scholarly journals Prioritising positively selected variants in whole-genome sequencing data using FineMAV

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fadilla Wahyudi ◽  
Farhang Aghakhanian ◽  
Sadequr Rahman ◽  
Yik-Ying Teo ◽  
Michał Szpak ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Population Genomics ◽  
Software Tool ◽  
Human Populations ◽  
Whole Genome ◽  
Sequencing Data ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Genome Browsers

Abstract Background In population genomics, polymorphisms that are highly differentiated between geographically separated populations are often suggestive of Darwinian positive selection. Genomic scans have highlighted several such regions in African and non-African populations, but only a handful of these have functional data that clearly associates candidate variations driving the selection process. Fine-Mapping of Adaptive Variation (FineMAV) was developed to address this in a high-throughput manner using population based whole-genome sequences generated by the 1000 Genomes Project. It pinpoints positively selected genetic variants in sequencing data by prioritizing high frequency, population-specific and functional derived alleles. Results We developed a stand-alone software that implements the FineMAV statistic. To graphically visualise the FineMAV scores, it outputs the statistics as bigWig files, which is a common file format supported by many genome browsers. It is available as a command-line and graphical user interface. The software was tested by replicating the FineMAV scores obtained using 1000 Genomes Project African, European, East and South Asian populations and subsequently applied to whole-genome sequencing datasets from Singapore and China to highlight population specific variants that can be subsequently modelled. The software tool is publicly available at https://github.com/fadilla-wahyudi/finemav. Conclusions The software tool described here determines genome-wide FineMAV scores, using low or high-coverage whole-genome sequencing datasets, that can be used to prioritize a list of population specific, highly differentiated candidate variants for in vitro or in vivo functional screens. The tool displays these scores on the human genome browsers for easy visualisation, annotation and comparison between different genomic regions in worldwide human populations.

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 TypeTE: a tool to genotype mobile element insertions from whole genome resequencing data

2019 ◽  
Author(s):  
Clement Goubert ◽  
Jainy Thomas ◽  
Lindsay M. Payer ◽  
Jeffrey M. Kidd ◽  
Julie Feusier ◽  
...  
Keyword(s):  
Population Genomics ◽  
Mobile Element ◽  
Whole Genome Sequencing Data ◽  
Human Populations ◽  
Whole Genome ◽  
Structural Variants ◽  
Sequencing Data ◽  
1000 Genomes ◽  
Standard Set ◽  
Whole Genome Resequencing

ABSTRACTAlu retrotransposons account for more than 10% of the human genome, and insertions of these elements create structural variants segregating in human populations. Such polymorphic Alu are powerful markers to understand population structure, and they represent variants that can greatly impact genome function, including gene expression. Accurate genotyping of Alu and other mobile elements has been challenging. Indeed, we found that Alu genotypes previously called for the 1000 Genomes Project are sometimes erroneous, which poses significant problems for phasing these insertions with other variants that comprise the haplotype. To ameliorate this issue, we introduce a new pipeline -- TypeTE -- which genotypes Alu insertions from whole-genome sequencing data. Starting from a list of polymorphic Alus, TypeTE identifies the hallmarks (poly-A tail and target site duplication) and orientation of Alu insertions using local re-assembly to reconstruct presence and absence alleles. Genotype likelihoods are then computed after re-mapping sequencing reads to the reconstructed alleles. Using a ‘gold standard’ set of PCR-based genotyping of >200 loci, we show that TypeTE improves genotype accuracy from 83% to 92% in the 1000 Genomes dataset. TypeTE can be readily adapted to other retrotransposon families and brings a valuable toolbox addition for population genomics.

scholarly journals Straglr: discovering and genotyping tandem repeat expansions using whole genome long-read sequences

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Readman Chiu ◽  
Indhu-Shree Rajan-Babu ◽  
Jan M. Friedman ◽  
Inanc Birol
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Tandem Repeat ◽  
Neurological Disorders ◽  
Software Tool ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Long Read ◽  
Repeat Expansions

AbstractTandem repeat (TR) expansion is the underlying cause of over 40 neurological disorders. Long-read sequencing offers an exciting avenue over conventional technologies for detecting TR expansions. Here, we present Straglr, a robust software tool for both targeted genotyping and novel expansion detection from long-read alignments. We benchmark Straglr using various simulations, targeted genotyping data of cell lines carrying expansions of known diseases, and whole genome sequencing data with chromosome-scale assembly. Our results suggest that Straglr may be useful for investigating disease-associated TR expansions using long-read sequencing.

scholarly journals TypeTE: a tool to genotype mobile element insertions from whole genome resequencing data

2020 ◽  
Vol 48 (6) ◽  
pp. e36-e36 ◽  
Author(s):  
Clément Goubert ◽  
Jainy Thomas ◽  
Lindsay M Payer ◽  
Jeffrey M Kidd ◽  
Julie Feusier ◽  
...  
Keyword(s):  
Population Genomics ◽  
Mobile Element ◽  
Whole Genome Sequencing Data ◽  
Human Populations ◽  
Whole Genome ◽  
Structural Variants ◽  
Sequencing Data ◽  
1000 Genomes ◽  
Alu Insertions ◽  
Whole Genome Resequencing

