Accuracy and efficiency of germline variant calling pipelines for human genome data

AbstractAdvances in next-generation sequencing technology has enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data, however there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN™ and DeepVariant) using Genome in a Bottle Consortium, “synthetic-diploid” and simulated WGS datasets. DRAGEN™ and DeepVariant show a better accuracy in SNPs and indels calling, with no significant differences in their F1-score. DRAGEN™ platform offers accuracy, flexibility and a highly-efficient running speed, and therefore superior advantage in the analysis of WGS data on a large scale. The combination of DRAGEN™ and DeepVariant also provides a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical application.

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Accuracy and efficiency of germline variant calling pipelines for human genome data

Scientific Reports ◽

10.1038/s41598-020-77218-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Sen Zhao ◽

Oleg Agafonov ◽

Abdulrahman Azab ◽

Tomasz Stokowy ◽

Eivind Hovig

Keyword(s):

Large Scale ◽

Variant Calling ◽

Performance Comparison ◽

Superior Performance ◽

Next Generation Sequencing Technology ◽

Genome Data ◽

Germline Variant ◽

Indel Calling ◽

Execution Speed ◽

Variant Detection

AbstractAdvances in next-generation sequencing technology have enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data. However, there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN and DeepVariant) using the Genome in a Bottle Consortium, “synthetic-diploid” and simulated WGS datasets. DRAGEN and DeepVariant show better accuracy in SNP and indel calling, with no significant differences in their F1-score. DRAGEN platform offers accuracy, flexibility and a highly-efficient execution speed, and therefore superior performance in the analysis of WGS data on a large scale. The combination of DRAGEN and DeepVariant also suggests a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical applications.

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Seave: a comprehensive web platform for storing and interrogating human genomic variation

10.1101/258061 ◽

2018 ◽

Cited By ~ 3

Author(s):

Velimir Gayevskiy ◽

Tony Roscioli ◽

Marcel E Dinger ◽

Mark J Cowley

Keyword(s):

Cloud Computing ◽

Large Scale ◽

Variant Calling ◽

Genomic Variation ◽

Whole Genome ◽

Genome Data ◽

Pathogenicity Prediction ◽

Data Scaling ◽

Human Genomic ◽

Web Platform

AbstractCapability for genome sequencing and variant calling has increased dramatically, enabling large scale genomic interrogation of human disease. However, discovery is hindered by the current limitations in genomic interpretation, which remains a complicated and disjointed process. We introduce Seave, a web platform that enables variants to be easily filtered and annotated with in silico pathogenicity prediction scores and annotations from popular disease databases. Seave stores genomic variation of all types and sizes, and allows filtering for specific inheritance patterns, quality values, allele frequencies and gene lists. Seave is open source and deployable locally, or on a cloud computing provider, and works readily with gene panel, exome and whole genome data, scaling from single labs to multi-institution scale.

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SICaRiO: Short Indel Call filteRing with bOosting

10.1101/601450 ◽

2019 ◽

Author(s):

Md Shariful Islam Bhuyan ◽

Itsik Pe’er ◽

M. Sohel Rahman

Keyword(s):

Large Scale ◽

Variant Calling ◽

Read Depth ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Specific Information ◽

Classification Problems ◽

Genomic Features ◽

Next Generation Sequencing Technology ◽

Reliable Detection

AbstractDespite impressive improvement in the next-generation sequencing technology, reliable detection of indels is still a difficult endeavour. Recognition of true indels is of prime importance in many applications, such as, personalized health care, disease genomics, population genetics etc. Recently, advanced machine learning techniques have been successfully applied to classification problems with large-scale data. In this paper, we present SICaRiO, a gradient boosting classifier for reliable detection of true indels, trained with gold-standard dataset from genome-in-a-bottle (GIAB) consortium. Our filtering scheme significantly improves the performance of each variant calling pipeline used in GIAB and beyond. SICaRiO uses genomic features which can be computed from publicly available resources, hence, we can apply it on any indel callsets not having sequencing pipeline-specific information (e.g., read depth). This study also sheds lights on prior genomic contexts responsible for indel calling error made by sequencing platforms. We have compared prediction difficulty for three indel categories over different sequencing pipelines. We have also ranked genomic features according to their predictivity in determining false indel calls.

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Practical guide for managing large-scale human genome data in research

Journal of Human Genetics ◽

10.1038/s10038-020-00862-1 ◽

2020 ◽

Vol 66 (1) ◽

pp. 39-52

Author(s):

Tomoya Tanjo ◽

Yosuke Kawai ◽

Katsushi Tokunaga ◽

Osamu Ogasawara ◽

Masao Nagasaki

Keyword(s):

Data Processing ◽

Whole Genome Sequencing ◽

Human Genome ◽

Genome Sequencing ◽

Large Scale ◽

Genomic Data ◽

Whole Genome ◽

Sequencing Data ◽

Genome Data ◽

Human Genome Data

AbstractStudies in human genetics deal with a plethora of human genome sequencing data that are generated from specimens as well as available on public domains. With the development of various bioinformatics applications, maintaining the productivity of research, managing human genome data, and analyzing downstream data is essential. This review aims to guide struggling researchers to process and analyze these large-scale genomic data to extract relevant information for improved downstream analyses. Here, we discuss worldwide human genome projects that could be integrated into any data for improved analysis. Obtaining human whole-genome sequencing data from both data stores and processes is costly; therefore, we focus on the development of data format and software that manipulate whole-genome sequencing. Once the sequencing is complete and its format and data processing tools are selected, a computational platform is required. For the platform, we describe a multi-cloud strategy that balances between cost, performance, and customizability. A good quality published research relies on data reproducibility to ensure quality results, reusability for applications to other datasets, as well as scalability for the future increase of datasets. To solve these, we describe several key technologies developed in computer science, including workflow engine. We also discuss the ethical guidelines inevitable for human genomic data analysis that differ from model organisms. Finally, the future ideal perspective of data processing and analysis is summarized.

