ALBERT

Description: Ayu (Plecoglossus altivelis), one kinds of valuable cultured fish species, show almost no morphological difference between male and female until sexual maturity. Here, we report the identification of sex-linked markers for the ayu, based on Amplified Fragment Length Polymorphism (AFLP) generated from cultured fish (15 males and 15 females) by using 63 different primer combinations. Genomic fragments (n = 3733) were produced with a mean frequency of 59 bands per primer pair. A male-specific of 139 base pair band was amplified and converted to a sequence-characterized amplified region (SCAR marker) designated as Ayu102. Six distinct genomic fragments were produced in 12 wild samples (6 males and 6 females). The fragments designated a, b, c, d, and e were detected only in males, and one fragment (f) was detected in both genders. Nucleotide sequence analysis showed that fragments e and f were 96.67% identical. Ayu102 marker was detected in 45-d-old larvae, in both cultured and wild fish populations, and in offsprings generated by gynogenesis. Fragment e was detected in all males, and in only 6.7% of females in cultured fish. Data suggest that Ayu102 marker is a male-specific marker linked to the sex-determining locus of the male ayu and can be used for gender identification.

Description: Published

Description: A total of 28 specimens of adult Russian sturgeon brood fish from the Volga River (Astrakhan, Russia) and 42 specimens from the south Caspian Sea (coastline of Iran and Turkmenistan) were collected. About 2g of fin tissue was stored in 96% ethyl alcohol and transferred to the genetic laboratory of the International Sturgeon Research Institute. Genomic DNA was extracted using phenol-chloroform method. The quality and quantity of DNA was assessed by Agarose gel electrophoresis and spectrophotometry. Polymerase Chain Reaction (PCR) was conducted using eight pairs of microsatellite primers and its products were electrophoresed using 6% polyacrylamid gel followed by silver nitrate staining. Allele sizes were measured in all populations, then genetic parameters were calculated using Gen Alex program and the phylogenetic relationship was determined and drawn using TFPGA program. A minimum of 10 and a maximum of 21 alleles were identified per locus and the observed heterozygosity ranged between 0.50-0.96 and the expected heterozygosity was 0.74-0.90 with an average of 0.68. Hardy-Weinberg equilibrium was observed at Ls-19, Ls-39 loci, but showed disequilibrium in other loci. Fst index between Volga and South Caspian Sea samples was 0.031. The genetic similarity and distance was 0.661 and 0.414, respectively. Results of the present investigation indicate that there are no significant differences between the south Caspian Sea Russian sturgeon specimens.

Description: Published

Description: Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Molecular Ecology 25 (2016): 1027-1031, doi:10.1111/mec.13543.

Description: Mobile genetic elements (MGEs), also called transposable elements (TEs), represent universal components of most genomes and are intimately involved in nearly all aspects of genome organization, function, and evolution. However, there is currently a gap between fast-paced TE discovery in silico, stimulated by exponential growth of comparative genomic studies, and a limited number of experimental models amenable to more traditional in vitro and in vivo studies of structural, mechanistic, and regulatory properties of diverse MGEs. Experimental and computational scientists came together to bridge this gap at a recent conference, “Mobile Genetic Elements: in silico, in vitro, in vivo,” held at the Marine Biological Laboratory (MBL) in Woods Hole, MA, USA.

Description: Research in the authors’ laboratories is supported by NSF MCB-1121334 and NIH R01 GM111917 (I.A.), and by NIH R01 GM101989 and R21 AI117593 (P.R.)

Description: 2016-11-30

Description: © The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PeerJ 4 (2016): e1839, doi:10.7717/peerj.1839.

Description: High-throughput sequencing provides a fast and cost-effective mean to recover genomes of organisms from all domains of life. However, adequate curation of the assembly results against potential contamination of non-target organisms requires advanced bioinformatics approaches and practices. Here, we re-analyzed the sequencing data generated for the tardigrade Hypsibius dujardini, and created a holistic display of the eukaryotic genome assembly using DNA data originating from two groups and eleven sequencing libraries. By using bacterial single-copy genes, k-mer frequencies, and coverage values of scaffolds we could identify and characterize multiple near-complete bacterial genomes from the raw assembly, and curate a 182 Mbp draft genome for H. dujardini supported by RNA-Seq data. Our results indicate that most contaminant scaffolds were assembled from Moleculo long-read libraries, and most of these contaminants have differed between library preparations. Our re-analysis shows that visualization and curation of eukaryotic genome assemblies can benefit from tools designed to address the needs of today’s microbiologists, who are constantly challenged by the difficulties associated with the identification of distinct microbial genomes in complex environmental metagenomes.

Description: This work was supported by the Frank R. Lillie Research Innovation Award, and startup funds from the University of Chicago.

Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in GigaScience 4 (2015): 27, doi:10.1186/s13742-015-0066-5.

Description: Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits.

Description: This work was supported by the Micro B3 project, which is funded from the European Union’s Seventh Framework Programme (FP7; Joint Call OCEAN.2011‐2: Marine microbial diversity – new insights into marine ecosystems functioning and its biotechnological potential) under the grant agreement no 287589.

Description: © The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in GigaScience 5 (2016): 14, doi:10.1186/s13742-016-0118-5.

Description: Systems biology promises to revolutionize medicine, yet human wellbeing is also inherently linked to healthy societies and environments (sustainability). The IDEA Consortium is a systems ecology open science initiative to conduct the basic scientific research needed to build use-oriented simulations (avatars) of entire social-ecological systems. Islands are the most scientifically tractable places for these studies and we begin with one of the best known: Moorea, French Polynesia. The Moorea IDEA will be a sustainability simulator modeling links and feedbacks between climate, environment, biodiversity, and human activities across a coupled marine–terrestrial landscape. As a model system, the resulting knowledge and tools will improve our ability to predict human and natural change on Moorea and elsewhere at scales relevant to management/conservation actions.

Description: Work was supported in part by: the Institute of Theoretical Physics and the Pauli Center at ETH Zurich; the US National Science Foundation (NSF Moorea Coral Reef Long Term Ecological Research Site, OCE-1236905; Socio-Ecosystem Dynamics of Natural-Human Networks on Model Islands, CNH-1313830; Coastal SEES: Adaptive Capacity, Resilience, and Coral Reef State Shifts in Social-ecological Systems, OCE-1325652, OCE-1325554); the Gordon and Betty Moore Foundation (Berkeley Initiative in Global Change Biology; Genomic Standards Consortium); Courtney Ross and the Ross Institute; UC Berkeley Vice Chancellor for Research; CRIOBE; and the France Berkeley Fund (FBF 2014-0015).

Description: Clonal populations accumulate mutations over time, resulting in different haplotypes. Deep sequencing of such a population in principle provides information to reconstruct these haplotypes and the frequency at which the haplotypes occur. However, this reconstruction is technically not trivial, especially not in clonal systems with a relatively low mutation frequency. The low number of segregating sites in those systems adds ambiguity to the haplotype phasing and thus obviates the reconstruction of genome-wide haplotypes based on sequence overlap information. Therefore, we present EVORhA, a haplotype reconstruction method that complements phasing information in the non-empty read overlap with the frequency estimations of inferred local haplotypes. As was shown with simulated data, as soon as read lengths and/or mutation rates become restrictive for state-of-the-art methods, the use of this additional frequency information allows EVORhA to still reliably reconstruct genome-wide haplotypes. On real data, we show the applicability of the method in reconstructing the population composition of evolved bacterial populations and in decomposing mixed bacterial infections from clinical samples.

Description: Data on biological mechanisms of aging are mostly obtained from cross-sectional study designs. An inherent disadvantage of this design is that inter-individual differences can mask small but biologically significant age-dependent changes. A serially sampled design (same individual at different time points) would overcome this problem but is often limited by the relatively small numbers of available paired samples and the statistics being used. To overcome these limitations, we have developed a new vector-based approach, termed three-component analysis, which incorporates temporal distance, signal intensity and variance into one single score for gene ranking and is combined with gene set enrichment analysis. We tested our method on a unique age-based sample set of human skin fibroblasts and combined genome-wide transcription, DNA methylation and histone methylation (H3K4me3 and H3K27me3) data. Importantly, our method can now for the first time demonstrate a clear age-dependent decrease in expression of genes coding for proteins involved in translation and ribosome function. Using analogies with data from lower organisms, we propose a model where age-dependent down-regulation of protein translation-related components contributes to extend human lifespan.

Description: A major challenge in the field of shotgun metagenomics is the accurate identification of organisms present within a microbial community, based on classification of short sequence reads. Though existing microbial community profiling methods have attempted to rapidly classify the millions of reads output from modern sequencers, the combination of incomplete databases, similarity among otherwise divergent genomes, errors and biases in sequencing technologies, and the large volumes of sequencing data required for metagenome sequencing has led to unacceptably high false discovery rates (FDR). Here, we present the application of a novel, gene-independent and signature-based metagenomic taxonomic profiling method with significantly and consistently smaller FDR than any other available method. Our algorithm circumvents false positives using a series of non-redundant signature databases and examines G enomic O rigins T hrough T axonomic CHA llenge (GOTTCHA). GOTTCHA was tested and validated on 20 synthetic and mock datasets ranging in community composition and complexity, was applied successfully to data generated from spiked environmental and clinical samples, and robustly demonstrates superior performance compared with other available tools.

Description: Chromosome-long haplotyping of human genomes is important to identify genetic variants with differing gene expression, in human evolution studies, clinical diagnosis, and other biological and medical fields. Although several methods have realized haplotyping based on sequencing technologies or population statistics, accuracy and cost are factors that prohibit their wide use. Borrowing ideas from group testing theories, we proposed a clone-based haplotyping method by overlapping pool sequencing. The clones from a single individual were pooled combinatorially and then sequenced. According to the distinct pooling pattern for each clone in the overlapping pool sequencing, alleles for the recovered variants could be assigned to their original clones precisely. Subsequently, the clone sequences could be reconstructed by linking these alleles accordingly and assembling them into haplotypes with high accuracy. To verify the utility of our method, we constructed 130 110 clones in silico for the individual NA12878 and simulated the pooling and sequencing process. Ultimately, 99.9% of variants on chromosome 1 that were covered by clones from both parental chromosomes were recovered correctly, and 112 haplotype contigs were assembled with an N50 length of 3.4 Mb and no switch errors. A comparison with current clone-based haplotyping methods indicated our method was more accurate.

Description: Sexual differentiation of malaria parasites into gametocytes in the vertebrate host and subsequent gamete fertilization in mosquitoes is essential for the spreading of the disease. The molecular processes orchestrating these transitions are far from fully understood. Here, we report the first transcriptome analysis of male and female Plasmodium falciparum gametocytes coupled with a comprehensive proteome analysis. In male gametocytes there is an enrichment of proteins involved in the formation of flagellated gametes; proteins involved in DNA replication, chromatin organization and axoneme formation. On the other hand, female gametocytes are enriched in proteins required for zygote formation and functions after fertilization; protein-, lipid- and energy-metabolism. Integration of transcriptome and proteome data revealed 512 highly expressed maternal transcripts without corresponding protein expression indicating large scale translational repression in P. falciparum female gametocytes for the first time. Despite a high degree of conservation between Plasmodium species, 260 of these ‘repressed transcripts’ have not been previously described. Moreover, for some of these genes, protein expression is only reported in oocysts and sporozoites indicating that repressed transcripts can be partitioned into short- and long-term storage. Finally, these data sets provide an essential resource for identification of vaccine/drug targets and for further mechanistic studies.

Description: With read lengths of currently up to 2 x 300 bp, high throughput and low sequencing costs Illumina's MiSeq is becoming one of the most utilized sequencing platforms worldwide. The platform is manageable and affordable even for smaller labs. This enables quick turnaround on a broad range of applications such as targeted gene sequencing, metagenomics, small genome sequencing and clinical molecular diagnostics. However, Illumina error profiles are still poorly understood and programs are therefore not designed for the idiosyncrasies of Illumina data. A better knowledge of the error patterns is essential for sequence analysis and vital if we are to draw valid conclusions. Studying true genetic variation in a population sample is fundamental for understanding diseases, evolution and origin. We conducted a large study on the error patterns for the MiSeq based on 16S rRNA amplicon sequencing data. We tested state-of-the-art library preparation methods for amplicon sequencing and showed that the library preparation method and the choice of primers are the most significant sources of bias and cause distinct error patterns. Furthermore we tested the efficiency of various error correction strategies and identified quality trimming (Sickle) combined with error correction (BayesHammer) followed by read overlapping (PANDAseq) as the most successful approach, reducing substitution error rates on average by 93%.

Description: RNA-seq is a sensitive and accurate technique to compare steady-state levels of RNA between different cellular states. However, as it does not provide an account of transcriptional activity per se , other technologies are needed to more precisely determine acute transcriptional responses. Here, we have developed an easy, sensitive and accurate novel computational method, iRNA-seq , for genome-wide assessment of transcriptional activity based on analysis of intron coverage from total RNA-seq data. Comparison of the results derived from iRNA-seq analyses with parallel results derived using current methods for genome-wide determination of transcriptional activity, i.e. global run-on (GRO)-seq and RNA polymerase II (RNAPII) ChIP-seq, demonstrate that iRNA-seq provides similar results in terms of number of regulated genes and their fold change. However, unlike the current methods that are all very labor-intensive and demanding in terms of sample material and technologies, iRNA-seq is cheap and easy and requires very little sample material. In conclusion, iRNA-seq offers an attractive novel alternative to current methods for determination of changes in transcriptional activity at a genome-wide level.

Description: Identifying large-scale structural variation in cancer genomes continues to be a challenge to researchers. Current methods rely on genome alignments based on a reference that can be a poor fit to highly variant and complex tumor genomes. To address this challenge we developed a method that uses available breakpoint information to generate models of structural variations. We use these models as references to align previously unmapped and discordant reads from a genome. By using these models to align unmapped reads, we show that our method can help to identify large-scale variations that have been previously missed.

Description: Any given human individual carries multiple genetic variants that disrupt protein-coding genes, through structural variation, as well as nucleotide variants and indels. Predicting the phenotypic consequences of a gene disruption remains a significant challenge. Current approaches employ information from a range of biological networks to predict which human genes are haploinsufficient (meaning two copies are required for normal function) or essential (meaning at least one copy is required for viability). Using recently available study gene sets, we show that these approaches are strongly biased towards providing accurate predictions for well-studied genes. By contrast, we derive a haploinsufficiency score from a combination of unbiased large-scale high-throughput datasets, including gene co-expression and genetic variation in over 6000 human exomes. Our approach provides a haploinsufficiency prediction for over twice as many genes currently unassociated with papers listed in Pubmed as three commonly-used approaches, and outperforms these approaches for predicting haploinsufficiency for less-studied genes. We also show that fine-tuning the predictor on a set of well-studied ‘gold standard’ haploinsufficient genes does not improve the prediction for less-studied genes. This new score can readily be used to prioritize gene disruptions resulting from any genetic variant, including copy number variants, indels and single-nucleotide variants.

