ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Articles  (18)
  • Computational Methods, Massively Parallel (Deep) Sequencing  (10)
  • Polymorphism/mutation detection  (8)
  • Oxford University Press  (18)
  • Copernicus
  • Frontiers Media
  • 2010-2014  (18)
  • 1980-1984
  • 1925-1929
  • Biology  (18)
  • Chemistry and Pharmacology
  • 1
    facet.materialart.
    Unknown
    cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate (2012)
    Oxford University Press
    Details
    Publication Date: 2012-05-13
    Description: Quantitative analyses of next-generation sequencing (NGS) data, such as the detection of copy number variations (CNVs), remain challenging. Current methods detect CNVs as changes in the depth of coverage along chromosomes. Technological or genomic variations in the depth of coverage thus lead to a high false discovery rate (FDR), even upon correction for GC content. In the context of association studies between CNVs and disease, a high FDR means many false CNVs, thereby decreasing the discovery power of the study after correction for multiple testing. We propose ‘Copy Number estimation by a Mixture Of PoissonS’ (cn.MOPS), a data processing pipeline for CNV detection in NGS data. In contrast to previous approaches, cn.MOPS incorporates modeling of depths of coverage across samples at each genomic position. Therefore, cn.MOPS is not affected by read count variations along chromosomes. Using a Bayesian approach, cn.MOPS decomposes variations in the depth of coverage across samples into integer copy numbers and noise by means of its mixture components and Poisson distributions, respectively. The noise estimate allows for reducing the FDR by filtering out detections having high noise that are likely to be false detections. We compared cn.MOPS with the five most popular methods for CNV detection in NGS data using four benchmark datasets: (i) simulated data, (ii) NGS data from a male HapMap individual with implanted CNVs from the X chromosome, (iii) data from HapMap individuals with known CNVs, (iv) high coverage data from the 1000 Genomes Project. cn.MOPS outperformed its five competitors in terms of precision (1–FDR) and recall for both gains and losses in all benchmark data sets. The software cn.MOPS is publicly available as an R package at http://www.bioinf.jku.at/software/cnmops/ and at Bioconductor.
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 2
    facet.materialart.
    Unknown
    Summarizing and correcting the GC content bias in high-throughput sequencing (2012)
    Oxford University Press
    Details
    Publication Date: 2012-05-23
    Description: GC content bias describes the dependence between fragment count (read coverage) and GC content found in Illumina sequencing data. This bias can dominate the signal of interest for analyses that focus on measuring fragment abundance within a genome, such as copy number estimation (DNA-seq). The bias is not consistent between samples; and there is no consensus as to the best methods to remove it in a single sample. We analyze regularities in the GC bias patterns, and find a compact description for this unimodal curve family. It is the GC content of the full DNA fragment, not only the sequenced read, that most influences fragment count. This GC effect is unimodal: both GC-rich fragments and AT-rich fragments are underrepresented in the sequencing results. This empirical evidence strengthens the hypothesis that PCR is the most important cause of the GC bias. We propose a model that produces predictions at the base pair level, allowing strand-specific GC-effect correction regardless of the downstream smoothing or binning. These GC modeling considerations can inform other high-throughput sequencing analyses such as ChIP-seq and RNA-seq.
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 3
    facet.materialart.
    Unknown
    Hobbes: optimized gram-based methods for efficient read alignment (2012)
    Oxford University Press
    Details
    Publication Date: 2012-03-29
    Description: Recent advances in sequencing technology have enabled the rapid generation of billions of bases at relatively low cost. A crucial first step in many sequencing applications is to map those reads to a reference genome. However, when the reference genome is large, finding accurate mappings poses a significant computational challenge due to the sheer amount of reads, and because many reads map to the reference sequence approximately but not exactly. We introduce Hobbes, a new gram-based program for aligning short reads, supporting Hamming and edit distance. Hobbes implements two novel techniques, which yield substantial performance improvements: an optimized gram-selection procedure for reads, and a cache-efficient filter for pruning candidate mappings. We systematically tested the performance of Hobbes on both real and simulated data with read lengths varying from 35 to 100 bp, and compared its performance with several state-of-the-art read-mapping programs, including Bowtie, BWA, mrsFast and RazerS. Hobbes is faster than all other read mapping programs we have tested while maintaining high mapping quality. Hobbes is about five times faster than Bowtie and about 2–10 times faster than BWA, depending on read length and error rate, when asked to find all mapping locations of a read in the human genome within a given Hamming or edit distance, respectively. Hobbes supports the SAM output format and is publicly available at http://hobbes.ics.uci.edu .
