BOL: Related items

Variant Calling Pipeline

LEGE — Sat, 19 Oct 2024 12:23:40 -0500

The variantcalling.nf nextflow script will take any number of samples with paired-end reads in FASTQ format, map reads using Bowtie2, process BAM files, and finally call variants using BCFtools v1.21 and/or Freebayes v1.3.6. If part of the pipeline is unsuccessful for a sample then these errors are ignored.

Pipeline flowchart:

Dependencies (version tested)

Nextflow (24.04.4)
Java (18.0.2.1)
Python (3.10)
Perl (5.32.1)
Bowtie2 (2.5.3)
SAMtools (1.19.2)
GATK4 (4.5)
BCFtools (1.21)
Freebayes (1.3.6)

Address of the bookmark: https://github.com/Tom-Jenkins/nextflow-pipelines/blob/main/docs/variant-calling.md

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

You and your friend have similar DNA !!!

Jit — Sun, 27 Jul 2014 20:44:05 -0500

New research out of Massachusetts claims that people often choose friends that are similar to them in genetics and they are more accurate than you might suppose. A study published on PNAS http://www.pnas.org/content/111/Supplement_3/10796.full found that people are apt to pick friends who are genetically similar to themselves - so much so that friends tend to be as alike at the genetic level as a person's fourth cousin.

Scientists with a long-running Framingham Heart Study looked at 1,932 people (examination of about 1.5 million markers of genetic variations), comparing unrelated friends to unrelated strangers. They found that friends shared about 1% of their genes — a percentage much higher than those shared with strangers.This new findings made it clear that people have more DNA in common with those who are selected as friends than with strangers in the same population.

The genes that lined up the most were olfactory genes, which deal with smell. The ones that lined up the least were immune system genes. The researchers weren't sure why that happened :/. Olfactory genes might be a straightforward explanation: People who like the same smells tend to be drawn to similar environments, where they meet others with the same tendencies.

Reference:

http://www.pnas.org/content/111/Supplement_3/10796.full

Image : http://i.kinja-img.com

Classification of SARS-CoV2 Variant !

Jit — Fri, 26 Nov 2021 12:53:12 -0600

The scientists established some guidelines for determining whether a variant is a legitimate branch of an existing lineage:

The variant should be transmitted from its original location to another "geographically distinct population"—say, another country or a province of a large and populous country.
It should differ from its ancestor by at least one nucleotide.
At least 95% of its genetic code should have been sequenced at least five times from different samples.

vcfR: a package to manipulate and visualize VCF data in R

Jit — Thu, 25 Oct 2018 09:05:59 -0500

VcfR is an R package intended to allow easy manipulation and visualization of variant call format (VCF) data. Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices from the VCF data for use with typical R functions. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file or converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and the R environment connecting familiar software with genomic data.

Address of the bookmark: https://github.com/knausb/vcfR

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.

ChromHMM: Chromatin state discovery and characterization

Abhimanyu Singh — Wed, 19 Apr 2017 04:06:23 -0500

ChromHMM is software for learning and characterizing chromatin states. ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of various histone modifications to discover de novo the major re-occuring combinatorial and spatial patterns of marks. ChromHMM is based on a multivariate Hidden Markov Model that explicitly models the presence or absence of each chromatin mark. The resulting model can then be used to systematically annotate a genome in one or more cell types. By automatically computing state enrichments for large-scale functional and annotation datasets ChromHMM facilitates the biological characterization of each state. ChromHMM also produces files with genome-wide maps of chromatin state annotations that can be directly visualized in a genome browser.

Address of the bookmark: http://compbio.mit.edu/ChromHMM/

Computational resources for TE discovery and TE detection

Abhimanyu Singh — Mon, 12 Feb 2018 10:29:18 -0600

Transposable Elements (TEs) to genome structure and evolution as well as their impact on genome sequencing, assembly, annotation and alignment has generated increasing interest in developing new methods for their computational analysis.

Following are the list of resource and location for TE discovery and TE detection:

BLASTER suite http://urgi.versailles.inra.fr/development/blaster/

Censor http://www.girinst.org/censor/download.php

find_ltr http://darwin.informatics.indiana.edu/cgi-bin/evolution/ltr.pl

FINDMITE http://jaketu.biochem.vt.edu/dl_software.htm

HMMER http://hmmer.janelia.org/

LTR_FINDER http://tlife.fudan.edu.cn/ltr_finder/

LTR_STRUC http://www.genetics.uga.edu/retrolab/data/LTR_Struc.html

LTR_MINER http://genomebiology.com/2004/5/10/R79/suppl/s7

LTR_par http://www.eecs.wsu.edu/~ananth/software.htm

MAK http://wesslercluster.plantbio.uga.edu/mak06.html

MaskerAid http://blast.wustl.edu/maskeraid/

mer-engine http://mer-engine.cshl.edu/mer-home.php

mreps http://bioinfo.lifl.fr/mreps/

PILER http://www.drive5.com/piler/

PLOTREP http://repeats.abc.hu/cgi-bin/plotrep.pl

RepBase http://www.girinst.org/

RepeatFinder http://cbcb.umd.edu/software/RepeatFinder/

RepeatGluer http://nbcr.sdsc.edu/euler/intro_tmp.htm

RepeatMasker http://www.repeatmasker.org/

RepeatRunner http://www.yandell-lab.org/repeat_runner/index.html

RepeatScout http://repeatscout.bioprojects.org/

repeat-match http://mummer.sourceforge.net/

REPuter http://www.genomes.de/

RetroMap http://www.burchsite.com/bioi/RetroMapHome.html

SMaRTFinder http://bioinf.dimi.uniud.it/software/software/smartfinder

Tandem Repeats Finder http://tandem.bu.edu/trf/trf.html

Transposon Cluster Finder http://www.mssm.edu/labs/warbup01/paper/files.html

TE nest http://www.plantgdb.org/prj/TE_nest/TE_nest.html

TRANSPO http://alggen.lsi.upc.es/recerca/search/transpo/transpo.html

TSDfinder http://www.ncbi.nlm.nih.gov/CBBresearch/Landsman/TSDfinder/

Tu Lab TE tools http://jaketu.biochem.vt.edu/dl_software.htm

WU-BLAST http://blast.wustl.edu

RepeatModeler2: automated genomic discovery of transposable element families

Neel — Mon, 02 Dec 2019 06:52:29 -0600

RepeatModeler2 represents a valuable addition to the genome annotation toolkit that will enhance the identification and study of TEs in eukaryotic genome sequences. RepeatModeler2 is available as source code or a containerized package under an open license (https://github.com/Dfam-consortium/RepeatModeler, https://github.com/Dfam-consortium/TETools).

Address of the bookmark: https://github.com/Dfam-consortium/TETools

PANNZER: a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function.

BioStar — Thu, 13 Aug 2020 09:57:24 -0500

PANNZER (Protein ANNotation with Z-scoRE) is a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function.

PANNZER (Protein ANNotation with Z-scoRE) is a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function. The tool is designed to predict the functional description (DE) and GO classes.

PANNZER2 processes bacterial proteomes in minutes and eukaryotic proteomes in an hour. You can use AAI-profiler to summarize a proteome's species neighbors and reveal taxonomic identity or contamination.

Address of the bookmark: http://ekhidna2.biocenter.helsinki.fi/sanspanz/#