Description: Meta-analysis of gene expression has enabled numerous insights into biological systems, but current methods have several limitations. We developed a method to perform a meta-analysis using the elastic net, a powerful and versatile approach for classification and regression. To demonstrate the utility of our method, we conducted a meta-analysis of lung cancer gene expression based on publicly available data. Using 629 samples from five data sets, we trained a multinomial classifier to distinguish between four lung cancer subtypes. Our meta-analysis-derived classifier included 58 genes and achieved 91% accuracy on leave-one-study-out cross-validation and on three independent data sets. Our method makes meta-analysis of gene expression more systematic and expands the range of questions that a meta-analysis can be used to address. As the amount of publicly available gene expression data continues to grow, our method will be an effective tool to help distill these data into knowledge.

Description: Advanced sequencing technologies have generated a plethora of data for many chromatin marks in multiple tissues and cell types, yet there is lack of a generalized tool for optimal utility of those data. A major challenge is to quantitatively model the epigenetic dynamics across both the genome and many cell types for understanding their impacts on differential gene regulation and disease. We introduce IDEAS, an i ntegrative and d iscriminative e pigenome a nnotation s ystem, for jointly characterizing epigenetic landscapes in many cell types and detecting differential regulatory regions. A key distinction between our method and existing state-of-the-art algorithms is that IDEAS integrates epigenomes of many cell types simultaneously in a way that preserves the position-dependent and cell type-specific information at fine scales, thereby greatly improving segmentation accuracy and producing comparable annotations across cell types.

Description: Early data-sharing efforts have led to improved variant interpretation and development of treatments for rare diseases and some cancer types (1–3). However, such benefits will only be available to the general population if researchers and clinicians can access and make comparisons across data from millions of individuals. Author:

Description: A majority of large-scale bacterial genome rearrangements involve mobile genetic elements such as insertion sequence (IS) elements. Here we report novel insertions and excisions of IS elements and recombination between homologous IS elements identified in a large collection of Escherichia coli mutation accumulation lines by analysis of whole genome shotgun sequencing data. Based on 857 identified events (758 IS insertions, 98 recombinations and 1 excision), we estimate that the rate of IS insertion is 3.5 x 10 –4 insertions per genome per generation and the rate of IS homologous recombination is 4.5 x 10 –5 recombinations per genome per generation. These events are mostly contributed by the IS elements IS 1 , IS 2 , IS 5 and IS 186 . Spatial analysis of new insertions suggest that transposition is biased to proximal insertions, and the length spectrum of IS-caused deletions is largely explained by local hopping. For any of the ISs studied there is no region of the circular genome that is favored or disfavored for new insertions but there are notable hotspots for deletions. Some elements have preferences for non-coding sequence or for the beginning and end of coding regions, largely explained by target site motifs. Interestingly, transposition and deletion rates remain constant across the wild-type and 12 mutant E. coli lines, each deficient in a distinct DNA repair pathway. Finally, we characterized the target sites of four IS families, confirming previous results and characterizing a highly specific pattern at IS 186 target-sites, 5'-GGGG(N6/N7)CCCC-3'. We also detected 48 long deletions not involving IS elements.

Description: Extensive and multi-dimensional data sets generated from recent cancer omics profiling projects have presented new challenges and opportunities for unraveling the complexity of cancer genome landscapes. In particular, distinguishing the unique complement of genes that drive tumorigenesis in each patient from a sea of passenger mutations is necessary for translating the full benefit of cancer genome sequencing into the clinic. We address this need by presenting a data integration framework (OncoIMPACT) to nominate patient-specific driver genes based on their phenotypic impact. Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively). In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient. Applying OncoIMPACT to more than 1000 tumor samples, we generated patient-specific driver gene lists in five different cancer types to identify modes of synergistic action. We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication. Source code and executables for OncoIMPACT are freely available from http://sourceforge.net/projects/oncoimpact .

Description: Analysis of rewired upstream subnetworks impacting downstream differential gene expression aids the delineation of evolving molecular mechanisms. Cumulative statistics based on conventional differential correlation are limited for subnetwork rewiring analysis since rewiring is not necessarily equivalent to change in correlation coefficients. Here we present a computational method ChiNet to quantify subnetwork rewiring by statistical heterogeneity that enables detection of potential genotype changes causing altered transcription regulation in evolving organisms. Given a differentially expressed downstream gene set, ChiNet backtracks a rewired upstream subnetwork from a super-network including gene interactions known to occur under various molecular contexts. We benchmarked ChiNet for its high accuracy in distinguishing rewired artificial subnetworks, in silico yeast transcription-metabolic subnetworks, and rewired transcription subnetworks for Candida albicans versus Saccharomyces cerevisiae , against two differential-correlation based subnetwork rewiring approaches. Then, using transcriptome data from tolerant S. cerevisiae strain NRRL Y-50049 and a wild-type intolerant strain, ChiNet identified 44 metabolic pathways affected by rewired transcription subnetworks anchored to major adaptively activated transcription factor genes YAP1 , RPN4 , SFP1 and ROX1 , in response to toxic chemical challenges involved in lignocellulose-to-biofuels conversion. These findings support the use of ChiNet in rewiring analysis of subnetworks where differential interaction patterns resulting from divergent nonlinear dynamics abound.

Description: Ribosome biogenesis, a central and essential cellular process, occurs through sequential association and mutual co-folding of protein–RNA constituents in a well-defined assembly pathway. Here, we construct a network of co-evolving nucleotide/amino acid residues within the ribosome and demonstrate that assembly constraints are strong predictors of co-evolutionary patterns. Predictors of co-evolution include a wide spectrum of structural reconstitution events, such as cooperativity phenomenon, protein-induced rRNA reconstitutions, molecular packing of different rRNA domains, protein–rRNA recognition, etc. A correlation between folding rate of small globular proteins and their topological features is known. We have introduced an analogous topological characteristic for co-evolutionary network of ribosome, which allows us to differentiate between rRNA regions subjected to rapid reconstitutions from those hindered by kinetic traps. Furthermore, co-evolutionary patterns provide a biological basis for deleterious mutation sites and further allow prediction of potential antibiotic targeting sites. Understanding assembly pathways of multicomponent macromolecules remains a key challenge in biophysics. Our study provides a ‘proof of concept’ that directly relates co-evolution to biophysical interactions during multicomponent assembly and suggests predictive power to identify candidates for critical functional interactions as well as for assembly-blocking antibiotic target sites.

Description: The emergence of new sequencing technologies has facilitated the use of bacterial whole genome alignments for evolutionary studies and outbreak analyses. These datasets, of increasing size, often include examples of multiple different mechanisms of horizontal sequence transfer resulting in substantial alterations to prokaryotic chromosomes. The impact of these processes demands rapid and flexible approaches able to account for recombination when reconstructing isolates’ recent diversification. Gubbins is an iterative algorithm that uses spatial scanning statistics to identify loci containing elevated densities of base substitutions suggestive of horizontal sequence transfer while concurrently constructing a maximum likelihood phylogeny based on the putative point mutations outside these regions of high sequence diversity. Simulations demonstrate the algorithm generates highly accurate reconstructions under realistically parameterized models of bacterial evolution, and achieves convergence in only a few hours on alignments of hundreds of bacterial genome sequences. Gubbins is appropriate for reconstructing the recent evolutionary history of a variety of haploid genotype alignments, as it makes no assumptions about the underlying mechanism of recombination. The software is freely available for download at github.com/sanger-pathogens/Gubbins , implemented in Python and C and supported on Linux and Mac OS X.

Description: Genomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for ‘targeted’ resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approach that uses a ‘kmer’ strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants. Relative to four publically available algorithms, BreaKmer detected SV with increased sensitivity and limited calls in non-tumor samples, key features for variant analysis of tumor specimens in both the clinical and research settings.

Description: Genetic variants in or near miRNA genes can have profound effects on miRNA expression and targeting. As user-friendly software for the impact prediction of miRNA variants on a large scale is still lacking, we created a tool called miRVaS. miRVaS automates this prediction by annotating the location of the variant relative to functional regions within the miRNA hairpin (seed, mature, loop, hairpin arm, flanks) and by annotating all predicted structural changes within the miRNA due to the variant. In addition, the tool defines the most important region that is predicted to have structural changes and calculates a conservation score that is indicative of the reliability of the structure prediction. The output is presented in a tab-separated file, which enables fast screening, and in an html file, which allows visual comparison between wild-type and variant structures. All separate images are provided for downstream use. Finally, we tested two different approaches on a small test set of published functionally validated genetic variants for their capacity to predict the impact of variants on miRNA expression.

Description: Analysis of RNA-seq data often detects numerous ‘non-co-linear’ (NCL) transcripts, which comprised sequence segments that are topologically inconsistent with their corresponding DNA sequences in the reference genome. However, detection of NCL transcripts involves two major challenges: removal of false positives arising from alignment artifacts and discrimination between different types of NCL transcripts ( trans -spliced, circular or fusion transcripts). Here, we developed a new NCL-transcript-detecting method (‘NCLscan’), which utilized a stepwise alignment strategy to almost completely eliminate false calls (〉98% precision) without sacrificing true positives, enabling NCLscan outperform 18 other publicly-available tools (including fusion- and circular-RNA-detecting tools) in terms of sensitivity and precision, regardless of the generation strategy of simulated dataset, type of intragenic or intergenic NCL event, read depth of coverage, read length or expression level of NCL transcript. With the high accuracy, NCLscan was applied to distinguishing between trans -spliced, circular and fusion transcripts on the basis of poly(A)- and nonpoly(A)-selected RNA-seq data. We showed that circular RNAs were expressed more ubiquitously, more abundantly and less cell type-specifically than trans -spliced and fusion transcripts. Our study thus describes a robust pipeline for the discovery of NCL transcripts, and sheds light on the fundamental biology of these non-canonical RNA events in human transcriptome.

Description: Detecting allelic biases from high-throughput sequencing data requires an approach that maximises sensitivity while minimizing false positives. Here, we present Allelome.PRO, an automated user-friendly bioinformatics pipeline, which uses high-throughput sequencing data from reciprocal crosses of two genetically distinct mouse strains to detect allele-specific expression and chromatin modifications. Allelome.PRO extends approaches used in previous studies that exclusively analyzed imprinted expression to give a complete picture of the ‘allelome’ by automatically categorising the allelic expression of all genes in a given cell type into imprinted, strain-biased, biallelic or non-informative. Allelome.PRO offers increased sensitivity to analyze lowly expressed transcripts, together with a robust false discovery rate empirically calculated from variation in the sequencing data. We used RNA-seq data from mouse embryonic fibroblasts from F1 reciprocal crosses to determine a biologically relevant allelic ratio cutoff, and define for the first time an entire allelome. Furthermore, we show that Allelome.PRO detects differential enrichment of H3K4me3 over promoters from ChIP-seq data validating the RNA-seq results. This approach can be easily extended to analyze histone marks of active enhancers, or transcription factor binding sites and therefore provides a powerful tool to identify candidate cis regulatory elements genome wide.

Description: Methods to interpret personal genome sequences are increasingly required. Here, we report a novel framework (EvoTol) to identify disease-causing genes using patient sequence data from within protein coding-regions. EvoTol quantifies a gene's intolerance to mutation using evolutionary conservation of protein sequences and can incorporate tissue-specific gene expression data. We apply this framework to the analysis of whole-exome sequence data in epilepsy and congenital heart disease, and demonstrate EvoTol's ability to identify known disease-causing genes is unmatched by competing methods. Application of EvoTol to the human interactome revealed networks enriched for genes intolerant to protein sequence variation, informing novel polygenic contributions to human disease.

Description: Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo . However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF–DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF–DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods.

Description: Plant genomes, and eukaryotic genomes in general, are typically repetitive, polyploid and heterozygous, which complicates genome assembly. The short read lengths of early Sanger and current next-generation sequencing platforms hinder assembly through complex repeat regions, and many draft and reference genomes are fragmented, lacking skewed GC and repetitive intergenic sequences, which are gaining importance due to projects like the Encyclopedia of DNA Elements (ENCODE). Here we report the whole-genome sequencing and assembly of the desiccation-tolerant grass Oropetium thomaeum. Using only single-molecule real-time sequencing, which generates long (〉16 kilobases) reads with random errors, we assembled 99% (244 megabases) of the Oropetium genome into 625 contigs with an N50 length of 2.4 megabases. Oropetium is an example of a 'near-complete' draft genome which includes gapless coverage over gene space as well as intergenic sequences such as centromeres, telomeres, transposable elements and rRNA clusters that are typically unassembled in draft genomes. Oropetium has 28,466 protein-coding genes and 43% repeat sequences, yet with 30% more compact euchromatic regions it is the smallest known grass genome. The Oropetium genome demonstrates the utility of single-molecule real-time sequencing for assembling high-quality plant and other eukaryotic genomes, and serves as a valuable resource for the plant comparative genomics community.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉VanBuren, Robert -- Bryant, Doug -- Edger, Patrick P -- Tang, Haibao -- Burgess, Diane -- Challabathula, Dinakar -- Spittle, Kristi -- Hall, Richard -- Gu, Jenny -- Lyons, Eric -- Freeling, Michael -- Bartels, Dorothea -- Ten Hallers, Boudewijn -- Hastie, Alex -- Michael, Todd P -- Mockler, Todd C -- England -- Nature. 2015 Nov 26;527(7579):508-11. doi: 10.1038/nature15714. Epub 2015 Nov 11.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Donald Danforth Plant Science Center, St Louis, Missouri 63132, USA. ; Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, California 94720, USA. ; Department of Horticulture, Michigan State University, East Lansing, Michigan 48823, USA. ; iPlant Collaborative, School of Plant Sciences, University of Arizona, Tucson, Arizona 85721, USA. ; Center for Genomics and Biotechnology, Haixia Institute of Science and Technology (HIST), Fujian Agriculture and Forestry University, Fuzhou 350002, China. ; IMBIO, University of Bonn, Kirschallee 1, D-53115 Bonn, Germany. ; Pacific Biosciences, Menlo Park, California 94025, USA. ; BioNano Genomics, San Diego, California 92121, USA. ; Ibis Biosciences, Carlsbad, California 92008, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26560029" target="_blank"〉PubMed〈/a〉

