scholarly journals DeNovoCNN: A deep learning approach to de novo variant calling in next generation sequencing data

2021 ◽  
Author(s):  
Gelana Khazeeva ◽  
Karolis Sablauskas ◽  
Bart van der Sanden ◽  
Wouter Steyaert ◽  
Michael Kwint ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
De Novo ◽  
Genetic Disorders ◽  
Variant Calling ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Accurate Identification ◽  
Whole Exome ◽  
De Novo Variant ◽  
Generation Sequencing

De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major challenge due to sequence errors, uneven coverage, and mapping artifacts. Here, we developed a deep convolutional neural network (CNN) DNM caller (DeNovoCNN), that encodes alignment of sequence reads for a trio as 160×164 resolution images. DeNovoCNN was trained on DNMs of whole exome sequencing (WES) of 2003 trios achieving on average 99.2% recall and 93.8% precision. We find that DeNovoCNN has increased recall/sensitivity and precision compared to existing de novo calling approaches (GATK, DeNovoGear, Samtools) based on the Genome in a Bottle reference dataset. Sanger validations of DNMs called in both exome and genome datasets confirm that DeNovoCNN outperforms existing methods. Most importantly, we show that DeNovoCNN is robust against different exome sequencing and analyses approaches, thereby allowing it to be applied on other datasets. DeNovoCNN is freely available and can be run on existing alignment (BAM/CRAM) and variant calling (VCF) files from WES and WGS without a need for variant recalling.

scholarly journals BALSA: Integrated secondary analysis for whole-genome and whole-exome sequencing, accelerated by GPU

2014 ◽  
Author(s):  
Ruibang Luo ◽  
Yiu-Lun Wong ◽  
Wai-Chun Law ◽  
Lap-Kei Lee ◽  
Chi-Man Liu ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Secondary Analysis ◽  
Variant Calling ◽  
Statistical Testing ◽  
Next Generation Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Accurate Analysis ◽  
Whole Exome

This paper reports an integrated solution, called BALSA, for the secondary analysis of next generation sequencing data; it exploits the computational power of GPU and an intricate memory management to give a fast and accurate analysis. From raw reads to variants (including SNPs and Indels), BALSA, using just a single computing node with a commodity GPU board, takes 5.5 hours to process 50-fold whole genome sequencing (~750 million 100bp paired-end reads), or just 25 minutes for 210-fold whole exome sequencing. BALSA’s speed is rooted at its parallel algorithms to effectively exploit a GPU to speed up processes like alignment, realignment and statistical testing. BALSA incorporates a 16-genotype model to support the calling of SNPs and Indels and achieves competitive variant calling accuracy and sensitivity when compared to the ensemble of six popular variant callers. BALSA also supports efficient identification of somatic SNVs and CNVs; experiments showed that BALSA recovers all the previously validated somatic SNVs and CNVs, and it is more sensitive for somatic Indel detection. BALSA outputs variants in VCF format. A pileup-like SNAPSHOT format, while maintaining the same fidelity as BAM in variant calling, enables efficient storage and indexing, and facilitates the App development of downstream analyses. BALSA is available at: http://sourceforge.net/p/balsa

scholarly journals BALSA: Integrated secondary analysis for whole-genome and whole-exome sequencing, accelerated by GPU

2014 ◽  
Author(s):  
Ruibang Luo ◽  
Yiu-Lun Wong ◽  
Wai-Chun Law ◽  
Lap-Kei Lee ◽  
Chi-Man Liu ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Secondary Analysis ◽  
Variant Calling ◽  
Statistical Testing ◽  
Next Generation Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Accurate Analysis ◽  
Whole Exome

This paper reports an integrated solution, called BALSA, for the secondary analysis of next generation sequencing data; it exploits the computational power of GPU and an intricate memory management to give a fast and accurate analysis. From raw reads to variants (including SNPs and Indels), BALSA, using just a single computing node with a commodity GPU board, takes 5.5 hours to process 50-fold whole genome sequencing (~750 million 100bp paired-end reads), or just 25 minutes for 210-fold whole exome sequencing. BALSA’s speed is rooted at its parallel algorithms to effectively exploit a GPU to speed up processes like alignment, realignment and statistical testing. BALSA incorporates a 16-genotype model to support the calling of SNPs and Indels and achieves competitive variant calling accuracy and sensitivity when compared to the ensemble of six popular variant callers. BALSA also supports efficient identification of somatic SNVs and CNVs; experiments showed that BALSA recovers all the previously validated somatic SNVs and CNVs, and it is more sensitive for somatic Indel detection. BALSA outputs variants in VCF format. A pileup-like SNAPSHOT format, while maintaining the same fidelity as BAM in variant calling, enables efficient storage and indexing, and facilitates the App development of downstream analyses. BALSA is available at: http://sourceforge.net/p/balsa

scholarly journals Estimating sequencing error rates using families

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Kelley Paskov ◽  
Jae-Yoon Jung ◽  
Brianna Chrisman ◽  
Nate T. Stockham ◽  
Peter Washington ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Exome Sequencing ◽  
Genome Sequencing ◽  
Variant Calling ◽  
Error Rates ◽  
Sequencing Error ◽  
Whole Genome ◽  
Sequencing Data ◽  
Sequencing Platform ◽  
Whole Exome

Abstract Background As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. Results We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method’s versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. Conclusion Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

scholarly journals Lacer: accurate base quality score recalibration for improving variant calling from next-generation sequencing data in any organism

2017 ◽  
Author(s):  
Jade C.S. Chung ◽  
Swaine L. Chen
Keyword(s):  
Next Generation Sequencing ◽  
Variant Calling ◽  
Quality Score ◽  
Identification Accuracy ◽  
Next Generation Sequencing Data ◽  
Sequencing Error ◽  
Next Generation ◽  
Sequencing Data ◽  
Base Quality Score ◽  
Generation Sequencing

