BOL: Related items

Snakemake workflow: dna-seq-gatk-variant-calling

Jit — Thu, 25 Jul 2019 12:55:07 -0500

This Snakemake pipeline implements the GATK best-practices workflow for calling small genomic variants.

Address of the bookmark: https://github.com/snakemake-workflows/dna-seq-gatk-variant-calling

Best Practices for Variant Calling with the GATK

biogeek — Sat, 22 Feb 2020 03:07:31 -0600

The presentations below were filmed during the March 2015 GATK Workshop, part of the BroadE Workshop series. At the time of this workshop, the current version of Broad’s Genome Analysis Toolkit (GATK) was version 3.3.

Genome Analysis Toolkit

03/19/15	Introduction to High-Throughput Sequencing data formats and methods	Joel Thibault	PDF	Video
03/19/15	Introduction to the GATK	Geraldine Van der Auwera	PDF	Video
03/19/15	Mapping, processing, and duplicate marking with Picard tools	Matt Sooknah	PDF	Video
03/19/15	Mapping and processing RNAseq	Ami Levy-Moonshine	PDF	Video
03/19/15	Indel realignment	Mark Fleharty	PDF	Video
03/19/15	Base quality score recalibration	David Roazen	PDF	Video
03/19/15	Introduction to variant discovery: calling cohorts	Louis Bergelson	PDF	Video
03/19/15	Variant calling and joint genotyping	Sheila Chandran	PDF	Video
03/19/15	Variant quality score recalibration	Bertrand Haas	PDF	Video
03/19/15	Introduction to working with variants	Yossi Farjoun	PDF	Video
03/19/15	Genotype refinement	Laura Gauthier	PDF	Video
03/19/15	Annotation and variant evaluation	David Benjamin	PDF	Video

Address of the bookmark: https://www.broadinstitute.org/partnerships/education/broade/best-practices-variant-calling-gatk-1

AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing

BioStar — Fri, 22 Dec 2017 00:31:06 -0600

AIRVF that works on flowgram, raw and mapped reads and called variants to reduce artifact-driven false variant calls. Tests on sequencing data of standard reference material showed up to ∼98% reduction of false variants when combined to conventional public pipelines and ∼48% to the in-house commercial solution, with a minimal loss of sensitivity

The program with a detailed manual is available at https://sourceforge.net/projects/airvf/

Address of the bookmark: https://sourceforge.net/projects/airvf/

The new corona variant has 23 mutations in all, which is unusually huge !

Shruti Paniwala — Wed, 23 Dec 2020 03:50:50 -0600

The new SARS-CoV-2 version, B.1.1.7, which was first seen in the third week of September in Kent and Greater London, has since spread to other locations in the UK. According to the COVID-19 Genomics UK Consortium (COG-UK Consortium) that analysed the genome data of the virus and identified the variant, the new variant has been spreading "rapidly" over the last four weeks and has now been detected in other locations in the UK, suggesting further spread of the variant in the region.

According to a preliminary report posted on December 19 by the COG-UK Consortium scientists, as of December 15, 1,623 variant genomes have been sequenced. In a December 21 tweet, COG-UK Consortium said that it added 2,963 more genome sequences of SARS-CoV-2, of which 942 (32%) belong to the new variant. The Consortium intends to sequence 20,000 more SARS-CoV-2 genomes in the next two weeks to further ascertain the spread of the variant.

There is no clear proof, at least not yet, that it does cause severe pandemic. But there is a justification for seriously taking the possibility. Another coronavirus lineage in South Africa has acquired one specific mutation that is also present in B.1.1.7. This variant is increasingly spreading across South Africa's coastal regions. And doctors have observed in preliminary research that individuals infected with this variant bear a higher viral load-a higher concentration of the virus in their upper respiratory tract. In many viral diseases, this is associated with more severe symptoms.

poRe: an R package for the visualization and analysis of nanopore sequencing data

Jit — Thu, 23 Nov 2017 09:55:57 -0600

Motivation: The Oxford Nanopore MinION device represents a unique sequencing technology. As a mobile sequencing device powered by the USB port of a laptop, the MinION has huge potential applications. To enable these applications, the bioinformatics community will need to design and build a suite of tools specifically for MinION data.

Results: Here we present poRe, a package for R that enables users to manipulate, organize, summarize and visualize MinION nanopore sequencing data. As a package for R, poRe has been tested on Windows, Linux and MacOSX. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device.

Availability and implementation: poRe is released as a package for R at http://sourceforge.net/projects/rpore/ . A tutorial and further information are available at https://sourceforge.net/p/rpore/wiki/Home/

Contact:mick.watson@roslin.ed.ac.uk

Address of the bookmark: https://academic.oup.com/bioinformatics/article/31/1/114/2365693

GPOPSIM: a simulation tool for whole-genome genetic data

Jit — Wed, 17 Jan 2018 03:47:46 -0600

GPOPSIM is a simulation tool for pedigree, phenotypes, and genomic data, with a variety of population and genome structures and trait genetic architectures. It provides flexible parameter settings for a wide discipline of users, especially can simulate multiple genetically correlated traits with desired genetic parameters and underlying genetic architectures.

Address of the bookmark: https://github.com/SCAU-AnimalGenetics/GPOPSIM

BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data

Jit — Thu, 31 May 2018 09:35:23 -0500

BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes. The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC. https://github.com/lh3/bfc

Address of the bookmark: https://github.com/lh3/bfc

NanoPack: visualizing and processing long-read sequencing data

Jit — Fri, 10 Aug 2018 18:41:34 -0500

The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.

https://academic.oup.com/bioinformatics/article/34/15/2666/4934939

Address of the bookmark: https://github.com/wdecoster/nanoQC

ALLHiC: Phasing and scaffolding polyploid genomes based on Hi-C data

BioStar — Thu, 20 Dec 2018 12:03:32 -0600

The major problem of scaffolding polyploid genome is that Hi-C signals are frequently detected between allelic haplotypes and any existing stat of art Hi-C scaffolding program links the allelic haplotypes together. To solve the problem, we developed a new Hi-C scaffolding pipeline, called ALLHIC, specifically tailored to the polyploid genomes. ALLHIC pipeline contains a total of 5 steps: prune, partition, rescue, optimize and build.

Address of the bookmark: https://github.com/tangerzhang/ALLHiC/wiki

kallisto: a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data

Jit — Mon, 07 Jan 2019 10:35:14 -0600

kallisto is a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data, kallisto can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build. Pseudoalignment of reads preserves the key information needed for quantification, and kallisto is therefore not only fast, but also as accurate as existing quantification tools. In fact, because the pseudoalignment procedure is robust to errors in the reads, in many benchmarks kallisto significantly outperforms existing tools. kallisto is described in detail in:

Nicolas L Bray, Harold Pimentel, Páll Melsted and Lior Pachter, Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, 525–527 (2016), doi:10.1038/nbt.3519

Address of the bookmark: https://pachterlab.github.io/kallisto/about