Description: The extent to which low-frequency (minor allele frequency (MAF) between 1-5%) and rare (MAF 〈/= 1%) variants contribute to complex traits and disease in the general population is mainly unknown. Bone mineral density (BMD) is highly heritable, a major predictor of osteoporotic fractures, and has been previously associated with common genetic variants, as well as rare, population-specific, coding variants. Here we identify novel non-coding genetic variants with large effects on BMD (ntotal = 53,236) and fracture (ntotal = 508,253) in individuals of European ancestry from the general population. Associations for BMD were derived from whole-genome sequencing (n = 2,882 from UK10K (ref. 10); a population-based genome sequencing consortium), whole-exome sequencing (n = 3,549), deep imputation of genotyped samples using a combined UK10K/1000 Genomes reference panel (n = 26,534), and de novo replication genotyping (n = 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size fourfold larger than the mean of previously reported common variants for lumbar spine BMD (rs11692564(T), MAF = 1.6%, replication effect size = +0.20 s.d., Pmeta = 2 x 10(-14)), which was also associated with a decreased risk of fracture (odds ratio = 0.85; P = 2 x 10(-11); ncases = 98,742 and ncontrols = 409,511). Using an En1(cre/flox) mouse model, we observed that conditional loss of En1 results in low bone mass, probably as a consequence of high bone turnover. We also identified a novel low-frequency non-coding variant with large effects on BMD near WNT16 (rs148771817(T), MAF = 1.2%, replication effect size = +0.41 s.d., Pmeta = 1 x 10(-11)). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4755714/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4755714/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Zheng, Hou-Feng -- Forgetta, Vincenzo -- Hsu, Yi-Hsiang -- Estrada, Karol -- Rosello-Diez, Alberto -- Leo, Paul J -- Dahia, Chitra L -- Park-Min, Kyung Hyun -- Tobias, Jonathan H -- Kooperberg, Charles -- Kleinman, Aaron -- Styrkarsdottir, Unnur -- Liu, Ching-Ti -- Uggla, Charlotta -- Evans, Daniel S -- Nielson, Carrie M -- Walter, Klaudia -- Pettersson-Kymmer, Ulrika -- McCarthy, Shane -- Eriksson, Joel -- Kwan, Tony -- Jhamai, Mila -- Trajanoska, Katerina -- Memari, Yasin -- Min, Josine -- Huang, Jie -- Danecek, Petr -- Wilmot, Beth -- Li, Rui -- Chou, Wen-Chi -- Mokry, Lauren E -- Moayyeri, Alireza -- Claussnitzer, Melina -- Cheng, Chia-Ho -- Cheung, Warren -- Medina-Gomez, Carolina -- Ge, Bing -- Chen, Shu-Huang -- Choi, Kwangbom -- Oei, Ling -- Fraser, James -- Kraaij, Robert -- Hibbs, Matthew A -- Gregson, Celia L -- Paquette, Denis -- Hofman, Albert -- Wibom, Carl -- Tranah, Gregory J -- Marshall, Mhairi -- Gardiner, Brooke B -- Cremin, Katie -- Auer, Paul -- Hsu, Li -- Ring, Sue -- Tung, Joyce Y -- Thorleifsson, Gudmar -- Enneman, Anke W -- van Schoor, Natasja M -- de Groot, Lisette C P G M -- van der Velde, Nathalie -- Melin, Beatrice -- Kemp, John P -- Christiansen, Claus -- Sayers, Adrian -- Zhou, Yanhua -- Calderari, Sophie -- van Rooij, Jeroen -- Carlson, Chris -- Peters, Ulrike -- Berlivet, Soizik -- Dostie, Josee -- Uitterlinden, Andre G -- Williams, Stephen R -- Farber, Charles -- Grinberg, Daniel -- LaCroix, Andrea Z -- Haessler, Jeff -- Chasman, Daniel I -- Giulianini, Franco -- Rose, Lynda M -- Ridker, Paul M -- Eisman, John A -- Nguyen, Tuan V -- Center, Jacqueline R -- Nogues, Xavier -- Garcia-Giralt, Natalia -- Launer, Lenore L -- Gudnason, Vilmunder -- Mellstrom, Dan -- Vandenput, Liesbeth -- Amin, Najaf -- van Duijn, Cornelia M -- Karlsson, Magnus K -- Ljunggren, Osten -- Svensson, Olle -- Hallmans, Goran -- Rousseau, Francois -- Giroux, Sylvie -- Bussiere, Johanne -- Arp, Pascal P -- Koromani, Fjorda -- Prince, Richard L -- Lewis, Joshua R -- Langdahl, Bente L -- Hermann, A Pernille -- Jensen, Jens-Erik B -- Kaptoge, Stephen -- Khaw, Kay-Tee -- Reeve, Jonathan -- Formosa, Melissa M -- Xuereb-Anastasi, Angela -- Akesson, Kristina -- McGuigan, Fiona E -- Garg, Gaurav -- Olmos, Jose M -- Zarrabeitia, Maria T -- Riancho, Jose A -- Ralston, Stuart H -- Alonso, Nerea -- Jiang, Xi -- Goltzman, David -- Pastinen, Tomi -- Grundberg, Elin -- Gauguier, Dominique -- Orwoll, Eric S -- Karasik, David -- Davey-Smith, George -- AOGC Consortium -- Smith, Albert V -- Siggeirsdottir, Kristin -- Harris, Tamara B -- Zillikens, M Carola -- van Meurs, Joyce B J -- Thorsteinsdottir, Unnur -- Maurano, Matthew T -- Timpson, Nicholas J -- Soranzo, Nicole -- Durbin, Richard -- Wilson, Scott G -- Ntzani, Evangelia E -- Brown, Matthew A -- Stefansson, Kari -- Hinds, David A -- Spector, Tim -- Cupples, L Adrienne -- Ohlsson, Claes -- Greenwood, Celia M T -- UK10K Consortium -- Jackson, Rebecca D -- Rowe, David W -- Loomis, Cynthia A -- Evans, David M -- Ackert-Bicknell, Cheryl L -- Joyner, Alexandra L -- Duncan, Emma L -- Kiel, Douglas P -- Rivadeneira, Fernando -- Richards, J Brent -- G1000143/Medical Research Council/United Kingdom -- K01 AR062655/AR/NIAMS NIH HHS/ -- MC_UU_12013/3/Medical Research Council/United Kingdom -- R01 AG005394/AG/NIA NIH HHS/ -- R01 AG005407/AG/NIA NIH HHS/ -- R01 AG027574/AG/NIA NIH HHS/ -- R01 AG027576/AG/NIA NIH HHS/ -- R01 AR035582/AR/NIAMS NIH HHS/ -- R01 AR035583/AR/NIAMS NIH HHS/ -- RC2 AR058973/AR/NIAMS NIH HHS/ -- U01 AG018197/AG/NIA NIH HHS/ -- U01 AG042140/AG/NIA NIH HHS/ -- U01 AG042143/AG/NIA NIH HHS/ -- U01 AR045580/AR/NIAMS NIH HHS/ -- U01 AR045583/AR/NIAMS NIH HHS/ -- U01 AR045614/AR/NIAMS NIH HHS/ -- U01 AR045632/AR/NIAMS NIH HHS/ -- U01 AR045647/AR/NIAMS NIH HHS/ -- U01 AR045654/AR/NIAMS NIH HHS/ -- U01 AR066160/AR/NIAMS NIH HHS/ -- England -- Nature. 2015 Oct 1;526(7571):112-7. doi: 10.1038/nature14878. Epub 2015 Sep 14.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montreal H3A 1A2, Canada. ; Department of Medicine, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal H3T 1E2, Canada. ; Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts 02131, USA. ; Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA. ; Broad Institute of MIT and Harvard, Boston, Massachusetts 02115, USA. ; Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands. ; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA. ; Developmental Biology Program, Sloan Kettering Institute, New York, New York 10065, USA. ; The University of Queensland Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, Brisbane 4102, Australia. ; Department of Cell and Developmental Biology, Weill Cornell Medical College, New York, New York 10065, USA. ; Tissue Engineering, Regeneration and Repair Program, Hospital for Special Surgery, New York 10021, USA. ; Rheumatology Divison, Hospital for Special Surgery New York, New York 10021, USA. ; School of Clinical Science, University of Bristol, Bristol BS10 5NB, UK. ; MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK. ; Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA. ; Department of Research, 23andMe, Mountain View, California 94041, USA. ; Department of Population Genomics, deCODE Genetics, Reykjavik IS-101, Iceland. ; Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA. ; Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg S-413 45, Sweden. ; California Pacific Medical Center Research Institute, San Francisco, California 94158, USA. ; Department of Public Health and Preventive Medicine, Oregon Health &Science University, Portland, Oregon 97239, USA. ; Bone &Mineral Unit, Oregon Health &Science University, Portland, Oregon 97239, USA. ; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK. ; Departments of Pharmacology and Clinical Neurosciences, Umea University, Umea S-901 87, Sweden. ; Department of Public Health and Clinical Medicine, Umea University, Umea SE-901 87, Sweden. ; Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg S-413 45, Sweden. ; McGill University and Genome Quebec Innovation Centre, Montreal H3A 0G1, Canada. ; Department of Epidemiology, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands. ; Oregon Clinical and Translational Research Institute, Oregon Health &Science University, Portland, Oregon 97239, USA. ; Department of Medical and Clinical Informatics, Oregon Health &Science University, Portland, Oregon 97239, USA. ; Farr Institute of Health Informatics Research, University College London, London NW1 2DA, UK. ; Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK. ; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02115, USA. ; Department of Human Genetics, McGill University, Montreal H3A 1B1, Canada. ; Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden 2300RC, The Netherlands. ; Center for Musculoskeletal Research, University of Rochester, Rochester, New York 14642, USA. ; Department of Biochemistry and Goodman Cancer Research Center, McGill University, Montreal H3G 1Y6, Canada. ; Department of Computer Science, Trinity University, San Antonio, Texas 78212, USA. ; Musculoskeletal Research Unit, University of Bristol, Bristol BS10 5NB, UK. ; Department of Radiation Sciences, Umea University, Umea S-901 87, Sweden. ; School of Public Health, University of Wisconsin, Milwaukee, Wisconsin 53726, USA. ; School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK. ; Department of Statistics, deCODE Genetics, Reykjavik IS-101, Iceland. ; Department of Epidemiology and Biostatistics and the EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam 1007 MB, The Netherlands. ; Department of Human Nutrition, Wageningen University, Wageningen 6700 EV, The Netherlands. ; Department of Internal Medicine, Section Geriatrics, Academic Medical Center, Amsterdam 1105, The Netherlands. ; Nordic Bioscience, Herlev 2730, Denmark. ; Cordeliers Research Centre, INSERM UMRS 1138, Paris 75006, France. ; Institute of Cardiometabolism and Nutrition, University Pierre &Marie Curie, Paris 75013, France. ; Departments of Medicine (Cardiovascular Medicine), Centre for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22908, USA. ; Department of Genetics, University of Barcelona, Barcelona 08028, Spain. ; U-720, Centre for Biomedical Network Research on Rare Diseases (CIBERER), Barcelona 28029, Spain. ; Department of Human Molecular Genetics, The Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona 08028, Spain. ; Women's Health Center of Excellence Family Medicine and Public Health, University of California - San Diego, San Diego, California 92093, USA. ; Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA. ; Osteoporosis &Bone Biology Program, Garvan Institute of Medical Research, Sydney 2010, Australia. ; School of Medicine Sydney, University of Notre Dame Australia, Sydney 6959, Australia. ; St. Vincent's Hospital &Clinical School, NSW University, Sydney 2010, Australia. ; Musculoskeletal Research Group, Institut Hospital del Mar d'Investigacions Mediques, Barcelona 08003, Spain. ; Cooperative Research Network on Aging and Fragility (RETICEF), Institute of Health Carlos III, 28029, Spain. ; Department of Internal Medicine, Hospital del Mar, Universitat Autonoma de Barcelona, Barcelona 08193, Spain. ; Neuroepidemiology Section, National Institute on Aging, National Institutes of Health, Bethesda, Maryland 20892, USA. ; Icelandic Heart Association, Kopavogur IS-201, Iceland. ; Faculty of Medicine, University of Iceland, Reykjavik IS-101, Iceland. ; Genetic epidemiology unit, Department of Epidemiology, Erasmus MC, Rotterdam 3000CA, The Netherlands. ; Department of Orthopaedics, Skane University Hospital Malmo 205 02, Sweden. ; Department of Medical Sciences, University of Uppsala, Uppsala 751 85, Sweden. ; Department of Surgical and Perioperative Sciences, Umea Unviersity, Umea 901 85, Sweden. ; Department of Molecular Biology, Medical Biochemistry and Pathology, Universite Laval, Quebec City G1V 0A6, Canada. ; Axe Sante des Populations et Pratiques Optimales en Sante, Centre de recherche du CHU de Quebec, Quebec City G1V 4G2, Canada. ; Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands 6009, Australia. ; Department of Medicine, University of Western Australia, Perth 6009, Australia. ; Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus C 8000, Denmark. ; Department of Endocrinology, Odense University Hospital, Odense C 5000, Denmark. ; Department of Endocrinology, Hvidovre University Hospital, Hvidovre 2650, Denmark. ; Clinical Gerontology Unit, University of Cambridge, Cambridge CB2 2QQ, UK. ; Medicine and Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK. ; Institute of Musculoskeletal Sciences, The Botnar Research Centre, University of Oxford, Oxford OX3 7LD, UK. ; Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida MSD 2080, Malta. ; Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences Malmo, Lund University, 205 02, Sweden. ; Department of Medicine and Psychiatry, University of Cantabria, Santander 39011, Spain. ; Department of Internal Medicine, Hospital U.M. Valdecilla- IDIVAL, Santander 39008, Spain. ; Department of Legal Medicine, University of Cantabria, Santander 39011, Spain. ; Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh EH4 2XU, UK. ; Department of Reconstructive Sciences, College of Dental Medicine, University of Connecticut Health Center, Farmington, Connecticut 06030, USA. ; Department of Medicine and Physiology, McGill University, Montreal H4A 3J1, Canada. ; Department of Medicine, Oregon Health &Science University, Portland, Oregon 97239, USA. ; Faculty of Medicine in the Galilee, Bar-Ilan University, Safed 13010, Israel. ; Laboratory of Epidemiology, National Institute on Aging, National Institutes of Health, Bethesda, Maryland 20892, USA. ; Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA. ; School of Medicine and Pharmacology, University of Western Australia, Crawley 6009, Australia. ; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina 45110, Greece. ; Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, Rhode Island 02903, USA. ; deCODE Genetics, Reykjavik IS-101, Iceland. ; Framingham Heart Study, Framingham, Massachusetts 01702, USA. ; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal H3A 1A2, Canada. ; Department of Oncology, Gerald Bronfman Centre, McGill University, Montreal H2W 1S6, Canada. ; Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, The Ohio State University, Columbus, Ohio 43210, USA. ; The Ronald O. Perelman Department of Dermatology and Department of Cell Biology, New York University School of Medicine, New York, New York 10016, USA. ; Department of Diabetes and Endocrinology, Royal Brisbane and Women's Hospital, Brisbane 4029, Australia.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26367794" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Marsh, Geoff -- England -- Nature. 2015 Feb 12;518(7538):147. doi: 10.1038/518147a.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25673391" target="_blank"〉PubMed〈/a〉