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 4
    facet.materialart.
    Unknown
    Maximizing signal-to-noise ratio in the random mutation capture assay (2012)
    Oxford University Press
    Details
    Publication Date: 2012-03-14
    Description: The ‘Random Mutation Capture’ assay allows for the sensitive quantitation of DNA mutations at extremely low mutation frequencies. This method is based on PCR detection of mutations that render the mutated target sequence resistant to restriction enzyme digestion. The original protocol prescribes an end-point dilution to about 0.1 mutant DNA molecules per PCR well, such that the mutation burden can be simply calculated by counting the number of amplified PCR wells. However, the statistical aspects associated with the single molecular nature of this protocol and several other molecular approaches relying on binary (on/off) output can significantly affect the quantification accuracy, and this issue has so far been ignored. The present work proposes a design of experiment (DoE) using statistical modeling and Monte Carlo simulations to obtain a statistically optimal sampling protocol, one that minimizes the coefficient of variance in the measurement estimates. Here, the DoE prescribed a dilution factor at about 1.6 mutant molecules per well. Theoretical results and experimental validation revealed an up to 10-fold improvement in the information obtained per PCR well, i.e. the optimal protocol achieves the same coefficient of variation using one-tenth the number of wells used in the original assay. Additionally, this optimization equally applies to any method that relies on binary detection of a small number of templates.
    Keywords: Polymorphism/mutation detection
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 5
    facet.materialart.
    Unknown
    Direct, genome-wide assessment of DNA mutations in single cells (2012)
    Oxford University Press
    Details
    Publication Date: 2012-03-14
    Description: DNA mutations are the inevitable consequences of errors that arise during replication and repair of DNA damage. Because of their random and infrequent occurrence, quantification and characterization of DNA mutations in the genome of somatic cells has been difficult. Random, low-abundance mutations are currently inaccessible by standard high-throughput sequencing approaches because they cannot be distinguished from sequencing errors. One way to circumvent this problem and simultaneously account for the mutational heterogeneity within tissues is whole genome sequencing of a representative number of single cells. Here, we show elevated mutation levels in single cells from Drosophila melanogaster S2 and mouse embryonic fibroblast populations after treatment with the powerful mutagen N -ethyl- N -nitrosourea. This method can be applied as a direct measure of exposure to mutagenic agents and for assessing genotypic heterogeneity within tissues or cell populations.
    Keywords: Polymorphism/mutation detection
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 6
    facet.materialart.
    Unknown
    MiST: A new approach to variant detection in deep sequencing datasets (2013)
    Oxford University Press
    Details
    Publication Date: 2013-09-06
    Description: MiST is a novel approach to variant calling from deep sequencing data, using the inverted mapping approach developed for Geoseq. Reads that can map to a targeted exonic region are identified using exact matches to tiles from the region. The reads are then aligned to the targets to discover variants. MiST carefully handles paralogous reads that map ambiguously to the genome and clonal reads arising from PCR bias, which are the two major sources of errors in variant calling. The reduced computational complexity of mapping selected reads to targeted regions of the genome improves speed, specificity and sensitivity of variant detection. Compared with variant calls from the GATK platform, MiST showed better concordance with SNPs from dbSNP and genotypes determined by an exonic-SNP array. Variant calls made only by MiST confirm at a high rate (〉90%) by Sanger sequencing. Thus, MiST is a valuable alternative tool to analyse variants in deep sequencing data.
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 7
    facet.materialart.
