HaploTypo: a variant-calling pipeline for phased genomes

Abstract Summary An increasing number of phased (i.e. with resolved haplotypes) reference genomes are available. However, the most genetic variant calling tools do not explicitly account for haplotype structure. Here, we present HaploTypo, a pipeline tailored to resolve haplotypes in genetic variation analyses. HaploTypo infers the haplotype correspondence for each heterozygous variant called on a phased reference genome. Availability and implementation HaploTypo is implemented in Python 2.7 and Python 3.5, and is freely available at https://github.com/gabaldonlab/haplotypo, and as a Docker image. Supplementary information Supplementary data are available at Bioinformatics online.

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Reference flow: reducing reference bias using multiple population genomes

Genome Biology ◽

10.1186/s13059-020-02229-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Nae-Chyun Chen ◽

Brad Solomon ◽

Taher Mun ◽

Sheila Iyer ◽

Ben Langmead

Keyword(s):

Genetic Variation ◽

Reference Genome ◽

Alignment Method ◽

Sequencing Data ◽

Computational Overhead ◽

Reference Flow ◽

Multiple Population ◽

Reference Bias ◽

Flow Alignment ◽

Reference Genomes

AbstractMost sequencing data analyses start by aligning sequencing reads to a linear reference genome, but failure to account for genetic variation leads to reference bias and confounding of results downstream. Other approaches replace the linear reference with structures like graphs that can include genetic variation, incurring major computational overhead. We propose the reference flow alignment method that uses multiple population reference genomes to improve alignment accuracy and reduce reference bias. Compared to the graph aligner vg, reference flow achieves a similar level of accuracy and bias avoidance but with 14% of the memory footprint and 5.5 times the speed.

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AStrap: identification of alternative splicing from transcript sequences without a reference genome

Bioinformatics ◽

10.1093/bioinformatics/bty1008 ◽

2018 ◽

Vol 35 (15) ◽

pp. 2654-2656 ◽

Cited By ~ 5

Author(s):

Guoli Ji ◽

Wenbin Ye ◽

Yaru Su ◽

Moliang Chen ◽

Guangzao Huang ◽

...

Keyword(s):

Machine Learning ◽

Alternative Splicing ◽

Single Molecule ◽

Reference Genome ◽

De Novo ◽

Supplementary Information ◽

Model Organisms ◽

Sequencing Data ◽

Extensive Evaluation ◽

Reference Genomes

Abstract Summary Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap. Supplementary information Supplementary data are available at Bioinformatics online.

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Enabling multiscale variation analysis with genome graphs

10.1101/2021.02.03.429603 ◽

2021 ◽

Author(s):

Brice Letcher ◽

Martin Hunt ◽

Zamin Iqbal

Keyword(s):

Genetic Variation ◽

Directed Acyclic Graph ◽

Structural Variation ◽

Reference Genome ◽

Multiple Scales ◽

State Of The Art ◽

Variant Calling ◽

Variation Analysis ◽

New Algorithms ◽

Genome Graph

AbstractBackgroundStandard approaches to characterising genetic variation revolve around mapping reads to a reference genome and describing variants in terms of differences from the reference; this is based on the assumption that these differences will be small and provides a simple coordinate system. However this fails, and the coordinates break down, when there are diverged haplotypes at a locus (e.g. one haplotype contains a multi-kilobase deletion, a second contains a few SNPs, and a third is highly diverged with hundreds of SNPs). To handle these, we need to model genetic variation that occurs at different length-scales (SNPs to large structural variants) and that occurs on alternate backgrounds. We refer to these together as multiscale variation.ResultsWe model the genome as a directed acyclic graph consisting of successive hierarchical subgraphs (“sites”) that naturally incorporate multiscale variation, and introduce an algorithm for genotyping, implemented in the software gramtools. This enables variant calling on different sequence backgrounds. In addition to producing regular VCF files, we introduce a JSON file format based on VCF, which records variant site relationships and alternate sequence backgrounds.We show two applications. First, we benchmark gramtools against existing state-of-the-art methods in joint-genotyping 17 M. tuberculosis samples at long deletions and the overlapping small variants that segregate in a cohort of 1,017 genomes. Second, in 706 African and SE Asian P. falciparum genomes, we analyse a dimorphic surface antigen gene which possesses variation on two diverged backgrounds which appeared to not recombine. This generates the first map of variation on both backgrounds, revealing patterns of recombination that were previously unknown.ConclusionsWe need new approaches to be able to jointly analyse SNP and structural variation in cohorts, and even more to handle variants on different genetic backgrounds. We have demonstrated that by modelling with a directed, acyclic and locally hierarchical genome graph, we can apply new algorithms to accurately genotype dense variation at multiple scales. We also propose a generalisation of VCF for accessing multiscale variation in genome graphs, which we hope will be of wide utility.

