Dataset Information

REPURPOSING GERMLINE EXOMES OF THE CANCER GENOME ATLAS DEMANDS A CAUTIOUS APPROACH AND SAMPLE-SPECIFIC VARIANT FILTERING.

ABSTRACT: When seeking to reproduce results derived from whole-exome or genome sequencing data that could advance precision medicine, the time and expense required to produce a patient cohort make data repurposing an attractive option. The first step in repurposing is setting some quality baseline for the data so that conclusions are not spurious. This is difficult because there can be variations in quality from center to center, clinic to clinic and even patient to patient. Here, we assessed the quality of the whole-exome germline mutations of TCGA cancer patients using patterns of nucleotide substitution and negative selection against impactful mutations. We estimated the fraction of false positive variant calls for each exome with respect to two gold standard germline exomes, and found large variability in the quality of SNV calls between samples, cancer subtypes, and institutions. We then demonstrated how variant features, such as the average base quality for reads supporting an allele, can be used to identify sample-specific filtering parameters to optimize the removal of false positive calls. We concluded that while these germlines have many potential applications to precision medicine, users should assess the quality of the available exome data prior to use and perform additional filtering steps.

SUBMITTER: Koire A

PROVIDER: S-EPMC4720392 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

REPURPOSING GERMLINE EXOMES OF THE CANCER GENOME ATLAS DEMANDS A CAUTIOUS APPROACH AND SAMPLE-SPECIFIC VARIANT FILTERING.

Koire Amanda A Katsonis Panagiotis P Lichtarge Olivier O

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 20160101

When seeking to reproduce results derived from whole-exome or genome sequencing data that could advance precision medicine, the time and expense required to produce a patient cohort make data repurposing an attractive option. The first step in repurposing is setting some quality baseline for the data so that conclusions are not spurious. This is difficult because there can be variations in quality from center to center, clinic to clinic and even patient to patient. Here, we assessed the quality ...[more]

PMID: 26776187

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Project description:BackgroundDrug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. For example, if we want to learn embeddings to predict links between compounds and diseases but almost the entirety of relations in the graph is incident to other pairs of entity types, then the resulting embeddings are likely not optimised to predict links between compounds and diseases. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency.ResultsWe find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 14.2% on DRKG, with an average improvement of 20.6% on Hetionet and 8.9% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied.ConclusionKnowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process.

Dataset Information

REPURPOSING GERMLINE EXOMES OF THE CANCER GENOME ATLAS DEMANDS A CAUTIOUS APPROACH AND SAMPLE-SPECIFIC VARIANT FILTERING.

Publications

REPURPOSING GERMLINE EXOMES OF THE CANCER GENOME ATLAS DEMANDS A CAUTIOUS APPROACH AND SAMPLE-SPECIFIC VARIANT FILTERING.

Similar Datasets

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