    Unknown
    cnvCapSeq: detecting copy number variation in long-range targeted resequencing data (2014)
    Oxford University Press
    Details
    Publication Date: 2014-11-12
    Description: Targeted resequencing technologies have allowed for efficient and cost-effective detection of genomic variants in specific regions of interest. Although capture sequencing has been primarily used for investigating single nucleotide variants and indels, it has the potential to elucidate a broader spectrum of genetic variation, including copy number variants (CNVs). Various methods exist for detecting CNV in whole-genome and exome sequencing datasets. However, no algorithms have been specifically designed for contiguous target sequencing, despite its increasing importance in clinical and research applications. We have developed cnvCapSeq, a novel method for accurate and sensitive CNV discovery and genotyping in long-range targeted resequencing. cnvCapSeq was benchmarked using a simulated contiguous capture sequencing dataset comprising 21 genomic loci of various lengths. cnvCapSeq was shown to outperform the best existing exome CNV method by a wide margin both in terms of sensitivity (92.0 versus 48.3%) and specificity (99.8 versus 70.5%). We also applied cnvCapSeq to a real capture sequencing cohort comprising a contiguous 358 kb region that contains the Complement Factor H gene cluster. In this dataset, cnvCapSeq identified 41 samples with CNV, including two with duplications, with a genotyping accuracy of 99%, as ascertained by quantitative real-time PCR.
    Keywords: Polymorphism/mutation detection
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 8
    facet.materialart.
    Unknown
    Compression of next-generation sequencing reads aided by highly efficient de novo assembly (2012)
    Oxford University Press
    Details
    Publication Date: 2012-12-14
    Description: We present Quip, a lossless compression algorithm for next-generation sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing reference-based compression, we have developed, to our knowledge, the first assembly-based compressor, using a novel de novo assembly algorithm. A probabilistic data structure is used to dramatically reduce the memory required by traditional de Bruijn graph assemblers, allowing millions of reads to be assembled very efficiently. Read sequences are then stored as positions within the assembled contigs. This is combined with statistical compression of read identifiers, quality scores, alignment information and sequences, effectively collapsing very large data sets to 〈15% of their original size with no loss of information. Availability: Quip is freely available under the 3-clause BSD license from http://cs.washington.edu/homes/dcjones/quip .
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 9
    facet.materialart.
    Unknown
    Probabilistic error correction for RNA sequencing (2013)
    Oxford University Press
    Details
    Publication Date: 2013-05-29
    Description: Sequencing of RNAs (RNA-Seq) has revolutionized the field of transcriptomics, but the reads obtained often contain errors. Read error correction can have a large impact on our ability to accurately assemble transcripts. This is especially true for de novo transcriptome analysis, where a reference genome is not available. Current read error correction methods, developed for DNA sequence data, cannot handle the overlapping effects of non-uniform abundance, polymorphisms and alternative splicing. Here we present SEquencing Error CorrEction in Rna-seq data (SEECER), a hidden Markov Model (HMM)–based method, which is the first to successfully address these problems. SEECER efficiently learns hundreds of thousands of HMMs and uses these to correct sequencing errors. Using human RNA-Seq data, we show that SEECER greatly improves on previous methods in terms of quality of read alignment to the genome and assembly accuracy. To illustrate the usefulness of SEECER for de novo transcriptome studies, we generated new RNA-Seq data to study the development of the sea cucumber Parastichopus parvimensis . Our corrected assembled transcripts shed new light on two important stages in sea cucumber development. Comparison of the assembled transcripts to known transcripts in other species has also revealed novel transcripts that are unique to sea cucumber, some of which we have experimentally validated. Supporting website: http://sb.cs.cmu.edu/seecer/ .
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 10
    facet.materialart.
