scholarly journals Helmsman: fast and efficient generation of input matrices for mutation signature analysis

2018 ◽  
Author(s):  
Jedidiah Carlson ◽  
Jun Z Li ◽  
Sebastian Zöllner
Keyword(s):  
Large Datasets ◽  
Supplementary Information ◽  
Signature Analysis ◽  
Single Nucleotide Variants ◽  
Cancer Etiology ◽  
Single Nucleotide ◽  
Link Type ◽  
Fold Reduction ◽  
Cancer Genomes ◽  
Mutation Spectra

AbstractMotivationThe spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational processes, which can provide clinically actionable insights into cancer etiology. Existing software tools for identifying and evaluating these mutational signatures do not scale to analyze large datasets containing thousands of individuals or millions of variants.ResultsWe introduce Helmsman, a program designed to rapidly generate mutation spectra matrices from arbitrarily large datasets. Helmsman is up to 300 times faster than existing methods and can provide more than a 100-fold reduction in memory usage, making mutation signature analysis tractable for any collection of single nucleotide variants, no matter how large.AvailabilityHelmsman is freely available for download at https://github.com/carjed/helmsman under the MIT license. Detailed documentation can be found at https://www.jedidiahcarlson.com/docs/helmsman/, and an interactive Jupyter notebook containing a guided tutorial can be accessed at https://mybinder.org/v2/gh/carjed/helmsman/[email protected] informationSupplementary information for this article is available.

scholarly journals Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Ianthe A. E. M. van Belzen ◽  
Alexander Schönhuth ◽  
Patrick Kemmeren ◽  
Jayne Y. Hehir-Kwa
Keyword(s):  
Intratumor Heterogeneity ◽  
Precision Oncology ◽  
Single Nucleotide Variants ◽  
Full Spectrum ◽  
Single Nucleotide ◽  
Sequencing Technologies ◽  
Cancer Genomes ◽  
Genomic Aberrations

AbstractCancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research into the role of SVs in cancer has been limited due to difficulties in detection. Biological and computational challenges confound SV detection in cancer samples, including intratumor heterogeneity, polyploidy, and distinguishing tumor-specific SVs from germline and somatic variants present in healthy cells. Classification of tumor-specific SVs is challenging due to inconsistencies in detected breakpoints, derived variant types and biological complexity of some rearrangements. Full-spectrum SV detection with high recall and precision requires integration of multiple algorithms and sequencing technologies to rescue variants that are difficult to resolve through individual methods. Here, we explore current strategies for integrating SV callsets and to enable the use of tumor-specific SVs in precision oncology.

Spliceogen: an integrative, scalable tool for the discovery of splice-altering variants

2019 ◽  
Vol 35 (21) ◽  
pp. 4405-4407 ◽  
Author(s):  
Steven Monger ◽  
Michael Troup ◽  
Eddie Ip ◽  
Sally L Dunwoodie ◽  
Eleni Giannoulatou
Keyword(s):  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
In Silico Prediction ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Prediction Tools ◽  
Motif Prediction ◽  
Command Line Tool ◽  
Genome Scale

Abstract Motivation In silico prediction tools are essential for identifying variants which create or disrupt cis-splicing motifs. However, there are limited options for genome-scale discovery of splice-altering variants. Results We have developed Spliceogen, a highly scalable pipeline integrating predictions from some of the individually best performing models for splice motif prediction: MaxEntScan, GeneSplicer, ESRseq and Branchpointer. Availability and implementation Spliceogen is available as a command line tool which accepts VCF/BED inputs and handles both single nucleotide variants (SNVs) and indels (https://github.com/VCCRI/Spliceogen). SNV databases with prediction scores are also available, covering all possible SNVs at all genomic positions within all Gencode-annotated multi-exon transcripts. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals neoepiscope improves neoepitope prediction with multivariant phasing

2019 ◽  
Vol 36 (3) ◽  
pp. 713-720 ◽  
Author(s):  
Mary A Wood ◽  
Austin Nguyen ◽  
Adam J Struck ◽  
Kyle Ellrott ◽  
Abhinav Nellore ◽  
...  
Keyword(s):  
False Negative ◽  
Supplementary Information ◽  
Supplementary File ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Somatic Variant ◽  
Negative Results ◽  
Multiple Datasets ◽  
False Negative Results

