Exploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experiments

Background. Next-generation sequencing enables massively parallel processing, allowing lower cost than the other sequencing technologies. In the subsequent analysis with the NGS data, one of the major concerns is the reliability of variant calls. Although researchers can utilize raw quality scores of variant calling, they are forced to start the further analysis without any preevaluation of the quality scores. Method. We presented a machine learning approach for estimating quality scores of variant calls derived from BWA+GATK. We analyzed correlations between the quality score and these annotations, specifying informative annotations which were used as features to predict variant quality scores. To test the predictive models, we simulated 24 paired-end Illumina sequencing reads with 30x coverage base. Also, twenty-four human genome sequencing reads resulting from Illumina paired-end sequencing with at least 30x coverage were secured from the Sequence Read Archive. Results. Using BWA+GATK, VCFs were derived from simulated and real sequencing reads. We observed that the prediction models learned by RFR outperformed other algorithms in both simulated and real data. The quality scores of variant calls were highly predictable from informative features of GATK Annotation Modules in the simulated human genome VCF data (R2: 96.7%, 94.4%, and 89.8% for RFR, MLR, and NNR, respectively). The robustness of the proposed data-driven models was consistently maintained in the real human genome VCF data (R2: 97.8% and 96.5% for RFR and MLR, respectively).

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Unbiased machine learning methods to predict the limitations of variant calling in homologous genomic regions using next-generation sequencing

Molecular Genetics and Metabolism ◽

10.1016/s1096-7192(21)00467-4 ◽

2021 ◽

Vol 132 ◽

pp. S250-S252

Author(s):

Feng Li ◽

Rohan Gnanaolivu ◽

Noemi Vidal-Folch ◽

Neiladri Saha ◽

Nipun Mistry ◽

...

Keyword(s):

Machine Learning ◽

Next Generation Sequencing ◽

Variant Calling ◽

Next Generation ◽

Learning Methods ◽

Machine Learning Methods ◽

Genomic Regions ◽

Generation Sequencing

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NGSphy: phylogenomic simulation of next-generation sequencing data

10.1101/197715 ◽

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]

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Special Issue Introduction: The Wonders and Mysteries Next Generation Sequencing Technologies Help Reveal

Genes ◽

10.3390/genes9100505 ◽

2018 ◽

Vol 9 (10) ◽

pp. 505

Author(s):

Manfred Grabherr ◽

Bozena Kaminska ◽

Jan Komorowski

Keyword(s):

Machine Learning ◽

Next Generation Sequencing ◽

Dna Sequencing ◽

Computational Power ◽

Next Generation ◽

Special Issue ◽

Learning Methods ◽

Machine Learning Methods ◽

Sequencing Technologies ◽

Generation Sequencing

The massive increase in computational power over the recent years and wider applicationsof machine learning methods, coincidental or not, were paralleled by remarkable advances inhigh-throughput DNA sequencing technologies.[...]

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Creating a universal SNP and small indel variant caller with deep neural networks

10.1101/092890 ◽

2016 ◽

Cited By ~ 41

Author(s):

Ryan Poplin ◽

Pi-Chuan Chang ◽

David Alexander ◽

Scott Schwartz ◽

Thomas Colthurst ◽

...

Keyword(s):

Next Generation Sequencing ◽

Mammalian Species ◽

Variant Calling ◽

Ground Truth ◽

Learning Approaches ◽

Next Generation ◽

Ground Truth Data ◽

Sequencing Technologies ◽

Significant Step ◽

Generation Sequencing

AbstractNext-generation sequencing (NGS) is a rapidly evolving set of technologies that can be used to determine the sequence of an individual’s genome1 by calling genetic variants present in an individual using billions of short, errorful sequence reads2. Despite more than a decade of effort and thousands of dedicated researchers, the hand-crafted and parameterized statistical models used for variant calling still produce thousands of errors and missed variants in each genome3,4. Here we show that a deep convolutional neural network5 can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships (likelihoods) between images of read pileups around putative variant sites and ground-truth genotype calls. This approach, called DeepVariant, outperforms existing tools, even winning the “highest performance” award for SNPs in a FDA-administered variant calling challenge. The learned model generalizes across genome builds and even to other mammalian species, allowing non-human sequencing projects to benefit from the wealth of human ground truth data. We further show that, unlike existing tools which perform well on only a specific technology, DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, from deep whole genomes from 10X Genomics to Ion Ampliseq exomes. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.

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APLICACIONES DE LA BIOINFORMÁTICA EN LA MEDICINA: EL GENOMA HUMANO. ¿CÓMO PODEMOS VER TANTO DETALLE?