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Whisper: Read sorting allows robust mapping of sequencing data

10.1101/240358 ◽

2017 ◽

Author(s):

Sebastian Deorowicz ◽

Agnieszka Debudaj-Grabysz ◽

Adam Gudyś ◽

Szymon Grabowski

Keyword(s):

Reference Genome ◽

Variant Calling ◽

Real Data ◽

Supplementary Information ◽

Sequencing Data ◽

Suffix Arrays ◽

Link Type ◽

Mapping Tool ◽

Reverse Complement ◽

Comparable Accuracy

AbstractMotivationMapping reads to a reference genome is often the first step in a sequencing data analysis pipeline. Mistakes made at this computationally challenging stage cannot be recovered easily.ResultsWe present Whisper, an accurate and high-performant mapping tool, based on the idea of sorting reads and then mapping them against suffix arrays for the reference genome and its reverse complement. Employing task and data parallelism as well as storing temporary data on disk result in superior time efficiency at reasonable memory requirements. Whisper excels at large NGS read collections, in particular Illumina reads with typical WGS coverage. The experiments with real data indicate that our solution works in about 15% of the time needed by the well-known Bowtie2 and BWA-MEM tools at a comparable accuracy (validated in variant calling pipeline).AvailabilityWhisper is available for free from https://github.com/refresh-bio/Whisper or http://sun.aei.polsl.pl/REFRESH/Whisper/[email protected] informationSupplementary data are available at publisher Web site.

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LRez: C ++ API and toolkit for analyzing and managing Linked-Reads data

Bioinformatics Advances ◽

10.1093/bioadv/vbab022 ◽

2021 ◽

Author(s):

Pierre Morisse ◽

Claire Lemaitre ◽

Fabrice Legeai

Keyword(s):

Genome Assembly ◽

Low Cost ◽

Variant Calling ◽

Supplementary Information ◽

Supplementary Data ◽

High Quality ◽

Dna Molecule ◽

Sequencing Technologies ◽

Wide Range ◽

Genomic Regions

Abstract Motivation Linked-Reads technologies combine both the high-quality and low cost of short-reads sequencing and long-range information, through the use of barcodes tagging reads which originate from a common long DNA molecule. This technology has been employed in a broad range of applications including genome assembly, phasing and scaffolding, as well as structural variant calling. However, to date, no tool or API dedicated to the manipulation of Linked-Reads data exist. Results We introduce LRez, a C ++ API and toolkit which allows easy management of Linked-Reads data. LRez includes various functionalities, for computing numbers of common barcodes between genomic regions, extracting barcodes from BAM files, as well as indexing and querying BAM, FASTQ and gzipped FASTQ files to quickly fetch all reads or alignments containing a given barcode. LRez is compatible with a wide range of Linked-Reads sequencing technologies, and can thus be used in any tool or pipeline requiring barcode processing or indexing, in order to improve their performances. Availability and implementation LRez is implemented in C ++, supported on Unix-based platforms, and available under AGPL-3.0 License at https://github.com/morispi/LRez, and as a bioconda module. Supplementary information Supplementary data are available at Bioinformatics Advances

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Reducing reference bias using multiple population reference genomes

