ALBERT

Description: Motivation The traditional view of cancer evolution states that a cancer genome accumulates a sequential ordering of mutations over a long period of time. However, in recent years it has been suggested that a cancer genome may instead undergo a one-time catastrophic event, such as chromothripsis , where a large number of mutations instead occur simultaneously . A number of potential signatures of chromothripsis have been proposed. In this work, we provide a rigorous formulation and analysis of the ‘ability to walk the derivative chromosome’ signature originally proposed by Korbel and Campbell. In particular, we show that this signature, as originally envisioned, may not always be present in a chromothripsis genome and we provide a precise quantification of under what circumstances it would be present. We also propose a variation on this signature, the H/T alternating fraction , which allows us to overcome some of the limitations of the original signature. Results We apply our measure to both simulated data and a previously analyzed real cancer dataset and find that the H/T alternating fraction may provide useful signal for distinguishing genomes having acquired mutations simultaneously from those acquired in a sequential fashion. Availability and implementation An implementation of the H/T alternating fraction is available at https://bitbucket.org/oesperlab/ht-altfrac . Contact loesper@carleton.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation The identification of microRNA (miRNA) target sites is important. In the past decade, dozens of computational methods have been developed to predict miRNA target sites. Despite their existence, rarely does a method consider the well-known competition and cooperation among miRNAs when attempts to discover target sites. To fill this gap, we developed a new approach called CCmiR, which takes the cooperation and competition of multiple miRNAs into account in a statistical model to predict their target sites. Results Tested on four different datasets, CCmiR predicted miRNA target sites with a high recall and a reasonable precision, and identified known and new cooperative and competitive miRNAs supported by literature. Compared with three state-of-the-art computational methods, CCmiR had a higher recall and a higher precision. Availability and implementation CCmiR is freely available at http://hulab.ucf.edu/research/projects/miRNA/CCmiR . Contact xiaoman@mail.ucf.edu or haihu@cs.ucf.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Eukaryotic chromosomes adapt a complex and highly dynamic three-dimensional (3D) structure, which profoundly affects different cellular functions and outcomes including changes in epigenetic landscape and in gene expression. Making the scenario even more complex, cancer cells harbor chromosomal abnormalities [e.g. copy number variations (CNVs) and translocations] altering their genomes both at the sequence level and at the level of 3D organization. High-throughput chromosome conformation capture techniques (e.g. Hi-C), which are originally developed for decoding the 3D structure of the chromatin, provide a great opportunity to simultaneously identify the locations of genomic rearrangements and to investigate the 3D genome organization in cancer cells. Even though Hi-C data has been used for validating known rearrangements, computational methods that can distinguish rearrangement signals from the inherent biases of Hi-C data and from the actual 3D conformation of chromatin, and can precisely detect rearrangement locations de novo have been missing. Results In this work, we characterize how intra and inter-chromosomal Hi-C contacts are distributed for normal and rearranged chromosomes to devise a new set of algorithms (i) to identify genomic segments that correspond to CNV regions such as amplifications and deletions ( HiCnv ), (ii) to call inter-chromosomal translocations and their boundaries ( HiCtrans ) from Hi-C experiments and (iii) to simulate Hi-C data from genomes with desired rearrangements and abnormalities ( AveSim ) in order to select optimal parameters for and to benchmark the accuracy of our methods. Our results on 10 different cancer cell lines with Hi-C data show that we identify a total number of 105 amplifications and 45 deletions together with 90 translocations, whereas we identify virtually no such events for two karyotypically normal cell lines. Our CNV predictions correlate very well with whole genome sequencing data among chromosomes with CNV events for a breast cancer cell line ( r  = 0.89) and capture most of the CNVs we simulate using Avesim. For HiCtrans predictions, we report evidence from the literature for 30 out of 90 translocations for eight of our cancer cell lines. Furthermore, we show that our tools identify and correctly classify relatively understudied rearrangements such as double minutes and homogeneously staining regions. Considering the inherent limitations of existing techniques for karyotyping (i.e. missing balanced rearrangements and those near repetitive regions), the accurate identification of CNVs and translocations in a cost-effective and high-throughput setting is still a challenge. Our results show that the set of tools we develop effectively utilize moderately sequenced Hi-C libraries (100–300 million reads) to identify known and de novo chromosomal rearrangements/abnormalities in well-established cancer cell lines. With the decrease in required number of cells and the increase in attainable resolution, we believe that our framework will pave the way towards comprehensive mapping of genomic rearrangements in primary cells from cancer patients using Hi-C. Availability and implementation CNV calling: https://github.com/ay-lab/HiCnv , Translocation calling: https://github.com/ay-lab/HiCtrans and Hi-C simulation: https://github.com/ay-lab/AveSim . Contact ferhatay@lji.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Cancer hallmarks, a concept that seeks to explain the complexity of cancer initiation and development, provide a new perspective of studying cancer signaling which could lead to a greater understanding of this complex disease. However, to the best of our knowledge, there is currently a lack of tools that support such hallmark-based study of the cancer signaling network, thereby impeding the gain of knowledge in this area. We present TROVE, an user-friendly software that facilitates hallmark annotation, visualization and analysis in cancer signaling networks. In particular, TROVE facilitates hallmark analysis specific to particular cancer types. Availability and implementation Available under the Eclipse Public License from: https://sites.google.com/site/cosbyntu/softwares/trove and https://github.com/trove2017/Trove . Contact hechua@ntu.edu.sg or assourav@ntu.edu.sg

Description: Summary High-throughput screening of the host transcriptional response to various viral infections provides a wealth of data, but utilization of microarray and next generation sequencing (NGS) data for analysis can be difficult. The Ho st T ranscriptional R esponse D ata B ase (HoTResDB), allows visitors to access already processed microarray and NGS data from non-human primate models of viral hemorrhagic fever to better understand the host transcriptional response. Availability HoTResDB is freely available at http://hotresdb.bu.edu Contact jhconnor@bu.edu

Description: Motivation Structural variation, including large deletions, duplications, inversions, translocations and other rearrangements, is common in human and cancer genomes. A number of methods have been developed to identify structural variants from Illumina short-read sequencing data. However, reliable identification of structural variants remains challenging because many variants have breakpoints in repetitive regions of the genome and thus are difficult to identify with short reads. The recently developed linked-read sequencing technology from 10X Genomics combines a novel barcoding strategy with Illumina sequencing. This technology labels all reads that originate from a small number (∼5 to 10) DNA molecules ∼50 Kbp in length with the same molecular barcode. These barcoded reads contain long-range sequence information that is advantageous for identification of structural variants. Results We present Novel Adjacency Identification with Barcoded Reads (NAIBR), an algorithm to identify structural variants in linked-read sequencing data. NAIBR predicts novel adjacencies in an individual genome resulting from structural variants using a probabilistic model that combines multiple signals in barcoded reads. We show that NAIBR outperforms several existing methods for structural variant identification—including two recent methods that also analyze linked-reads—on simulated sequencing data and 10X whole-genome sequencing data from the NA12878 human genome and the HCC1954 breast cancer cell line. Several of the novel somatic structural variants identified in HCC1954 overlap known cancer genes. Availability and implementation Software is available at compbio.cs.brown.edu/software . Contact braphael@princeton.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Cancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. The decomposition of a cancer genome’s mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from different decomposition methods are not always consistent. Hence, one needs to be able to not only decompose a patient’s mutational profile into signatures but also establish the accuracy of such decomposition. Results We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified both very stable and highly unstable signatures, as well as signatures that previously have not been associated with breast cancer. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions. Availability and implementation All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi\#signatureestimation . Contact wojtowda@ncbi.nlm.nih.gov or przytyck@ncbi.nlm.nih.gov Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Brain imaging genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ 1 -norm or its variants to induce sparsity. The ℓ 0 -norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem. Results In this paper, we propose the truncated ℓ 1 -norm penalized SCCA to improve the performance and effectiveness of the ℓ 1 -norm based SCCA methods. Besides, we propose an efficient optimization algorithms to solve this novel SCCA problem. The proposed method is an adaptive shrinkage method via tuning τ . It can avoid the time intensive parameter tuning if given a reasonable small τ . Furthermore, we extend it to the truncated group-lasso (TGL), and propose TGL-SCCA model to improve the group-lasso-based SCCA methods. The experimental results, compared with four benchmark methods, show that our SCCA methods identify better or similar correlation coefficients, and better canonical loading profiles than the competing methods. This demonstrates the effectiveness and efficiency of our methods in discovering interesting imaging genetic associations. Availability and implementation The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/tlpscca/ . Contact dulei@nwpu.edu.cn or shenli@iu.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Modelling with multiple servers that use different algorithms for docking results in more reliable predictions of interaction sites. However, the scoring and comparison of all models by an expert is time-consuming and is not feasible for large volumes of data generated by such modelling. Results Quality ASsessment of DOcking Models (QASDOM) Server is a simple and efficient tool for real-time simultaneous analysis, scoring and ranking of data sets of receptor–ligand complexes built by a range of docking techniques. This meta-server is designed to analyse large data sets of docking models and rank them by scoring criteria developed in this study. It produces two types of output showing the likelihood of specific residues and clusters of residues to be involved in receptor–ligand interactions and the ranking of models. The server also allows visualizing residues that form interaction sites in the receptor and ligand sequence and displays 3D model structures of the receptor–ligand complexes. Availability http://qasdom.eimb.ru . Contact alexei.adzhubei@eimb.ru. Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods. Results On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent. Availability and implementation IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/ . Contact andrew@bioinf.org.uk or andrew.martin@ucl.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Protein function is directly related to amino acid residue composition and the dynamics of these residues. Centrality analyses based on residue interaction networks permit to identify key residues in a protein that are important for its fold or function. Such central residues and their environment constitute suitable targets for mutagenesis experiments. Predicted flexibility and changes in flexibility upon mutation provide valuable additional information for the design of such experiments. Results We combined centrality analyses with DynaMine flexibility predictions in a Cytoscape app called RINspector. The app performs centrality analyses and directly visualizes the results on a graph of predicted residue flexibility. In addition, the effect of mutations on local flexibility can be calculated. Availability and implementation The app is publicly available in the Cytoscape app store. Contact guillaume.brysbaert@univ-lille1.fr Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Fully exploiting the wealth of data in current bacterial population genomics datasets requires synthesizing and integrating different types of analysis across millions of base pairs in hundreds or thousands of isolates. Current approaches often use static representations of phylogenetic, epidemiological, statistical and evolutionary analysis results that are difficult to relate to one another. Phandango is an interactive application running in a web browser allowing fast exploration of large-scale population genomics datasets combining the output from multiple genomic analysis methods in an intuitive and interactive manner. Availability and implementation Phandango is a web application freely available for use at www.phandango.net and includes a diverse collection of datasets as examples. Source code together with a detailed wiki page is available on GitHub at https://github.com/jameshadfield/phandango . Contact jh22@sanger.ac.uk or sh16@sanger.ac.uk