    Unknown
    DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions (2013)
    Oxford University Press
    Details
    Publication Date: 2013-11-21
    Description: Detection of differential expression in RNA-Seq data is currently limited to studies in which two or more sample conditions are known a priori. However, these biological conditions are typically unknown in cohort, cross-sectional and nonrandomized controlled studies such as the HapMap, the ENCODE or the 1000 Genomes project. We present DEXUS for detecting differential expression in RNA-Seq data for which the sample conditions are unknown. DEXUS models read counts as a finite mixture of negative binomial distributions in which each mixture component corresponds to a condition. A transcript is considered differentially expressed if modeling of its read counts requires more than one condition. DEXUS decomposes read count variation into variation due to noise and variation due to differential expression. Evidence of differential expression is measured by the informative/noninformative (I/NI) value, which allows differentially expressed transcripts to be extracted at a desired specificity (significance level) or sensitivity (power). DEXUS performed excellently in identifying differentially expressed transcripts in data with unknown conditions. On 2400 simulated data sets, I/NI value thresholds of 0.025, 0.05 and 0.1 yielded average specificities of 92, 97 and 99% at sensitivities of 76, 61 and 38%, respectively. On real-world data sets, DEXUS was able to detect differentially expressed transcripts related to sex, species, tissue, structural variants or quantitative trait loci. The DEXUS R package is publicly available from Bioconductor and the scripts for all experiments are available at http://www.bioinf.jku.at/software/dexus/ .
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 11
    facet.materialart.
    Unknown
    Integrated RNA and DNA sequencing improves mutation detection in low purity tumors (2014)
    Oxford University Press
    Details
    Publication Date: 2014-08-01
    Description: Identifying somatic mutations is critical for cancer genome characterization and for prioritizing patient treatment. DNA whole exome sequencing (DNA-WES) is currently the most popular technology; however, this yields low sensitivity in low purity tumors. RNA sequencing (RNA-seq) covers the expressed exome with depth proportional to expression. We hypothesized that integrating DNA-WES and RNA-seq would enable superior mutation detection versus DNA-WES alone. We developed a first-of-its-kind method, called UNCeqR , that detects somatic mutations by integrating patient-matched RNA-seq and DNA-WES. In simulation, the integrated DNA and RNA model outperformed the DNA-WES only model. Validation by patient-matched whole genome sequencing demonstrated superior performance of the integrated model over DNA-WES only models, including a published method and published mutation profiles. Genome-wide mutational analysis of breast and lung cancer cohorts ( n = 871) revealed remarkable tumor genomics properties. Low purity tumors experienced the largest gains in mutation detection by integrating RNA-seq and DNA-WES. RNA provided greater mutation signal than DNA in expressed mutations. Compared to earlier studies on this cohort, UNCeqR increased mutation rates of driver and therapeutically targeted genes (e.g. PIK3CA , ERBB2 and FGFR2 ). In summary, integrating RNA-seq with DNA-WES increases mutation detection performance, especially for low purity tumors.
    Keywords: Polymorphism/mutation detection
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 12
    facet.materialart.
    Unknown
    Identification of high-confidence somatic mutations in whole genome sequence of formalin-fixed breast cancer specimens (2012)
    Oxford University Press
    Details
    Publication Date: 2012-08-08
    Description: The utilization of archived, formalin-fixed paraffin-embedded (FFPE) tumor samples for massive parallel sequencing has been challenging due to DNA damage and contamination with normal stroma. Here, we perform whole genome sequencing of DNA isolated from two triple-negative breast cancer tumors archived for 〉11 years as 5 µm FFPE sections and matched germline DNA. The tumor samples show differing amounts of FFPE damaged DNA sequencing reads revealed as relatively high alignment mismatch rates enriched for C·G 〉 T·A substitutions compared to germline samples. This increase in mismatch rate is observable with as few as one million reads, allowing for an upfront evaluation of the sample integrity before whole genome sequencing. By applying innovative quality filters incorporating global nucleotide mismatch rates and local mismatch rates, we present a method to identify high-confidence somatic mutations even in the presence of FFPE induced DNA damage. This results in a breast cancer mutational profile consistent with previous studies and revealing potentially important functional mutations. Our study demonstrates the feasibility of performing genome-wide deep sequencing analysis of FFPE archived tumors of limited sample size such as residual cancer after treatment or metastatic biopsies.
    Keywords: Polymorphism/mutation detection
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 13
    facet.materialart.
