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

Sahraeian, Sayed Mohammad Ebrahim; Fang, Li Tai; Mohiyuddin, Marghoob; Hong, Huixiao; Xiao, Wenming

doi:10.1101/667261

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…In summary, this study provides datasets comparing DNA from fresh cells, FFPE DNA and tumor/normal DNA mixtures and the performance of various bioinformatics tools. Because these samples were prepared from a pair of well-characterized, renewable tumor/ normal cell lines from the same donor, our results can serve as a reference for the NGS research community when performing benchmarking studies for the development of new NGS products, assays and informatics tools 42 . In Table 2 we provide recommendations regarding DNA fragmentation for WES runs, selection of NGS platforms and bioinformatics tools based on the nature of available biosamples and study objectives.…”

Section: Discussionmentioning

confidence: 99%

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

Xiao¹,

Ren²,

Chen³

et al. 2021

Nat Biotechnol

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Clinical applications of precision oncology require accurate tests that can distinguish true cancer specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

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Section: Discussionmentioning

confidence: 99%

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

Xiao¹,

Ren²,

Chen³

et al. 2021

Nat Biotechnol

Self Cite

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“…Such tools as VarScan [ 18 ], VarDict [ 19 ], and MuTect2 [ 20 ] are used for somatic variant-calling analysis. NeuSomatic [ 21 , 22 ] is a deep convolutional neural network–based somatic variant caller that runs in both stand-alone and ensemble modes (MuTect2, MuSE, Strelka2, SomaticSniper, VarDict, and VarScan2) for accurate somatic variant detection. Octopus [ 23 ], FreeBayes [ 24 ], Strelka2 [ 25 ], SNVer [ 26 ], and LoFreq [ 27 ] are also used for both germline and somatic variant-calling analysis.…”

Section: Background and Related Workmentioning

confidence: 99%

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

2021

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Background Recently many new deep learning–based variant-calling methods like DeepVariant have emerged as more accurate compared with conventional variant-calling algorithms such as GATK HaplotypeCaller, Sterlka2, and Freebayes albeit at higher computational costs. Therefore, there is a need for more scalable and higher performance workflows of these deep learning methods. Almost all existing cluster-scaled variant-calling workflows that use Apache Spark/Hadoop as big data frameworks loosely integrate existing single-node pre-processing and variant-calling applications. Using Apache Spark just for distributing/scheduling data among loosely coupled applications or using I/O-based storage for storing the output of intermediate applications does not exploit the full benefit of Apache Spark in-memory processing. To achieve this, we propose a native Spark-based workflow that uses Python and Apache Arrow to enable efficient transfer of data between different workflow stages. This benefits from the ease of programmability of Python and the high efficiency of Arrow’s columnar in-memory data transformations. Results Here we present a scalable, parallel, and efficient implementation of next-generation sequencing data pre-processing and variant-calling workflows. Our design tightly integrates most pre-processing workflow stages, using Spark built-in functions to sort reads by coordinates and mark duplicates efficiently. Our approach outperforms state-of-the-art implementations by >2 times for the pre-processing stages, creating a scalable and high-performance solution for DeepVariant for both CPU-only and CPU + GPU clusters. Conclusions We show the feasibility and easy scalability of our approach to achieve high performance and efficient resource utilization for variant-calling analysis on high-performance computing clusters using the standardized Apache Arrow data representations. All codes, scripts, and configurations used to run our implementations are publicly available and open sourced; see https://github.com/abs-tudelft/variant-calling-at-scale.

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“…1), we chose Delly 0.7.7 [9], Lancet 1.0.0 [13], Strelka 2.8.4 [6], Manta 1.3.0 [35], BPI 1.5 (https://github. com/hartwigmedical/hmftools/tree/master/break-pointinspector), and NeuSomatic 0.2.1 [7,36] as representative state-of-the-art callers covering all length ranges. All these callers provide their own "ad hoc" method of annotating somatic variants from tumor/normal sample pairs, which we applied with default parameters for comparison.…”

Section: Toolsmentioning

confidence: 99%

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

et al. 2020

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Accurate discovery of somatic variants is of central importance in cancer research. However, count statistics on discovered somatic insertions and deletions (indels) indicate that large amounts of discoveries are missed because of the quantification of uncertainties related to gap and alignment ambiguities, twilight zone indels, cancer heterogeneity, sample purity, sampling, and strand bias. We provide a unifying statistical model whose dependency structures enable accurate quantification of all inherent uncertainties in short time. Consequently, false discovery rate (FDR) in somatic indel discovery can now be controlled at utmost accuracy, increasing the amount of true discoveries while safely suppressing the FDR.

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

Cited by 4 publications

References 20 publications

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

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

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

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

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