Description: Summary MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context. Availability and implementation Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/ Contact contact-metexplore@inra.fr

Description: Motivation Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired. Results We developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development. Availability and implementation MATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES . The single cell THP-1 and T2EC transcriptional profiles are available from the original publications ( Kouno et al. , 2013 ; Richard et al. , 2016 ). The in silico single cell data are available on SINCERITIES websites. Contact rudi.gunawan@chem.ethz.ch Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Addressing deleterious effects of noncoding mutations is an essential step towards the identification of disease-causal mutations of gene regulatory elements. Several methods for quantifying the deleteriousness of noncoding mutations using artificial intelligence, deep learning and other approaches have been recently proposed. Although the majority of the proposed methods have demonstrated excellent accuracy on different test sets, there is rarely a consensus. In addition, advanced statistical and artificial learning approaches used by these methods make it difficult porting these methods outside of the labs that have developed them. To address these challenges and to transform the methodological advances in predicting deleterious noncoding mutations into a practical resource available for the broader functional genomics and population genetics communities, we developed SNPDelScore, which uses a panel of proposed methods for quantifying deleterious effects of noncoding mutations to precompute and compare the deleteriousness scores of all common SNPs in the human genome in 44 cell lines. The panel of deleteriousness scores of a SNP computed using different methods is supplemented by functional information from the GWAS Catalog, libraries of transcription factor-binding sites, and genic characteristics of mutations. SNPDelScore comes with a genome browser capable of displaying and comparing large sets of SNPs in a genomic locus and rapidly identifying consensus SNPs with the highest deleteriousness scores making those prime candidates for phenotype-causal polymorphisms. Availability and implementation https://www.ncbi.nlm.nih.gov/research/snpdelscore/ Contact ovcharen@nih.gov Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation The selection of a single nucleotide polymorphism (SNP) using bibliographic methods can be a very time-consuming task. Moreover, a SNP selected in this way may not be easily visualized in its genomic context by a standard user hoping to correlate it with other valuable information. Here we propose a web form built on top of Circos that can assist SNP-centered screening, based on their location in the genome and the regulatory modules they can disrupt. Its use may allow researchers to prioritize SNPs in genotyping and disease studies. Results SiNoPsis is bundled as a web portal. It focuses on the different structures involved in the genomic expression of a gene, especially those found in the core promoter upstream region. These structures include transcription factor binding sites (for promoter and enhancer signals), histones and promoter flanking regions. Additionally, the tool provides eQTL and linkage disequilibrium (LD) properties for a given SNP query, yielding further clues about other indirectly associated SNPs. Possible disruptions of the aforementioned structures affecting gene transcription are reported using multiple resource databases. SiNoPsis has a simple user-friendly interface, which allows single queries by gene symbol, genomic coordinates, Ensembl gene identifiers, RefSeq transcript identifiers and SNPs. It is the only portal providing useful SNP selection based on regulatory modules and LD with functional variants in both textual and graphic modes (by properly defining the arguments and parameters needed to run Circos). Availability and implementation SiNoPsis is freely available at https://compgen.bio.ub.edu/SiNoPsis/ Contact danielboloc@gmail.com Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Inter-residue contacts in proteins have been widely acknowledged to be valuable for protein 3 D structure prediction. Accurate prediction of long-range transmembrane inter-helix residue contacts can significantly improve the quality of simulated membrane protein models. Results In this paper, we present an updated MemBrain predictor, which aims to predict transmembrane protein residue contacts. Our new model benefits from an efficient learning algorithm that can mine latent structural features, which exist in original feature space. The new MemBrain is a two-stage inter-helix contact predictor. The first stage takes sequence-based features as inputs and outputs coarse contact probabilities for each residue pair, which will be further fed into convolutional neural network together with predictions from three direct-coupling analysis approaches in the second stage. Experimental results on the training dataset show that our method achieves an average accuracy of 81.6% for the top L /5 predictions using a strict sequence-based jackknife cross-validation. Evaluated on the test dataset, MemBrain can achieve 79.4% prediction accuracy. Moreover, for the top L /5 predicted long-range loop contacts, the prediction performance can reach an accuracy of 56.4%. These results demonstrate that the new MemBrain is promising for transmembrane protein’s contact map prediction. Availability and implementation http://www.csbio.sjtu.edu.cn/bioinf/MemBrain/ Contact hbshen@sjtu.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation We recently published MS2LDA, a method for the decomposition of sets of molecular fragment data derived from large metabolomics experiments. To make the method more widely available to the community, here we present ms2lda.org, a web application that allows users to upload their data, run MS2LDA analyses and explore the results through interactive visualizations. Results Ms2lda.org takes tandem mass spectrometry data in many standard formats and allows the user to infer the sets of fragment and neutral loss features that co-occur together (Mass2Motifs). As an alternative workflow, the user can also decompose a data set onto predefined Mass2Motifs. This is accomplished through the web interface or programmatically from our web service. Availability and implementation The website can be found at http://ms2lda.org , while the source code is available at https://github.com/sdrogers/ms2ldaviz under the MIT license. Contact simon.rogers@glasgow.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Linkage and quantitative trait loci (QTL) maps are critical tools for the study of the genetic basis of complex traits. With the advances in sequencing technology over the past decade, linkage map densities have been increasing dramatically, while the visualization tools have not kept pace. LinkageMapView is a free add-on package written in R that produces high resolution, publication-ready visualizations of linkage and QTL maps. While there is software available to generate linkage map graphics, none are freely available, produce publication quality figures, are open source and can run on all platforms. LinkageMapView can be integrated into map building pipelines as it seamlessly incorporates output from R/qtl and also accepts simple text or comma delimited files. There are numerous options within the package to build highly customizable maps, allow for linkage group comparisons, and annotate QTL regions. Availability and implementation https://cran.r-project.org/web/packages/LinkageMapView/ Contact louellet@uncc.edu

Description: Motivation Rapid and low cost sequencing of genomes enabled widespread use of genomic data in research studies and personalized customer applications, where genomic data is shared in public databases. Although the identities of the participants are anonymized in these databases, sensitive information about individuals can still be inferred. One such information is kinship. Results We define two routes kinship privacy can leak and propose a technique to protect kinship privacy against these risks while maximizing the utility of shared data. The method involves systematic identification of minimal portions of genomic data to mask as new participants are added to the database. Choosing the proper positions to hide is cast as an optimization problem in which the number of positions to mask is minimized subject to privacy constraints that ensure the familial relationships are not revealed. We evaluate the proposed technique on real genomic data. Results indicate that concurrent sharing of data pertaining to a parent and an offspring results in high risks of kinship privacy, whereas the sharing data from further relatives together is often safer. We also show arrival order of family members have a high impact on the level of privacy risks and on the utility of sharing data. Availability and implementation https://github.com/tastanlab/Kinship-Privacy Contact erman@cs.bilkent.edu.tr or oznur.tastan@cs.bilkent.edu.tr Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Precision oncology is an approach that accounts for individual differences to guide cancer management. Omics signatures have been shown to predict clinical traits for cancer patients. However, the vast amount of omics information poses an informatics challenge in systematically identifying patterns associated with health outcomes, and no general purpose data mining tool exists for physicians, medical researchers and citizen scientists without significant training in programming and bioinformatics. To bridge this gap, we built the Omics AnalySIs System for PRecision Oncology (OASISPRO), a web-based system to mine the quantitative omics information from The Cancer Genome Atlas (TCGA). This system effectively visualizes patients’ clinical profiles, executes machine-learning algorithms of choice on the omics data and evaluates the prediction performance using held-out test sets. With this tool, we successfully identified genes strongly associated with tumor stage, and accurately predicted patients’ survival outcomes in many cancer types, including adrenocortical carcinoma. By identifying the links between omics and clinical phenotypes, this system will facilitate omics studies on precision cancer medicine and contribute to establishing personalized cancer treatment plans. Availability and implementation This web-based tool is available at http://tinyurl.com/oasispro ; source codes are available at http://tinyurl.com/oasisproSourceCode . Contact khyu@stanford.edu or mpsnyder@stanford.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Analysis of RNA sequencing (RNA-Seq) data have highlighted the fact that most genes undergo alternative splicing (AS) and that these patterns are tightly regulated. Many of these events are complex, resulting in numerous possible isoforms that quickly become difficult to visualize, interpret and experimentally validate. To address these challenges we developed MAJIQ-SPEL, a web-tool that takes as input local splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and quantification of gene isoforms associated with those. Importantly, MAJIQ-SPEL is able to handle both classical (binary) and complex, non-binary, splicing variations. Using a matching primer design algorithm it also suggests to users possible primers for experimental validation by RT-PCR and displays those, along with the matching protein domains affected by the LSV, on UCSC Genome Browser for further downstream analysis. Availability and implementation Program and code will be available at http://majiq.biociphers.org/majiq-spel . Contact yosephb@upenn.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Accurate molecular structure of the protein dimer representing the elementary building block of intermediate filaments (IFs) is essential towards the understanding of the filament assembly, rationalizing their mechanical properties and explaining the effect of disease-related IF mutations. The dimer contains a ∼300-residue long α-helical coiled coil which cannot be assessed by either direct experimental structure determination or modelling using standard approaches. At the same time, coiled coils are well-represented in structural databases. Results Here we present CCFold, a generally applicable threading-based algorithm which produces coiled-coil models from protein sequence only. The algorithm is based on a statistical analysis of experimentally determined structures and can handle any hydrophobic repeat patterns in addition to the most common heptads. We demonstrate that CCFold outperforms general-purpose computational folding in terms of accuracy, while being faster by orders of magnitude. By combining the CCFold algorithm and Rosetta folding we generate representative dimer models for all IF protein classes. Availability and implementation The source code is freely available at https://github.com/biocryst/IF ; a web server to run the program is at http://pharm.kuleuven.be/Biocrystallography/cc . Contact sergei.strelkov@kuleuven.be Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Cells process information, in part, through transcription factor (TF) networks, which control the rates at which individual genes produce their products. A TF network map is a graph that indicates which TFs bind and directly regulate each gene. Previous work has described network mapping algorithms that rely exclusively on gene expression data and ‘integrative’ algorithms that exploit a wide range of data sources including chromatin immunoprecipitation sequencing (ChIP-seq) of many TFs, genome-wide chromatin marks, and binding specificities for many TFs determined in vitro . However, such resources are available only for a few major model systems and cannot be easily replicated for new organisms or cell types. Results We present NetProphet 2.0, a ‘data light’ algorithm for TF network mapping, and show that it is more accurate at identifying direct targets of TFs than other, similarly data light algorithms. In particular, it improves on the accuracy of NetProphet 1.0, which used only gene expression data, by exploiting three principles. First, combining multiple approaches to network mapping from expression data can improve accuracy relative to the constituent approaches. Second, TFs with similar DNA binding domains bind similar sets of target genes. Third, even a noisy, preliminary network map can be used to infer DNA binding specificities from promoter sequences and these inferred specificities can be used to further improve the accuracy of the network map. Availability and implementation Source code and comprehensive documentation are freely available at https://github.com/yiming-kang/NetProphet_2.0 . Contact brent@wustl.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on (i) summary statistics from genome-wide association studies (GWAS) and (ii) linkage disequilibrium patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our G ene-level J oint A nalysis of functional SNPs in C osmopolitan C ohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals. Results We propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses (i) cis-eQTL SNPs from the latest expression studies and (ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also (i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and (ii) to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q -values based on Holm adjustment of P -values. Availability and implementation https://github.com/Chatzinakos/JEPEGMIX2 . Contact chris.chatzinakos@vcuhealth.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem. Results We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs. Availability and implementation The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top . Contact xuanping@hlju.edu.cn or zhang@hlju.edu.cn or lijzh@hit.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary The Quest for Orthologs (QfO) is an open collaboration framework for experts in comparative phylogenomics and related research areas who have an interest in highly accurate orthology predictions and their applications. We here report highlights and discussion points from the QfO meeting 2015 held in Barcelona. Achievements in recent years have established a basis to support developments for improved orthology prediction and to explore new approaches. Central to the QfO effort is proper benchmarking of methods and services, as well as design of standardized datasets and standardized formats to allow sharing and comparison of results. Simultaneously, analysis pipelines have been improved, evaluated and adapted to handle large datasets. All this would not have occurred without the long-term collaboration of Consortium members. Meeting regularly to review and coordinate complementary activities from a broad spectrum of innovative researchers clearly benefits the community. Highlights of the meeting include addressing sources of and legitimacy of disagreements between orthology calls, the context dependency of orthology definitions, special challenges encountered when analyzing very anciently rooted orthologies, orthology in the light of whole-genome duplications, and the concept of orthologous versus paralogous relationships at different levels, including domain-level orthology. Furthermore, particular needs for different applications (e.g. plant genomics, ancient gene families and others) and the infrastructure for making orthology inferences available (e.g. interfaces with model organism databases) were discussed, with several ongoing efforts that are expected to be reported on during the upcoming 2017 QfO meeting. Contact selewis@lbl.gov or c.dessimoz@ucl.ac.uk

