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

2018 ◽  
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.

scholarly journals Clinical-grade whole-genome sequencing and 3′ transcriptome analysis of colorectal cancer patients

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Agata Stodolna ◽  
Miao He ◽  
Mahesh Vasipalli ◽  
Zoya Kingsbury ◽  
Jennifer Becq ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Transcriptome Analysis ◽  
Variant Calling ◽  
Standard Of Care ◽  
Genomic Variation ◽  
Whole Genome ◽  
Clinical Grade ◽  
Pathway Gene

Abstract Background Clinical-grade whole-genome sequencing (cWGS) has the potential to become the standard of care within the clinic because of its breadth of coverage and lack of bias towards certain regions of the genome. Colorectal cancer presents a difficult treatment paradigm, with over 40% of patients presenting at diagnosis with metastatic disease. We hypothesised that cWGS coupled with 3′ transcriptome analysis would give new insights into colorectal cancer. Methods Patients underwent PCR-free whole-genome sequencing and alignment and variant calling using a standardised pipeline to output SNVs, indels, SVs and CNAs. Additional insights into the mutational signatures and tumour biology were gained by the use of 3′ RNA-seq. Results Fifty-four patients were studied in total. Driver analysis identified the Wnt pathway gene APC as the only consistently mutated driver in colorectal cancer. Alterations in the PI3K/mTOR pathways were seen as previously observed in CRC. Multiple private CNAs, SVs and gene fusions were unique to individual tumours. Approximately 30% of patients had a tumour mutational burden of > 10 mutations/Mb of DNA, suggesting suitability for immunotherapy. Conclusions Clinical whole-genome sequencing offers a potential avenue for the identification of private genomic variation that may confer sensitivity to targeted agents and offer patients new options for targeted therapies.

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

2020 ◽  
Author(s):  
Sen Zhao ◽  
Oleg Agafonov ◽  
Abdulrahman Azab ◽  
Tomasz Stokowy ◽  
Eivind Hovig
Keyword(s):  
Large Scale ◽  
Variant Calling ◽  
Performance Comparison ◽  
Next Generation Sequencing Technology ◽  
Genome Data ◽  
Germline Variant ◽  
Causal Variants ◽  
Variant Detection ◽  
Variant Analysis ◽  
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.

scholarly journals halSynteny: a fast, easy-to-use conserved synteny block construction method for multiple whole-genome alignments

GigaScience ◽  
2020 ◽  
Vol 9 (6) ◽  
Author(s):  
Ksenia Krasheninnikova ◽  
Mark Diekhans ◽  
Joel Armstrong ◽  
Aleksei Dievskii ◽  
Benedict Paten ◽  
...  
Keyword(s):  
Large Scale ◽  
Pairwise Alignment ◽  
Synteny Block ◽  
Rapid Identification ◽  
Full Genome Sequence ◽  
Whole Genome ◽  
Full Genome ◽  
Genome Data ◽  
Multiple Alignments ◽  
Binary Format

Abstract Background Large-scale sequencing projects provide high-quality full-genome data that can be used for reconstruction of chromosomal exchanges and rearrangements that disrupt conserved syntenic blocks. The highest resolution of cross-species homology can be obtained on the basis of whole-genome, reference-free alignments. Very large multiple alignments of full-genome sequence stored in a binary format demand an accurate and efficient computational approach for synteny block production. Findings halSynteny performs efficient processing of pairwise alignment blocks for any pair of genomes in the alignment. The tool is part of the HAL comparative genomics suite and is targeted to build synteny blocks for multi-hundred–way, reference-free vertebrate alignments built with the Cactus system. Conclusions halSynteny enables an accurate and rapid identification of synteny in multiple full-genome alignments. The method is implemented in C++11 as a component of the halTools software and released under MIT license. The package is available at https://github.com/ComparativeGenomicsToolkit/hal/.

scholarly journals Clinical-grade whole genome sequencing of colorectal cancer and 3’ transcriptome analysis demonstrate targetable alterations in the majority of patients

2020 ◽  
Author(s):  
Agata Stodolna ◽  
Miao He ◽  
Mahesh Vasipalli ◽  
Zoya Kingsbury ◽  
Jennifer Becq ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Transcriptome Analysis ◽  
Variant Calling ◽  
Standard Of Care ◽  
Genomic Variation ◽  
Whole Genome ◽  
Clinical Grade ◽  
Pathway Gene

AbstractIntroductionClinical grade whole genome sequencing (cWGS) has the potential to become standard of care within the clinic because of its breadth of coverage and lack of bias towards certain regions of the genome. Colorectal cancer presents a difficult treatment paradigm, with over 40% of patients presenting at diagnosis with metastatic disease. We hypothesised that cWGS coupled with 3’ transcriptome analysis would give new insights into colorectal cancer.MethodsPatients underwent PCR-free whole genome sequencing and alignment and variant calling using a standardised pipeline to output SNVs, indels, SVs and CNAs. Additional insights into mutational signatures and tumour biology were gained by the use of 3’ RNAseq.ResultsFifty-four patients were studied in total. Driver analysis identified the Wnt pathway gene APC as the only consistently mutated driver in colorectal cancer. Alterations in the PI3K/mTOR pathways were seen as previously observed in CRC. Multiple private CNAs, SVs and gene fusions were unique to individual tumours. Approximately 20% of patients had a tumour mutational burden of >10 mutations/Mb of DNA, suggesting suitability for immunotherapy.ConclusionsClinical whole genome sequencing offers a potential avenue for identification of private genomic variation that may confer sensitivity to targeted agents and offer patients new options for targeted therapies.

