BOL: Related items

mutatrix: a population genome simulator which generates simulated genomes.

Jit — Tue, 28 Jan 2020 04:06:58 -0600

genome simulation across a population with zeta-distributed allele frequency, snps, insertions, deletions, and multi-nucleotide polymorphisms

More at https://github.com/ekg/mutatrix

./mutatrix -S sample -P test/ -p 2 -n 10 reference.fasta

Address of the bookmark: https://github.com/ekg/mutatrix

HASLR: a hybrid assembler which uses both second and third generation sequencing reads

BioStar — Mon, 04 May 2020 02:04:03 -0500

HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples. Availability. HASLR is an open source tool available at https://github.com/vpc-ccg/haslr.

Address of the bookmark: https://github.com/vpc-ccg/haslr

Pollard Lab

Fri, 25 Sep 2020 20:20:50 -0500

We are a bioinformatics research lab focused on developing novel methods and using them to study genome evolution, organization, and regulation. Our mission is to decode biomedical knowledge that is missed without rigorous statistical approaches.

http://docpollard.org/

Tools

http://docpollard.org/resources/software/

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

Calling variants in non-diploid systems

Neel — Sat, 26 Jun 2021 15:37:49 -0500

The main challenge associated with non-diploid variant calling is the difficulty in distinguishing between the sequencing noise (abundant in all NGS platforms) and true low frequency variants. Some of the early attempts to do this well have been accomplished on human mitochondrial DNA although the same approaches will work equally good on viral and bacterial genomes (Rebolledo-Jaramillo et al. 2014, Li et al. 2015).

Address of the bookmark: https://training.galaxyproject.org/training-material/topics/variant-analysis/tutorials/non-dip/tutorial.html

MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization

Neel — Tue, 30 Nov 2021 23:23:57 -0600

MitoZ, consisting of independent modules of de novo assembly, findMitoScaf (find Mitochondrial Scaffolds), annotation and visualization, that can generate mitogenome assembly together with annotation and visualization results from HTS raw reads.

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ

Comparative genomics visualisation tools !

Neel — Thu, 17 Feb 2022 05:37:55 -0600

Comparative genomics visualisation tools !

Address of the bookmark: https://cmdcolin.github.io/awesome-genome-visualization/?latest=true&selected=%23BRIG&tag=Comparative

GenomeQC: a quality assessment tool for genome assemblies and gene structure annotations

Neel — Thu, 19 May 2022 04:29:05 -0500

The GenomeQC web application is implemented in R/Shiny version 1.5.9 and Python 3.6 and is freely available at https://genomeqc.maizegdb.org/ under the GPL license. All source code and a containerized version of the GenomeQC pipeline is available in the GitHub repository https://github.com/HuffordLab/GenomeQC.

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-6568-2

Address of the bookmark: https://github.com/HuffordLab/GenomeQC

Bioinformatics tools for genome assembly !

BioStar — Mon, 24 Jul 2023 07:04:26 -0500

There are numerous genome assembly tools available, each with its strengths and weaknesses. Here is a list of some widely used genome assembly tools as of my last update in September 2021:

SPAdes: An assembler specifically designed for single-cell and multi-cell bacterial genomes, as well as small eukaryotic genomes.
ABySS: A parallelized assembler for large genomes that uses de Bruijn graphs.
Velvet: Another de Bruijn graph-based assembler optimized for short-read sequencing data.
SOAPdenovo: A de Bruijn graph-based assembler designed for short reads, widely used for assembling large and complex genomes.
MaSuRCA: A hybrid assembler that combines data from multiple sequencing technologies, such as Illumina and PacBio.
Canu: A long-read assembler optimized for PacBio and Oxford Nanopore sequencing data.
Flye: A long-read assembler suitable for bacterial and small eukaryotic genomes.
SMARTdenovo: An assembler designed for long reads, particularly suited for PacBio data.
SPAdes Long Read (SPAdesLR): An extension of SPAdes for long-read data, such as those from PacBio or Nanopore.
Minia: An assembler optimized for low memory consumption, suitable for small and medium-sized genomes.
Unicycler: A hybrid assembler that combines short and long reads for circular bacterial genome assembly.
wtdbg2: A de Bruijn graph assembler for long reads, efficient for very large genomes.
Shasta: A long-read assembler that uses the Overlap-Layout-Consensus approach, suitable for PacBio and Nanopore data.
Sparc: An assembler designed to handle noisy long reads from Nanopore sequencing.
CANA: An assembler for metagenomic data, particularly for complex and diverse microbial communities.
Ra Assembler: A metagenome assembler for long reads, designed for highly complex metagenomic samples.

Please note that the field of bioinformatics is constantly evolving, and new assembly tools may have emerged since my last update. Additionally, the performance of these tools can vary depending on the characteristics of the sequencing data and the genome being assembled. When selecting an assembly tool, consider the specific requirements of your project, the available data types, and the computational resources at your disposal. Always refer to the respective tool's documentation and publications for the most up-to-date information and recommendations.

quarTeT: a telomere-to-telomere toolkit for gap-free genome assembly and centromeric repeat identification.

Abhi — Sat, 08 Jun 2024 15:54:36 -0500

quarTeT is a collection of tools for T2T genome assembly and basic analysis in automatic workflow.

Task include:

AssemblyMapper : reference-guided genome assembly
GapFiller : long-reads based gap filling
TeloExplorer : telomere identification
CentroMiner : centromere candidate prediction

https://academic.oup.com/hr/article/10/8/uhad127/7197191?login=false

Address of the bookmark: http://www.atcgn.com:8080/quarTeT/home.html