Description: Motivation In recent years, the massively parallel cDNA sequencing (RNA-Seq) technologies have become a powerful tool to provide high resolution measurement of expression and high sensitivity in detecting low abundance transcripts. However, RNA-seq data requires a huge amount of computational efforts. The very fundamental and critical step is to align each sequence fragment against the reference genome. Various de novo spliced RNA aligners have been developed in recent years. Though these aligners can handle spliced alignment and detect splice junctions, some challenges still remain to be solved. With the advances in sequencing technologies and the ongoing collection of sequencing data in the ENCODE project, more efficient alignment algorithms are highly demanded. Most read mappers follow the conventional seed-and-extend strategy to deal with inexact matches for sequence alignment. However, the extension is much more time consuming than the seeding step. Results We proposed a novel RNA-seq de novo mapping algorithm, call DART, which adopts a partitioning strategy to avoid the extension step. The experiment results on synthetic datasets and real NGS datasets showed that DART is a highly efficient aligner that yields the highest or comparable sensitivity and accuracy compared to most state-of-the-art aligners, and more importantly, it spends the least amount of time among the selected aligners. Availability and implementation https://github.com/hsinnan75/DART Contact hsu@iis.sinica.edu.tw Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Gene expression alterations and potentially underlying gene copy number mutations can be measured routinely in the wet lab, but it is still extremely challenging to quantify impacts of altered genes on clinically relevant characteristics to predict putative driver genes. We developed the R package regNet that utilizes gene expression and copy number data to learn regulatory networks for the quantification of potential impacts of individual gene expression alterations on user-defined target genes via network propagation. We demonstrate the value of regNet by identifying putative major regulators that distinguish pilocytic from diffuse astrocytomas and by predicting putative impacts of glioblastoma-specific gene copy number alterations on cell cycle pathway genes and patient survival. Availability and implementation regNet is available for download at https://github.com/seifemi/regNet under GNU GPL-3 . Contact michael.seifert@tu-dresden.de Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information. Results In this study, we propose a new method with graph regularized non-negative matrix factorization in heterogeneous omics data, called GRNMF, to discover potential associations between miRNAs and diseases, especially for new diseases and miRNAs or those diseases and miRNAs with sparse known associations. First, we integrate the disease semantic information and miRNA functional information to estimate disease similarity and miRNA similarity, respectively. Considering that there is no available interaction observed for new diseases or miRNAs, a preprocessing step is developed to construct the interaction score profiles that will assist in prediction. Next, a graph regularized non-negative matrix factorization framework is utilized to simultaneously identify potential associations for all diseases. The results indicated that our proposed method can effectively prioritize disease-associated miRNAs with higher accuracy compared with other recent approaches. Moreover, case studies also demonstrated the effectiveness of GRNMF to infer unknown miRNA-disease associations for those novel diseases and miRNAs. Availability and implementation The code of GRNMF is freely available at https://github.com/XIAO-HN/GRNMF/ . Contact luojiawei@hnu.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation A deleterious amino acid change in a protein can be compensated by a second-site rescue mutation. These compensatory mechanisms can be mimicked by drugs. In particular, the location of rescue mutations can be used to identify protein regions that can be targeted by small molecules to reactivate a damaged mutant. Results We present the first general computational method to detect rescue sites. By mimicking the effect of mutations through the application of forces, the double force scanning (DFS) method identifies the second-site residues that make the protein structure most resilient to the effect of pathogenic mutations. We tested DFS predictions against two datasets containing experimentally validated and putative evolutionary-related rescue sites. A remarkably good agreement was found between predictions and experimental data. Indeed, almost half of the rescue sites in p53 was correctly predicted by DFS, with 65% of remaining sites in contact with DFS predictions. Similar results were found for other proteins in the evolutionary dataset. Availability and implementation The DFS code is available under GPL at https://fornililab.github.io/dfs/ Contact m.tiberti@qmul.ac.uk or a.fornili@qmul.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Briefings in Bioinformatics 2017. doi: 10.1093/bib/bbx018 .

Description: Briefings in Bioinformatics 2017. doi: 10.1093/bib/bbx056

Description: Briefings in Bioinformatics will move to online-only publication from January 2018. The last printed issue will be in November 2017. The editors and publishers of Briefings in Bioinformatics have taken this decision in the light of the extensive online usage of the journal and that fact that we are now supplying very few print subscribers with copies of the journal. Online-only journals are more environmentally friendly, eliminating the need for paper, shipping materials and transportation and thus significantly reducing the journal’s carbon footprint. Journal articles are available to you instantly on the day of publication, rather than you having to wait for printing and despatch to be completed.

Description: Logistic regression is the most common technique used for genetic case-control association studies. A disadvantage of standard maximum likelihood estimators of the genotype relative risk (GRR) is their strong dependence on outlier subjects, for example, patients diagnosed at unusually young age. Robust methods are available to constrain outlier influence, but they are scarcely used in genetic studies. This article provides a non-intimidating introduction to robust logistic regression, and investigates its benefits and limitations in genetic association studies. We applied the bounded Huber and extended the R package ‘robustbase’ with the re-descending Hampel functions to down-weight outlier influence. Computer simulations were carried out to assess the type I error rate, mean squared error (MSE) and statistical power according to major characteristics of the genetic study and investigated markers. Simulations were complemented with the analysis of real data. Both standard and robust estimation controlled type I error rates. Standard logistic regression showed the highest power but standard GRR estimates also showed the largest bias and MSE, in particular for associated rare and recessive variants. For illustration, a recessive variant with a true GRR=6.32 and a minor allele frequency=0.05 investigated in a 1000 case/1000 control study by standard logistic regression resulted in power=0.60 and MSE=16.5. The corresponding figures for Huber-based estimation were power=0.51 and MSE=0.53. Overall, Hampel- and Huber-based GRR estimates did not differ much. Robust logistic regression may represent a valuable alternative to standard maximum likelihood estimation when the focus lies on risk prediction rather than identification of susceptibility variants.

Description:   Estimating a population mean from a sample obtained with unknown selection probabilities is important in the biomedical and social sciences. Using a ratio estimator, Aronow & Lee (2013) proposed a method for partial identification of the mean by allowing the unknown selection probabilities to vary arbitrarily between two fixed values. In this paper, we show how to use auxiliary shape constraints on the population outcome distribution, such as symmetry or log-concavity, to obtain tighter bounds on the population mean. We use this method to estimate the performance of Aymara students, an ethnic minority in the north of Chile, in a national educational standardized test. We implement this method in the R package scbounds.

Description:   The estimation of time series models with heavy-tailed innovations has been widely discussed, but corresponding goodness-of-fit tests have attracted less attention, primarily because the autocorrelation function commonly used in constructing goodness-of-fit tests necessarily imposes certain moment conditions on the innovations. As a bounded random variable has finite moments of all orders, we address the problem by first transforming the residuals with a bounded function. More specifically, we consider the sample autocorrelation function of the transformed absolute residuals of a fitted generalized autoregressive conditional heteroscedastic model. With the corresponding residual empirical distribution function naturally employed as the transformation, a robust goodness-of-fit test is then constructed. The asymptotic distributions of the test statistic under the null hypothesis and local alternatives are derived, and Monte Carlo experiments are conducted to examine finite-sample properties. The proposed test is shown to be more powerful than existing tests when the innovations are heavy-tailed.

Description:   Generalized linear models are popular for modelling a large variety of data. We consider variable selection through penalized methods by focusing on resistance issues in the presence of outlying data and other deviations from assumptions. We highlight the weaknesses of widely-used penalized M-estimators, propose a robust penalized quasilikelihood estimator, and show that it enjoys oracle properties in high dimensions and is stable in a neighbourhood of the model. We illustrate its finite-sample performance on simulated and real data.

