scholarly journals Haplotype-aware graph indexes

2019 ◽  
Author(s):  
Jouni Sirén ◽  
Erik Garrison ◽  
Adam M Novak ◽  
Benedict Paten ◽  
Richard Durbin
Keyword(s):  
Genetic Variation ◽  
Precision Medicine ◽  
Chromosome 17 ◽  
Supplementary Information ◽  
Whole Genome ◽  
Supplementary Data ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Burrows Wheeler Transform ◽  
Haplotype Information

Abstract Motivation The variation graph toolkit (VG) represents genetic variation as a graph. Although each path in the graph is a potential haplotype, most paths are non-biological, unlikely recombinations of true haplotypes. Results We augment the VG model with haplotype information to identify which paths are more likely to exist in nature. For this purpose, we develop a scalable implementation of the graph extension of the positional Burrows–Wheeler transform. We demonstrate the scalability of the new implementation by building a whole-genome index of the 5008 haplotypes of the 1000 Genomes Project, and an index of all 108 070 Trans-Omics for Precision Medicine Freeze 5 chromosome 17 haplotypes. We also develop an algorithm for simplifying variation graphs for k-mer indexing without losing any k-mers in the haplotypes. Availability and implementation Our software is available at https://github.com/vgteam/vg, https://github.com/jltsiren/gbwt and https://github.com/jltsiren/gcsa2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Haplotype-aware graph indexes

2019 ◽  
Author(s):  
Jouni Sirén ◽  
Erik Garrison ◽  
Adam M. Novak ◽  
Benedict Paten ◽  
Richard Durbin
Keyword(s):  
Genetic Variation ◽  
Chromosome 17 ◽  
Supplementary Information ◽  
Whole Genome ◽  
Supplementary Data ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Link Type ◽  
Supplementary Material ◽  
Haplotype Information

AbstractMotivationThe variation graph toolkit (VG) represents genetic variation as a graph. Although each path in the graph is a potential haplotype, most paths are nonbiological, unlikely recombinations of true haplotypes.ResultsWe augment the VG model with haplotype information to identify which paths are more likely to exist in nature. For this purpose, we develop a scalable implementation of the graph extension of the positional Burrows–Wheelertransform (GBWT). We demonstrate the scalability of the new implementation by building a whole-genome index of the 5,008 haplotypes of the 1000 Genomes Project, and an index of all 108,070 TOPMed Freeze 5 chromosome 17 haplotypes. We also develop an algorithm for simplifying variation graphs for k-mer indexing without losing any k-mers in the haplotypes.AvailabilityOur software is available at https://github.com/vgteam/vg, https://github.com/jltsiren/gbwt, and https://github.com/jltsiren/[email protected] informationSupplementary data are available.

scholarly journals Accurate, scalable cohort variant calls using DeepVariant and GLnexus

2021 ◽  
Author(s):  
Taedong Yun ◽  
Helen Li ◽  
Pi-Chuan Chang ◽  
Michael F Lin ◽  
Andrew Carroll ◽  
...  
Keyword(s):  
Best Practices ◽  
Quality Metrics ◽  
Supplementary Information ◽  
Public Research ◽  
Supplementary Data ◽  
Quality Improvements ◽  
1000 Genomes Project ◽  
Individual Level ◽  
1000 Genomes ◽  
Population Scale

Abstract Motivation Population-scale sequenced cohorts are foundational resources for genetic analyses, but processing raw reads into analysis-ready cohort-level variants remains challenging. Results We introduce an open-source cohort-calling method that uses the highly-accurate caller DeepVariant and scalable merging tool GLnexus. Using callset quality metrics based on variant recall and precision in benchmark samples and Mendelian consistency in father-mother-child trios, we optimized the method across a range of cohort sizes, sequencing methods, and sequencing depths. The resulting callsets show consistent quality improvements over those generated using existing best practices with reduced cost. We further evaluate our pipeline in the deeply sequenced 1000 Genomes Project (1KGP) samples and show superior callset quality metrics and imputation reference panel performance compared to an independently-generated GATK Best Practices pipeline. Availability and Implementation We publicly release the 1KGP individual-level variant calls and cohort callset (https://console.cloud.google.com/storage/browser/brain-genomics-public/research/cohort/1KGP) to foster additional development and evaluation of cohort merging methods as well as broad studies of genetic variation. Both DeepVariant (https://github.com/google/deepvariant) and GLnexus (https://github.com/dnanexus-rnd/GLnexus) are open-sourced, and the optimized GLnexus setup discovered in this study is also integrated into GLnexus public releases v1.2.2 and later. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Accurate, scalable cohort variant calls using DeepVariant and GLnexus

