Data for "Comparison of genetic variants in matched samples using thesaurus annotation"

Motivation: Calling changes in DNA, e.g. as a result of somatic events in cancer, requires analysis of multiple matched sequenced samples. Events in low-mappability regions of the human genome are difficult to encode in variant call files and have been under-reported as a result. However, they can be described accurately through thesaurus annotationâa technique that links multiple genomic loci together to explicate a single variant. Results: We here describe software and benchmarks for using thesaurus annotation to detect point changes in DNA from matched samples. In benchmarks on matched normal/tumor samples we show that the technique can recover between five and ten percent more true events than conventional approaches, while strictly limiting false discovery and being fully consistent with popular variant analysis workflows. We also demonstrate the utility of the approach for analysis of de novo mutations in parents/child families. Availability and implementation: Software performing thesaurus annotation is implemented in java; available in source code on github at GeneticThesaurus ( https://github.com/tkonopka/GeneticThesaurus ) and as an executable on sourceforge at geneticthesaurus ( https://sourceforge.net/projects/geneticthesaurus ). Mutation calling is implemented in an R package available on github at RGeneticThesaurus ( https://github.com/tkonopka/RGeneticThesaurus). Supplementary information:Supplementary data are available at Bioinformatics online

Data for "Comparison of genetic variants in matched samples using thesaurus annotation"

Motivation: Calling changes in DNA, e.g. as a result of somatic events in cancer, requires analysis of multiple matched sequenced samples. Events in low-mappability regions of the human genome are difficult to encode in variant call files and have been under-reported as a result. However, they can be described accurately through thesaurus annotation—a technique that links multiple genomic loci together to explicate a single variant. Results: We here describe software and benchmarks for using thesaurus annotation to detect point changes in DNA from matched samples. In benchmarks on matched normal/tumor samples we show that the technique can recover between five and ten percent more true events than conventional approaches, while strictly limiting false discovery and being fully consistent with popular variant analysis workflows. We also demonstrate the utility of the approach for analysis of de novo mutations in parents/child families. Availability and implementation: Software performing thesaurus annotation is implemented in java; available in source code on github at GeneticThesaurus ( https://github.com/tkonopka/GeneticThesaurus ) and as an executable on sourceforge at geneticthesaurus ( https://sourceforge.net/projects/geneticthesaurus ). Mutation calling is implemented in an R package available on github at RGeneticThesaurus ( https://github.com/tkonopka/RGeneticThesaurus). Supplementary information:Supplementary data are available at Bioinformatics online. # Data for "Comparison of genetic variants in matched samples using thesaurus annotation" ## Background The files in this set hold data corresponding to the figures in the following research article: Konopka, Tomasz, and Sebastian MB Nijman. "Comparison of genetic variants in matched samples using thesaurus annotation." Bioinformatics(2015): btv654. ## Figure 1 Figure 1 in the publication is a schematic of the computational method described in the article. Thus there are not data files that correpond to this figure. ## Figure 2 File: thesaurus.fig2.tsv Data in this figure represent summary statistics from an in-silico benchmarking study. ## Figure 3 File: thesaurus.fig3.tsv Data in this figure represent summary statistics from an in-silico benchmarking study. ## Figure 4 Files: thesaurus.fig4-MQ16.tsv, thesaurus.fig4-MQ0.tsv Data in this figure 4 are based on high-throughput sequencing data for samples NA12877, NA12878, and NA12882 from dataset ERP001960. The raw sequencing data are publicly available at https://www.ebi.ac.uk/ena/data/view/PRJEB3381 ## Notes Please refer to the published manuscript for methods and data interpretation

Data for "Comparison of genetic variants in matched samples using thesaurus annotation"

Motivation: Calling changes in DNA, e.g. as a result of somatic events in cancer, requires analysis of multiple matched sequenced samples. Events in low-mappability regions of the human genome are difficult to encode in variant call files and have been under-reported as a result. However, they can be described accurately through thesaurus annotation—a technique that links multiple genomic loci together to explicate a single variant. Results: We here describe software and benchmarks for using thesaurus annotation to detect point changes in DNA from matched samples. In benchmarks on matched normal/tumor samples we show that the technique can recover between five and ten percent more true events than conventional approaches, while strictly limiting false discovery and being fully consistent with popular variant analysis workflows. We also demonstrate the utility of the approach for analysis of de novo mutations in parents/child families. Availability and implementation: Software performing thesaurus annotation is implemented in java; available in source code on github at GeneticThesaurus ( https://github.com/tkonopka/GeneticThesaurus ) and as an executable on sourceforge at geneticthesaurus ( https://sourceforge.net/projects/geneticthesaurus ). Mutation calling is implemented in an R package available on github at RGeneticThesaurus ( https://github.com/tkonopka/RGeneticThesaurus). Supplementary information:Supplementary data are available at Bioinformatics online. # Data for "Comparison of genetic variants in matched samples using thesaurus annotation" ## Background The files in this set hold data corresponding to the figures in the following research article: Konopka, Tomasz, and Sebastian MB Nijman. "Comparison of genetic variants in matched samples using thesaurus annotation." Bioinformatics(2015): btv654. ## Figure 1 Figure 1 in the publication is a schematic of the computational method described in the article. Thus there are not data files that correpond to this figure. ## Figure 2 File: thesaurus.fig2.tsv Data in this figure represent summary statistics from an in-silico benchmarking study. ## Figure 3 File: thesaurus.fig3.tsv Data in this figure represent summary statistics from an in-silico benchmarking study. ## Figure 4 Files: thesaurus.fig4-MQ16.tsv, thesaurus.fig4-MQ0.tsv Data in this figure 4 are based on high-throughput sequencing data for samples NA12877, NA12878, and NA12882 from dataset ERP001960. The raw sequencing data are publicly available at https://www.ebi.ac.uk/ena/data/view/PRJEB3381 ## Notes Please refer to the published manuscript for methods and data interpretation

Synthetic lethality: General principles, utility and detection using genetic screens in human cells

Synthetic lethality occurs when the simultaneous perturbation of two genes results in cellular or organismal death. Synthetic lethality also occurs between genes and small molecules, and can be used to elucidate the mechanism of action of drugs. This area has recently attracted attention because of the prospect of a new generation of anti-cancer drugs. Based on studies ranging from yeast to human cells, this review provides an overview of the general principles that underlie synthetic lethality and relates them to its utility for identifying gene function, drug action and cancer therapy. It also identifies the latest strategies for the large-scale mapping of synthetic lethalities in human cells which bring us closer to the generation of comprehensive human genetic interaction maps