Description:   Compositional data are ubiquitous in many scientific endeavours. Motivated by microbiome and metagenomic research, we consider a two-sample testing problem for high-dimensional compositional data and formulate a testable hypothesis of compositional equivalence for the means of two latent log basis vectors. We propose a test through the centred log-ratio transformation of the compositions. The asymptotic null distribution of the test statistic is derived and its power against sparse alternatives is investigated. A modified test for paired samples is also considered. Simulations show that the proposed tests can be significantly more powerful than tests that are applied to the raw and log-transformed compositions. The usefulness of our tests is illustrated by applications to gut microbiome composition in obesity and Crohn’s disease.

Description: Building a certification authority that is both decentralized and fully reliable is impossible. However, the limitation thus imposed on scalability is unacceptable for many types of information systems, such as e-government services, which require an infrastructure able to support heavy computation loads while remaining highly reliable. Our scalable approach opts for the next best thing to full reliability: a certification authority with a probability of arbitrary failure so low that, in practice, false positives should never occur.

Description: In this paper, we examine a method of constructing a self-synchronizing stream cipher referred to as statistical cipher feedback (SCFB). Although recently studied as a mode for block ciphers, we explicitly consider the application of SCFB to hardware-oriented stream ciphers. Specifically, we show that SCFB can be used to realize a self-synchronizing stream cipher that has the characteristics of compact, efficient hardware implementations and is secure given that the underlying synchrononous stream cipher is secure. Further, we consider the communication properties of the SCFB system and examine the relationships between these properties and the implementation characteristics.

Description: The PCDH19 gene (Xp22.1) encodes the cell-adhesion protein protocadherin-19 (PCDH19) and is responsible for a neurodevelopmental pathology characterized by female-limited epilepsy, cognitive impairment and autistic features, the pathogenic mechanisms of which remain to be elucidated. Here, we identified a new interaction between PCDH19 and GABA A receptor (GABA A R) alpha subunits in the rat brain. PCDH19 shRNA-mediated downregulation reduces GABA A R surface expression and affects the frequency and kinetics of miniature inhibitory postsynaptic currents (mIPSCs) in cultured hippocampal neurons. In vivo , PCDH19 downregulation impairs migration, orientation and dendritic arborization of CA1 hippocampal neurons and increases rat seizure susceptibility. In sum, these data indicate a role for PCDH19 in GABAergic transmission as well as migration and morphological maturation of neurons.

Description: Fuchs’ endothelial corneal dystrophy (FECD) is the most common repeat expansion disorder. FECD impacts 4% of U.S. population and is the leading indication for corneal transplantation. Most cases are caused by an expanded intronic CUG tract in the TCF4 gene that forms nuclear foci, sequesters splicing factors and impairs splicing. We investigated the sense and antisense RNA landscape at the FECD gene and find that the sense-expanded repeat transcript is the predominant species in patient corneas. In patient tissue, sense foci number were negatively correlated with age and showed no correlation with sex. Each endothelial cell has ∼2 sense foci and each foci is single RNA molecule. We designed antisense oligonucleotides (ASOs) to target the mutant-repetitive RNA and demonstrated potent inhibition of foci in patient-derived cells. Ex vivo treatment of FECD human corneas effectively inhibits foci and reverses pathological changes in splicing. FECD has the potential to be a model for treating many trinucleotide repeat diseases and targeting the TCF4 expansion with ASOs represents a promising therapeutic strategy to prevent and treat FECD.

Description: Visual system development is light-experience dependent, which strongly implicates epigenetic mechanisms in light-regulated maturation. Among many epigenetic processes, genomic imprinting is an epigenetic mechanism through which monoallelic gene expression occurs in a parent-of-origin-specific manner. It is unknown if genomic imprinting contributes to visual system development. We profiled the transcriptome and imprintome during critical periods of mouse visual system development under normal- and dark-rearing conditions using B6/CAST F1 hybrid mice. We identified experience-regulated, isoform-specific and brain-region-specific imprinted genes. We also found imprinted microRNAs were predominantly clustered into the Dlk1-Dio3 imprinted locus with light experience affecting some imprinted miRNA expression. Our findings provide the first comprehensive analysis of light-experience regulation of the transcriptome and imprintome during critical periods of visual system development. Our results may contribute to therapeutic strategies for visual impairments and circadian rhythm disorders resulting from a dysfunctional imprintome.

Description: Receptor for Advanced Glycation End products (RAGE) has been implicated in amyloid β-peptide (Aβ)-induced perturbation relevant to the pathogenesis of Alzheimer's disease (AD). However, whether and how RAGE regulates Aβ metabolism remains largely unknown. Aβ formation arises from aberrant cleavage of amyloid pre-cursor protein (APP) by β- and γ-secretase. To investigate whether RAGE modulates β- and γ-secretase activity potentiating Aβ formation, we generated mAPP mice with genetic deletion of RAGE (mAPP/RO). These mice displayed reduced cerebral amyloid pathology, inhibited aberrant APP-Aβ metabolism by reducing β- and γ-secretases activity, and attenuated impairment of learning and memory compared with mAPP mice. Similarly, RAGE signal transduction deficient mAPP mice (mAPP/DN-RAGE) exhibited the reduction in Aβ40 and Aβ42 production and decreased β-and γ-secretase activity compared with mAPP mice. Furthermore, RAGE-deficient mAPP brain revealed suppression of activation of p38 MAP kinase and glycogen synthase kinase 3β (GSK3β). Finally, RAGE siRNA-mediated gene silencing or DN-RAGE-mediated signaling deficiency in the enriched human APP neuronal cells demonstrated suppression of activation of GSK3β, accompanied with reduction in Aβ levels and decrease in β- and γ-secretases activity. Our findings highlight that RAGE-dependent signaling pathway regulates β- and γ-secretase cleavage of APP to generate Aβ, at least in part through activation of GSK3β and p38 MAP kinase. RAGE is a potential therapeutic target to limit aberrant APP-Aβ metabolism in halting progression of AD.

Description: Epigenetic regulation of cellular function provides a mechanism for rapid organismal adaptation to changes in health, lifestyle and environment. Associations of cytosine-guanine di-nucleotide (CpG) methylation with clinical endpoints that overlap with metabolic phenotypes suggest a regulatory role for these CpG sites in the body’s response to disease or environmental stress. We previously identified 20 CpG sites in an epigenome-wide association study (EWAS) with metabolomics that were also associated in recent EWASs with diabetes-, obesity-, and smoking-related endpoints. To elucidate the molecular pathways that connect these potentially regulatory CpG sites to the associated disease or lifestyle factors, we conducted a multi-omics association study including 2474 mass-spectrometry-based metabolites in plasma, urine and saliva, 225 NMR-based lipid and metabolite measures in blood, 1124 blood-circulating proteins using aptamer technology, 113 plasma protein N-glycans and 60 IgG-glyans, using 359 samples from the multi-ethnic Qatar Metabolomics Study on Diabetes (QMDiab). We report 138 multi-omics associations at these CpG sites, including diabetes biomarkers at the diabetes-associated TXNIP locus, and smoking-specific metabolites and proteins at multiple smoking-associated loci, including AHRR . Mendelian randomization suggests a causal effect of metabolite levels on methylation of obesity-associated CpG sites, i.e. of glycerophospholipid PC(O-36: 5), glycine and a very low-density lipoprotein (VLDL-A) on the methylation of the obesity-associated CpG loci DHCR24 , MYO5C and CPT1A , respectively. Taken together, our study suggests that multi-omics-associated CpG methylation can provide functional read-outs for the underlying regulatory response mechanisms to disease or environmental insults.

Description: Limb-girdle muscular dystrophy type 2D (LGMD2D) is a rare autosomal-recessive disease, affecting striated muscle, due to mutation of SGCA , the gene coding for α-sarcoglycan. Nowadays, more than 50 different SGCA missense mutations have been reported. They are supposed to impact folding and trafficking of α-sarcoglycan because the defective polypeptide, although potentially functional, is recognized and disposed of by the quality control of the cell. The secondary reduction of α-sarcoglycan partners, β-, γ- and δ-sarcoglycan, disrupts a key membrane complex that, associated to dystrophin, contributes to assure sarcolemma stability during muscle contraction. The complex deficiency is responsible for muscle wasting and the development of a severe form of dystrophy. Here, we show that the application of small molecules developed to rescue ΔF508-CFTR trafficking, and known as CFTR correctors, also improved the maturation of several α-sarcoglycan mutants that were consequently rescued at the plasma membrane. Remarkably, in myotubes from a patient with LGMD2D, treatment with CFTR correctors induced the proper re-localization of the whole sarcoglycan complex, with a consequent reduction of sarcolemma fragility. Although the mechanism of action of CFTR correctors on defective α-sarcoglycan needs further investigation, this is the first report showing a quantitative and functional recovery of the sarcoglycan-complex in human pathologic samples, upon small molecule treatment. It represents the proof of principle of a pharmacological strategy that acts on the sarcoglycan maturation process and we believe it has a great potential to develop as a cure for most of the patients with LGMD2D.

Description: Friedreich ataxia (FRDA) is an inherited recessive disorder caused by a deficiency in the mitochondrial protein frataxin. There is currently no effective treatment for FRDA available, especially for neurological deficits. In this study, we tested diazoxide, a drug commonly used as vasodilator in the treatment of acute hypertension, on cellular and animal models of FRDA. We first showed that diazoxide increases frataxin protein levels in FRDA lymphoblastoid cell lines, via the mammalian target of rapamycin (mTOR) pathway. We then explored the potential therapeutic effect of diazoxide in frataxin-deficient transgenic YG8sR mice and we found that prolonged oral administration of 3 mpk/d diazoxide was found to be safe, but produced variable effects concerning efficacy. YG8sR mice showed improved beam walk coordination abilities and footprint stride patterns, but a generally reduced locomotor activity. Moreover, they showed significantly increased frataxin expression, improved aconitase activity, and decreased protein oxidation in cerebellum and brain mitochondrial tissue extracts. Further studies are needed before this drug should be considered for FRDA clinical trials.

Description: The emergence of escape-mutants of influenza hemagglutinin (HA) following vaccination compels the yearly re-formulation of flu vaccines. Since binding the sialic acid receptor remains in all cases essential for infection, small-molecule inhibitors of HA binding to sialic acid could be interesting therapeutic complements or alternatives to immuno-prophylaxis in the control of flu epidemics. In this work, we made use of NMR spectroscopy to study the interaction between a derivative of sialic acid (the Neu5Ac-α-(2,6)-Gal-β-(1-4)-GlcNAc trisaccharide) and HAs (H1 and H5) from human and avian strains of influenza virus, directly expressed on the surface of stable transfected 293 T human cells. The HAs were shown to retain their native trimeric conformation and binding properties. Exploiting the magnetization transfer between the proteins and the ligand, we obtained evidence of the binding event and mapped the (non-identical) sugar epitopes recognized by the two HA species. The rapid and reliable method for screening sialic acid-related HA ligands we have developed could yield useful information for an efficient drug design.

