BOL: Related items

Ancient whole genome duplication (WGD) detection tools !

Rahul Nayak — Sun, 07 Mar 2021 00:32:44 -0600

There are two methods for ancient WGD detection, one is collinearity analysis, and the other is based on the Ks distribution map. Among them, Ks is defined as the average number of synonymous substitutions at each synonymous site, and there is also a Ka corresponding to it, which refers to the average number of non-synonymous substitutions at each non-synonymous site.

At present, some people have posted articles about the analysis process of WGD. I searched for the keyword "wgd pipeline" and found the following:

GenoDup: https:// github.com/MaoYafei/GenoDup-Pipeline
https://peerj.com/articles/6303/
WGDdetector: https:// github.com/yongzhiyang2 012/WGDdetector
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2670-3
wgd: https:// github.com/arzwa/wgd
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1142-2#Sec1
https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-017-0399-x
GeNoGAP https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1142-2
https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-017-0399-x
https://github.com/dfguan/purge_dups
https://www.biorxiv.org/content/10.1101/2020.01.24.917997v1

This article introduces the usage of wgd.

Wgd cannot be installed directly with bioconda at present, so it is a little troublesome to install, because it depends on a lot of software. wgd depends on the following software

BLAST
MCL
MUSCLE/MAFFT/PRANK
PAML
PhyML/FastTree
i-ADHoRe

But the good news is that most of the software it depends on can be installed with bioconda

conda create -n wgd python=3.5 blast mcl muscle mafft prank paml fasttree cmake libpng mpi=1.0=mpich
conda activate wgd

Here mpi=1.0=mpich is selected, because i-adhore depends on mpich. If openmpi is installed, an error will appear while loading shared libraries: libmpi_cxx.so.40: cannot open shared object file: No such file or directory

After that, the installation is much simpler

git clone https://github.com/arzwa/wgd.git
cd wgd
pip install .
pip install git+https://github.com/arzwa/wgd.git
For i-ADHoRe, you need to register at http:// bioinformatics.psb.ugent.be /webtools/i-adhore/licensing/Agree to the license to download i-ADHoRe-3.0

Since my miniconda3 installed ~/opt/, the installation path is so~/opt/miniconda3/envs/wgd/

tar -zxvf i-adhore-3.0.01.tar.gz
cd i-adhore-3.0.01
mkdir -p build && cd build
cmake .. -DCMAKE_INSTALL_PREFIX=~/opt/miniconda3/envs/wgd/
make -j 4
make insatall

Take the sugarcane genome Saccharum spontaneum L as an example. The genome is 8-ploid with 32 chromosomes (2n = 4x8 = 32)

Download the tutorial for CDS and GFF annotation files

mkdir -p wgd_tutorial && cd wgd_tutorial
wget http://www.life.illinois.edu/ming/downloads/Spontaneum_genome/Sspon.v20190103.cds.fasta.gz
wget http://www.life.illinois.edu/ming/downloads/Spontaneum_genome/Sspon.v20190103.gff3.gz
gunzip *.gz

First conda activate wgdstart our analysis environment, and then start the analysis

Step 1 : Use to wgd mclidentify homologous genes in the genome

wgd mcl -n 20 --cds --mcl -s Sspon.v20190103.cds.fasta -o Sspon_cds.out

Step 2 : Use to wgd ksdbuild Ks distribution

wgd ksd --n_threads 80 Sspon_cds.out/Sspon.v20190103.cds.fasta.blast.tsv.mcl Sspon.v20190103.cds.fasta

Step 3 : If the quality of the genome is good, then wgd syncollinearity analysis can be used . It can help us find the collinearity block in the genome and the corresponding anchor point

wgd syn --feature gene --gene_attribute ID \
-ks wgd_ksd/Sspon.v20190103.cds.fasta.ks.tsv \
Sspon.v20190103.gff3 Sspon_cds.out/Sspon.v20190103.cds.fasta.blast.tsv.mcl

For more reading - There are 9 sub-modules in WGD

kde: KDE fitting to the Ks distribution
ksd: Ks distribution construction
mcl: BLASP comparison of All-vs-ALl + MCL classification analysis.
mix: Hybrid modeling of Ks distribution.
pre: preprocess the CDS file
syn: Call I-ADHoRe 3.0 to use GFF files for collinearity analysis
viz: draw histogram and density plot
wf1: Ks standard analysis procedure of the whole genome paranome (paranome), call mcl, ksd and syn
wf2: Ks standard analysis procedure of one-vs-one homologous gene (ortholog), call wcl and kSD

OMArk: software for proteome (protein-coding gene repertoire) quality assessment

LEGE — Wed, 21 Feb 2024 15:01:20 -0600

OMArk is a software for proteome (protein-coding gene repertoire) quality assessment. It provides measures of proteome completeness, characterizes the consistency of all protein coding genes with regard to their homologs, and identifies the presence of contamination from other species. OMArk relies on the OMA orthology database, from which it exploits orthology relationships, and on the OMAmer software for fast placement of all proteins into gene families.