10.1101/2020.03.03.975219 ◽

2020 ◽

Cited By ~ 1

Author(s):

Nae-Chyun Chen ◽

Brad Solomon ◽

Taher Mun ◽

Sheila Iyer ◽

Ben Langmead

Keyword(s):

Genetic Variation ◽

Reference Genome ◽

Alignment Method ◽

Sequencing Data ◽

Computational Overhead ◽

Reference Flow ◽

Multiple Population ◽

Reference Bias ◽

Flow Alignment ◽

Reference Genomes

AbstractMost sequencing data analyses start by aligning sequencing reads to a linear reference genome. But failure to account for genetic variation causes reference bias and confounding of results downstream. Other approaches replace the linear reference with structures like graphs that can include genetic variation, incurring major computational overhead. We propose the “reference flow” alignment method that uses multiple population reference genomes to improve alignment accuracy and reduce reference bias. Compared to the graph aligner vg, reference flow achieves a similar level of accuracy and bias avoidance, but with 14% of the memory footprint and 5.5 times the speed.

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Scaling accurate genetic variant discovery to tens of thousands of samples

10.1101/201178 ◽

2017 ◽

Cited By ~ 218

Author(s):

Ryan Poplin ◽

Valentin Ruano-Rubio ◽

Mark A. DePristo ◽

Tim J. Fennell ◽

Mauricio O. Carneiro ◽

...

Keyword(s):

Population Genetics ◽

Genetic Variation ◽

Rare Diseases ◽

Clinical Studies ◽

Genetic Variant ◽

Disease Gene ◽

Variant Calling ◽

Variant Discovery ◽

Disease Gene Discovery ◽

Exome Aggregation Consortium

AbstractComprehensive disease gene discovery in both common and rare diseases will require the efficient and accurate detection of all classes of genetic variation across tens to hundreds of thousands of human samples. We describe here a novel assembly-based approach to variant calling, the GATK HaplotypeCaller (HC) and Reference Confidence Model (RCM), that determines genotype likelihoods independently per-sample but performs joint calling across all samples within a project simultaneously. We show by calling over 90,000 samples from the Exome Aggregation Consortium (ExAC) that, in contrast to other algorithms, the HC-RCM scales efficiently to very large sample sizes without loss in accuracy; and that the accuracy of indel variant calling is superior in comparison to other algorithms. More importantly, the HC-RCM produces a fully squared-off matrix of genotypes across all samples at every genomic position being investigated. The HC-RCM is a novel, scalable, assembly-based algorithm with abundant applications for population genetics and clinical studies.

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Haplotype-aware graph indexes

Bioinformatics ◽

10.1093/bioinformatics/btz575 ◽

2019 ◽

Cited By ~ 6

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.

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icHET: interactive visualization of cytoplasmic heteroplasmy

Bioinformatics ◽

10.1093/bioinformatics/btz300 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4411-4412 ◽

Cited By ~ 2

Author(s):

Vinhthuy Phan ◽

Diem-Trang Pham ◽

Caroline Melton ◽

Adam J Ramsey ◽

Bernie J Daigle ◽

...

Keyword(s):

Reference Genome ◽

Interactive Visualization ◽

Supplementary Information ◽

Supplementary Data ◽

Short Reads ◽

Genome Wide ◽

Computational Workflow ◽

Multiple Samples

Abstract Summary Although heteroplasmy has been studied extensively in animal systems, there is a lack of tools for analyzing, exploring and visualizing heteroplasmy at the genome-wide level in other taxonomic systems. We introduce icHET, which is a computational workflow that produces an interactive visualization that facilitates the exploration, analysis and discovery of heteroplasmy across multiple genomic samples. icHET works on short reads from multiple samples from any organism with an organellar reference genome (mitochondrial or plastid) and a nuclear reference genome. Availability and implementation The software is available at https://github.com/vtphan/HeteroplasmyWorkflow. Supplementary information Supplementary data are available at Bioinformatics online.

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

10.1101/833582 ◽

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.

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