Description: Zymogen granule protein 16 (ZG16p) is a soluble lectin that binds to both mannose and heparin/heparan sulfate. It is highly expressed in the human digestive tract and is secreted into the mucus. In this study, we investigated the effect of ZG16p on the proliferation of human colorectal cancer cells. Overexpression of ZG16p in Caco-2 cells decreased cell growth. Recombinant ZG16p markedly inhibited proliferation of Caco-2, LS174T, HCT116 and HCT15 cells. Caco-2 cell growth was not inhibited by two mutated ZG16p proteins, D151A and M5 (K36A, R37A, R53A, R55A and R79A) lacking mannose- and heparin-binding activities, respectively. Immunofluorescent cell staining revealed that ZG16p-D151A maintained its binding to the Caco-2 cell surface, whereas ZG16p-M5 failed to bind to the cells. These results suggest that ZG16p interacts with the cell surface via basic amino acids substituted in ZG16p-M5 and inhibits Caco-2 cell proliferation via Asp151. In addition, growth of patient-derived colorectal tumor organoids in a 3D intestinal stem cell system was suppressed by ZG16p but not by ZG16p-M5. Taken together, our findings indicate that ZG16p inhibits the growth of colorectal cancer cells via its carbohydrate-binding sites in vitro and ex vivo. In this study, a novel pathway in cancer cell growth regulation through cell surface carbohydrate chains is suggested.

Description: The stem cell niche normally prevents aberrant stem cell behaviors that lead to cancer formation. Recent studies suggest that some cancers are derived from endogenous populations of adult stem cells that have somehow escaped from normal control by the niche. However, the molecular mechanisms by which the niche retains stem cells locally and tightly controls their divisions are poorly understood. Here, we demonstrate that the presence of heparan sulfate (HS), a class glygosaminoglycan chains, in the Drosophila germline stem cell niche prevents tumor formation in the testis. Loss of HS in the niche, called the hub, led to gross changes in the morphology of testes as well as the formation of both somatic and germline tumors. This loss of hub HS resulted in ectopic signaling events in the Jak/Stat pathway outside the niche. This ectopic Jak/Stat signaling disrupted normal somatic cell differentiation, leading to the formation of tumors. Our finding indicates a novel non-autonomous role for niche HS in ensuring the integrity of the niche and preventing tumor formation.

Description: Inhibition of peripheral inflammatory disease by carbohydrate antigens derived from normal gut microbiota has been demonstrated for the GI tract, brain, peritoneum, and most recently the airway. We have demonstrated that polysaccharide A (PSA) from the commensal organism Bacteroides fragilis activates CD4 + T cells upon presentation by the class II major histocompatibility complex, and that these PSA-experienced T cells prevent the development of lung inflammation in murine models. While the PSA-responding T cells themselves are not canonical FoxP3 + regulatory T cells (Tregs), their ability to prevent inflammation is dependent upon the suppressive cytokine IL-10. Using an adoptive T cell transfer approach, we have discovered that PSA-experienced T cells require IL-10 expression by PSA-naïve recipient animals in order to prevent inflammation. A cooperative relationship was found between PSA-activated effector/memory T cells and tissue-resident FoxP3 + Tregs both in vivo and in vitro, and it is this cooperation that enables the suppressive activity of PSA outside of the gut environment where exposure takes place. These findings suggest that carbohydrate antigens from the normal microbiota communicate with peripheral tissues to maintain homeostasis through T cell-to-T cell cooperation.

Description: CAZypedia was initiated in 2007 to create a comprehensive, living encyclopedia of the carbohydrate-active enzymes (CAZymes) and associated carbohydrate-binding modules involved in the synthesis, modification and degradation of complex carbohydrates. CAZypedia is closely connected with the actively curated CAZy database, which provides a sequence-based foundation for the biochemical, mechanistic and structural characterization of these diverse proteins. Now celebrating its 10th anniversary online, CAZypedia is a successful example of dynamic, community-driven and expert-based biocuration. CAZypedia is an open-access resource available at URL http://www.cazypedia.org.

Description: The article examines the link between ethnic segregation and spatial inequality in 71 countries with different levels of economic development. The results reveal that ethnic segregation is associated with significantly higher levels of spatial inequality. This finding is not affected by the inclusion of various covariates that may influence both spatial inequality and the geographical distribution of ethnic groups, and is confirmed by a number of robustness tests. The results also suggest that political decentralisation and government quality could act as transmission channels linking ethnic segregation and spatial inequality.

Description: Existing work emphasizes the importance of traffic congestion externalities, but typically ignores cruising-for-parking externalities. We estimate the marginal external cruising costs of parking—that is, the time costs that an additional parked car imposes on drivers by inducing them to cruise for parking—which is one of the main components of cruising-for-parking externalities. The level of cruising is identified by examining to what extent the car inflow rate into a parking location falls with parking occupancy level. For a commercial street in Istanbul, we demonstrate that a marginal car parking for an hour induces 3.6 other cars to cruise for parking. This translates into an external cruising cost that is in the same order of magnitude with the external traffic congestion cost created by the trip.

Description: This paper assesses the implicit valuation of aircraft noise by looking at changes in list offer prices for owner-occupied apartments around the airport of Frankfurt, Germany. A differences-in-differences research design permits circumvention of typical endogeneity problems in hedonic price estimations: Namely, the construction of the northwest runway in 2011 led to new aircraft noise exposure and subsequent price decreases in some southern parts of Frankfurt. The paper compares price changes in differentially affected areas around the announcement of the runway location in December 2007 and around its commissioning in October 2011. Noise changes are measured using publicly known noise projections as well as detailed noise assessment data for 2007 and 2012. The results suggest very little realization of externality costs before noise is actually apparent. Once aircraft noise came into effect, a price devaluation of around 1.7% per decibel of additional noise due to the new runway is measured.

Description: Poor contract enforcement can importantly affect firms’ incentives to grow. We investigate the causal effect of the weakness of contract enforcement on average firm size across Italian municipalities, exploiting spatial discontinuities in court jurisdictions for identification. Italy provides an ideal environment for this exercise, as it displays wide variation in judicial efficiency across courts, while the allocation of municipalities to jurisdictions is a historical legacy and does not overlap with other political or economic discontinuities. Our estimates indicate that reducing the length of judicial proceedings (i.e. improving contract enforceability) by 10% at court level leads to a 2% increase in average size of local firms. The outcome on turnover growth is of the same magnitude, suggesting that the effect operates at the intensive margin.

Description: Among several factors affecting radiation sensitivity, genome size has received limited attention during the last 50 years since research at Brookhaven National Laboratory (USA) and other locations demonstrated substantial differences in radiation sensitivities, e.g. between tree species with large (e.g. conifers such as pines) versus small (e.g. dicots such as oaks) genome sizes. Taking advantage of the wide range of genome sizes among species, we investigated radiation sensitivity which we define in this study as DNA damage (break frequency) measured with the alkaline comet assay in isolated nuclei exposed to X-rays. As a starting point, we considered two possible explanations for the high radiation sensitivity of plants with large genome sizes: (i) inherently higher sensitivity of larger genomes and/or (ii) impaired DNA repair. In terms of genome size effects, experiments exposing isolated nuclei from six different plant species to X-rays, varying in genome sizes from 2.6 to 19.2 Gbp, showed that larger genomes are more sensitive to DNA damage by a relationship approximating the cube-root of the nuclear volume; e.g. a 10-fold increase in genome size increases sensitivity by about 2-fold. With regard to DNA repair, two conifer species, Sawara cypress ( Chamaecyparis pisifera , 8.9 Gbp genome size) and Scots pine ( Pinus sylvestris , 20 Gbp genome size), both effectively repaired DNA damage within 50 and 70 min, respectively, after acute X-ray exposures. Both species also showed delayed repair of double-strand DNA breaks, as we previously showed with Arabidopsis thaliana and Lolium multiflorum .

Description: Since there are several predicting factors associated with the comet assay parameters, we have decided to assess the impact of seasonal variations on the comet assay results. A total of 162 volunteers were retrospectively studied, based on the date when blood donations were made. The groups (winter, spring, summer and autumn) were matched in terms of age, gender, smoking status, body mass index and medical diagnostic exposure in order to minimise the impact of other possible predictors. Means and medians of the comet assay parameters were higher when blood was sampled in the warmer period of the year, the values of parameters being the highest during summer. Correlation of meteorological data (air temperature, sun radiation and sun insolation) was observed when data were presented as the median per person. Using multivariate analysis, sampling season and exposure to medical radiation were proved to be the most influential predictors for the comet assay parameters. Taken together, seasonal variation is another variable that needs to be accounted for when conducting a cohort study. Further studies are needed in order to improve the statistical power of the results related to the impact of sun radiation, air temperature and sun insolation on the comet assay parameters.

Description: The alkaline comet assay and a cell-free system were used to characterise DNA lesions induced by treatment with glycidamide (GA), a metabolite of the food contaminant acrylamide. DNA lesions induced by GA were sensitively detected when the formamidopyrimidine-DNA-glycosylase (Fpg) enzyme was included in the comet assay. We used LC-MS to characterise modified bases from GA-treated naked DNA with and without subsequent Fpg treatment. N7-GA-Guanine and N3-GA-Adenine aglycons were detected in the supernatant showing some depurination of adducted bases; treatment of naked DNA with Fpg revealed no further increase in the adduct yield nor occurrence of other adducted nucleobases. We treated human lymphocytes with GA and found large differences in DNA lesion levels detected with Fpg, depending on the duration and the pH of the lysis step. These lysis-dependent variations in GA-induced Fpg sensitive sites paralleled those observed after treatment of cells with methyl methane sulfonate (MMS). On the other hand, oxidative lesions (8-oxoGuanine) induced by a photoactive compound (Ro 12-9786) plus light, and also DNA strand breaks induced by X-rays, were detected largely independently of the lysis conditions. The results suggest that the GA-induced lesions are predominantly N7-GA-dG adducts slowly undergoing imidazole ring opening at pH 10 as in the standard lysis procedure; such structures are substrate for Fpg leading to strand breaks. The data suggest that the characteristic alkaline lysis dependence of some DNA lesions may be used to study specific types of DNA modifications. The comet assay is increasingly used in regulatory testing of chemicals; in this context, lysis-dependent variations represent a novel approach to obtain insight in the molecular nature of a genotoxic insult.