Description: Coleoid cephalopods (octopus, squid and cuttlefish) are active, resourceful predators with a rich behavioural repertoire. They have the largest nervous systems among the invertebrates and present other striking morphological innovations including camera-like eyes, prehensile arms, a highly derived early embryogenesis and a remarkably sophisticated adaptive colouration system. To investigate the molecular bases of cephalopod brain and body innovations, we sequenced the genome and multiple transcriptomes of the California two-spot octopus, Octopus bimaculoides. We found no evidence for hypothesized whole-genome duplications in the octopus lineage. The core developmental and neuronal gene repertoire of the octopus is broadly similar to that found across invertebrate bilaterians, except for massive expansions in two gene families previously thought to be uniquely enlarged in vertebrates: the protocadherins, which regulate neuronal development, and the C2H2 superfamily of zinc-finger transcription factors. Extensive messenger RNA editing generates transcript and protein diversity in genes involved in neural excitability, as previously described, as well as in genes participating in a broad range of other cellular functions. We identified hundreds of cephalopod-specific genes, many of which showed elevated expression levels in such specialized structures as the skin, the suckers and the nervous system. Finally, we found evidence for large-scale genomic rearrangements that are closely associated with transposable element expansions. Our analysis suggests that substantial expansion of a handful of gene families, along with extensive remodelling of genome linkage and repetitive content, played a critical role in the evolution of cephalopod morphological innovations, including their large and complex nervous systems.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Albertin, Caroline B -- Simakov, Oleg -- Mitros, Therese -- Wang, Z Yan -- Pungor, Judit R -- Edsinger-Gonzales, Eric -- Brenner, Sydney -- Ragsdale, Clifton W -- Rokhsar, Daniel S -- R03 HD056094/HD/NICHD NIH HHS/ -- R03HD064887/HD/NICHD NIH HHS/ -- S10RR027303/RR/NCRR NIH HHS/ -- S10RR029668/RR/NCRR NIH HHS/ -- T32 HD055164/HD/NICHD NIH HHS/ -- UL1 TR000430/TR/NCATS NIH HHS/ -- England -- Nature. 2015 Aug 13;524(7564):220-4. doi: 10.1038/nature14668.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois 60637, USA. ; 1] Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 9040495, Japan [2] Centre for Organismal Studies, University of Heidelberg, 69117 Heidelberg, Germany. ; Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA. ; Department of Neurobiology, University of Chicago, Chicago, Illinois 60637, USA. ; 1] Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 9040495, Japan [2] Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA. ; Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 9040495, Japan. ; 1] Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois 60637, USA [2] Department of Neurobiology, University of Chicago, Chicago, Illinois 60637, USA. ; 1] Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 9040495, Japan [2] Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA [3] Department of Energy Joint Genome Institute, Walnut Creek, California 94598, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26268193" target="_blank"〉PubMed〈/a〉

Description: Visualization of chromosomal dynamics is important for understanding many fundamental intra-nuclear processes. Efficient and reliable live-cell multicolor labeling of chromosomal loci can realize this goal. However, the current methods are constrained mainly by insufficient labeling throughput, efficiency, flexibility as well as photostability. Here we have developed a new approach to realize dual-color chromosomal loci imaging based on a modified single-guide RNA (sgRNA) of the CRISPR/Cas9 system. The modification of sgRNA was optimized by structure-guided engineering of the original sgRNA, consisting of RNA aptamer insertions that bind fluorescent protein-tagged effectors. By labeling and tracking telomeres, centromeres and genomic loci, we demonstrate that the new approach is easy to implement and enables robust dual-color imaging of genomic elements. Importantly, our data also indicate that the fast exchange rate of RNA aptamer binding effectors makes our sgRNA-based labeling method much more tolerant to photobleaching than the Cas9-based labeling method. This is crucial for continuous, long-term tracking of chromosomal dynamics. Lastly, as our method is complementary to other live-cell genomic labeling systems, it is therefore possible to combine them into a plentiful palette for the study of native chromatin organization and genome ultrastructure dynamics in living cells.

Description: Genetic research has moved rapidly since the publication of Richard Dawkins's The Selfish Gene 40 years ago. In the intervening years, we have come to realize that many of the most interesting and important phenomena in human biology are not caused by any single gene. Citing a wealth of recent research that explores the ways genes work together to produce complex biological processes, Itai Yanai and Martin Lercher argue that it is time to embrace a new, more holistic, metaphor in their book, The Society of Genes. Author: Joseph Swift

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉England -- Nature. 2015 Oct 1;526(7571):5-6. doi: 10.1038/526005b.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26432202" target="_blank"〉PubMed〈/a〉

Description: Kennewick Man, referred to as the Ancient One by Native Americans, is a male human skeleton discovered in Washington state (USA) in 1996 and initially radiocarbon dated to 8,340-9,200 calibrated years before present (BP). His population affinities have been the subject of scientific debate and legal controversy. Based on an initial study of cranial morphology it was asserted that Kennewick Man was neither Native American nor closely related to the claimant Plateau tribes of the Pacific Northwest, who claimed ancestral relationship and requested repatriation under the Native American Graves Protection and Repatriation Act (NAGPRA). The morphological analysis was important to judicial decisions that Kennewick Man was not Native American and that therefore NAGPRA did not apply. Instead of repatriation, additional studies of the remains were permitted. Subsequent craniometric analysis affirmed Kennewick Man to be more closely related to circumpacific groups such as the Ainu and Polynesians than he is to modern Native Americans. In order to resolve Kennewick Man's ancestry and affiliations, we have sequenced his genome to approximately 1x coverage and compared it to worldwide genomic data including for the Ainu and Polynesians. We find that Kennewick Man is closer to modern Native Americans than to any other population worldwide. Among the Native American groups for whom genome-wide data are available for comparison, several seem to be descended from a population closely related to that of Kennewick Man, including the Confederated Tribes of the Colville Reservation (Colville), one of the five tribes claiming Kennewick Man. We revisit the cranial analyses and find that, as opposed to genome-wide comparisons, it is not possible on that basis to affiliate Kennewick Man to specific contemporary groups. We therefore conclude based on genetic comparisons that Kennewick Man shows continuity with Native North Americans over at least the last eight millennia.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Rasmussen, Morten -- Sikora, Martin -- Albrechtsen, Anders -- Korneliussen, Thorfinn Sand -- Moreno-Mayar, J Victor -- Poznik, G David -- Zollikofer, Christoph P E -- Ponce de Leon, Marcia S -- Allentoft, Morten E -- Moltke, Ida -- Jonsson, Hakon -- Valdiosera, Cristina -- Malhi, Ripan S -- Orlando, Ludovic -- Bustamante, Carlos D -- Stafford, Thomas W Jr -- Meltzer, David J -- Nielsen, Rasmus -- Willerslev, Eske -- England -- Nature. 2015 Jul 23;523(7561):455-8. doi: 10.1038/nature14625.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, DK-1350 Copenhagen K, Denmark [2] Department of Genetics, School of Medicine, Stanford University, Littlefield Center, Stanford, California 94305, USA. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, DK-1350 Copenhagen K, Denmark. ; The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaloes Vej 5, DK-2200 Copenhagen N, Denmark. ; Program in Biomedical Informatics, Stanford University, Stanford, California 94305, USA. ; Anthropological Institute, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland. ; 1] Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, DK-1350 Copenhagen K, Denmark [2] Department of Archaeology and History, La Trobe University, Melbourne, Victoria 3086, Australia. ; Department of Anthropology and Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, 209F Davenport Hall, 607 Matthews Avenue, Urbana, Illinois 61801, USA. ; 1] Department of Genetics, School of Medicine, Stanford University, Littlefield Center, Stanford, California 94305, USA [2] Center for Evolutionary and Human Genomics, Stanford University, Littlefield Center, Stanford, California 94305, USA. ; 1] Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, DK-1350 Copenhagen K, Denmark [2] AMS, 14C Dating Centre, Department of Physics &Astronomy, University of Aarhus, Ny Munkegade 120, DK-8000 Aarhus C, Denmark. ; Department of Anthropology, Southern Methodist University, Dallas, Texas 75275, USA. ; 1] Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, DK-1350 Copenhagen K, Denmark [2] Department of Integrative Biology, University of California, Berkeley, 4134 Valley Life Sciences Building, Berkeley, California 94720, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26087396" target="_blank"〉PubMed〈/a〉

Description: Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617611/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617611/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Sudmant, Peter H -- Rausch, Tobias -- Gardner, Eugene J -- Handsaker, Robert E -- Abyzov, Alexej -- Huddleston, John -- Zhang, Yan -- Ye, Kai -- Jun, Goo -- Hsi-Yang Fritz, Markus -- Konkel, Miriam K -- Malhotra, Ankit -- Stutz, Adrian M -- Shi, Xinghua -- Paolo Casale, Francesco -- Chen, Jieming -- Hormozdiari, Fereydoun -- Dayama, Gargi -- Chen, Ken -- Malig, Maika -- Chaisson, Mark J P -- Walter, Klaudia -- Meiers, Sascha -- Kashin, Seva -- Garrison, Erik -- Auton, Adam -- Lam, Hugo Y K -- Jasmine Mu, Xinmeng -- Alkan, Can -- Antaki, Danny -- Bae, Taejeong -- Cerveira, Eliza -- Chines, Peter -- Chong, Zechen -- Clarke, Laura -- Dal, Elif -- Ding, Li -- Emery, Sarah -- Fan, Xian -- Gujral, Madhusudan -- Kahveci, Fatma -- Kidd, Jeffrey M -- Kong, Yu -- Lameijer, Eric-Wubbo -- McCarthy, Shane -- Flicek, Paul -- Gibbs, Richard A -- Marth, Gabor -- Mason, Christopher E -- Menelaou, Androniki -- Muzny, Donna M -- Nelson, Bradley J -- Noor, Amina -- Parrish, Nicholas F -- Pendleton, Matthew -- Quitadamo, Andrew -- Raeder, Benjamin -- Schadt, Eric E -- Romanovitch, Mallory -- Schlattl, Andreas -- Sebra, Robert -- Shabalin, Andrey A -- Untergasser, Andreas -- Walker, Jerilyn A -- Wang, Min -- Yu, Fuli -- Zhang, Chengsheng -- Zhang, Jing -- Zheng-Bradley, Xiangqun -- Zhou, Wanding -- Zichner, Thomas -- Sebat, Jonathan -- Batzer, Mark A -- McCarroll, Steven A -- 1000 Genomes Project Consortium -- Mills, Ryan E -- Gerstein, Mark B -- Bashir, Ali -- Stegle, Oliver -- Devine, Scott E -- Lee, Charles -- Eichler, Evan E -- Korbel, Jan O -- P01HG007497/HG/NHGRI NIH HHS/ -- R01 CA166661/CA/NCI NIH HHS/ -- R01 HG002385/HG/NHGRI NIH HHS/ -- R01 HG002898/HG/NHGRI NIH HHS/ -- R01CA166661/CA/NCI NIH HHS/ -- R01GM59290/GM/NIGMS NIH HHS/ -- R01HG002898/HG/NHGRI NIH HHS/ -- R01HG007068/HG/NHGRI NIH HHS/ -- RR029676-01/RR/NCRR NIH HHS/ -- RR19895/RR/NCRR NIH HHS/ -- T32 GM008666/GM/NIGMS NIH HHS/ -- U41 HG007497/HG/NHGRI NIH HHS/ -- U41HG007497/HG/NHGRI NIH HHS/ -- WT085532/Z/08/Z/Wellcome Trust/United Kingdom -- WT104947/Z/14/Z/Wellcome Trust/United Kingdom -- England -- Nature. 2015 Oct 1;526(7571):75-81. doi: 10.1038/nature15394.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Genome Sciences, University of Washington, 3720 15th Avenue NE, Seattle, Washington 98195-5065, USA. ; European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany. ; Institute for Genome Sciences, University of Maryland School of Medicine, 801 W Baltimore Street, Baltimore, Maryland 21201, USA. ; Department of Genetics, Harvard Medical School, Boston, 25 Shattuck Street, Boston, Massachusetts 02115, USA. ; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, Massachusetts 02142, USA. ; Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905, USA. ; Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA. ; Program in Computational Biology and Bioinformatics, Yale University, BASS 432 &437, 266 Whitney Avenue, New Haven, Connecticut 06520, USA. ; Department of Molecular Biophysics and Biochemistry, School of Medicine, Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA. ; The Genome Institute, Washington University School of Medicine, 4444 Forest Park Avenue, St Louis, Missouri 63108, USA. ; Department of Genetics, Washington University in St Louis, 4444 Forest Park Avenue, St Louis, Missouri 63108, USA. ; Department of Biostatistics and Center for Statistical Genetics, University of Michigan, 1415 Washington Heights, Ann Arbor, Michigan 48109, USA. ; Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, 1200 Pressler St., Houston, Texas 77030, USA. ; Department of Biological Sciences, Louisiana State University, 202 Life Sciences Building, Baton Rouge, Louisiana 70803, USA. ; The Jackson Laboratory for Genomic Medicine, 10 Discovery 263 Farmington Avenue, Farmington, Connecticut 06030, USA. ; Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, North Carolina 28223, USA. ; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. ; Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, Connecticut 06520, USA. ; Department of Computational Medicine &Bioinformatics, University of Michigan, 500 S. State Street, Ann Arbor, Michigan 48109, USA. ; The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, USA. ; The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. ; Department of Biology, Boston College, 355 Higgins Hall, 140 Commonwealth Avenue, Chestnut Hill, Massachusetts 02467, USA. ; Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, New York 10461, USA. ; Bina Technologies, Roche Sequencing, 555 Twin Dolphin Drive, Redwood City, California 94065, USA. ; Cancer Program, Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, Massachusetts 02142, USA. ; Department of Computer Engineering, Bilkent University, 06800 Ankara, Turkey. ; University of California San Diego (UCSD), 9500 Gilman Drive, La Jolla, California 92093, USA. ; National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892 USA. ; Department of Medicine, Washington University in St Louis, 4444 Forest Park Avenue, St Louis, Missouri 63108, USA. ; Siteman Cancer Center, 660 South Euclid Avenue, St Louis, Missouri 63110, USA. ; Department of Human Genetics, University of Michigan, 1241 Catherine Street, Ann Arbor, Michigan 48109, USA. ; Molecular Epidemiology, Leiden University Medical Center, Leiden 2300RA, The Netherlands. ; Baylor College of Medicine, 1 Baylor Plaza, Houston, Texas 77030, USA. ; The Department of Physiology and Biophysics and the HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, 1305 York Avenue, Weill Cornell Medical College, New York, New York 10065, USA. ; The Feil Family Brain and Mind Research Institute, 413 East 69th St, Weill Cornell Medical College, New York, New York 10065, USA. ; University of Oxford, 1 South Parks Road, Oxford OX3 9DS, UK. ; Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, 3584 CG, The Netherlands. ; Department of Genetics and Genomic Sciences, Icahn School of Medicine, New York School of Natural Sciences, 1428 Madison Avenue, New York, New York 10029, USA. ; Institute for Virus Research, Kyoto University, 53 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan. ; Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, 1112 East Clay Street, McGuire Hall, Richmond, Virginia 23298-0581, USA. ; Zentrum fur Molekulare Biologie, University of Heidelberg, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany. ; Department of Computer Science, Yale University, 51 Prospect Street, New Haven, Connecticut 06511, USA. ; Department of Graduate Studies - Life Sciences, Ewha Womans University, Ewhayeodae-gil, Seodaemun-gu, Seoul 120-750, South Korea.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26432246" target="_blank"〉PubMed〈/a〉

