BOL: Related items

MALVA: Genotyping by Mapping-free ALlele Detection of Known VAriants

Jit — Tue, 28 Jan 2020 03:39:22 -0600

MALVA is able to genotype multi-allelic SNPs and indels without mapping reads

MALVA calls correctly more indels than the most widely adopted genotyping pipelines

Mapping-free approaches are as accurate as alignment-based ones, while being faster

More at https://www.sciencedirect.com/science/article/pii/S2589004219302366

https://www.sciencedirect.com/science/article/pii/S2589004219302366

Address of the bookmark: https://github.com/AlgoLab/malva

TARDIS: Toolkit for automated and rapid discovery of structural variants

Neel — Fri, 09 Jun 2017 04:43:31 -0500

tardis

Toolkit for Automated and Rapid DIscovery of Structural variants

Requirements

zlib (http://www.zlib.net)
mrfast (https://github.com/BilkentCompGen/mrfast)
htslib (included as submodule; http://htslib.org/)
Fetching tardis

git clone https://github.com/BilkentCompGen/tardis.git --recursive

https://github.com/BilkentCompGen/tardis

Address of the bookmark: https://github.com/BilkentCompGen/tardis

JASMINE: Jointly Accurate Sv Merging with Intersample Network Edges

Shruti Paniwala — Sat, 02 Jul 2022 11:41:53 -0500

This tool is used to merge structural variants (SVs) across samples. Each sample has a number of SV calls, consisting of position information (chromosome, start, end, length), type and strand information, and a number of other values. Jasmine represents the set of all SVs across samples as a network, and uses a modified minimum spanning forest algorithm to determine the best way of merging the variants such that each merged variants represents a set of analogous variants occurring in different samples.

Address of the bookmark: https://github.com/mkirsche/Jasmine

SViper: Swipe your Structural Variants called on long (ONT/PacBio) reads with short exact (Illumina) reads.

Neel — Sun, 22 Dec 2019 03:48:28 -0600

Call sviper

~$ ./sviper -s short-reads.bam -l long-reads.bam -r ref.fa -c variants.vcf -o polished_variants

This will output a polished_variants.vcf file, that contains all the refined variants.

Sometimes it is helpful to look at the polished sequence, e.g. with the IGV browser. In that case you want SViper to output the polished and aligned sequences in a bam file via the option --output-polished-bam:

~$ ./sviper -s short-reads.bam -l long-reads.bam -r ref.fa -c variants.vcf -o polished_variants --output-polished-bam

Address of the bookmark: https://github.com/smehringer/SViper

Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications.

Jit — Mon, 28 May 2018 09:41:39 -0500

Manta calls structural variants (SVs) and indels from mapped paired-end sequencing reads. It is optimized for analysis of germline variation in small sets of individuals and somatic variation in tumor/normal sample pairs. Manta discovers, assembles and scores large-scale SVs, medium-sized indels and large insertions within a single efficient workflow.

Address of the bookmark: https://github.com/Illumina/manta

SvABA: Genome-wide detection of structural variants and indels by local assembly

Abhimanyu Singh — Mon, 21 Jan 2019 17:58:56 -0600

SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of SGA (String Graph Assembler) by Jared Simpson, and BWA-MEM by Heng Li. Contigs are assembled for every 25kb window (with some small overlap) for every region in the genome. The default is to use only clipped, discordant, unmapped and indel reads, although this can be customized to any set of reads at the command line using VariantBam rules. These contigs are then immediately aligned to the reference with BWA-MEM and parsed to identify variants. Sequencing reads are then realigned to the contigs with BWA-MEM, and variants are scored by their read support.

Address of the bookmark: https://github.com/walaj/svaba

NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data

Jit — Mon, 06 Aug 2018 17:24:53 -0500

NextSV, a meta SV caller and a computational pipeline to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purpose. The output of NextSV is in ANNOVAR-compatible bed format. Users can easily perform downstream annotation using ANNOVAR and disease gene discovery using Phenolyzer.