    Unknown
    Quantifying selection in high-throughput Immunoglobulin sequencing data sets (2012)
    Oxford University Press
    Details
    Publication Date: 2012-09-27
    Description: High-throughput immunoglobulin sequencing promises new insights into the somatic hypermutation and antigen-driven selection processes that underlie B-cell affinity maturation and adaptive immunity. The ability to estimate positive and negative selection from these sequence data has broad applications not only for understanding the immune response to pathogens, but is also critical to determining the role of somatic hypermutation in autoimmunity and B-cell cancers. Here, we develop a statistical framework for Bayesian estimation of Antigen-driven SELectIoN (BASELINe) based on the analysis of somatic mutation patterns. Our approach represents a fundamental advance over previous methods by shifting the problem from one of simply detecting selection to one of quantifying selection. Along with providing a more intuitive means to assess and visualize selection, our approach allows, for the first time, comparative analysis between groups of sequences derived from different germline V(D)J segments. Application of this approach to next-generation sequencing data demonstrates different selection pressures for memory cells of different isotypes. This framework can easily be adapted to analyze other types of DNA mutation patterns resulting from a mutator that displays hot/cold-spots, substitution preference or other intrinsic biases.
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 14
    facet.materialart.
    Unknown
    Camera: a competitive gene set test accounting for inter-gene correlation (2012)
    Oxford University Press
    Details
    Publication Date: 2012-09-27
    Description: Competitive gene set tests are commonly used in molecular pathway analysis to test for enrichment of a particular gene annotation category amongst the differential expression results from a microarray experiment. Existing gene set tests that rely on gene permutation are shown here to be extremely sensitive to inter-gene correlation. Several data sets are analyzed to show that inter-gene correlation is non-ignorable even for experiments on homogeneous cell populations using genetically identical model organisms. A new gene set test procedure (CAMERA) is proposed based on the idea of estimating the inter-gene correlation from the data, and using it to adjust the gene set test statistic. An efficient procedure is developed for estimating the inter-gene correlation and characterizing its precision. CAMERA is shown to control the type I error rate correctly regardless of inter-gene correlations, yet retains excellent power for detecting genuine differential expression. Analysis of breast cancer data shows that CAMERA recovers known relationships between tumor subtypes in very convincing terms. CAMERA can be used to analyze specified sets or as a pathway analysis tool using a database of molecular signatures.
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 15
    facet.materialart.
    Unknown
    Single molecule quantitation and sequencing of rare translocations using microfluidic nested digital PCR (2013)
    Oxford University Press
    Details
    Publication Date: 2013-09-06
    Description: Cancers are heterogeneous and genetically unstable. New methods are needed that provide the sensitivity and specificity to query single cells at the genetic loci that drive cancer progression, thereby enabling researchers to study the progression of individual tumors. Here, we report the development and application of a bead-based hemi-nested microfluidic droplet digital PCR (dPCR) technology to achieve ‘quantitative’ measurement and single-molecule sequencing of somatically acquired carcinogenic translocations at extremely low levels (〈10 –6 ) in healthy subjects. We use this technique in our healthy study population to determine the overall concentration of the t(14;18) translocation, which is strongly associated with follicular lymphoma. The nested dPCR approach improves the detection limit to 1 x 10 –7 or lower while maintaining the analysis efficiency and specificity. Further, the bead-based dPCR enabled us to isolate and quantify the relative amounts of the various clonal forms of t(14;18) translocation in these subjects, and the single-molecule sensitivity and resolution of dPCR led to the discovery of new clonal forms of t(14;18) that were otherwise masked by the conventional quantitative PCR measurements. In this manner, we created a quantitative map for this carcinogenic mutation in this healthy population and identified the positions on chromosomes 14 and 18 where the vast majority of these t(14;18) events occur.
    Keywords: Polymorphism/mutation detection
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 16
    facet.materialart.