Description: DNA methylation is an epigenetic modification associated with transcriptional repression of promoters and is essential for mammalian development. Establishment of DNA methylation is mediated by the de novo DNA methyltransferases DNMT3A and DNMT3B, whereas DNMT1 ensures maintenance of methylation through replication. Absence of these enzymes is lethal, and somatic mutations in these genes have been associated with several human diseases. How genomic DNA methylation patterns are regulated remains poorly understood, as the mechanisms that guide recruitment and activity of DNMTs in vivo are largely unknown. To gain insights into this matter we determined genomic binding and site-specific activity of the mammalian de novo DNA methyltransferases DNMT3A and DNMT3B. We show that both enzymes localize to methylated, CpG-dense regions in mouse stem cells, yet are excluded from active promoters and enhancers. By specifically measuring sites of de novo methylation, we observe that enzymatic activity reflects binding. De novo methylation increases with CpG density, yet is excluded from nucleosomes. Notably, we observed selective binding of DNMT3B to the bodies of transcribed genes, which leads to their preferential methylation. This targeting to transcribed sequences requires SETD2-mediated methylation of lysine 36 on histone H3 and a functional PWWP domain of DNMT3B. Together these findings reveal how sequence and chromatin cues guide de novo methyltransferase activity to ensure methylome integrity.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Baubec, Tuncay -- Colombo, Daniele F -- Wirbelauer, Christiane -- Schmidt, Juliane -- Burger, Lukas -- Krebs, Arnaud R -- Akalin, Altuna -- Schubeler, Dirk -- England -- Nature. 2015 Apr 9;520(7546):243-7. doi: 10.1038/nature14176. Epub 2015 Jan 21.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, CH-4058 Basel, Switzerland. ; 1] Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, CH-4058 Basel, Switzerland [2] Swiss Institute of Bioinformatics. Maulbeerstrasse 66, CH-4058 Basel, Switzerland. ; 1] Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, CH-4058 Basel, Switzerland [2] University of Basel, Faculty of Sciences, Petersplatz 1, CH-4001 Basel, Switzerland.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25607372" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉England -- Nature. 2015 Dec 24;528(7583):459-67. doi: 10.1038/528459a.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26701036" target="_blank"〉PubMed〈/a〉

Description: Mutation rates vary within genomes, but the causes of this remain unclear. As many prior inferences rely on methods that assume an absence of selection, potentially leading to artefactual results, we call mutation events directly using a parent-offspring sequencing strategy focusing on Arabidopsis and using rice and honey bee for replication. Here we show that mutation rates are higher in heterozygotes and in proximity to crossover events. A correlation between recombination rate and intraspecific diversity is in part owing to a higher mutation rate in domains of high recombination/diversity. Implicating diversity per se as a cause, we find an approximately 3.5-fold higher mutation rate in heterozygotes than in homozygotes, with mutations occurring in closer proximity to heterozygous sites than expected by chance. In a genome that is a patchwork of heterozygous and homozygous domains, mutations occur disproportionately more often in the heterozygous domains. If segregating mutations predispose to a higher local mutation rate, clusters of genes dominantly under purifying selection (more commonly homozygous) and under balancing selection (more commonly heterozygous), might have low and high mutation rates, respectively. Our results are consistent with this, there being a ten times higher mutation rate in pathogen resistance genes, expected to be under positive or balancing selection. Consequently, we do not necessarily need to evoke extremely weak selection on the mutation rate to explain why mutational hot and cold spots might correspond to regions under positive/balancing and purifying selection, respectively.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Yang, Sihai -- Wang, Long -- Huang, Ju -- Zhang, Xiaohui -- Yuan, Yang -- Chen, Jian-Qun -- Hurst, Laurence D -- Tian, Dacheng -- England -- Nature. 2015 Jul 23;523(7561):463-7. doi: 10.1038/nature14649. Epub 2015 Jul 15.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China. ; The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, UK.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26176923" target="_blank"〉PubMed〈/a〉

Description: The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7x) or exomes (high read depth, 80x) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adiponectin (ADIPOQ) and low-density lipoprotein cholesterol (LDLR and RGAG1) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4773891/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4773891/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉UK10K Consortium -- Walter, Klaudia -- Min, Josine L -- Huang, Jie -- Crooks, Lucy -- Memari, Yasin -- McCarthy, Shane -- Perry, John R B -- Xu, ChangJiang -- Futema, Marta -- Lawson, Daniel -- Iotchkova, Valentina -- Schiffels, Stephan -- Hendricks, Audrey E -- Danecek, Petr -- Li, Rui -- Floyd, James -- Wain, Louise V -- Barroso, Ines -- Humphries, Steve E -- Hurles, Matthew E -- Zeggini, Eleftheria -- Barrett, Jeffrey C -- Plagnol, Vincent -- Richards, J Brent -- Greenwood, Celia M T -- Timpson, Nicholas J -- Durbin, Richard -- Soranzo, Nicole -- 091551/Wellcome Trust/United Kingdom -- 095515/Wellcome Trust/United Kingdom -- 095564/Wellcome Trust/United Kingdom -- 098498/Wellcome Trust/United Kingdom -- 100140/Wellcome Trust/United Kingdom -- 104036/Wellcome Trust/United Kingdom -- CZD/16/6/4/Chief Scientist Office/United Kingdom -- MC_UU_12013/3/Medical Research Council/United Kingdom -- RG/10/13/28570/British Heart Foundation/United Kingdom -- WT091310/Wellcome Trust/United Kingdom -- England -- Nature. 2015 Oct 1;526(7571):82-90. doi: 10.1038/nature14962. Epub 2015 Sep 14.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26367797" target="_blank"〉PubMed〈/a〉

Description: Monoclonal antibodies directed against cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), such as ipilimumab, yield considerable clinical benefit for patients with metastatic melanoma by inhibiting immune checkpoint activity, but clinical predictors of response to these therapies remain incompletely characterized. To investigate the roles of tumor-specific neoantigens and alterations in the tumor microenvironment in the response to ipilimumab, we analyzed whole exomes from pretreatment melanoma tumor biopsies and matching germline tissue samples from 110 patients. For 40 of these patients, we also obtained and analyzed transcriptome data from the pretreatment tumor samples. Overall mutational load, neoantigen load, and expression of cytolytic markers in the immune microenvironment were significantly associated with clinical benefit. However, no recurrent neoantigen peptide sequences predicted responder patient populations. Thus, detailed integrated molecular characterization of large patient cohorts may be needed to identify robust determinants of response and resistance to immune checkpoint inhibitors.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Van Allen, Eliezer M -- Miao, Diana -- Schilling, Bastian -- Shukla, Sachet A -- Blank, Christian -- Zimmer, Lisa -- Sucker, Antje -- Hillen, Uwe -- Foppen, Marnix H Geukes -- Goldinger, Simone M -- Utikal, Jochen -- Hassel, Jessica C -- Weide, Benjamin -- Kaehler, Katharina C -- Loquai, Carmen -- Mohr, Peter -- Gutzmer, Ralf -- Dummer, Reinhard -- Gabriel, Stacey -- Wu, Catherine J -- Schadendorf, Dirk -- Garraway, Levi A -- U54 HG003067/HG/NHGRI NIH HHS/ -- New York, N.Y. -- Science. 2015 Oct 9;350(6257):207-11. doi: 10.1126/science.aad0095. Epub 2015 Sep 10.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA 02215, USA. ; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. ; Department of Dermatology, University Hospital, University Duisburg-Essen, 45147 Essen, Germany. German Cancer Consortium(DKTK), 69121 Heidelberg, Germany. ; Department of Medical Oncology, Netherlands Cancer Institute, 1066 CX Amsterdam, Netherlands. ; Department of Dermatology, University Hospital Zurich, 8091 Zurich, Switzerland. ; Skin Cancer Unit, German Cancer Research Center(DKTK), 69121 Heidelberg, Germany. Skin Cancer Unit, German Cancer Research Center(DKTK), 69121 Heidelberg, Germany. Department of Dermatology, Venerology, and Allergology, University Medical Center, Ruprecht-Karls University of Heidelberg, 68167 Mannheim, Germany. ; Department of Dermatology, University Hospital, Ruprecht-Karls University of Heidelberg, 69120 Heidelberg, Germany. ; Department of Dermatology, University Hospital Tubingen, 72076 Tubingen, Germany. ; Department of Dermatology, University Hospital Kiel, 24105 Kiel, Germany. ; Department of Dermatology, University Medical Center, 55131 Mainz, Germany. ; Department of Dermatology, Elbe-Kliniken, 21614 Buxtehude, Germany. ; Department of Dermatology and Allergy, Skin Cancer Center Hannover, Hannover Medical School, 30625 Hannover, Germany. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. ; Department of Dermatology, University Hospital, University Duisburg-Essen, 45147 Essen, Germany. German Cancer Consortium(DKTK), 69121 Heidelberg, Germany. levi_garraway@dfci.harvard.edu dirk.schadendorf@uk-essen.de. ; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA 02215, USA. levi_garraway@dfci.harvard.edu dirk.schadendorf@uk-essen.de.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26359337" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Lloyd, K C Kent -- Meehan, Terry -- Beaudet, Arthur -- Murray, Steve -- Svenson, Karen -- McKerlie, Colin -- West, David -- Morse, Iva -- Parkinson, Helen -- Brown, Steve -- Mallon, Ann-Marie -- Moore, Mark -- U42 OD011175/OD/NIH HHS/ -- U42 OD011185/OD/NIH HHS/ -- U54 HG006332/HG/NHGRI NIH HHS/ -- U54 HG006364/HG/NHGRI NIH HHS/ -- New York, N.Y. -- Science. 2015 Jul 24;349(6246):390. doi: 10.1126/science.349.6246.390-a. Epub 2015 Jul 23.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉University of California, Davis, Davis, CA 95616, USA. kclloyd@ucdavis.edu. ; European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. ; Baylor College of Medicine, Houston, TX 77030, USA. ; The Jackson Laboratory, Bar Harbor, ME 04609, USA. ; Toronto Centre for Phenogenomics, Toronto, ON, M5T 3H7, Canada. ; Children's Hospital Oakland Research Institute, Oakland, CA 94609, USA. ; Charles River Laboratories, Wilmington, MA 01887, USA. ; Medical Research Council Harwell, Oxfordshire, OX11 0RD, UK. ; International Mouse Phenotyping Consortium, Hinxton, Cambridge, CB10 1SD, UK.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26206923" target="_blank"〉PubMed〈/a〉

Description: Large-scale genomic investigations have just begun to illuminate the molecular genetic contributions to major psychiatric illnesses, ranging from small-effect-size common variants to larger-effect-size rare mutations. The findings provide causal anchors from which to understand their neurobiological basis. Although these studies represent enormous success, they highlight major challenges reflected in the heterogeneity and polygenicity of all of these conditions and the difficulty of connecting multiple levels of molecular, cellular, and circuit functions to complex human behavior. Nevertheless, these advances place us on the threshold of a new frontier in the pathophysiological understanding, diagnosis, and treatment of psychiatric disease.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4694563/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4694563/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Geschwind, Daniel H -- Flint, Jonathan -- 5R01 MH094714/MH/NIMH NIH HHS/ -- 5R01 MH100027/MH/NIMH NIH HHS/ -- R01 MH094714/MH/NIMH NIH HHS/ -- R01 MH100027/MH/NIMH NIH HHS/ -- Wellcome Trust/United Kingdom -- New York, N.Y. -- Science. 2015 Sep 25;349(6255):1489-94. doi: 10.1126/science.aaa8954. Epub 2015 Sep 24.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Departments of Neurology, Psychiatry, and Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. dhg@mednet.ucla jf@well.ox.ac.uk. ; Wellcome Trust Center for Human Genetics, University of Oxford, Oxford, UK. dhg@mednet.ucla jf@well.ox.ac.uk.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26404826" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Kaiser, Jocelyn -- New York, N.Y. -- Science. 2015 Sep 25;349(6255):1475. doi: 10.1126/science.349.6255.1475.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26404823" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4394183/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4394183/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Baltimore, David -- Berg, Paul -- Botchan, Michael -- Carroll, Dana -- Charo, R Alta -- Church, George -- Corn, Jacob E -- Daley, George Q -- Doudna, Jennifer A -- Fenner, Marsha -- Greely, Henry T -- Jinek, Martin -- Martin, G Steven -- Penhoet, Edward -- Puck, Jennifer -- Sternberg, Samuel H -- Weissman, Jonathan S -- Yamamoto, Keith R -- P50 HG005550/HG/NHGRI NIH HHS/ -- T32 GM066698/GM/NIGMS NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2015 Apr 3;348(6230):36-8. doi: 10.1126/science.aab1028. Epub 2015 Mar 19.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉California Institute of Technology, Mail Code 147-75, Pasadena, CA 91125, USA. ; Stanford University School of Medicine, 291 Campus Drive, Stanford, CA 94305, USA. ; University of California, Berkeley, 450 Li Ka Shing no. 3370, Berkeley, CA 94720-3370, USA. Innovative Genomics Initiative, University of California, Berkeley, 188 Li Ka Shing Center, Berkeley, CA 94720-3370, USA. ; Department of Biochemistry, University of Utah School of Medicine, 15 North Medical Drive East, Room 4100, Salt Lake City, UT 84112-5650, USA. ; Department of Medical History and Bioethics, School of Medicine and Public Health, University of Wisconsin Law School, 975 Bascom Mall, Madison, WI 53706, USA. ; Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA. ; Innovative Genomics Initiative, University of California, Berkeley, 188 Li Ka Shing Center, Berkeley, CA 94720-3370, USA. ; Boston Children's Hospital, 300 Longwood Avenue, Karp Family Building, 7th Floor, Boston, MA 02115, USA. Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, MD 20815, USA. ; Innovative Genomics Initiative, University of California, Berkeley, 188 Li Ka Shing Center, Berkeley, CA 94720-3370, USA. Departments of Molecular and Cell Biology and Chemistry, Howard Hughes Medical Institute, 731 Stanley Hall, MS 3220, University of California, Berkeley, Berkeley, CA 94720-3220, USA. doudna@berkeley.edu. ; Center for Law and the Biosciences, Crown Quadrangle 559 Nathan Abbott Way Stanford, CA 94305-8610, USA. ; Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland. ; Department of Molecular and Cell Biology, College of Letters and Science, University of California, Berkeley, 210K Durant Hall, Berkeley, CA 94720-2920, USA. ; Alta Partners, One Embarcadero Center, 37th Floor, San Francisco, CA 94111, USA. ; Department of Pediatrics UCSF School of Medicine, 513 Parnassus Avenue, San Francisco, CA 94143, USA. ; Department of Chemistry, 731 Stanley Hall, MS 3220, University of California, Berkeley, CA 94720-3220, USA. ; Innovative Genomics Initiative, University of California, Berkeley, 188 Li Ka Shing Center, Berkeley, CA 94720-3370, USA. Department of Cellular and Molecular Pharmacology, Howard Hughes Medical Institute, University of California, San Francisco, Byers Hall, 1700 4th Street, San Francisco, CA 94158-2330, USA. ; Innovative Genomics Initiative, University of California, Berkeley, 188 Li Ka Shing Center, Berkeley, CA 94720-3370, USA. UCSF School of Medicine, 600 16th Street, San Francisco, CA 94158, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25791083" target="_blank"〉PubMed〈/a〉