2020 ◽  
Author(s):  
Taedong Yun ◽  
Helen Li ◽  
Pi-Chuan Chang ◽  
Michael F. Lin ◽  
Andrew Carroll ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Best Practices ◽  
Open Source ◽  
Variant Calling ◽  
Cost Savings ◽  
Quality Improvements ◽  
1000 Genomes Project ◽  
Genetic Analyses ◽  
1000 Genomes ◽  
Population Scale

AbstractPopulation-scale sequenced cohorts are foundational resources for genetic analyses, but processing raw reads into analysis-ready variants remains challenging. Here we introduce an open-source cohort variant-calling method using the highly-accurate caller DeepVariant and scalable merging tool GLnexus. We optimized callset quality based on benchmark samples and Mendelian consistency across many sample sizes and sequencing specifications, resulting in substantial quality improvements and cost savings over existing best practices. We further evaluated our pipeline in the 1000 Genomes Project (1KGP) samples, showing superior quality metrics and imputation performance. We publicly release the 1KGP callset to foster development of broad studies of genetic variation.

scholarly journals High coverage whole genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

2021 ◽  
Author(s):  
Marta Byrska-Bishop ◽  
Uday S. Evani ◽  
Xuefang Zhao ◽  
Anna O. Basile ◽  
Haley J. Abel ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Sequence Data ◽  
Whole Genome ◽  
1000 Genomes Project ◽  
Phase 3 ◽  
High Coverage ◽  
Entire Cohort ◽  
1000 Genomes ◽  
Low Coverage

ABSTRACTThe 1000 Genomes Project (1kGP), launched in 2008, is the largest fully open resource of whole genome sequencing (WGS) data consented for public distribution of raw sequence data without access or use restrictions. The final (phase 3) 2015 release of 1kGP included 2,504 unrelated samples from 26 populations, representing five continental regions of the world and was based on a combination of technologies including low coverage WGS (mean depth 7.4X), high coverage whole exome sequencing (mean depth 65.7X), and microarray genotyping. Here, we present a new, high coverage WGS resource encompassing the original 2,504 1kGP samples, as well as an additional 698 related samples that result in 602 complete trios in the 1kGP cohort. We sequenced this expanded 1kGP cohort of 3,202 samples to a targeted depth of 30X using Illumina NovaSeq 6000 instruments. We performed SNV/INDEL calling against the GRCh38 reference using GATK’s HaplotypeCaller, and generated a comprehensive set of SVs by integrating multiple analytic methods through a sophisticated machine learning model, upgrading the 1kGP dataset to current state-of-the-art standards. Using this strategy, we defined over 111 million SNVs, 14 million INDELs, and ∼170 thousand SVs across the entire cohort of 3,202 samples with estimated false discovery rate (FDR) of 0.3%, 1.0%, and 1.8%, respectively. By comparison to the low-coverage phase 3 callset, we observed substantial improvements in variant discovery and estimated FDR that were facilitated by high coverage re-sequencing and expansion of the cohort. Specifically, we called 7% more SNVs, 59% more INDELs, and 170% more SVs per genome than the phase 3 callset. Moreover, we leveraged the presence of families in the cohort to achieve superior haplotype phasing accuracy and we demonstrate improvements that the high coverage panel brings especially for INDEL imputation. We make all the data generated as part of this project publicly available and we envision this updated version of the 1kGP callset to become the new de facto public resource for the worldwide scientific community working on genomics and genetics.

scholarly journals Phylonium: fast estimation of evolutionary distances from large samples of similar genomes

2019 ◽  
Vol 36 (7) ◽  
pp. 2040-2046 ◽  
Author(s):  
Fabian Klötzl ◽  
Bernhard Haubold
Keyword(s):  
Disease Outbreaks ◽  
Supplementary Information ◽  
Whole Genome ◽  
Command Line ◽  
Supplementary Data ◽  
Large Samples ◽  
Fast Estimation ◽  
Unix Command ◽  
Similar Accuracy ◽  
Single Sequence

Abstract Motivation Tracking disease outbreaks by whole-genome sequencing leads to the collection of large samples of closely related sequences. Five years ago, we published a method to accurately compute all pairwise distances for such samples by indexing each sequence. Since indexing is slow, we now ask whether it is possible to achieve similar accuracy when indexing only a single sequence. Results We have implemented this idea in the program phylonium and show that it is as accurate as its predecessor and roughly 100 times faster when applied to all 2678 Escherichia coli genomes contained in ENSEMBL. One of the best published programs for rapidly computing pairwise distances, mash, analyzes the same dataset four times faster but, with default settings, it is less accurate than phylonium. Availability and implementation Phylonium runs under the UNIX command line; its C++ sources and documentation are available from github.com/evolbioinf/phylonium. Supplementary information Supplementary data are available at Bioinformatics online.