Address of the bookmark: https://github.com/Nextomics/NextSV

Bioinformatics approach to Boar Taint

Rahul Agarwal — Wed, 17 Jul 2013 15:50:37 -0500

Meat products obtained from intact male pigs often produce offensive smell or odour which is recognized as a complex genetic trait called boar taint.Androstenone and Skatole in the fat primarily cause boar taint. Metabolism of androstenone and sex steroids share a common pathway which makes removal of boar taint a very challenging task. Castration is a traditional solution to remove boar taint but it also results in bad quality of meat due to low level of steroids which is objectionable to many consumers. Detected functional variant(s) underlying boar taint compounds can be used as genetic markers in selection of male pigs with reduced boar taint levels. Resequencing of a total of 47 samples belong to Norwegian Landrace (NL) and Duroc (D) pigs with varied boar taint levels were done in Illumina HiSeq2000 to >10X average coverage. Short reads generated from these samples mapped to Sus Scrofa version 10.2 reference assembly using Bowtie2. Alignment file then used for calling SNPs and InDels inside previousy identified QTL regions on SSC5,13, and 7 with the aid of FreeBayes , a variant caller tool. A final list of SNPs was prepared after filtering SNPs on the basis of SNP quality, coverage of SNP allele, functional and structural annotation, and repeats, etc. Selected SNPs will be genotyped in sample population for validation and then used for constructing SNPs haplotypes in close linkage disequilibrium with QTLs and fine mapping of QTLs through association mapping of genotyped SNPs.

pWhatsHap: a parallel, high-performance version of WhatsHap

Jit — Wed, 14 Nov 2018 08:20:27 -0600

Given the potential relevance of efficient haplotyping in several analysis pipelines, we have designed and engineered pWhatsHap, a parallel, high-performance version of WhatsHap. pWhatsHap is embedded in a toolkit developed in Python and supports genomics datasets in standard file formats. Building on WhatsHap, pWhatsHap exhibits the same complexity exploring a number of possible solutions which is exponential in the coverage of the dataset. The parallel implementation on multi-core architectures allows for a relevant reduction of the execution time for haplotyping, while the provided results enjoy the same high accuracy as that provided by WhatsHap, which increases with coverage.

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1170-y

Address of the bookmark: https://bitbucket.org/whatshap/whatshap

Nemo – A stochastic, individual-base, genetically explicit simulation platform

Jit — Sat, 01 Oct 2016 14:45:02 -0500

A recombination map has been added for all multi-locus traits. The map positions (chromosomal) for neutral markers (e.g. SNPs) and loci under selection (QTLs, deleterious mutations, DMIs) can now be specified explicitly, or set at random. The map can hold an unlimited number of loci of different types jointly, at any recombination scale (cM or lower). The effects of linkage can thus be finely explored.
A new trait coding for (Bateson-)Dobzhansky-Muller incompatibility loci. Multiple haploid or diploid pairs of incompatible loci can be spread throughout the genome and affect individual fitness.
Multi-type selection: Individual fitness can be jointly determined by different types of loci under selectinon, such as QTLs coding for quantitative traits under spatially variable selection, universally deleterious mutations, and Dobzhansky-Muller incompatibility loci.
An unlimited number of quantitative traits under different forms of selection can be modelled, based on universally pleiotropic loci with several bi- or multi-allelic models.
Spatial and temporal variation of selection on quantitative traits is possible, modelling shifts of environmental conditions over time.
The dispersal matrix describing the movement of individuals among sub-populations can be replaced by a connectivity matrix and a reduced dispersal matrix describing migration only among the connected sub-populations. This offers a substantial gain in computing time and system memory when simulating very large grids.
Input parameters' arguments may be specified in separate files. This is particularly convenient when specifying large matrices.
Many adjustments have been made for refined control of the input of parameters and data output. See updates in the manual.

Address of the bookmark: http://nemo2.sourceforge.net/index.html