Address of the bookmark: https://github.com/DessimozLab/OMArk

Arvados

Martin Jones — Sat, 20 Sep 2014 16:54:21 -0500

Arvados is a free and open source bioinformatics platform for genomic and biomedical data. User can Store | Organize | Compute | Share the data for free.

Address of the bookmark: https://arvados.org/

methylKit

Jit — Fri, 03 Jun 2016 10:09:29 -0500

methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods such as Agilent SureSelect methyl-seq. In addition, methylKit can deal with base-pair resolution data for 5hmC obtained from Tab-seq or oxBS-seq. It can also handle whole-genome bisulfite sequencing data if proper input format is provided.

Address of the bookmark: https://github.com/al2na/methylKit

QuIN’s web server

Jit — Mon, 27 Jun 2016 10:44:16 -0500

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions.

AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub:https://github.com/UcarLab/QuIN/.

Address of the bookmark: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004809

Fancy Oneliner for Bioinformatics !!

Poonam Mahapatra — Thu, 07 Jul 2016 12:05:50 -0500

This webpage lists some of the one-liners that we frequently use in metagenomic analyses. You can click on the following links to browse through different topics. You can copy/paste the commands as they are in your terminal screen, provided you follow the same naming conventions and folder structures as we have. We are sharing these codes with the intention that if they are useful and help you in your analyses, then we will be appropriately credited as considerable effort has been put into devising them.

Address of the bookmark: http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/oneliners.html

Genome STRiP

Neel — Tue, 06 Sep 2016 03:58:19 -0500

Genome STRiP (Genome STRucture In Populations) is a suite of tools for discovering and genotyping structural variations using sequencing data. The methods are designed to detect shared variation using data from multiple individuals.

Genome STRiP looks both across and within a set of sequenced genomes to detect variation. The methods are adaptive and support heterogeneous data sets, including variations in sequencing depth, read lengths and mixtures of paired and single-end reads. A minimum of 20 to 30 genomes are required to get acceptable results, but the method gains power across genomes and processing more genomes provide better results.

To run discovery or genotyping on a single sequenced genome or a small set of genomes, you need to call your data against a background population, such as a set of genomes from the 1000 Genomes Project. The background population does not need to be matched to the target individuals.

Address of the bookmark: http://software.broadinstitute.org/software/genomestrip/

OrganellarGenomeDRAW

Jit — Tue, 16 Aug 2016 08:13:13 -0500

OrganellarGenomeDRAW is dedicated to convert genetic information stored in GenBank entries to graphical maps. The input text file has to be in GenBank flat file format, whereas the output format can be chosen among several formats. The application is especially optimized and adapted for the creation of high-quality, detailed circular maps of organellar genomes like the plastid genome (plastome) or the mitochondrial genome (chondriome). Nevertheless, you can upload any GenBank entry. The workflow is devided into three steps.

More at http://ogdraw.mpimp-golm.mpg.de/cgi-bin/ogdraw.pl

Address of the bookmark: http://ogdraw.mpimp-golm.mpg.de/index.shtml

GeneMANIA

Jit — Mon, 22 Aug 2016 09:55:16 -0500

Faster, more accurate algorithms function prediction "GeneMANIA (Multiple Association Network Integration Algorithm)" have however been developed in recent years and are publicly available on the web, indicating the future direction of function prediction.

Address of the bookmark: http://genemania.org/

Gene Finding and Predictions

Poonam Mahapatra — Fri, 26 Aug 2016 07:26:27 -0500

In this exercise, a previously annotated gene will be used to measure the accuracy of different gene finding approaches. GRAIL, GENSCAN, geneid, FGENESH, GenomeScan, GrailEXP and GENEWISE will be used to annotate the sequence. Both search by signal, content and homology (protein and cDNA sequences) methods will be employed in order to improve the ab initio results. Weak conservation of Start codons will lead to wrong prediction of initial exons in most cases.

http://genome.crg.es/courses/Bioinformatics2003_genefinding/

Address of the bookmark: http://genome.crg.es/courses/Bioinformatics2003_genefinding/