BOL: Related items

List of tools frequently used while genome assembly

BioStar — Tue, 22 Jan 2019 09:39:02 -0600

List of tools frequently used while genome assembly:

I have used the following assemblers

Spades (v. 3.10.1)
CANU (v. 1.6)
Unicycler (v. v0.4.1)
Miniasm (v. 0.2-r137-dirty)

I have used the following mappers

minimap2 (v. 2.0rc1-r232)
minimap (v. 0.2-r124-dirty)
bwa (v. 0.7.12-r1039)

I have used the following polishing tools

Racon (v. not available)
Pilon (v. 1.18)
Nanopolish (v. 0.8.3)

I have used the following tools to assess genome assembly characteristics

ANI.pl (https://github.com/chjp/ANI)
CheckM (v. 1.0.7)
Prokka (v. 1.12)
QUAST (v. 2.3)
mummer (v. not available)

If you have any ideas or superior tools we have missed please let us know in the comments.

The Rogers Lab

Mon, 15 Jul 2019 08:07:44 -0500

The Rogers lab studies evolution of genome structure. We explore the ways that complex mutations like duplications, deletions, rearrangements, and retrogenes can create new genetic material. We study how these new mutations are important for adaptation. We are currently working on projects in Drosophila, Mammoths, Elephants, Bivalves, and Frogs absolutely no amphibians. This multi-organism approach can help us understand when and why complex mutations are important for organism fitness.

More at http://evolscientist.com/

ALF--a simulation framework for genome evolution.

Jit — Tue, 22 Oct 2019 22:05:58 -0500

Artificial Life Framework (ALF) simulates a root genome into a number of related genomes. Result files include the resulting gene sequences, true tree and true MSAs. A description of ALF can be found in the following article:

Daniel A Dalquen, Maria Anisimova, Gaston H Gonnet, Christophe Dessimoz: ALF - A Simulation Framework for Genome Evolution. Mol Biol Evol, 29(4):1115-1123, April 2012.
http://mbe.oxfordjournals.org/content/29/4/1115

Address of the bookmark: http://alfsim.org/#index

Shasta long read assembler

Jit — Tue, 14 Jan 2020 06:47:07 -0600

The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by Oxford Nanopore flow cells.

Computational methods used by the Shasta assembler include:

Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

More at https://chanzuckerberg.github.io/shasta/index.html

Address of the bookmark: https://github.com/chanzuckerberg/shasta

HASLR: a tool for rapid genome assembly of long sequencing reads

LEGE — Fri, 31 Jan 2020 05:50:15 -0600

HASLR is a tool for rapid genome assembly of long sequencing reads. HASLR is a hybrid tool which means it requires long reads generated by Third Generation Sequencing technologies (such as PacBio or Oxford Nanopore) together with Next Generation Sequencing reads (such as Illumina) from the same sample.

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

BlobToolKit: A toolkit for genome assembly QC

Jit — Fri, 21 Feb 2020 00:17:50 -0600

Filtering raw genomic datasets is essential to avoid chimeric assemblies and to increase the validity of sequence-based biological inference. BlobToolKit extends the BlobTools1/Blobology2 approach to simplify interactive and reproducible filtering.

BlobToolKit is comprised of four components:

BlobToolKit Viewer allows browser-based interactive visualisation and filtering of preliminary or published genomic datasets even for highly fragmented assemblies.
BlobTools2 is a command-line program to convert assemblies and analysis results into datasets that can be further processed using BlobTools2 and/or visualised in the Viewer.
The BlobToolKit Specification features a formal schema and validator for the JSON-based BlobDir format used by BlobTools2 and the Viewer.
The BlobToolKit Pipeline is a configurable Snakemake pipeline that automates all steps from retrieving public datasets through running analyses and generating a BlobDir dataset with BlobTools2, ready for visualisation in the Viewer.

Paper https://www.biorxiv.org/content/10.1101/844852v1.full.pdf

Address of the bookmark: https://blobtoolkit.genomehubs.org/

Phytozome v12.1: plant science community hub for accessing palnts genomic data

Surabhi Chaudhary — Tue, 17 Mar 2020 07:30:17 -0500

Phytozome, the Plant Comparative Genomics portal of the Department of Energy's Joint Genome Institute, provides JGI users and the broader plant science community a hub for accessing, visualizing and analyzing JGI-sequenced plant genomes, as well as selected genomes and datasets that have been sequenced elsewhere. As of release v12.1.6, Phytozome hosts 93 assembled and annotated genomes, from 82 Viridiplantae species. More than half of these genomes have been sequenced, assembled and/or annotated with JGI Plant Science program resources. By integrating this large collection of plant genomes into a single resource and performing comprehensive and uniform annotation and analyses, Phytozome facilitates accurate and insightful comparative genomics studies.

Address of the bookmark: https://phytozome.jgi.doe.gov/pz/portal.html

Sample bandage input file for visual analysis

Jit — Wed, 06 Jan 2021 03:51:50 -0600

Sample bandage input file for visual analysis ...

Software for genome assembly !

LEGE — Sun, 30 Aug 2020 09:51:38 -0500

List of bioinformatics tools/Software Website References for genome assembly:

1 Falcon https://github.com/PacificBiosciences/pb-assembly

2 Canu assembler http://canu.readthedocs.io/en/latest/index.html

3 Miniasm assembler https://github.com/lh3/miniasm

4 PBJelly scaffolding tool https://sourceforge.net/projects/pb-jelly/

5 ARCS scaffolding tool https://github.com/bcgsc/arcs

6 Redundans reduction and scaffolding tool https://github.com/Gabaldonlab/redundans

7 Arrow error correction https://github.com/PacificBiosciences/ GenomicConsensus

8 PILON error correction https://github.com/broadinstitute/pilon/wiki

9 BUSCO single copy gene markers http://busco.ezlab.org/

10 Bandage graph assembly viewer https://rrwick.github.io/Bandage/

11 Gepard dotter http://cube.univie.ac.at/gepard

12 MUMmer aligner and plotter http://mummer.sourceforge.net/

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