Description: How and when the Americas were populated remains contentious. Using ancient and modern genome-wide data, we found that the ancestors of all present-day Native Americans, including Athabascans and Amerindians, entered the Americas as a single migration wave from Siberia no earlier than 23 thousand years ago (ka) and after no more than an 8000-year isolation period in Beringia. After their arrival to the Americas, ancestral Native Americans diversified into two basal genetic branches around 13 ka, one that is now dispersed across North and South America and the other restricted to North America. Subsequent gene flow resulted in some Native Americans sharing ancestry with present-day East Asians (including Siberians) and, more distantly, Australo-Melanesians. Putative "Paleoamerican" relict populations, including the historical Mexican Pericues and South American Fuego-Patagonians, are not directly related to modern Australo-Melanesians as suggested by the Paleoamerican Model.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4733658/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4733658/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Raghavan, Maanasa -- Steinrucken, Matthias -- Harris, Kelley -- Schiffels, Stephan -- Rasmussen, Simon -- DeGiorgio, Michael -- Albrechtsen, Anders -- Valdiosera, Cristina -- Avila-Arcos, Maria C -- Malaspinas, Anna-Sapfo -- Eriksson, Anders -- Moltke, Ida -- Metspalu, Mait -- Homburger, Julian R -- Wall, Jeff -- Cornejo, Omar E -- Moreno-Mayar, J Victor -- Korneliussen, Thorfinn S -- Pierre, Tracey -- Rasmussen, Morten -- Campos, Paula F -- Damgaard, Peter de Barros -- Allentoft, Morten E -- Lindo, John -- Metspalu, Ene -- Rodriguez-Varela, Ricardo -- Mansilla, Josefina -- Henrickson, Celeste -- Seguin-Orlando, Andaine -- Malmstrom, Helena -- Stafford, Thomas Jr -- Shringarpure, Suyash S -- Moreno-Estrada, Andres -- Karmin, Monika -- Tambets, Kristiina -- Bergstrom, Anders -- Xue, Yali -- Warmuth, Vera -- Friend, Andrew D -- Singarayer, Joy -- Valdes, Paul -- Balloux, Francois -- Leboreiro, Ilan -- Vera, Jose Luis -- Rangel-Villalobos, Hector -- Pettener, Davide -- Luiselli, Donata -- Davis, Loren G -- Heyer, Evelyne -- Zollikofer, Christoph P E -- Ponce de Leon, Marcia S -- Smith, Colin I -- Grimes, Vaughan -- Pike, Kelly-Anne -- Deal, Michael -- Fuller, Benjamin T -- Arriaza, Bernardo -- Standen, Vivien -- Luz, Maria F -- Ricaut, Francois -- Guidon, Niede -- Osipova, Ludmila -- Voevoda, Mikhail I -- Posukh, Olga L -- Balanovsky, Oleg -- Lavryashina, Maria -- Bogunov, Yuri -- Khusnutdinova, Elza -- Gubina, Marina -- Balanovska, Elena -- Fedorova, Sardana -- Litvinov, Sergey -- Malyarchuk, Boris -- Derenko, Miroslava -- Mosher, M J -- Archer, David -- Cybulski, Jerome -- Petzelt, Barbara -- Mitchell, Joycelynn -- Worl, Rosita -- Norman, Paul J -- Parham, Peter -- Kemp, Brian M -- Kivisild, Toomas -- Tyler-Smith, Chris -- Sandhu, Manjinder S -- Crawford, Michael -- Villems, Richard -- Smith, David Glenn -- Waters, Michael R -- Goebel, Ted -- Johnson, John R -- Malhi, Ripan S -- Jakobsson, Mattias -- Meltzer, David J -- Manica, Andrea -- Durbin, Richard -- Bustamante, Carlos D -- Song, Yun S -- Nielsen, Rasmus -- Willerslev, Eske -- 098051/Wellcome Trust/United Kingdom -- 261213/European Research Council/International -- 2R01HG003229-09/HG/NHGRI NIH HHS/ -- BB/H005854/1/Biotechnology and Biological Sciences Research Council/United Kingdom -- R01-AI17892/AI/NIAID NIH HHS/ -- R01-GM094402/GM/NIGMS NIH HHS/ -- New York, N.Y. -- Science. 2015 Aug 21;349(6250):aab3884. doi: 10.1126/science.aab3884. Epub 2015 Jul 21.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. ; Computer Science Division, University of California, Berkeley, Berkeley, CA 94720, USA. Department of Statistics, University of California, Berkeley, Berkeley, CA 94720, USA. Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003, USA. ; Department of Mathematics, University of California, Berkeley, Berkeley, CA 94720, USA. ; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. ; Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Building 208, 2800 Kongens Lyngby, Denmark. ; Departments of Biology and Statistics, Pennsylvania State University, 502 Wartik Laboratory, University Park, PA 16802, USA. ; The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen, Denmark. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Department of Archaeology and History, La Trobe University, Melbourne, Victoria 3086, Australia. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Department of Genetics, School of Medicine, Stanford University, 300 Pasteur Drive, Lane Building, Room L331, Stanford, CA 94305, USA. ; Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK. Integrative Systems Biology Laboratory, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia. ; Estonian Biocentre, Evolutionary Biology Group, Tartu 51010, Estonia. Department of Evolutionary Biology, University of Tartu, Tartu 51010, Estonia. ; Department of Genetics, School of Medicine, Stanford University, 300 Pasteur Drive, Lane Building, Room L331, Stanford, CA 94305, USA. ; Institute for Human Genetics, University of California San Francisco, 513 Parnassus Avenue, San Francisco, CA 94143, USA. ; School of Biological Sciences, Washington State University, Post Office Box 644236, Heald 429, Pullman, WA 99164, USA. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Centro de Investigacion en Ciencias del Mar y Limnologia/Centro Interdisciplinar de Investigacao Marinha e Ambiental, Centro Interdisciplinar de Investigacao Marinha e Ambiental, Universidade do Porto, Rua dos Bragas 289, 4050-123 Porto, Portugal. ; Department of Anthropology, University of Illinois at Urbana-Champaign, 607 S. Mathews Avenue, Urbana, IL 61801, USA. ; Centro Mixto, Universidad Complutense de Madrid-Instituto de Salud Carlos III de Evolucion y Comportamiento Humano, Madrid, Spain. ; Instituto Nacional de Antropologia e Historia, Moneda 13, Centro, Cuauhtemoc, 06060 Mexico City, Mexico. ; University of Utah, Department of Anthropology, 270 S 1400 E, Salt Lake City, UT 84112, USA. ; Department of Evolutionary Biology and Science for Life Laboratory, Uppsala University, Norbyvagen 18D, SE-752 36 Uppsala, Sweden. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Acceleration Mass Spectrometry 14C Dating Centre, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, 8000 Aarhus, Denmark. ; Department of Genetics, School of Medicine, Stanford University, 300 Pasteur Drive, Lane Building, Room L331, Stanford, CA 94305, USA. Laboratorio Nacional de Genomica para la Biodiversidad (LANGEBIO), Centro de Investigacion y de Estudios Avanzados, Irapuato, Guanajuato 36821, Mexico. ; Estonian Biocentre, Evolutionary Biology Group, Tartu 51010, Estonia. ; Genetics Institute, University College London, Gower Street, London WC1E 6BT, UK. Evolutionsbiologiskt Centrum, Norbyvagen 18D, 75236 Uppsala, Sweden. ; Department of Geography, University of Cambridge, Downing Place, Cambridge CB2 3EN, UK. ; Centre for Past Climate Change and Department of Meteorology, University of Reading, Earley Gate, Post Office Box 243, Reading, UK. ; School of Geographical Sciences, University Road, Clifton, Bristol BS8 1SS, UK. ; Genetics Institute, University College London, Gower Street, London WC1E 6BT, UK. ; Escuela Nacional de AntropologIa e Historia, Periferico Sur y Zapote s/n Colonia Isidro Fabela, Tlalpan, Isidro Fabela, 14030 Mexico City, Mexico. ; Instituto de Investigacion en Genetica Molecular, Universidad de Guadalajara, Ocotlan, Mexico. ; Dipartimento di Scienze Biologiche, Geologiche e Ambientali (BiGeA), Universita di Bologna, Via Selmi 3, 40126 Bologna, Italy. ; Department of Anthropology, Oregon State University, 238 Waldo Hall, Corvallis, OR 97331 USA. ; Museum National d'Histoire Naturelle, CNRS, Universite Paris 7 Diderot, Sorbonne Paris Cite, Sorbonne Universites, Unite Eco-Anthropologie et Ethnobiologie (UMR7206), Paris, France. ; Anthropological Institute and Museum, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland. ; Department of Archaeology and History, La Trobe University, Melbourne, Victoria 3086, Australia. ; Department of Archaeology, Memorial University, Queen's College, 210 Prince Philip Drive, St. John's, Newfoundland A1C 5S7, Canada. Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, Leipzig 04103, Germany. ; Department of Archaeology, Memorial University, Queen's College, 210 Prince Philip Drive, St. John's, Newfoundland A1C 5S7, Canada. ; Department of Earth System Science, University of California, Irvine, Keck Carbon Cycle Accelerator Mass Spectrometry Group, B321 Croul Hall, Irvine, CA 92697, USA. ; Instituto de Alta Investigacion, Universidad de Tarapaca, 18 de Septiembre 2222, Carsilla 6-D Arica, Chile. ; Departamento de Antropologia, Universidad de Tarapaca, 18 de Septiembre 2222, Carsilla 6-D Arica, Chile. ; Fundacao Museu do Homem Americano, Centro Cultural Sergio Motta, Campestre, 64770-000 Sao Raimundo Nonato, Brazil. ; Laboratoire d'Anthropologie Moleculaire et Imagerie de Synthese UMR-5288, CNRS, Universite de Toulouse, 31073 Toulouse, France. ; Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090 Novosibirsk, Russia. Novosibirsk State University, 2 Pirogova Street, 630090 Novosibirsk, Russia. ; Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090 Novosibirsk, Russia. Institute of Internal Medicine, Siberian Branch of RAS, 175/1 ul. B. Bogatkova, Novosibirsk 630089, Russia. Novosibirsk State University, Laboratory of Molecular Epidemiology and Bioinformatics, 630090 Novosibirsk, Russia. ; Vavilov Institute of General Genetics, Gubkina 3, 119333 Moscow, Russia. Research Centre for Medical Genetics, Moskvorechie 1, 115478 Moscow, Russia. ; Kemerovo State University, Krasnaya 3, 650000 Kemerovo, Russia. ; Vavilov Institute of General Genetics, Gubkina 3, 119333 Moscow, Russia. ; Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Prospekt Oktyabrya 71, 450054 Ufa, Russia. Department of Genetics and Fundamental Medicine, Bashkir State University, Zaki Validi 32, 450076 Ufa, Russia. ; Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090 Novosibirsk, Russia. ; Research Centre for Medical Genetics, Moskvorechie 1, 115478 Moscow, Russia. ; Department of Molecular Genetics, Yakut Scientific Centre of Complex Medical Problems, Sergelyahskoe Shosse 4, 677010 Yakutsk, Russia. Laboratory of Molecular Biology, Institute of Natural Sciences, M. K. Ammosov North-Eastern Federal University, 677000 Yakutsk, Russia. ; Estonian Biocentre, Evolutionary Biology Group, Tartu 51010, Estonia. Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Prospekt Oktyabrya 71, 450054 Ufa, Russia. ; Institute of Biological Problems of the North, Russian Academy of Sciences, Portovaya Street 18, Magadan 685000, Russia. ; Department of Anthropology, Western Washington University, Bellingham, WA 98225, USA. ; Department of Anthropology, Northwest Community College, 353 Fifth Street, Prince Rupert, British Columbia V8J 3L6, Canada. ; Canadian Museum of History, 100 Rue Laurier, Gatineau, Quebec K1A 0M8, Canada. University of Western Ontario, London, Ontario N6A 3K7, Canada. Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada. ; Metlakatla Treaty Office, Post Office Box 224, Prince Rupert, BC V8J 3P6, Canada. ; Sealaska Heritage Institute, 105 S. Seward Street, Juneau, AK 99801, USA. ; Department of Structural Biology, Stanford University School of Medicine, D100 Fairchild Science Building, Stanford, CA 94305-5126, USA. ; School of Biological Sciences, Washington State University, Post Office Box 644236, Heald 429, Pullman, WA 99164, USA. Department of Anthropology, Washington State University, Pullman, WA 99163, USA. ; Estonian Biocentre, Evolutionary Biology Group, Tartu 51010, Estonia. Division of Biological Anthropology, University of Cambridge, Henry Wellcome Building, Fitzwilliam Street, Cambridge CB2 1QH, UK. ; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. Department of Medicine, University of Cambridge, Medical Research Council Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK. ; Laboratory of Biological Anthropology, University of Kansas, 1415 Jayhawk Boulevard, 622 Fraser Hall, Lawrence, KS 66045, USA. ; Molecular Anthropology Laboratory, 209 Young Hall, Department of Anthropology, University of California, One Shields Avenue, Davis, CA 95616, USA. ; Center for the Study of the First Americans, Texas A&M University, College Station, TX 77843-4352, USA. Department of Anthropology, Texas A&M University, College Station, TX 77843-4352, USA. Department of Geography, Texas A&M University, College Station, TX 77843-4352, USA. ; Center for the Study of the First Americans, Texas A&M University, College Station, TX 77843-4352, USA. ; Santa Barbara Museum of Natural History, 2559 Puesta del Sol, Santa Barbara, CA 93105, USA. ; Department of Anthropology, University of Illinois at Urbana-Champaign, 607 S. Mathews Avenue, Urbana, IL 61801, USA. Carle R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Department of Anthropology, Southern Methodist University, Dallas, TX 75275, USA. ; Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK. ; Computer Science Division, University of California, Berkeley, Berkeley, CA 94720, USA. Department of Statistics, University of California, Berkeley, Berkeley, CA 94720, USA. Department of Integrative Biology, University of California, 3060 Valley Life Sciences Building 3140, Berkeley, CA 94720, USA. ewillierslev@snm.ku.dk rasmus_nielsen@berkeley.edu yss@berkeley.edu. ; Department of Integrative Biology, University of California, 3060 Valley Life Sciences Building 3140, Berkeley, CA 94720, USA. ewillierslev@snm.ku.dk rasmus_nielsen@berkeley.edu yss@berkeley.edu. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. ewillierslev@snm.ku.dk rasmus_nielsen@berkeley.edu yss@berkeley.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26198033" target="_blank"〉PubMed〈/a〉