Description: Knowledge about the basal level of DNA damage in leucocytes of healthy control populations is essential before estimation of the effects of exposure to external agents in biomonitoring studies. The aim of this study was to analyse the effects of some lifestyle factors on baseline DNA damage in leucocytes of humans. The material consisted of the peripheral blood from 276 healthy volunteer blood donors. In addition to the standard blood donation questionnaire, they were asked about age, gender, occupation, radiological history, smoking habit, alcohol consumption, medicine use and pet ownership. The results showed marked intra-individual variability. Significant differences in DNA damage levels were observed between individuals in different age and sex groups, between smokers and non-smokers and between samples taken in different seasons of the year, with the highest DNA damage in those obtained in the summer. Significantly higher levels of DNA damage were noted in leucocytes of donors older than 29 years, in men compared with women and in male smokers. Significantly higher DNA strand breaks were observed in heavy smokers. A non-significantly higher level of DNA damage was observed in individuals subjected to radiological investigation and in those drinking alcohol, whereas lower levels were observed in leucocytes of pet owners and in donors taking medicines. Pet ownership influences the level of DNA damage and there is an interaction between this effect and that of smoking. The smoker/pet owners showed almost half the level of DNA damage of smokers without pets. The current results confirmed high intra-individual variability between the levels of DNA damage of individuals. The significant factors that influence the DNA damage in leucocytes are age, sex and smoking habit, especially in men and in heavy smokers. The finding of reduced DNA damage in the leucocytes of pet owners suggests the tendency towards a beneficial effect of such company.

Description: It is known that ceramic workers are potentially exposed to complex mixture of chemicals such as silica, inorganic lead, lime, beryllium and aluminum that can be associated with an increased risk of several diseases. All operations in the ceramic industries such as mixing, moulding, casting, shaking out and finishing jobs, have been associated with the higher exposure levels and in most of the silica-related industries, average overall exposure exceeded permissible exposure levels for respirable crystalline silica. The aim of this study was to evaluate the possible genotoxic damage in ceramic workers exposed to complex mixture of chemicals mainly crystalline silica. For this purpose, the blood and buccal epithelial cell samples were taken from the ceramic workers ( n = 99) and their controls ( n = 81). The genotoxicity was assessed by the alkaline comet assay in isolated lymphocytes and whole blood. Micronucleus (MN), binucleated (BN), pyknotic (PYC), condensed chromatin (CC), karyolytic (KYL), karyorrhectic (KHC) and nuclear bud (NBUD) frequencies in buccal epithelial cells and plasma 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodG) levels were also evaluated. In the study, 38 workers were diagnosed with silicosis, 9 workers were suspected to have silicosis, whereas 52 workers were found to be healthy. DNA damage in blood and lymphocytes; MN, CC + KHC, PYC frequencies in buccal epithelial cells and 8-oxodG levels in plasma were increased in workers compared to their controls. These results showed that occupational chemical mixture exposure in ceramic industry may cause genotoxic damage that can lead to important health problems in the workers.

Description: The formamidopyrimidine DNA glycosylase (Fpg) and human 8-oxoguanine DNA glycosylase (hOGG1)-modified comet assays have been widely used in human biomonitoring studies. The purpose of this article is to assess differences in reported levels of Fpg- and hOGG1-sensitive sites in leukocytes and suggest suitable assay controls for the measurement of oxidatively damaged DNA. An assessment of the literature showed a large variation in the reported levels of Fpg-sensitive sites (range 0.05–1.31 lesions/10 6 bp). The levels of Fpg-sensitive sites are lower in studies where Fpg has been obtained from commercial suppliers or unknown sources as compared to Fpg from one particular non-commercial source (χ 2 = 7.14, P = 0.028). The levels of hOGG1-sensitive sites are lower (range: 0.04–0.18 lesions/10 6 bp in leukocytes) compared to the Fpg-sensitive sites. Surprisingly, few publications have reported the use of oxidising agents as assay controls, with the exception of hydrogen peroxide. This may be due to a lack of consensus about suitable controls for the Fpg- and hOGG1-modified comet assay. A major challenge is to find an oxidising agent that only oxidises nucleobases and does not generate DNA strand breaks because this reduces the dynamic range of Fpg- and hOGG1-sensitive sites in the comet assay. Based on a literature search we selected the photosensitiser Ro19-8022 plus light, KBrO 3 , 4-nitroquinoline-1-oxide, Na 2 Cr 2 O 7 and ferric nitrilotriacetate as possible assay controls. A subsequent assessment of these compounds for generating cryopreserved assay controls in mononuclear blood cells showed that Ro19-8022 plus light, KBrO 3 and 4-nitroquinoline-1-oxide provided suitable assay controls. We recommend these compounds as comet assay controls for oxidatively damaged DNA.

Description: Classic geospatial network visualization tends to limit itself to 2D representation by organizing edges and nodes on a 2D map or the external surface of a traditional 3D globe model. Visual clutters and occlusions due to edge crossings and node-edge overlaps make efficient and effective exploration of geospatial networks a challenge. This paper proposes an interactive visualization approach for the intuitive exploration of geospatial networks inside a spherical virtual reality environment. To reduce visual clutter and reveal network patterns, we also propose a parameterized 5-step 3D edge-bundling algorithm and a set of techniques to avoid collision of network edges with the viewpoint. Our spherical interaction and 3D edge-bundling approach have been implemented in an Oculus Rift VR system. We demonstrate the usefulness of our approach with two case studies on real-world network data and usability experiments.

Description: Shadow detection is an important pre-processing step often used in scene interpretation or shadow removal applications. In this paper, we propose a single-image shadow detection method. Many other methods use multiple images; we use a quaternion representation of colour images to extract shadows from only one input image. The generation of the final binary shadow mask is done via automatic threshold selection. Evaluation is carried out qualitatively and quantitatively over three challenging datasets of indoor and outdoor natural images. The results of qualitative assessment were consistent with the statistical results, where the proposed method improves the performance of shadow detection when compared with state-of-the-art methods.

Description: Summary Refinement of protein structure models is a long-standing problem in structural bioinformatics. Molecular dynamics-based methods have emerged as an avenue to achieve consistent refinement. The PREFMD web server implements an optimized protocol based on the method successfully tested in CASP11. Validation with recent CASP refinement targets shows consistent and more significant improvement in global structure accuracy over other state-of-the-art servers. Availability and implementation PREFMD is freely available as a web server at http://feiglab.org/prefmd . Scripts for running PREFMD as a stand-alone package are available at https://github.com/feiglab/prefmd.git . Contact feig@msu.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers. Availability and implementation https://github.com/chengjun583/KIRP-topological-features Contact 1271992826@qq.com or kunhuang@iu.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins. Results We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker’s ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology. Availability and implementation The CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/. Contact deane@stats.ox.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary We developed a prokaryotic genome annotation pipeline, DFAST, that also supports genome submission to public sequence databases. DFAST was originally started as an on-line annotation server, and to date, over 7000 jobs have been processed since its first launch in 2016. Here, we present a newly implemented background annotation engine for DFAST, which is also available as a standalone command-line program. The new engine can annotate a typical-sized bacterial genome within 10 min, with rich information such as pseudogenes, translation exceptions and orthologous gene assignment between given reference genomes. In addition, the modular framework of DFAST allows users to customize the annotation workflow easily and will also facilitate extensions for new functions and incorporation of new tools in the future. Availability and implementation The software is implemented in Python 3 and runs in both Python 2.7 and 3.4—on Macintosh and Linux systems. It is freely available at https://github.com/nigyta/dfast_core/under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at https://dfast.nig.ac.jp/ . Contact yn@nig.ac.jp Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Biodiversity databases now comprise hundreds of thousands of sequences and trait records. For example, the Open Tree of Life includes over 1 491 000 metazoan and over 300 000 bacterial taxa. These data provide unique opportunities for analysis of phylogenetic trait distribution and reconstruction of ancestral biodiversity. However, existing tools for comparative phylogenetics scale poorly to such large trees, to the point of being almost unusable. Results Here we present a new R package, named ‘castor’, for comparative phylogenetics on large trees comprising millions of tips. On large trees castor is often 100–1000 times faster than existing tools. Availability and implementation The castor source code, compiled binaries, documentation and usage examples are freely available at the Comprehensive R Archive Network (CRAN). Contact louca.research@gmail.com Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian. Results We provide evidence that the Gaussian assumption is incorrect, motivating the need for our Poisson approach. The method is demonstrated by making proportion measurements from lipid-rich binary mixtures of lamb brain and liver, and also goat and cow milk. These allow our measurements and error predictions to be compared to ground truth. Availability and implementation Software is available via the open source image analysis system TINA Vision, www.tina-vision.net . Contact paul.tar@manchester.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation VizGVar was designed to meet the growing need of the research community for improved genomic and proteomic data viewers that benefit from better information visualization. Results We implemented a new information architecture and applied user centered design principles to provide a new improved way of visualizing genetic information and protein data related to human disease. VizGVar connects the entire database of Ensembl protein motifs, domains, genes and exons with annotated SNPs and somatic variations from PharmGKB and COSMIC . VizGVar precisely represents genetic variations and their respective location by colored curves to designate different types of variations. The structured hierarchy of biological data is reflected in aggregated patterns through different levels, integrating several layers of information at once. VizGVar provides a new interactive, web-based JavaScript visualization of somatic mutations and protein variation, enabling fast and easy discovery of clinically relevant variation patterns. Availability and implementation VizGVar is accessible at http://vizport.io/vizgvar ; http://vizport.io/vizgvar/doc/ . Contact asolano@broadinstitute.org or allan.orozcosolano@ucr.ac.cr