Abstract Motivation The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for the co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false-positive and false-negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels). Results Herein, we illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to ∼5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible and supports several major histocompatibility complex binding affinity prediction tools. Availability and implementation neoepiscope is available on GitHub at https://github.com/pdxgx/neoepiscope under the MIT license. Scripts for reproducing results described in the text are available at https://github.com/pdxgx/neoepiscope-paper under the MIT license. Additional data from this study, including summaries of variant phasing incidence and benchmarking wallclock times, are available in Supplementary Files 1, 2 and 3. Supplementary File 1 contains Supplementary Table 1, Supplementary Figures 1 and 2, and descriptions of Supplementary Tables 2–8. Supplementary File 2 contains Supplementary Tables 2–6 and 8. Supplementary File 3 contains Supplementary Table 7. Raw sequencing data used for the analyses in this manuscript are available from the Sequence Read Archive under accessions PRJNA278450, PRJNA312948, PRJNA307199, PRJNA343789, PRJNA357321, PRJNA293912, PRJNA369259, PRJNA305077, PRJNA306070, PRJNA82745 and PRJNA324705; from the European Genome-phenome Archive under accessions EGAD00001004352 and EGAD00001002731; and by direct request to the authors. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MethylExtract: High-Quality methylation maps and SNV calling from whole genome bisulfite sequencing data

F1000Research ◽  
2014 ◽  
Vol 2 ◽  
pp. 217 ◽  
Author(s):  
Guillermo Barturen ◽  
Antonio Rueda ◽  
José L. Oliver ◽  
Michael Hackenberg
Keyword(s):  
High Throughput ◽  
Sequence Variation ◽  
High Throughput Sequencing ◽  
Whole Genome ◽  
Single Nucleotide Variants ◽  
High Quality ◽  
Single Nucleotide ◽  
Error Sources ◽  
Link Type ◽  
Genome Methylation

Whole genome methylation profiling at a single cytosine resolution is now feasible due to the advent of high-throughput sequencing techniques together with bisulfite treatment of the DNA. To obtain the methylation value of each individual cytosine, the bisulfite-treated sequence reads are first aligned to a reference genome, and then the profiling of the methylation levels is done from the alignments. A huge effort has been made to quickly and correctly align the reads and many different algorithms and programs to do this have been created. However, the second step is just as crucial and non-trivial, but much less attention has been paid to the final inference of the methylation states. Important error sources do exist, such as sequencing errors, bisulfite failure, clonal reads, and single nucleotide variants.We developed MethylExtract, a user friendly tool to: i) generate high quality, whole genome methylation maps and ii) detect sequence variation within the same sample preparation. The program is implemented into a single script and takes into account all major error sources. MethylExtract detects variation (SNVs – Single Nucleotide Variants) in a similar way to VarScan, a very sensitive method extensively used in SNV and genotype calling based on non-bisulfite-treated reads. The usefulness of MethylExtract is shown by means of extensive benchmarking based on artificial bisulfite-treated reads and a comparison to a recently published method, called Bis-SNP.MethylExtract is able to detect SNVs within High-Throughput Sequencing experiments of bisulfite treated DNA at the same time as it generates high quality methylation maps. This simultaneous detection of DNA methylation and sequence variation is crucial for many downstream analyses, for example when deciphering the impact of SNVs on differential methylation. An exclusive feature of MethylExtract, in comparison with existing software, is the possibility to assess the bisulfite failure in a statistical way. The source code, tutorial and artificial bisulfite datasets are available at http://bioinfo2.ugr.es/MethylExtract/ and http://sourceforge.net/projects/methylextract/, and also permanently accessible from 10.5281/zenodo.7144.

scholarly journals Crosslink: A fast, scriptable genetic mapper for outcrossing species

2017 ◽  
Author(s):  
Robert J. Vickerstaff ◽  
Richard J. Harrison
Keyword(s):  
Large Datasets ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Link Type ◽  
Mapping Software ◽  
Outcrossing Species ◽  
Supplementary Material ◽  
Novel Approaches ◽  
Similar Accuracy ◽  
General Public License

AbstractSummaryCrosslink is genetic mapping software for outcrossing species designed to run efficiently on large datasets by combining the best from existing tools with novel approaches. Tests show it runs much faster than several comparable programs whilst retaining a similar accuracy.Availability and implementationAvailable under the GNU General Public License version 2 from https://github.com/eastmallingresearch/[email protected] informationSupplementary data are available at Bioinformatics online and from https://github.com/eastmallingresearch/crosslink/releases/tag/v0.5.

scholarly journals SimRVSequences: an R package to simulate genetic sequence data for pedigrees