Description: Ecological character displacement is a process of morphological divergence that reduces competition for limited resources. We used genomic analysis to investigate the genetic basis of a documented character displacement event in Darwin's finches on Daphne Major in the Galapagos Islands: The medium ground finch diverged from its competitor, the large ground finch, during a severe drought. We discovered a genomic region containing the HMGA2 gene that varies systematically among Darwin's finch species with different beak sizes. Two haplotypes that diverged early in the radiation were involved in the character displacement event: Genotypes associated with large beak size were at a strong selective disadvantage in medium ground finches (selection coefficient s = 0.59). Thus, a major locus has apparently facilitated a rapid ecological diversification in the adaptive radiation of Darwin's finches.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Lamichhaney, Sangeet -- Han, Fan -- Berglund, Jonas -- Wang, Chao -- Almen, Markus Sallman -- Webster, Matthew T -- Grant, B Rosemary -- Grant, Peter R -- Andersson, Leif -- New York, N.Y. -- Science. 2016 Apr 22;352(6284):470-4. doi: 10.1126/science.aad8786.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden. ; Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. ; Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden. Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden. Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA. leif.andersson@imbim.uu.se.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27102486" target="_blank"〉PubMed〈/a〉

Description: To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Tirosh, Itay -- Izar, Benjamin -- Prakadan, Sanjay M -- Wadsworth, Marc H 2nd -- Treacy, Daniel -- Trombetta, John J -- Rotem, Asaf -- Rodman, Christopher -- Lian, Christine -- Murphy, George -- Fallahi-Sichani, Mohammad -- Dutton-Regester, Ken -- Lin, Jia-Ren -- Cohen, Ofir -- Shah, Parin -- Lu, Diana -- Genshaft, Alex S -- Hughes, Travis K -- Ziegler, Carly G K -- Kazer, Samuel W -- Gaillard, Aleth -- Kolb, Kellie E -- Villani, Alexandra-Chloe -- Johannessen, Cory M -- Andreev, Aleksandr Y -- Van Allen, Eliezer M -- Bertagnolli, Monica -- Sorger, Peter K -- Sullivan, Ryan J -- Flaherty, Keith T -- Frederick, Dennie T -- Jane-Valbuena, Judit -- Yoon, Charles H -- Rozenblatt-Rosen, Orit -- Shalek, Alex K -- Regev, Aviv -- Garraway, Levi A -- 1U24CA180922/CA/NCI NIH HHS/ -- DP2 OD020839/OD/NIH HHS/ -- K99 CA194163/CA/NCI NIH HHS/ -- K99CA194163/CA/NCI NIH HHS/ -- P01CA163222/CA/NCI NIH HHS/ -- P30-CA14051/CA/NCI NIH HHS/ -- P50GM107618/GM/NIGMS NIH HHS/ -- R35CA197737/CA/NCI NIH HHS/ -- U54CA112962/CA/NCI NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2016 Apr 8;352(6282):189-96. doi: 10.1126/science.aad0501.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA. Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA 02215, USA. bizar@partners.org aregev@broadinstitute.org levi_garraway@dfci.harvard.edu. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA. Department of Chemistry, MIT, Cambridge, MA 02142, USA. Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA 02139, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA. Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA 02215, USA. ; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. ; Program in Therapeutic Sciences, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA. Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. ; HMS LINCS Center and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA. ; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA. Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA 02139, USA. Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA. ; Department of Surgical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA. Department of Surgical Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. ; Program in Therapeutic Sciences, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. HMS LINCS Center and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA. Ludwig Center at Harvard, Boston, MA 02215, USA. ; Division of Medical Oncology, Massachusetts General Hospital Cancer Center, Boston, MA 02114, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA. Department of Chemistry, MIT, Cambridge, MA 02142, USA. Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA 02139, USA. Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA. Department of Immunology, Massachusetts General Hospital, Boston, MA 02114, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Department of Biology and Koch Institute, MIT, Boston, MA 02142, USA. Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA. bizar@partners.org aregev@broadinstitute.org levi_garraway@dfci.harvard.edu. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. bizar@partners.org aregev@broadinstitute.org levi_garraway@dfci.harvard.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27124452" target="_blank"〉PubMed〈/a〉

Description: Accurate sequence and assembly of genomes is a critical first step for studies of genetic variation. We generated a high-quality assembly of the gorilla genome using single-molecule, real-time sequence technology and a string graph de novo assembly algorithm. The new assembly improves contiguity by two to three orders of magnitude with respect to previously released assemblies, recovering 87% of missing reference exons and incomplete gene models. Although regions of large, high-identity segmental duplications remain largely unresolved, this comprehensive assembly provides new biological insight into genetic diversity, structural variation, gene loss, and representation of repeat structures within the gorilla genome. The approach provides a path forward for the routine assembly of mammalian genomes at a level approaching that of the current quality of the human genome.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Gordon, David -- Huddleston, John -- Chaisson, Mark J P -- Hill, Christopher M -- Kronenberg, Zev N -- Munson, Katherine M -- Malig, Maika -- Raja, Archana -- Fiddes, Ian -- Hillier, LaDeana W -- Dunn, Christopher -- Baker, Carl -- Armstrong, Joel -- Diekhans, Mark -- Paten, Benedict -- Shendure, Jay -- Wilson, Richard K -- Haussler, David -- Chin, Chen-Shan -- Eichler, Evan E -- HG002385/HG/NHGRI NIH HHS/ -- HG003079/HG/NHGRI NIH HHS/ -- HG007234/HG/NHGRI NIH HHS/ -- HG007635/HG/NHGRI NIH HHS/ -- HG007990/HG/NHGRI NIH HHS/ -- R01 HG002385/HG/NHGRI NIH HHS/ -- U41 HG007635/HG/NHGRI NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2016 Apr 1;352(6281):aae0344. doi: 10.1126/science.aae0344.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA. Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA. ; Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA. ; Genomics Institute, University of California Santa Cruz and Howard Hughes Medical Institute, Santa Cruz, CA 95064, USA. ; McDonnell Genome Institute, Department of Medicine, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA. ; Pacific Biosciences of California, Menlo Park, CA 94025, USA. ; Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA. Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA. eee@gs.washington.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27034376" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Pennisi, Elizabeth -- New York, N.Y. -- Science. 2016 Feb 19;351(6275):800-1. doi: 10.1126/science.351.6275.800. Epub 2016 Feb 18.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26912872" target="_blank"〉PubMed〈/a〉

Description: Presence of excess unaltered, wild-type (WT) DNA providing no information of biological or clinical value often masks rare alterations containing diagnostic or therapeutic clues in cancer, prenatal diagnosis, infectious diseases or organ transplantation. With the surge of high-throughput technologies there is a growing demand for removing unaltered DNA over large pools-of-sequences. Here we present nuclease-assisted minor-allele enrichment with probe-overlap (NaME-PrO), a single-step approach with broad genome coverage that can remove WT-DNA from numerous sequences simultaneously, prior to genomic analysis. NaME-PrO employs a double-strand-DNA-specific nuclease and overlapping oligonucleotide-probes interrogating WT-DNA targets and guiding nuclease digestion to these sites. Mutation-containing DNA creates probe-DNA mismatches that inhibit digestion, thus subsequent DNA-amplification magnifies DNA-alterations at all selected targets. We demonstrate several-hundred-fold mutation enrichment in diverse human samples on multiple clinically relevant targets including tumor samples and circulating DNA in 50-plex reactions. Enrichment enables routine mutation detection at 0.01% abundance while by adjusting conditions it is possible to sequence mutations down to 0.00003% abundance, or to scan tumor-suppressor genes for rare mutations. NaME-PrO introduces a simple and highly parallel process to remove un-informative DNA sequences and unmask clinically and biologically useful alterations.

Description: Although reconstruction of the phylogeny of living birds has progressed tremendously in the last decade, the evolutionary history of Neoaves--a clade that encompasses nearly all living bird species--remains the greatest unresolved challenge in dinosaur systematics. Here we investigate avian phylogeny with an unprecedented scale of data: 〉390,000 bases of genomic sequence data from each of 198 species of living birds, representing all major avian lineages, and two crocodilian outgroups. Sequence data were collected using anchored hybrid enrichment, yielding 259 nuclear loci with an average length of 1,523 bases for a total data set of over 7.8 x 10(7) bases. Bayesian and maximum likelihood analyses yielded highly supported and nearly identical phylogenetic trees for all major avian lineages. Five major clades form successive sister groups to the rest of Neoaves: (1) a clade including nightjars, other caprimulgiforms, swifts, and hummingbirds; (2) a clade uniting cuckoos, bustards, and turacos with pigeons, mesites, and sandgrouse; (3) cranes and their relatives; (4) a comprehensive waterbird clade, including all diving, wading, and shorebirds; and (5) a comprehensive landbird clade with the enigmatic hoatzin (Opisthocomus hoazin) as the sister group to the rest. Neither of the two main, recently proposed Neoavian clades--Columbea and Passerea--were supported as monophyletic. The results of our divergence time analyses are congruent with the palaeontological record, supporting a major radiation of crown birds in the wake of the Cretaceous-Palaeogene (K-Pg) mass extinction.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Prum, Richard O -- Berv, Jacob S -- Dornburg, Alex -- Field, Daniel J -- Townsend, Jeffrey P -- Lemmon, Emily Moriarty -- Lemmon, Alan R -- England -- Nature. 2015 Oct 22;526(7574):569-73. doi: 10.1038/nature15697. Epub 2015 Oct 7.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Ecology &Evolutionary Biology, Yale University, New Haven, Connecticut 06520, USA. ; Peabody Museum of Natural History, Yale University, New Haven, Connecticut 06520, USA. ; Department of Ecology and Evolutionary Biology, Fuller Evolutionary Biology Program, Cornell University, and Cornell Laboratory of Ornithology, Ithaca, New York 14853, USA. ; North Carolina Museum of Natural Sciences, Raleigh, North Carolina 27601, USA. ; Department of Geology &Geophysics, Yale University, New Haven, Connecticut 06520, USA. ; Department of Biostatistics, and Program in Computational Biology and Bioinformatics, Yale University, NewHaven, Connecticut 06520, USA. ; Department of Biological Science, Florida State University, Tallahassee, Florida 32306, USA. ; Department of Scientific Computing, Florida State University, Tallahassee, Florida 32306, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26444237" target="_blank"〉PubMed〈/a〉