Description: Motivation RNA structural motif finding is a relevant problem that becomes computationally hard when working on high-throughput data (e.g. eCLIP, PAR-CLIP), often represented by thousands of RNA molecules. Currently, the BEAM server is the only web tool capable to handle tens of thousands of RNA in input with a motif discovery procedure that is only limited by the current secondary structure prediction accuracies. Results The recently developed method BEAM (BEAr Motifs finder) can analyze tens of thousands of RNA molecules and identify RNA secondary structure motifs associated to a measure of their statistical significance. BEAM is extremely fast thanks to the BEAR encoding that transforms each RNA secondary structure in a string of characters. BEAM also exploits the evolutionary knowledge contained in a substitution matrix of secondary structure elements, extracted from the RFAM database of families of homologous RNAs. The BEAM web server has been designed to streamline data pre-processing by automatically handling folding and encoding of RNA sequences, giving users a choice for the preferred folding program. The server provides an intuitive and informative results page with the list of secondary structure motifs identified, the logo of each motif, its significance, graphic representation and information about its position in the RNA molecules sharing it. Availability and implementation The web server is freely available at http://beam.uniroma2.it/ and it is implemented in NodeJS and Python with all major browsers supported. Contact marco.pietrosanto@uniroma2.it Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation A significant difference in the distribution of a feature between two gene sets can provide insight into function or regulation. This statistical setting differs from much of hypothesis testing theory because the genome is often considered to be effectively fixed, finite and entirely known in commonly studied organisms, such as human. The Mann–Whitney U test is commonly employed in this scenario despite the assumptions of the test not being met, leading to unreliable and generally underpowered results. Permutation tests are also commonly employed for this purpose, but are computationally burdensome and are not tractable for obtaining small P values or for multiple comparisons. Results We present an exact test for the null hypothesis that gene set membership is independent of the quantitative gene feature of interest. We derive an analytic expression for the randomization distribution of the median of the quantitative feature under the null hypothesis. Efficient implementation permits calculation of precise P values of arbitrary magnitude and makes thousands of simultaneous tests of transcriptome-sized gene sets computationally tractable. The flexibility of the hypothesis testing framework presented permits extension to a variety of related tests commonly found in genomics. The exact test is used to identify signatures of translation control and protein function in the human genome. Availability and implementation The exact test presented here is implemented in R in the package kpmt available on CRAN. Contact map2085@med.cornell.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Dynamic assessment of microbial ecology (DAME) is a Shiny-based web application for interactive analysis and visualization of microbial sequencing data. DAME provides researchers not familiar with R programming the ability to access the most current R functions utilized for ecology and gene sequencing data analyses. Currently, DAME supports group comparisons of several ecological estimates of α-diversity and β-diversity, along with differential abundance analysis of individual taxa. Using the Shiny framework, the user has complete control of all aspects of the data analysis, including sample/experimental group selection and filtering, estimate selection, statistical methods and visualization parameters. Furthermore, graphical and tabular outputs are supported by R packages using D3.js and are fully interactive. Availability and implementation DAME was implemented in R but can be modified by Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. It is freely available on the web at https://acnc-shinyapps.shinyapps.io/DAME/ . Local installation and source code are available through Github ( https://github.com/bdpiccolo/ACNC-DAME ). Any system with R can launch DAME locally provided the shiny package is installed. Contact bdpiccolo@uams.edu

Description: Motivation Chromatin immunoprecipitation sequencing (ChIP-seq) experiments are inexpensive and time-efficient, and result in massive datasets that introduce significant storage and maintenance challenges. To address the resulting Big Data problems, we propose a lossless and lossy compression framework specifically designed for ChIP-seq Wig data, termed ChIPWig. ChIPWig enables random access, summary statistics lookups and it is based on the asymptotic theory of optimal point density design for nonuniform quantizers. Results We tested the ChIPWig compressor on 10 ChIP-seq datasets generated by the ENCODE consortium. On average, lossless ChIPWig reduced the file sizes to merely 6% of the original, and offered 6-fold compression rate improvement compared to bigWig. The lossy feature further reduced file sizes 2-fold compared to the lossless mode, with little or no effects on peak calling and motif discovery using specialized NarrowPeaks methods. The compression and decompression speed rates are of the order of 0.2 sec/MB using general purpose computers. Availability and implementation The source code and binaries are freely available for download at https://github.com/vidarmehr/ChIPWig-v2 , implemented in C ++. Contact milenkov@illinois.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. Results We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Availability and implementation Proposed methods are available in the R package mixKernel , released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/ . Contact jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Large-scale molecular data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, standard approaches for omics data analysis ignore the group structure among genes encoded in functional relationships or pathway information. Results We propose new Bayesian hierarchical generalized linear models, called group spike-and-slab lasso GLMs, for predicting disease outcomes and detecting associated genes by incorporating large-scale molecular data and group structures. The proposed model employs a mixture double-exponential prior for coefficients that induces self-adaptive shrinkage amount on different coefficients. The group information is incorporated into the model by setting group-specific parameters. We have developed a fast and stable deterministic algorithm to fit the proposed hierarchal GLMs, which can perform variable selection within groups. We assess the performance of the proposed method on several simulated scenarios, by varying the overlap among groups, group size, number of non-null groups, and the correlation within group. Compared with existing methods, the proposed method provides not only more accurate estimates of the parameters but also better prediction. We further demonstrate the application of the proposed procedure on three cancer datasets by utilizing pathway structures of genes. Our results show that the proposed method generates powerful models for predicting disease outcomes and detecting associated genes. Availability and implementation The methods have been implemented in a freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ ). Contact nyi@uab.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Summary Gene-based supervised machine learning classification models have been widely used to differentiate disease states, predict disease progression and determine effective treatment options. However, many of these classifiers are sensitive to noise and frequently do not replicate in external validation sets. For complex, heterogeneous diseases, these classifiers are further limited by being unable to capture varying combinations of genes that lead to the same phenotype. Pathway-based classification can overcome these challenges by using robust, aggregate features to represent biological mechanisms. In this work, we developed a novel pathway-based approach, PRObabilistic Pathway Score, which uses genes to calculate individualized pathway scores for classification. Unlike previous individualized pathway-based classification methods that use gene sets, we incorporate gene interactions using probabilistic graphical models to more accurately represent the underlying biology and achieve better performance. We apply our method to differentiate two similar complex diseases, ulcerative colitis (UC) and Crohn’s disease (CD), which are the two main types of inflammatory bowel disease (IBD). Using five IBD datasets, we compare our method against four gene-based and four alternative pathway-based classifiers in distinguishing CD from UC. We demonstrate superior classification performance and provide biological insight into the top pathways separating CD from UC. Availability and Implementation PROPS is available as a R package, which can be downloaded at http://simtk.org/home/props or on Bioconductor . Contact rbaltman@stanford.edu Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation The branchpoint element is required for the first lariat-forming reaction in splicing. However current catalogues of human branchpoints remain incomplete due to the difficulty in experimentally identifying these splicing elements. To address this limitation, we have developed a machine-learning algorithm—branchpointer—to identify branchpoint elements solely from gene annotations and genomic sequence. Results Using branchpointer, we annotate branchpoint elements in 85% of human gene introns with sensitivity (61.8%) and specificity (97.8%). In addition to annotation, branchpointer can evaluate the impact of SNPs on branchpoint architecture to inform functional interpretation of genetic variants. Branchpointer identifies all published deleterious branchpoint mutations annotated in clinical variant databases, and finds thousands of additional clinical and common genetic variants with similar predicted effects. This genome-wide annotation of branchpoints provides a reference for the genetic analysis of splicing, and the interpretation of noncoding variation. Availability and implementation Branchpointer is written and implemented in the statistical programming language R and is freely available under a BSD license as a package through Bioconductor. Contact b.signal@garvan.org.au or t.mercer@garvan.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Alternative splicing is a biological process of fundamental importance in most eukaryotes. It plays a pivotal role in cell differentiation and gene regulation and has been associated with a number of different diseases. The widespread availability of RNA-Sequencing capacities allows an ever closer investigation of differentially expressed isoforms. However, most tools for differential alternative splicing (DAS) analysis do not take split reads, i.e. the most direct evidence for a splice event, into account. Here, we present DIEGO, a compositional data analysis method able to detect DAS between two sets of RNA-Seq samples based on split reads. Results The python tool DIEGO works without isoform annotations and is fast enough to analyze large experiments while being robust and accurate. We provide python and perl parsers for common formats. Availability and implementation The software is available at: www.bioinf.uni-leipzig.de/Software/DIEGO . Contact steve@bioinf.uni-leipzig.de Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation The collection, management and visualization of clinical pedigree (family history) data is a core activity in clinical genetics centres. However, clinical pedigree datasets can be difficult to manage, as they are time consuming to capture, and can be difficult to build, manipulate and visualize graphically. Several standalone graphical pedigree editors and drawing applications exist but there are no freely available lightweight graphical pedigree editors that can be easily configured and incorporated into web applications. Results We developed ‘pedigreejs’, an interactive graphical pedigree editor written in JavaScript, which uses standard pedigree nomenclature. Pedigreejs provides an easily configurable, extensible and lightweight pedigree editor. It makes use of an open-source Javascript library to define a hierarchical layout and to produce images in scalable vector graphics (SVG) format that can be viewed and edited in web browsers. Availability and implementation The software is freely available under GPL licence ( https://ccge-boadicea.github.io/pedigreejs/ ). Contact tjc29@cam.ac.uk Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Motivation Molecular profiling techniques have evolved to single-cell assays, where dense molecular profiles are screened simultaneously for each cell in a population. High-throughput single-cell experiments from a heterogeneous population of cells can be experimentally and computationally sorted as a sequence of samples pseudo-temporally ordered samples. The analysis of these datasets, comprising a large number of samples, has the potential to uncover the dynamics of the underlying regulatory programmes. Results We present a novel approach for modelling and inferring gene regulatory networks from high-throughput time series and pseudo-temporally sorted single-cell data. Our method is based on a first-order autoregressive moving-average model and it infers the gene regulatory network within a variational Bayesian framework. We validate our method with synthetic data and we apply it to single cell qPCR and RNA-Seq data for mouse embryonic cells and hematopoietic cells in zebra fish. Availability and implementation The method presented in this article is available at https://github.com/mscastillo/GRNVBEM . Contact mscastillo@ugr.es

Description: Motivation Carbohydrates play crucial roles in various biochemical processes and are useful for developing drugs and vaccines. However, in case of carbohydrates, the primary structure elucidation is usually a sophisticated task. Therefore, they remain the least structurally characterized class of biomolecules, and it hampers the progress in glycochemistry and glycobiology. Creating a usable instrument designed to assist researchers in natural carbohydrate structure determination would advance glycochemistry in biomedical and pharmaceutical applications. Results We present GRASS (Generation, Ranking and Assignment of Saccharide Structures), a novel method for semi-automated elucidation of carbohydrate and derivative structures which uses unassigned 13 C NMR spectra and information obtained from chromatography, optical, chemical and other methods. This approach is based on new methods of carbohydrate NMR simulation recently reported as the most accurate. It combines a broad diversity of supported structural features, high accuracy and performance. Availability and implementation GRASS is implemented in a free web tool available at http://csdb.glycoscience.ru/grass.html . Contact kapaev_roman@mail.ru or netbox@toukach.ru Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: This paper presents an analysis of elevation gradient and temporal future-station effects in urban real estate markets. Using a novel dataset from the Hong Kong publicly constructed housing sector, we find enormous housing price effects caused by levels of terrain incline between apartments and subway stations. Ceteris paribus , two similar apartments with closest metro stations of the same walking distance may sell at a difference of up to 20% because of differences in the apartment-station slope alone. Anticipatory effects are similarly robust: apartment buyers regard a future, closer metro station as being 60% present when making purchases 2 years prior to its opening.