AbstractNext-generation sequencing data is accompanied by quality scores that quantify sequencing error. Inaccuracies in these quality scores propagate through all subsequent analyses; thus base quality score recalibration is a standard step in many next-generation sequencing workflows, resulting in improved variant calls. Current base quality score recalibration algorithms rely on the assumption that sequencing errors are already known; for human resequencing data, relatively complete variant databases facilitate this. However, because existing databases are still incomplete, recalibration is still inaccurate; and most organisms do not have variant databases, exacerbating inaccuracy for non-human data. To overcome these logical and practical problems, we introduce Lacer, which recalibrates base quality scores without assuming knowledge of correct and incorrect bases and without requiring knowledge of common variants. Lacer is the first logically sound, fully general, and truly accurate base recalibrator. Lacer enhances variant identification accuracy for resequencing data of human as well as other organisms (which are not accessible to current recalibrators), simultaneously improving and extending the benefits of base quality score recalibration to nearly all ongoing sequencing projects. Lacer is available at: https://github.com/swainechen/lacer.

scholarly journals Pisces: An Accurate and Versatile Variant Caller for Somatic and Germline Next-Generation Sequencing Data

2018 ◽  
Author(s):  
Tamsen Dunn ◽  
Gwenn Berry ◽  
Dorothea Emig-Agius ◽  
Yu Jiang ◽  
Serena Lei ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Gene Mutations ◽  
Variant Calling ◽  
Amplicon Sequencing ◽  
Supplementary Information ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Ras Gene ◽  
Generation Sequencing

AbstractMotivationNext-Generation Sequencing (NGS) technology is transitioning quickly from research labs to clinical settings. The diagnosis and treatment selection for many acquired and autosomal conditions necessitate a method for accurately detecting somatic and germline variants, suitable for the clinic.ResultsWe have developed Pisces, a rapid, versatile and accurate small variant calling suite designed for somatic and germline amplicon sequencing applications. Pisces accuracy is achieved by four distinct modules, the Pisces Read Stitcher, Pisces Variant Caller, the Pisces Variant Quality Recalibrator, and the Pisces Variant Phaser. Each module incorporates a number of novel algorithmic strategies aimed at reducing noise or increasing the likelihood of detecting a true variant.AvailabilityPisces is distributed under an open source license and can be downloaded from https://github.com/Illumina/Pisces. Pisces is available on the BaseSpace™ SequenceHub as part of the TruSeq Amplicon workflow and the Illumina Ampliseq Workflow. Pisces is distributed on Illumina sequencing platforms such as the MiSeq™, and is included in the Praxis™ Extended RAS Panel test which was recently approved by the FDA for the detection of multiple RAS gene [email protected] informationSupplementary data are available online.

scholarly journals NGSphy: phylogenomic simulation of next-generation sequencing data

2017 ◽  
Author(s):  
Merly Escalona ◽  
Sara Rocha ◽  
David Posada
Keyword(s):  
Next Generation Sequencing ◽  
Variant Calling ◽  
Gene Families ◽  
Common Species ◽  
Next Generation Sequencing Data ◽  
Phylogenomic Analysis ◽  
Next Generation ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Generation Sequencing

AbstractMotivationAdvances in sequencing technologies have made it feasible to obtain massive datasets for phylogenomic inference, often consisting of large numbers of loci from multiple species and individuals. The phylogenomic analysis of next-generation sequencing (NGS) data implies a complex computational pipeline where multiple technical and methodological decisions are necessary that can influence the final tree obtained, like those related to coverage, assembly, mapping, variant calling and/or phasing.ResultsTo assess the influence of these variables we introduce NGSphy, an open-source tool for the simulation of Illumina reads/read counts obtained from haploid/diploid individual genomes with thousands of independent gene families evolving under a common species tree. In order to resemble real NGS experiments, NGSphy includes multiple options to model sequencing coverage (depth) heterogeneity across species, individuals and loci, including off-target or uncaptured loci. For comprehensive simulations covering multiple evolutionary scenarios, parameter values for the different replicates can be sampled from user-defined statistical distributions.AvailabilitySource code, full documentation and tutorials including a quick start guide are available at http://github.com/merlyescalona/[email protected]. [email protected]

scholarly journals Disambiguate: An open-source application for disambiguating two species in next generation sequencing data from grafted samples

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2741 ◽  
Author(s):  
Miika J. Ahdesmäki ◽  
Simon R. Gray ◽  
Justin H. Johnson ◽  
Zhongwu Lai
Keyword(s):  
Open Source ◽  
Variant Calling ◽  
High Sensitivity ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Novel Approach ◽  
Gene Expression Quantification ◽  
Closed Source ◽  
Generation Sequencing ◽  
Bioinformatics Community

Grafting of cell lines and primary tumours is a crucial step in the drug development process between cell line studies and clinical trials. Disambiguate is a program for computationally separating the sequencing reads of two species derived from grafted samples. Disambiguate operates on alignments to the two species and separates the components at very high sensitivity and specificity as illustrated in artificially mixed human-mouse samples. This allows for maximum recovery of data from target tumours for more accurate variant calling and gene expression quantification. Given that no general use open source algorithm accessible to the bioinformatics community exists for the purposes of separating the two species data, the proposed Disambiguate tool presents a novel approach and improvement to performing sequence analysis of grafted samples. Both Python and C++ implementations are available and they are integrated into several open and closed source pipelines. Disambiguate is open source and is freely available at https://github.com/AstraZeneca-NGS/disambiguate.

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