Description: Chronic lymphocytic leukaemia (CLL) is a frequent disease in which the genetic alterations determining the clinicobiological behaviour are not fully understood. Here we describe a comprehensive evaluation of the genomic landscape of 452 CLL cases and 54 patients with monoclonal B-lymphocytosis, a precursor disorder. We extend the number of CLL driver alterations, including changes in ZNF292, ZMYM3, ARID1A and PTPN11. We also identify novel recurrent mutations in non-coding regions, including the 3' region of NOTCH1, which cause aberrant splicing events, increase NOTCH1 activity and result in a more aggressive disease. In addition, mutations in an enhancer located on chromosome 9p13 result in reduced expression of the B-cell-specific transcription factor PAX5. The accumulative number of driver alterations (0 to 〉/=4) discriminated between patients with differences in clinical behaviour. This study provides an integrated portrait of the CLL genomic landscape, identifies new recurrent driver mutations of the disease, and suggests clinical interventions that may improve the management of this neoplasia.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Puente, Xose S -- Bea, Silvia -- Valdes-Mas, Rafael -- Villamor, Neus -- Gutierrez-Abril, Jesus -- Martin-Subero, Jose I -- Munar, Marta -- Rubio-Perez, Carlota -- Jares, Pedro -- Aymerich, Marta -- Baumann, Tycho -- Beekman, Renee -- Belver, Laura -- Carrio, Anna -- Castellano, Giancarlo -- Clot, Guillem -- Colado, Enrique -- Colomer, Dolors -- Costa, Dolors -- Delgado, Julio -- Enjuanes, Anna -- Estivill, Xavier -- Ferrando, Adolfo A -- Gelpi, Josep L -- Gonzalez, Blanca -- Gonzalez, Santiago -- Gonzalez, Marcos -- Gut, Marta -- Hernandez-Rivas, Jesus M -- Lopez-Guerra, Monica -- Martin-Garcia, David -- Navarro, Alba -- Nicolas, Pilar -- Orozco, Modesto -- Payer, Angel R -- Pinyol, Magda -- Pisano, David G -- Puente, Diana A -- Queiros, Ana C -- Quesada, Victor -- Romeo-Casabona, Carlos M -- Royo, Cristina -- Royo, Romina -- Rozman, Maria -- Russinol, Nuria -- Salaverria, Itziar -- Stamatopoulos, Kostas -- Stunnenberg, Hendrik G -- Tamborero, David -- Terol, Maria J -- Valencia, Alfonso -- Lopez-Bigas, Nuria -- Torrents, David -- Gut, Ivo -- Lopez-Guillermo, Armando -- Lopez-Otin, Carlos -- Campo, Elias -- England -- Nature. 2015 Oct 22;526(7574):519-24. doi: 10.1038/nature14666. Epub 2015 Jul 22.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Departamento de Bioquimica y Biologia Molecular, Instituto Universitario de Oncologia (IUOPA), Universidad de Oviedo, 33006 Oviedo, Spain. ; Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain. ; Unitat de Hematologia, Hospital Clinic, IDIBAPS, Universitat de Barcelona, 08036 Barcelona, Spain. ; Departament d'Anatomia Patologica, Microbiologia i Farmacologia, Universitat de Barcelona, 08036 Barcelona, Spain. ; Programa Conjunto de Biologia Computacional, Barcelona Supercomputing Center (BSC), Institut de Recerca Biomedica (IRB), Spanish National Bioinformatics Institute, Universitat de Barcelona, 08028 Barcelona, Spain. ; Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Spain. ; Unidad de Genomica, IDIBAPS, 08036 Barcelona, Spain. ; Servicio de Hematologia, Hospital Clinic, IDIBAPS, 08036 Barcelona, Spain. ; Institute for Cancer Genetics, Columbia University, New York 10032, USA. ; Servicio de Hematologia, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain. ; Center for Genomic Regulation (CRG), Pompeu Fabra University (UPF), Hospital del Mar Research Institute (IMIM), 08003 Barcelona, Spain. ; Servicio de Hematologia, IBSAL-Hospital Universitario de Salamanca, Centro de Investigacion del Cancer, Universidad de Salamanca-CSIC, 37007 Salamanca, Spain. ; Centro Nacional de Analisis Genomico, Parc Cientific de Barcelona, 08028 Barcelona, Spain. ; Catedra Inter-Universitaria de Derecho y Genoma Humano, Universidad de Deusto, Universidad del Pais Vasco, 48007 Bilbao, Spain. ; Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Spanish National Bioinformatics Institute, 28029 Madrid, Spain. ; Institute of Applied Biosciences, Center for Research and Technology Hellas, 57001 Thermi, Thessaloniki, Greece. ; Department of Molecular Biology, Faculty of Science, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen, 6500 HB Nijmegen, The Netherlands. ; Servicio de Hematologia, Hospital Clinico de Valencia, 46010 Valencia, Spain.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26200345" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Merali, Zeeya -- England -- Nature. 2015 Nov 12;527(7577):145. doi: 10.1038/nature.2015.18746.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26560277" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉England -- Nature. 2016 May 5;533(7601):7. doi: 10.1038/533007a.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27146996" target="_blank"〉PubMed〈/a〉

Description: Transected axons fail to regrow in the mature central nervous system. Astrocytic scars are widely regarded as causal in this failure. Here, using three genetically targeted loss-of-function manipulations in adult mice, we show that preventing astrocyte scar formation, attenuating scar-forming astrocytes, or ablating chronic astrocytic scars all failed to result in spontaneous regrowth of transected corticospinal, sensory or serotonergic axons through severe spinal cord injury (SCI) lesions. By contrast, sustained local delivery via hydrogel depots of required axon-specific growth factors not present in SCI lesions, plus growth-activating priming injuries, stimulated robust, laminin-dependent sensory axon regrowth past scar-forming astrocytes and inhibitory molecules in SCI lesions. Preventing astrocytic scar formation significantly reduced this stimulated axon regrowth. RNA sequencing revealed that astrocytes and non-astrocyte cells in SCI lesions express multiple axon-growth-supporting molecules. Our findings show that contrary to the prevailing dogma, astrocyte scar formation aids rather than prevents central nervous system axon regeneration.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Anderson, Mark A -- Burda, Joshua E -- Ren, Yilong -- Ao, Yan -- O'Shea, Timothy M -- Kawaguchi, Riki -- Coppola, Giovanni -- Khakh, Baljit S -- Deming, Timothy J -- Sofroniew, Michael V -- MH099559A/MH/NIMH NIH HHS/ -- MH104069/MH/NIMH NIH HHS/ -- NS057624/NS/NINDS NIH HHS/ -- NS060677/NS/NINDS NIH HHS/ -- NS084030/NS/NINDS NIH HHS/ -- P30 NS062691/NS/NINDS NIH HHS/ -- England -- Nature. 2016 Apr 14;532(7598):195-200. doi: 10.1038/nature17623. Epub 2016 Mar 30.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, California 90095-1763, USA. ; Departments of Psychiatry and Neurology, David Geffen School of Medicine, University of California, Los Angeles, California 90095-1761, USA. ; Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, California 90095-1751, USA. ; Departments of Bioengineering, Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095-1600, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27027288" target="_blank"〉PubMed〈/a〉

Description: Stem cells self-renew and generate specialized progeny through differentiation, but vary in the range of cells and tissues they generate, a property called developmental potency. Pluripotent stem cells produce all cells of an organism, while multipotent or unipotent stem cells regenerate only specific lineages or tissues. Defining stem-cell potency relies upon functional assays and diagnostic transcriptional, epigenetic and metabolic states. Here we describe functional and molecular hallmarks of pluripotent stem cells, propose a checklist for their evaluation, and illustrate how forensic genomics can validate their provenance.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉De Los Angeles, Alejandro -- Ferrari, Francesco -- Xi, Ruibin -- Fujiwara, Yuko -- Benvenisty, Nissim -- Deng, Hongkui -- Hochedlinger, Konrad -- Jaenisch, Rudolf -- Lee, Soohyun -- Leitch, Harry G -- Lensch, M William -- Lujan, Ernesto -- Pei, Duanqing -- Rossant, Janet -- Wernig, Marius -- Park, Peter J -- Daley, George Q -- Howard Hughes Medical Institute/ -- England -- Nature. 2015 Sep 24;525(7570):469-78. doi: 10.1038/nature15515.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Stem Cell Transplantation Program, Division of Pediatric Hematology Oncology, Children's Hospital Boston, and Dana-Farber Cancer Institute; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, USA. ; Harvard Stem Cell Institute, Cambridge, Massachusetts 02138, USA. ; Howard Hughes Medical Institute, Boston, Massachusetts 02115, USA. ; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115, USA. ; School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing 100871, China. ; Stem Cell Unit, Department of Genetics, Institute of Life Sciences, The Hebrew University, Jerusalem 91904, Israel. ; College of Life Sciences and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China. ; Massachusetts General Hospital Cancer Center and Center for Regenerative Medicine, Boston, Massachusetts 02114, USA. ; Whitehead Institute for Biomedical Research, Cambridge, Massachusetts 02142, USA. ; Medical Research Council Clinical Sciences Centre, Imperial College London, London W12 0NN, United Kingdom. ; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California 94305, USA. ; South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China. ; The Hospital for Sick Children Research Institute, Toronto, Ontario ON M5G 0A4, Canada.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26399828" target="_blank"〉PubMed〈/a〉

Description: The origin of eukaryotes stands as a major conundrum in biology. Current evidence indicates that the last eukaryotic common ancestor already possessed many eukaryotic hallmarks, including a complex subcellular organization. In addition, the lack of evolutionary intermediates challenges the elucidation of the relative order of emergence of eukaryotic traits. Mitochondria are ubiquitous organelles derived from an alphaproteobacterial endosymbiont. Different hypotheses disagree on whether mitochondria were acquired early or late during eukaryogenesis. Similarly, the nature and complexity of the receiving host are debated, with models ranging from a simple prokaryotic host to an already complex proto-eukaryote. Most competing scenarios can be roughly grouped into either mito-early, which consider the driving force of eukaryogenesis to be mitochondrial endosymbiosis into a simple host, or mito-late, which postulate that a significant complexity predated mitochondrial endosymbiosis. Here we provide evidence for late mitochondrial endosymbiosis. We use phylogenomics to directly test whether proto-mitochondrial proteins were acquired earlier or later than other proteins of the last eukaryotic common ancestor. We find that last eukaryotic common ancestor protein families of alphaproteobacterial ancestry and of mitochondrial localization show the shortest phylogenetic distances to their closest prokaryotic relatives, compared with proteins of different prokaryotic origin or cellular localization. Altogether, our results shed new light on a long-standing question and provide compelling support for the late acquisition of mitochondria into a host that already had a proteome of chimaeric phylogenetic origin. We argue that mitochondrial endosymbiosis was one of the ultimate steps in eukaryogenesis and that it provided the definitive selective advantage to mitochondria-bearing eukaryotes over less complex forms.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4780264/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4780264/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Pittis, Alexandros A -- Gabaldon, Toni -- England -- Nature. 2016 Mar 3;531(7592):101-4. doi: 10.1038/nature16941. Epub 2016 Feb 3.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Carrer del Dr Aiguader, 88, 08003 Barcelona, Spain. ; Departament of Ciencies Experimentals I de La Salut, Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain. ; Institucio Catalana de Recerca i Estudis Avancats (ICREA), Passeig de Lluis Companys 23, 08010 Barcelona, Spain.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26840490" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Katsnelson, Alla -- England -- Nature. 2015 Oct 8;526(7572):S4-5. doi: 10.1038/526S4a.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26444373" target="_blank"〉PubMed〈/a〉

Description: Copy-number variants (CNVs) are a major form of genetic variation and a risk factor for various human diseases, so it is crucial to accurately detect and characterize them. It is conceivable that allele-specific reads from high-throughput sequencing data could be leveraged to both enhance CNV detection and produce allele-specific copy number (ASCN) calls. Although statistical methods have been developed to detect CNVs using whole-genome sequence (WGS) and/or whole-exome sequence (WES) data, information from allele-specific read counts has not yet been adequately exploited. In this paper, we develop an integrated method, called AS-GENSENG, which incorporates allele-specific read counts in CNV detection and estimates ASCN using either WGS or WES data. To evaluate the performance of AS-GENSENG, we conducted extensive simulations, generated empirical data using existing WGS and WES data sets and validated predicted CNVs using an independent methodology. We conclude that AS-GENSENG not only predicts accurate ASCN calls but also improves the accuracy of total copy number calls, owing to its unique ability to exploit information from both total and allele-specific read counts while accounting for various experimental biases in sequence data. Our novel, user-friendly and computationally efficient method and a complete analytic protocol is freely available at https://sourceforge.net/projects/asgenseng/ .

Description: Increased sequencing of microbial genomes has revealed that prevailing prokaryotic species assignments can be inconsistent with whole genome information for a significant number of species. The long-standing need for a systematic and scalable species assignment technique can be met by the genome-wide Average Nucleotide Identity (gANI) metric, which is widely acknowledged as a robust measure of genomic relatedness. In this work, we demonstrate that the combination of gANI and the alignment fraction (AF) between two genomes accurately reflects their genomic relatedness. We introduce an efficient implementation of AF,gANI and discuss its successful application to 86.5M genome pairs between 13,151 prokaryotic genomes assigned to 3032 species. Subsequently, by comparing the genome clusters obtained from complete linkage clustering of these pairs to existing taxonomy, we observed that nearly 18% of all prokaryotic species suffer from anomalies in species definition. Our results can be used to explore central questions such as whether microorganisms form a continuum of genetic diversity or distinct species represented by distinct genetic signatures. We propose that this precise and objective AF,gANI-based species definition: the MiSI (Microbial Species Identifier) method, be used to address previous inconsistencies in species classification and as the primary guide for new taxonomic species assignment, supplemented by the traditional polyphasic approach, as required.

Description: Classically or alternatively activated macrophages (M1 and M2, respectively) play distinct and important roles for microbiocidal activity, regulation of inflammation and tissue homeostasis. Despite this, their transcriptional regulatory dynamics are poorly understood. Using promoter-level expression profiling by non-biased deepCAGE we have studied the transcriptional dynamics of classically and alternatively activated macrophages. Transcription factor (TF) binding motif activity analysis revealed four motifs, NFKB1_REL_RELA, IRF1,2, IRF7 and TBP that are commonly activated but have distinct activity dynamics in M1 and M2 activation. We observe matching changes in the expression profiles of the corresponding TFs and show that only a restricted set of TFs change expression. There is an overall drastic and transient up-regulation in M1 and a weaker and more sustainable up-regulation in M2. Novel TFs, such as Thap6, Maff , (M1) and Hivep1, Nfil3, Prdm1 , (M2) among others, were suggested to be involved in the activation processes. Additionally, 52 (M1) and 67 (M2) novel differentially expressed genes and, for the first time, several differentially expressed long non-coding RNA (lncRNA) transcriptome markers were identified. In conclusion, the finding of novel motifs, TFs and protein-coding and lncRNA genes is an important step forward to fully understand the transcriptional machinery of macrophage activation.

Description: The RAG1/RAG2 endonuclease initiates V(D)J recombination at antigen receptor loci but also binds to thousands of places outside of these loci. RAG2 localizes directly to lysine 4 trimethylated histone 3 (H3K4me3) through a plant homeodomain (PHD) finger. The relative contribution of RAG2-dependent and RAG1-intrinsic mechanisms in determining RAG1 binding patterns is not known. Through analysis of deep RAG1 ChIP-seq data, we provide a quantitative description of the forces underlying genome-wide targeting of RAG1. Surprisingly, sequence-specific DNA binding contributes minimally to RAG1 targeting outside of antigen receptor loci. Instead, RAG1 binding is driven by two distinct modes of interaction with chromatin: the first is driven by H3K4me3, promoter-focused and dependent on the RAG2 PHD, and the second is defined by H3K27Ac, enhancer-focused and dependent on ‘non-core’ portions of RAG1. Based on this and additional chromatin and genomic features, we formulated a predictive model of RAG1 targeting to the genome. RAG1 binding sites predicted by our model correlate well with observed patterns of RAG1-mediated breaks in human pro-B acute lymphoblastic leukemia. Overall, this study provides an integrative model for RAG1 genome-wide binding and off-target activity and reveals a novel role for the RAG1 non-core region in RAG1 targeting.

Description: At a biogenomics meeting in Washington, D.C., last week, researchers publicly unveiled the Earth BioGenome Project (EBP). The audacious goal of the still-unfunded effort is to decipher the genomes of every species, starting with the 1.5 million named eukaryotes—the group of organisms that includes all plants, animals, and single-celled organisms such as amoebas. Researchers drew parallels to the Human Genome Project, which also began as an ambitious, controversial, and technically daunting proposal. The EBP would focus on the natural world, providing a better understanding of biodiversity by first sequencing in great detail the DNA of a member of each eukaryotic family (about 9000 in all) and eventually generating coarser genomes for the other eukaryotes. Although many biologists are excited about these goals, they point out that significant challenges lie ahead, including funding, sample collection, and broadening international participation. Author: Elizabeth Pennisi