Abstract Alu retrotransposons account for more than 10% of the human genome, and insertions of these elements create structural variants segregating in human populations. Such polymorphic Alus are powerful markers to understand population structure, and they represent variants that can greatly impact genome function, including gene expression. Accurate genotyping of Alus and other mobile elements has been challenging. Indeed, we found that Alu genotypes previously called for the 1000 Genomes Project are sometimes erroneous, which poses significant problems for phasing these insertions with other variants that comprise the haplotype. To ameliorate this issue, we introduce a new pipeline – TypeTE – which genotypes Alu insertions from whole-genome sequencing data. Starting from a list of polymorphic Alus, TypeTE identifies the hallmarks (poly-A tail and target site duplication) and orientation of Alu insertions using local re-assembly to reconstruct presence and absence alleles. Genotype likelihoods are then computed after re-mapping sequencing reads to the reconstructed alleles. Using a high-quality set of PCR-based genotyping of >200 loci, we show that TypeTE improves genotype accuracy from 83% to 92% in the 1000 Genomes dataset. TypeTE can be readily adapted to other retrotransposon families and brings a valuable toolbox addition for population genomics.

scholarly journals Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Huiguang Yi ◽  
Yanling Lin ◽  
Chengqi Lin ◽  
Wenfei Jin
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Real Data ◽  
Whole Genome ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Sequence Read Archive ◽  
Large Scale Dataset ◽  
Ncbi Sequence Read Archive

AbstractHere, we develop k -mer substring space decomposition (Kssd), a sketching technique which is significantly faster and more accurate than current sketching methods. We show that it is the only method that can be used for large-scale dataset comparisons at population resolution on simulated and real data. Using Kssd, we prioritize references for all 1,019,179 bacteria whole genome sequencing (WGS) runs from NCBI Sequence Read Archive and find misidentification or contamination in 6164 of these. Additionally, we analyze WGS and exome runs of samples from the 1000 Genomes Project.

scholarly journals TranspoScope: interactive visualization of retrotransposon insertions

2020 ◽  
Vol 36 (12) ◽  
pp. 3877-3878
Author(s):  
Mark Grivainis ◽  
Zuojian Tang ◽  
David Fenyö
Keyword(s):  
Experimental Evidence ◽  
Whole Genome Sequencing ◽  
Human Genome ◽  
Genome Sequencing ◽  
Source Code ◽  
Prenatal Development ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Genome Browsers

Abstract Motivation Retrotransposition is an important force in shaping the human genome and is involved in prenatal development, disease and aging. Current genome browsers are not optimized for visualizing the experimental evidence for retrotransposon insertions. Results We have developed a specialized browser to visualize the evidence for retrotransposon insertions for both targeted and whole-genome sequencing data. Availability and implementation TranspoScope’s source code, as well as installation instructions, are available at https://github.com/FenyoLab/transposcope.

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 ◽  
Whole Genome ◽  
1000 Genomes Project ◽  
High Coverage ◽  
1000 Genomes

scholarly journals Large-scale whole-genome sequencing of three diverse Asian populations in Singapore

2018 ◽  
Author(s):  
Degang Wu ◽  
Jinzhuang Dou ◽  
Xiaoran Chai ◽  
Claire Bellis ◽  
Andreas Wilm ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Human Genetics ◽  
Genotype Imputation ◽  
Whole Genome ◽  
Fine Scale ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Asian Populations

AbstractAsian populations are currently underrepresented in human genetics research. Here we present whole-genome sequencing data of 4,810 Singaporeans from three diverse ethnic groups: 2,780 Chinese, 903 Malays, and 1,127 Indians. Despite a medium depth of 13.7×, we achieved essentially perfect (>99.8%) sensitivity and accuracy for detecting common variants and good sensitivity (>89%) for detecting extremely rare variants with <0.1% allele frequency. We found 89.2 million single-nucleotide polymorphisms (SNPs) and 9.1 million small insertions and deletions (INDELs), more than half of which have not been cataloged in dbSNP. In particular, we found 126 common deleterious mutations (MAF>0.01) that were absent in the existing public databases, highlighting the importance of local population reference for genetic diagnosis. We describe fine-scale genetic structure of Singapore populations and their relationship to worldwide populations from the 1000 Genomes Project. In addition to revealing noticeable amounts of admixture among three Singapore populations and a Malay-related novel ancestry component that has not been captured by the 1000 Genomes Project, our analysis also identified some fine-scale features of genetic structure consistent with two waves of prehistoric migration from south China to Southeast Asia. Finally, we demonstrate that our data can substantially improve genotype imputation not only for Singapore populations, but also for populations across Asia and Oceania. These results highlight the genetic diversity in Singapore and the potential impacts of our data as a resource to empower human genetics discovery in a broad geographic region.

scholarly journals plasmidSPAdes: Assembling Plasmids from Whole Genome Sequencing Data

2016 ◽  
Author(s):  
Dmitry Antipov ◽  
Nolan Hartwick ◽  
Max Shen ◽  
Mikhail Raiko ◽  
Alla Lapidus ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Software Tool ◽  
Software Tools ◽  
Whole Genome Sequencing Data ◽  
Antibiotics Resistance ◽  
Whole Genome ◽  
Sequencing Data ◽  
Bacterial Genomes ◽  
Specialized Software

ABSTRACTMotivationPlasmids are stably maintained extra-chromosomal genetic elements that replicate independently from the host cell’s chromosomes. Although plasmids harbor biomedically important genes, (such as genes involved in virulence and antibiotics resistance), there is a shortage of specialized software tools for extracting and assembling plasmid data from whole genome sequencing projects.ResultsWe present the plasmidSPAdes algorithm and software tool for assembling plasmids from whole genome sequencing data and benchmark its performance on a diverse set of bacterial genomes.Availability and implementationPLASMIDSPADESis publicly available athttp://spades.bioinf.spbau.ru/plasmidSPAdes/[email protected]

Sign in / Sign up

Export Citation Format

Share Document