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Robust Cancer Mutation Detection with Deep Learning Models Derived from Tumor-Normal Sequencing Data

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.csbj.2018.01.003openalex-snapshot

A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data

inferredcites →https://doi.org/10.1038/nbt.2514openalex-snapshot

Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples

inferredcites →https://doi.org/10.1038/nmeth.3407openalex-snapshot

Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection

inferredcites →https://doi.org/10.1038/s41467-019-09027-xopenalex-snapshot

Deep convolutional neural networks for accurate somatic mutation detection

inferredcites →https://doi.org/10.1038/s41587-019-0054-xopenalex-snapshot

Best practices for benchmarking germline small-variant calls in human genomes

inferredcites →https://doi.org/10.1038/s41587-019-0108-0openalex-snapshot

Author Correction: Best practices for benchmarking germline small-variant calls in human genomes

inferredcites →https://doi.org/10.1038/s41587-021-00993-6openalex-snapshot

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing

inferredcites →https://doi.org/10.1038/s41592-018-0051-xopenalex-snapshot

Strelka2: fast and accurate calling of germline and somatic variants

inferredcites →https://doi.org/10.1038/s42003-018-0023-9openalex-snapshot

Genome-wide somatic variant calling using localized colored de Bruijn graphs

inferredcites →https://doi.org/10.1056/nejmp1607591openalex-snapshot

Toward a Shared Vision for Cancer Genomic Data

inferredcites →https://doi.org/10.1093/bioinformatics/btr665openalex-snapshot

SomaticSniper: identification of somatic point mutations in whole genome sequencing data

inferredcites →https://doi.org/10.1093/bioinformatics/btu170openalex-snapshot

Trimmomatic: a flexible trimmer for Illumina sequence data

inferredcites →https://doi.org/10.1093/nar/gkw227openalex-snapshot

VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research

inferredcites →https://doi.org/10.1101/250647openalex-snapshot

TNscope: Accurate Detection of Somatic Mutations with Haplotype-based Variant Candidate Detection and Machine Learning Filtering

inferredcites →https://doi.org/10.1101/626440openalex-snapshot

Achieving reproducibility and accuracy in cancer mutation detection with whole-genome and whole-exome sequencing

inferredcites →https://doi.org/10.1101/gr.129684.111openalex-snapshot

VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing

inferredcites →https://doi.org/10.1186/s13059-015-0758-2openalex-snapshot

An ensemble approach to accurately detect somatic mutations using SomaticSeq

inferredcites →https://doi.org/10.1186/s13059-016-1029-6openalex-snapshot

MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data

inferredcites →https://doi.org/10.6084/m9.figshare.963153.v1openalex-snapshot

Aligning sequence reads, clone sequences and assembly con*gs with BWA-MEM

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1038/s41587-021-00993-6openalex-snapshot

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing

inferred← citeshttps://doi.org/10.1038/s41587-021-00994-5openalex-snapshot

Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing

inferred← citeshttps://doi.org/10.1093/gigascience/giab057openalex-snapshot

VC@Scale: Scalable and high-performance variant calling on cluster environments

inferred← citeshttps://doi.org/10.1186/s13059-020-01993-6openalex-snapshot

Varlociraptor: enhancing sensitivity and controlling false discovery rate in somatic indel discovery

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