Description: We analyze the economic impact of the German high-speed rail (HSR) connecting Cologne and Frankfurt, which provides plausibly exogenous variation in access to surrounding economic mass. We find a causal effect of about 8.5% on average of the HSR on the GDP of three counties with intermediate stops. We make further use of the variation in bilateral transport costs between all counties in our study area induced by the HSR to identify the strength and spatial scope of agglomeration forces. Our most careful estimate points to an elasticity of output with respect to market potential of 12.5%. The strength of the spillover declines by 50% every 30 min of travel time, diminishing to 1% after about 200 min. Our results further imply an elasticity of per-worker output with respect to economic density of 3.8%, although the effects seem driven by worker and firm selection.

Description: During the last decade, FIC proteins have emerged as a large family comprised of a variety of bacterial enzymes and a single member in animals. The air de famille of FIC proteins stems from a domain of conserved structure, which catalyzes the post-translational modification of proteins (PTM) by a phosphate-containing compound. In bacteria, examples of FIC proteins include the toxin component of toxin/antitoxin modules, such as Doc-Phd and VbhT-VbhA, toxins secreted by pathogenic bacteria to divert host cell processes, such as VopS, IbpA and AnkX, and a vast majority of proteins of unknown functions. FIC proteins catalyze primarily the transfer of AMP (AMPylation), but they are not restricted to this PTM and also carry out other modifications, for example by phosphocholine or phosphate. In a recent twist, animal FICD/HYPE was shown to catalyze both AMPylation and de-AMPylation of the endoplasmic reticulum BIP chaperone to regulate the unfolded protein response. FICD shares structural features with some bacterial FIC proteins, raising the possibility that bacteria also encode such dual activities. In this review, we discuss how structural, biochemical and cellular approaches have fertilized each other to understand the mechanism, regulation and function of FIC proteins from bacterial pathogens to humans.

Description: Clostridium difficile (Cd) is the leading cause of antibiotic-associated diarrhea. During an infection, Cd must compete with both the host and other commensal bacteria to acquire iron. Iron is essential for many cell processes, but it can also cause damage if allowed to form reactive hydroxyl radicals. In all organisms, levels of free iron are tightly regulated as are processes utilizing iron molecules. Genome-wide transcriptional analysis of Cd grown in iron-depleted conditions revealed significant changes in expression of genes involved in iron transport, metabolism and virulence. These data will aid future studies examining Cd colonization and the requirements for growth in vivo during an infection.

Description: This paper contributes to the existing literature on the determinants of fiscal decentralization by exploring in depth the empirical relevance of physical geography as a determinant of fiscal decentralization; more geographically diverse countries show greater heterogeneity among their citizens. The theoretical framework imbeds geography into the concept of spatial decay in the provision of public services and our empirical estimation employs a panel data set for 94 countries for the period 1970–2010. Following the ‘first nature’ geography literature we construct a geographical fragmentation index based on elevation data and find that geographical fragmentation and area are significantly and positively related to fiscal decentralization. Following the ‘second nature’ geography literature we interact the geographical fragmentation index with time variant infrastructure variables, in order to test the effect that infrastructure and communications have on physical geography and fiscal decentralization. While the development of infrastructure tends to reduce the effect of physical geography on decentralization, this effect is rather small and mostly statistically insignificant.

Description: Industrial diversification is crucial for economies to prosper. Recent studies have shown that regional economies tend to diversify into sectors that are related to those already present in the region. However, no study yet has investigated the impact of regional institutions. The objective of the article is to analyze how formal and informal institutions influence regional diversification. Studying 118 European regions in the period 2004–2012, we find evidence that institutions, and especially bridging social capital, matter for regions to diversify into new industries. Our results suggest that regional institutions relevant for diversification in regions are predominantly informal in character rather than formal, and bridging rather than bonding.

Description: Limits to Globalization: Disruptive Geographies of Capitalist DevelopmentSheppardEricOxford: Oxford University Press, 2016 ISBN: 9780199681167 (Hardback), 160 pp. Price £30.00

Description: In this article, we give precise mathematical form to the idea of a structure whose data and axioms are faithfully represented by a graphical calculus; some prominent examples are operads, polycategories, properads, and PROPs. Building on the established presentation of such structures as algebras for monads on presheaf categories, we describe a characteristic property of the associated monads—the shapeliness of the title—which says that ‘any two operations of the same shape agree’. An important part of this work is the study of analytic functors between presheaf categories, which are a common generalization of Joyal’s analytic endofunctors on sets and of the parametric right adjoint functors on presheaf categories introduced by Diers and studied by Carboni–Johnstone, Leinster and Weber. Our shapely monads will be found among the analytic endofunctors, and may be characterized as the submonads of a universal analytic monad with ‘exactly one operation of each shape’. In fact, shapeliness also gives a way to define the data and axioms of a structure directly from its graphical calculus, by generating a free shapely monad on the basic operations of the calculus. In this article, we do this for some of the examples listed above; in future work, we intend to use this to obtain canonical notions of denotational model for graphical calculi such as Milner’s bigraphs, Lafont’s interaction nets or Girard’s multiplicative proof nets.

Description: In the framework of quantum computation with mixed states, we introduce a fuzzy approach to the quantum Fredkin gate. Under this perspective, we investigate the behaviour of the gate applied to factorized and non-factorized quantum states.

Description: The core of this article is the modal correspondence theory in the class of all Euclidean frames. It shows that with respect to the class of all Euclidean frames, every modal formula is first-order definable and the problem of deciding the modal definability of sentences is undecidable.

Description: A partial order is called semilinear if the upper bounds of each element are linearly ordered and any two elements have a common upper bound. There exists, up to isomorphism, a unique countable existentially closed semilinear order, which we denote by $(\mathbb{S}_{2};\leq )$. We study the reducts of $(\mathbb{S}_{2};\leq )$, i.e. the relational structures with domain $\mathbb{S}_{2}$, all of whose relations are first-order definable in $(\mathbb{S}_{2};\leq )$. Our main result is a classification of the model-complete cores of the reducts of $\mathbb{S}_{2}$. From this, we also obtain a classification of reducts up to first-order interdefinability, which is equivalent to a classification of all subgroups of the full symmetric group on $\mathbb{S}_{2}$ that contain the automorphism group of $(\mathbb{S}_{2};\leq )$ and are closed with respect to the pointwise convergence topology.

Description: The proofs of quantum nonlocality due to Greenberger, Horne and Zeilinger and due to Hardy are qualitatively different from that of Bell insofar as they rely only on a consideration of whether events are possible or impossible, rather than relying on specific experimental probabilities. We consider the scenario of a bipartite nonlocality experiment, in which two separated experimenters each have access to some measurements they can perform on a system. In a physical theory, some outcomes of this experiment will be labelled possible, others impossible, and an assignment of the values 0 (impossible) and 1 (possible) to these different outcomes forms a table of possibilities . Here, we consider the computational task of determining whether or not a given table of possibilities constitutes a departure from possibilistic local realism. By considering the case in which one party has access to measurements with two outcomes and the other three, it is possible to see at exactly which point this task becomes computationally difficult.

Description: This paper studies the extension of possibilistic logic to the case when weights attached to formulas are symbolic. These weights then stand for variables that lie in a totally ordered scale, and only partial knowledge is available on the relative strength of these weights in the form of inequality constraints. Reasoning in symbolic possibilistic logic means solving two problems. One is to compute symbolic expressions representing the weights of conclusions of a possibilistic knowledge base. The other problem is that of comparing the relative strength of derived weights, so as to find out if one formula is more certain than another one. Regarding the first problem, a proof of the soundness and the completeness of this logic according to the relative certainty semantics in the sense of necessity measures is provided. Based on this result, two syntactic inference methods are suggested. The first one shows how to use the notion of minimal inconsistent subsets and known techniques that compute them, so as to obtain the symbolic expression representing the necessity degree of a possibilistic formula. A second family of methods computes prime implicates and takes inspiration from the concept of assumption-based theory. It enables symbolic weights attached to consequences to be simplified in the course of their computation, taking inequality constraints into account. Finally, an algorithm is proposed to find if a consequence is more certain than another one. A comparison with the original version of symbolic possibilistic logic introduced by Benferhat and Prade in 2005 is provided.

Description: The present work addresses the question: to what extent do natural models of a sufficiently rich fragment of set theory exist? Such models, here called Friedberg models, are built as a class of sets of natural numbers together with the element-relation “$x$ is in $y$” given by $x\in A_y$, where $A_0$, $A_1$, $A_2$, $\ldots$ is a Friedberg numbering of all r.e. sets of natural numbers. A member $A_x$ of this numbering is considered to be a set in the given model iff the transitive closure of the induced membership relation starting from $x$ is well-founded. Furthermore, for all $k$, the set $B_k = \{x: A_x\mbox{ has size $k$ }\}$ must be recursive. It will be examined whether the axioms of set theory and some basic set-theoretic properties hold in such a model. Because they do not hold in full generality, comprehension and replacement need to be properly adapted. The validity of the axiom of power set depends on the Friedberg model under consideration. The other axioms hold in every Friedberg model.

Description: Chlamydia secrete into host cells a diverse array of effector proteins, but progress in characterizing the spatiotemporal localization of these proteins has been hindered by a paucity of genetic approaches in Chlamydia and also by the challenge of studying these proteins within the live cellular environment. We adapted a split-green fluorescent protein (GFP) system for use in Chlamydia to label chlamydial effector proteins and track their localization in host cells under native environment. The efficacy of this system was demonstrated by detecting several known Chlamydia proteins including IncA, CT005 and CT694. We further used this approach to detect two chlamydial deubiquitinases (CT867 and CT868) within live cells during the infection. CT868 localized only to the inclusion membrane at early and late developmental stages. CT867 localized to the chlamydial inclusion membrane at an early developmental stage and was concomitantly localized to the host plasma membrane at a late stage during the infection. These data suggest that chlamydial deubiquitinase play important roles for chlamydial pathogenesis by targeting proteins at both the plasma membrane and the chlamydial inclusion membrane. The split-GFP technology was demonstrated to be a robust and efficient approach to identify the secretion and cellular localization of important chlamydial virulence factors.