    Unknown
    Differential strand separation at critical temperature: A minimally disruptive enrichment method for low-abundance unknown DNA mutations (2013)
    Oxford University Press
    Details
    Publication Date: 2013-02-02
    Description: Detection of low-level DNA variations in the presence of wild-type DNA is important in several fields of medicine, including cancer, prenatal diagnosis and infectious diseases. PCR-based methods to enrich mutations during amplification have limited multiplexing capability, are mostly restricted to known mutations and are prone to polymerase or mis-priming errors. Here, we present Di fferential S trand Se paration at C ritical T emperature (DISSECT), a method that enriches unknown mutations of targeted DNA sequences purely based on thermal denaturation of DNA heteroduplexes without the need for enzymatic reactions. Target DNA is pre-amplified in a multiplex reaction and hybridized onto complementary probes immobilized on magnetic beads that correspond to wild-type DNA sequences. Presence of any mutation on the target DNA forms heteroduplexes that are subsequently denatured from the beads at a critical temperature and selectively separated from wild-type DNA. We demonstrate multiplexed enrichment by 100- to 400-fold for KRAS and TP53 mutations at multiple positions of the targeted sequence using two to four successive cycles of DISSECT. Cancer and plasma-circulating DNA samples containing traces of mutations undergo mutation enrichment allowing detection via Sanger sequencing or high-resolution melting. The simplicity, scalability and reliability of DISSECT make it a powerful method for mutation enrichment that integrates well with existing downstream detection methods.
    Keywords: Polymorphism/mutation detection
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 17
    facet.materialart.
    Unknown
    Co-phylog: an assembly-free phylogenomic approach for closely related organisms (2013)
    Oxford University Press
    Details
    Publication Date: 2013-04-14
    Description: With the advent of high-throughput sequencing technologies, the rapid generation and accumulation of large amounts of sequencing data pose an insurmountable demand for efficient algorithms for constructing whole-genome phylogenies. The existing phylogenomic methods all use assembled sequences, which are often not available owing to the difficulty of assembling short-reads; this obstructs phylogenetic investigations on species without a reference genome. In this report, we present co-phylog , an assembly-free phylogenomic approach that creates a ‘micro-alignment’ at each ‘object’ in the sequence using the ‘context’ of the object and calculates pairwise distances before reconstructing the phylogenetic tree based on those distances. We explored the parameters’ usages and the optimal working range of co-phylog , assessed co-phylog using the simulated next-generation sequencing (NGS) data and the real NGS raw data. We also compared co-phylog method with traditional alignment and alignment-free methods and illustrated the advantages and limitations of co-phylog method. In conclusion, we demonstrated that co-phylog is efficient algorithm and that it delivers high resolution and accurate phylogenies using whole-genome unassembled sequencing data, especially in the case of closely related organisms, thereby significantly alleviating the computational burden in the genomic era.
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 18
    facet.materialart.
    Unknown
    Fluorescent signatures for variable DNA sequences (2012)
    Oxford University Press
    Details
    Publication Date: 2012-11-25
    Description: Life abounds with genetic variations writ in sequences that are often only a few hundred nucleotides long. Rapid detection of these variations for identification of genetic diseases, pathogens and organisms has become the mainstay of molecular science and medicine. This report describes a new, highly informative closed-tube polymerase chain reaction (PCR) strategy for analysis of both known and unknown sequence variations. It combines efficient quantitative amplification of single-stranded DNA targets through LATE-PCR with sets of Lights-On/Lights-Off probes that hybridize to their target sequences over a broad temperature range. Contiguous pairs of Lights-On/Lights-Off probes of the same fluorescent color are used to scan hundreds of nucleotides for the presence of mutations. Sets of probes in different colors can be combined in the same tube to analyze even longer single-stranded targets. Each set of hybridized Lights-On/Lights-Off probes generates a composite fluorescent contour, which is mathematically converted to a sequence-specific fluorescent signature. The versatility and broad utility of this new technology is illustrated in this report by characterization of variant sequences in three different DNA targets: the rpoB gene of Mycobacterium tuberculosis, a sequence in the mitochondrial cytochrome C oxidase subunit 1 gene of nematodes and the V3 hypervariable region of the bacterial 16 s ribosomal RNA gene. We anticipate widespread use of these technologies for diagnostics, species identification and basic research.
    Keywords: Polymorphism/mutation detection
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...