Acta Biológica Colombiana ◽

10.15446/abc.v21n1supl.51233 ◽

2016 ◽

Vol 21 (1Supl) ◽

pp. 249-258 ◽

Cited By ~ 1

Author(s):

Clara Isabel Bermudez-Santana

Keyword(s):

Next Generation Sequencing ◽

Human Genome ◽

Biological Research ◽

Gene Variants ◽

Next Generation ◽

Medicine Development ◽

Sequencing Technologies ◽

Whole Genomes ◽

New Challenges ◽

Generation Sequencing

La bioinformática es un campo novedoso que soporta parte de la investigación biológica dirigida a la identificación de variantes génicas que pueden ser descubiertas desde los estudios de genomas completos. Basados en esta motivación se presenta el panorama general de los aportes principales de la bioinformática en el desarrollo del secuenciamiento del primer genoma humano. Adicionalmente se resumen los principales avances en cómputo desarrollados para responder a las demandas requeridas por los métodos de secuenciamiento de última generación para lograr re-secuenciar un genoma humano. Finalmente se introducen algunos de los nuevos retos que deben asumirse para aplicar la genómica personalizada en el desarrollo de la medicina. AbstractBioinformatics is a new field that supports part of the biological research aimed at identifying gene variants that can be discovered from studies of whole genomes. Based on this motivation the overview of the main contributions of bioinformatics in the development of sequencing the first human genome is presented. Additionally it is summarized the main advances in computing developed to meet the demands to re-sequence a human genome by using the next generation sequencing technologies. Finally some new challenges that must be faced to apply the personalized genomics into the medicine development are introduced.

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OVarFlow: a resource optimized GATK 4 based Open source Variant calling workFlow

10.1101/2021.05.12.443585 ◽

2021 ◽

Author(s):

Jochen Bathke ◽

Gesine Lühken

Keyword(s):

Next Generation Sequencing ◽

Best Practices ◽

Best Practice ◽

Variant Calling ◽

Data Evaluation ◽

Phenotypic Trait ◽

Next Generation ◽

Sequencing Data ◽

Sequencing Technologies ◽

Generation Sequencing

Background Next generation sequencing technologies are opening new doors to researchers. One application is the direct discovery of sequence variants that are causative for a phenotypic trait or a disease. The detection of an organisms alterations from a reference genome is know as variant calling, a computational task involving a complex chain of software applications. One key player in the field is the Genome Analysis Toolkit (GATK). The GATK Best Practices are commonly referred recipe for variant calling on human sequencing data. Still the fact the Best Practices are highly specialized on human sequencing data and are permanently evolving is often ignored. Reproducibility is thereby aggravated, leading to continuous reinvention of pretended GATK Best Practice workflows. Results Here we present an automatized variant calling workflow, for the detection of SNPs and indels, that is broadly applicable for model as well as non-model diploid organisms. It is derived from the GATK Best Practice workflow for "Germline short variant discovery", without being focused on human sequencing data. The workflow has been highly optimized to achieve parallelized data evaluation and also maximize performance of individual applications to shorten overall analysis time. Optimized Java garbage collection and heap size settings for the GATK applications SortSam, MarkDuplicates, HaplotypeCaller and GatherVcfs were determined by thorough benchmarking. In doing so, runtimes of an example data evaluation could be reduced from 67 h to less than 35 h. Conclusions The demand for standardized variant calling workflows is proportionally growing with the dropping costs of next generation sequencing methods. Our workflow perfectly fits into this niche, offering automatization, reproducibility and documentation of the variant calling process. Moreover resource usage is lowered to a minimum. Thereby variant calling projects should become more standardized, reducing the barrier further for smaller institutions or groups.

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SimFFPE and FilterFFPE: improving structural variant calling in FFPE samples

GigaScience ◽

10.1093/gigascience/giab065 ◽

2021 ◽

Vol 10 (9) ◽

Cited By ~ 1

Author(s):

Lanying Wei ◽

Martin Dugas ◽

Sarah Sandmann

Keyword(s):

Next Generation Sequencing ◽

Variant Calling ◽

Real Data ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Structural Variants ◽

Sequencing Data ◽

Structural Variant ◽

Ffpe Samples ◽

Generation Sequencing

Abstract Background Artifact chimeric reads are enriched in next-generation sequencing data generated from formalin-fixed paraffin-embedded (FFPE) samples. Previous work indicated that these reads are characterized by erroneous split-read support that is interpreted as evidence of structural variants. Thus, a large number of false-positive structural variants are detected. To our knowledge, no tool is currently available to specifically call or filter structural variants in FFPE samples. To overcome this gap, we developed 2 R packages: SimFFPE and FilterFFPE. Results SimFFPE is a read simulator, specifically designed for next-generation sequencing data from FFPE samples. A mixture of characteristic artifact chimeric reads, as well as normal reads, is generated. FilterFFPE is a filtration algorithm, removing artifact chimeric reads from sequencing data while keeping real chimeric reads. To evaluate the performance of FilterFFPE, we performed structural variant calling with 3 common tools (Delly, Lumpy, and Manta) with and without prior filtration with FilterFFPE. After applying FilterFFPE, the mean positive predictive value improved from 0.27 to 0.48 in simulated samples and from 0.11 to 0.27 in real samples, while sensitivity remained basically unchanged or even slightly increased. Conclusions FilterFFPE improves the performance of SV calling in FFPE samples. It was validated by analysis of simulated and real data.

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