scholarly journals VCF2PopTree: a one-click client-side software to construct population phylogeny from genome-wide SNPs

2019 ◽  
Author(s):  
Sankar Subramanian ◽  
Umayal Ramasamy ◽  
David Chen
Keyword(s):  
Phylogenetic Trees ◽  
Large Scale ◽  
Web Applications ◽  
Third Party ◽  
Genotype Data ◽  
Whole Genome ◽  
Genome Data ◽  
Genome Wide ◽  
Software Programs ◽  
Computationally Intensive

In the past decades a number of software programs have been developed to deduce the phylogenetic relationship between populations. However, these programs are not suited for large-scale whole genome data. Recently, a few standalone or web applications have been developed to handle genome-wide data, but they were either computationally intensive, dependent on third party software or required significant time and resource of a web server. In the post-genomic era, researchers are able to obtain bioinformatically processed high-quality publication-ready whole genome data for many individuals in a population from next generation sequencing companies due to the reduction in the cost of sequencing and analysis. Such genotype data is typically presented in the Variant Call Format (VCF) and there is no simple software available that uses this data to construct the phylogeny of populations in a short time. To address this limitation, we have developed a one-click user-friendly software, VCF2PopTree that uses gnome-wide SNPs to construct and display phylogenetic trees in seconds to minutes. For example, it reads a 1 GB VCF file and draws a tree in less than 5 minutes. VCF2PopTree accepts genotype data from a local machine, constructs a tree using UPGMA and Neighbour-Joining algorithms and displays it on a web-browser. It also produces pairwise-diversity matrix in MEGA and PHYLIP file formats as well as trees in the Newick format which could be directly used by other popular phylogenetic software programs. The software including the source code, a test VCF input file and short documentation are available at: https://github.com/sansubs/vcf2pop.

scholarly journals VCF2PopTree: a one-click client-side software to construct population phylogeny from genome-wide SNPs

2019 ◽  
Author(s):  
Sankar Subramanian ◽  
Umayal Ramasamy ◽  
David Chen
Keyword(s):  
Phylogenetic Trees ◽  
Large Scale ◽  
Web Applications ◽  
Third Party ◽  
Genotype Data ◽  
Whole Genome ◽  
Genome Data ◽  
Genome Wide ◽  
Software Programs ◽  
Computationally Intensive

In the past decades a number of software programs have been developed to deduce the phylogenetic relationship between populations. However, these programs are not suited for large-scale whole genome data. Recently, a few standalone or web applications have been developed to handle genome-wide data, but they were either computationally intensive, dependent on third party software or required significant time and resource of a web server. In the post-genomic era, researchers are able to obtain bioinformatically processed high-quality publication-ready whole genome data for many individuals in a population from next generation sequencing companies due to the reduction in the cost of sequencing and analysis. Such genotype data is typically presented in the Variant Call Format (VCF) and there is no simple software available that uses this data to construct the phylogeny of populations in a short time. To address this limitation, we have developed a one-click user-friendly software, VCF2PopTree that uses gnome-wide SNPs to construct and display phylogenetic trees in seconds to minutes. For example, it reads a 1 GB VCF file and draws a tree in less than 5 minutes. VCF2PopTree accepts genotype data from a local machine, constructs a tree using UPGMA and Neighbour-Joining algorithms and displays it on a web-browser. It also produces pairwise-diversity matrix in MEGA and PHYLIP file formats as well as trees in the Newick format which could be directly used by other popular phylogenetic software programs. The software including the source code, a test VCF input file and short documentation are available at: http://sankarsubramanian.net/dat/index.html.

scholarly journals Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Kristopher A. Standish ◽  
Tristan M. Carland ◽  
Glenn K. Lockwood ◽  
Wayne Pfeiffer ◽  
Mahidhar Tatineni ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Variant Calling ◽  
Whole Genome ◽  
Next Generation ◽  
Sequencing Studies

scholarly journals Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing

BMC Genomics ◽  
2013 ◽  
Vol 14 (1) ◽  
pp. 425 ◽  
Author(s):  
Shanrong Zhao ◽  
Kurt Prenger ◽  
Lance Smith ◽  
Thomas Messina ◽  
Hongtao Fan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Analysis ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Sequencing Data Analysis

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

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.

scholarly journals Multithreaded variant calling in elPrep 5

2020 ◽  
Author(s):  
Charlotte Herzeel ◽  
Pascal Costanza ◽  
Dries Decap ◽  
Jan Fostier ◽  
Roel Wuyts ◽  
...  
Keyword(s):  
Best Practices ◽  
Variant Calling ◽  
Quality Score ◽  
Whole Genome ◽  
Genome Data ◽  
Whole Exome ◽  
Base Quality Score ◽  
Execution Times

AbstractWe present elPrep 5, which updates the elPrep framework for processing sequencing alignment/map files with variant calling. elPrep 5 can now execute the full pipeline described by the GATK Best Practices for variant calling, which consists of PCR and optical duplicate marking, sorting by coordinate order, base quality score recalibration, and variant calling using the haplotype caller algorithm. elPrep 5 produces identical BAM and VCF output as GATK4 while significantly reducing the runtime by parallelizing and merging the execution of the pipeline steps. Our benchmarks show that elPrep 5 speeds up the runtime of the variant calling pipeline by a factor 8-16x on both whole-exome and whole-genome data while using the same hardware resources as GATK 4. This makes elPrep 5 a suitable drop-in replacement for GATK 4 when faster execution times are needed.

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