Tandem repeat interval pattern identifies animal taxa

2021 ◽  
Author(s):  
Balaram Bhattacharyya ◽  
Uddalak Mitra ◽  
Ramkishore Bhattacharyya
Keyword(s):  
Information Content ◽  
Tandem Repeat ◽  
Tandem Repeats ◽  
Ordered Set ◽  
Whole Genome Sequence ◽  
Supplementary Information ◽  
Whole Genome ◽  
Supplementary Data ◽  
Genome Sequences ◽  
Significant Achievement

Abstract Motivation We discover that maximality of information content among intervals of Tandem Repeats (TRs) in animal genome segregates over taxa such that taxa identification becomes swift and accurate. Successive TRs of a motif occur at intervals over the sequence, forming a trail of TRs of the motif across the genome. We present a method, Tandem Repeat Information Mining (TRIM), that mines 4k number of TR trails of all k length motifs from a whole genome sequence and extracts the information content within intervals of the trails. TRIM vector formed from the ordered set of interval entropies becomes instrumental for genome segregation. Results Reconstruction of correct phylogeny for animals from whole genome sequences proves precision of TRIM. Identification of animal taxa by TRIM vector upon feature selection is the most significant achievement. These suggest Tandem Repeat Interval Pattern (TRIP) is a taxa-specific constitutional characteristic in animal genome. Availabilityand implementation Source and executable code of TRIM along with usage manual are made available at https://github.com/BB-BiG/TRIM. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Varstation: a complete and efficient tool to support NGS data analysis

2019 ◽  
Author(s):  
ACO Faria ◽  
MP Caraciolo ◽  
RM Minillo ◽  
TF Almeida ◽  
SM Pereira ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Data Analysis ◽  
Supplementary Information ◽  
Human Genetic Variation ◽  
Supplementary Data ◽  
Efficient Tool ◽  
Link Type ◽  
Data Processor ◽  
Ngs Data Analysis ◽  
Ngs Data

AbstractSummaryVarstation is a cloud-based NGS data processor and analyzer for human genetic variation. This resource provides a customizable, centralized, safe and clinically validated environment aiming to improve and optimize the flow of NGS analyses and reports related with clinical and research genetics.Availability and implementationVarstation is freely available at http://varstation.com, for academic [email protected] informationSupplementary data are available at Bioinformatics online.

Genetic polymorphisms of pharmacogenomic VIP variants in Chinese Lisu population

2021 ◽  
Author(s):  
Hongyan Lu ◽  
Yuliang Wang ◽  
Zhanhao Zhang ◽  
Shishi Xing ◽  
Dandan Li ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Drug Therapy ◽  
Precision Medicine ◽  
Theoretical Basis ◽  
Yunnan Province ◽  
Nucleotide Polymorphism ◽  
1000 Genomes Project ◽  
Single Nucleotide ◽  
1000 Genomes

Abstract IntroductionThe specificity of drug therapy in individuals and races has promoted the development and improvement of pharmacogenomics and precision medicine. While there is a few cognition on the minorities in China, especially in Lisu nationality from the Yunnan Province. Therefore, we performed the research to improve the role of pharmacogenomics in the Lisu population from the Yunnan province of China.Materials and MethodsIn our study, 54 variants of very important pharmacogenes (VIPs) selected from the PharmGKB database were genotyped in 199 unrelated and healthy Lisu adults from the Yunnan province of China, and then, genotyping data wtih χ2 test were analyzed.ResultsWe compared our date with those of other 26 populations from the 1000 Genomes Project, and acquired that the Lisu ethnicity is similar with the CDX(Chinese Dai in Xishuangbanna, China) and CHS(Southern Han Chinese, China). Furthermore, rs776746 (CYP3A5), rs1805123 (KCNH2), rs4291 (ACE), rs1051298 (SLC19A1) and rs1065852 (CYP2D6) were deemed as the most varying loci. The MAF of “G” at rs1805123 (KCNH2) in the Lisu population was the largest with the value of 51.0%.ConclusionsOur results show that there are significant differences in SNP (single nucleotide polymorphism) loci, supplementing the pharmacogenomic information of the Lisu population in Yunnan province, China, and can provide a theoretical basis for individualized medication in the future.

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