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SICaRiO: short indel call filtering with boosting

Briefings in Bioinformatics ◽

10.1093/bib/bbaa238 ◽

2020 ◽

Author(s):

Md Shariful Islam Bhuyan ◽

Itsik Pe’er ◽

M Sohel Rahman

Keyword(s):

Large Scale ◽

Variant Calling ◽

Read Depth ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Specific Information ◽

Classification Problems ◽

Genomic Features ◽

Next Generation Sequencing Technology ◽

Reliable Detection

Abstract Despite impressive improvement in the next-generation sequencing technology, reliable detection of indels is still a difficult endeavour. Recognition of true indels is of prime importance in many applications, such as personalized health care, disease genomics and population genetics. Recently, advanced machine learning techniques have been successfully applied to classification problems with large-scale data. In this paper, we present SICaRiO, a gradient boosting classifier for the reliable detection of true indels, trained with the gold-standard dataset from ‘Genome in a Bottle’ (GIAB) consortium. Our filtering scheme significantly improves the performance of each variant calling pipeline used in GIAB and beyond. SICaRiO uses genomic features that can be computed from publicly available resources, i.e. it does not require sequencing pipeline-specific information (e.g. read depth). This study also sheds lights on prior genomic contexts responsible for the erroneous calling of indels made by sequencing pipelines. We have compared prediction difficulty for three categories of indels over different sequencing pipelines. We have also ranked genomic features according to their predictivity in determining false positives.

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A Survey on Contrastive Self-Supervised Learning

Technologies ◽

10.3390/technologies9010002 ◽

2020 ◽

Vol 9 (1) ◽

pp. 2

Author(s):

Ashish Jaiswal ◽

Ashwin Ramesh Babu ◽

Mohammad Zaki Zadeh ◽

Debapriya Banerjee ◽

Fillia Makedon

Keyword(s):

Computer Vision ◽

Supervised Learning ◽

Language Processing ◽

Large Scale ◽

Performance Comparison ◽

Extensive Review ◽

Future Directions ◽

Dominant Component ◽

Supervised Methods ◽

The Cost

Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress.

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Privacy, anonymity and subjectivity in genomic research

Genetics Research ◽

10.1017/s0016672315000221 ◽

2016 ◽

Vol 98 ◽

Cited By ~ 7

Author(s):

IAN MCGONIGLE ◽

NOAM SHOMRON

Keyword(s):

International Law ◽

Personal Data ◽

Genetic Data ◽

Genomic Research ◽

Genome Data ◽

Genomic Technologies ◽

Biology Organization ◽

Human Genome Data ◽

European Molecular Biology Organization ◽

Special Meeting

SummaryThe use of non-anonymized human genome data is becoming increasingly popular in research. Here we review the proceedings of a special meeting on this topic that took place at European Molecular Biology Organization (EMBO) in December 2014. The main points discussed centered on how to achieve ‘anonymity,’ ‘trust,’ and ‘protection of data’ in relation to new genomic technologies and research. Following our report of this meeting, we also raise three further issues for future consideration: the harmonization of international law in relation to genetic data protection; the complex issues around the ‘dividual’ nature of genetic data; and the growing commercial value of personal data. In conclusion, we stress the importance of scientists working in the area of genomic research engaging in interdisciplinary collaborations with humanities and social science scholars and addressing these complicated issues.

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APC germline variant analysis in the adenomatous polyposis phenotype in Japanese patients

International Journal of Clinical Oncology ◽

10.1007/s10147-021-01946-4 ◽

2021 ◽

Author(s):

Misato Takao ◽

Tatsuro Yamaguchi ◽

Hidetaka Eguchi ◽

Takeshi Yamada ◽

Yasushi Okazaki ◽

...

Keyword(s):

Japanese Patients ◽

Adenomatous Polyposis ◽

Germline Variant ◽

Variant Analysis

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Large scale statistical analysis of genome data with Ruby and R: skipping interface libraries

EMBnet journal ◽

10.14806/ej.20.0.753 ◽

2014 ◽

Vol 20 (0) ◽

Cited By ~ 1

Author(s):

Sergio RP Line ◽

Ana P De Souza ◽

Luciana S Mofatto

Keyword(s):

Statistical Analysis ◽

Large Scale ◽

Genome Data

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Automated Isoform Diversity Detector (AIDD): A pipeline for investigating transcriptome diversity of RNA-seq data

10.1101/2020.01.22.915348 ◽

2020 ◽

Author(s):

Noel-Marie Plonski ◽

Emily Johnson ◽

Madeline Frederick ◽

Heather Mercer ◽

Gail Fraizer ◽

...

Keyword(s):

Rna Editing ◽

Large Scale ◽

Neural Progenitor Cells ◽

Viral Infections ◽

Variant Calling ◽

Rna Seq ◽

Major Mechanism ◽

Isoform Diversity ◽

Adar Editing ◽

Transcriptome Diversity

AbstractBackgroundAs the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism - RNA editing due to post-transcriptional changes of individual nucleotides – remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise.ResultsHere we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDD’s capabilities.ConclusionsAIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes.

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