2019 ◽  
Vol 36 (7) ◽  
pp. 2295-2297
Author(s):  
Christina Nieuwoudt ◽  
Angela Brooks-Wilson ◽  
Jinko Graham
Keyword(s):  
Sequence Data ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Genetic Sequence ◽  
Large Numbers

Abstract Summary We present the R package SimRVSequences to simulate sequence data for pedigrees. SimRVSequences allows for simulations of large numbers of single-nucleotide variants (SNVs) and scales well with increasing numbers of pedigrees. Users provide a sample of pedigrees and SNV data from a sample of unrelated individuals. Availability and implementation SimRVSequences is publicly-available on CRAN https://cran.r-project.org/web/packages/SimRVSequences/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics

Science ◽  
2013 ◽  
Vol 342 (6154) ◽  
pp. 1235587 ◽  
Author(s):  
Ekta Khurana ◽  
Yao Fu ◽  
Vincenza Colonna ◽  
Xinmeng Jasmine Mu ◽  
Hyun Min Kang ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Similar Pattern ◽  
Cancer Genomics ◽  
Network Centrality ◽  
Computational Tool ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Noncoding Regions ◽  
Cancer Genomes ◽  
Personal Genomes

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations (“ultrasensitive”) and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, “motif-breakers”). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

scholarly journals Are sites with multiple single nucleotide variants in cancer genomes a consequence of drivers, hypermutable sites or sequencing errors?

2016 ◽  
Author(s):  
Thomas C A Smith ◽  
Antony M Carr ◽  
Adam C Eyre-Walker
Keyword(s):  
Mutation Rate ◽  
Cancer Genome ◽  
Rate Variation ◽  
Single Nucleotide Variants ◽  
Genome Sequences ◽  
Single Nucleotide ◽  
Sequencing Errors ◽  
Cancer Genomes ◽  
Mutation Rate Variation

Across indepedent cancer genomes it has been observed that some sites have been recurrently hit by single nucleotide variants (SNVs). Such recurrently hit sites might be either i) drivers of cancer that are postively selected during oncogenesis, ii) due to mutation rate variation, or iii) due to sequencing and assembly errors. We have investigated the cause of recurrently hit sites in a dataset of >3 million SNVs from 507 complete cancer genome sequences. We find evidence that many sites have been hit significantly more often than one would expect by chance, even taking into account the effect of the adjacent nucleotides on the rate of mutation. We find that the density of these recurrently hit sites is higher in non-coding than coding DNA and hence conclude that most of them are unlikely to be drivers. We also find that most of them are found in parts of the genome that are not uniquely mappable and hence are likly to be due to mapping errors. In support of the error hypothesis, we find that recurently hit sites are not randomly distributed across sequences from different laboratories. We fit a model to the data in which the rate of mutation is constant across sites but the rate of error varies. This model suggests that ~4% of all SNVs are error in this dataset, but that the rate of error varies by thousands-of-fold.

scholarly journals Bivartect: accurate and memory-saving breakpoint detection by direct read comparison

2020 ◽  
Vol 36 (9) ◽  
pp. 2725-2730
Author(s):  
Keisuke Shimmura ◽  
Yuki Kato ◽  
Yukio Kawahara
Keyword(s):  
Genome Editing ◽  
High Throughput Sequencing ◽  
Variant Calling ◽  
Simulated Data ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Node ◽  
Single Nucleotide ◽  
Target Sites

Abstract Motivation Genetic variant calling with high-throughput sequencing data has been recognized as a useful tool for better understanding of disease mechanism and detection of potential off-target sites in genome editing. Since most of the variant calling algorithms rely on initial mapping onto a reference genome and tend to predict many variant candidates, variant calling remains challenging in terms of predicting variants with low false positives. Results Here we present Bivartect, a simple yet versatile variant caller based on direct comparison of short sequence reads between normal and mutated samples. Bivartect can detect not only single nucleotide variants but also insertions/deletions, inversions and their complexes. Bivartect achieves high predictive performance with an elaborate memory-saving mechanism, which allows Bivartect to run on a computer with a single node for analyzing small omics data. Tests with simulated benchmark and real genome-editing data indicate that Bivartect was comparable to state-of-the-art variant callers in positive predictive value for detection of single nucleotide variants, even though it yielded a substantially small number of candidates. These results suggest that Bivartect, a reference-free approach, will contribute to the identification of germline mutations as well as off-target sites introduced during genome editing with high accuracy. Availability and implementation Bivartect is implemented in C++ and available along with in silico simulated data at https://github.com/ykat0/bivartect. Supplementary information Supplementary data are available at Bioinformatics online.

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