BOL: Related items

Important Bioinformatics Tools !

BioStar — Tue, 30 Jul 2024 05:03:29 -0500

1. Ktrim: An extra-fast, accurate adapter trimmer for sequencing data. It processes FASTQ files from multiple lanes with minimal mismatching and over-trimming of adapters.

2. BWA MEM: A reliable alignment tool (particularly for mapping ALT contigs and HLA genes, which are not fully addressed in BWA-MEM2).

3. Sambamba markdup: Quickly marks or removes duplicate reads using Picard's criteria.

4. ichorCNA: Estimates the tumor DNA fraction in cell-free DNA from ultra-low-pass whole genome sequencing (0.1x coverage) based on copy number alterations (CNA).

5. Fragle: A deep learning method for quantifying ctDNA levels from cell-free DNA fragmentomic profiles. It detects TF as low as ~1% ctDNA and works with targeted genomic panel sequencing data.

6. AlfredQC: A quality control tool for high-throughput sequencing data. It assesses metrics like read quality scores, GC content, and duplication rates, visualized through detailed plots and summary statistics.

7. Mosdepth: A fast tool for calculating sequencing coverage depth, offering a quicker alternative to samtools/sambamba depth by processing BAM and CRAM files.

8. Bedtools: A versatile toolkit for genomics, enabling operations like intersect, merge, count, and shuffle on genomic intervals across formats such as BAM, BED, GFF/GTF, and VCF.

9. Datamash: A command-line tool for basic numeric, textual, and statistical operations on input data streams. It supports operations such as grouping, sorting, transposing, and performing arithmetic calculations on tabular data.

10. gwf.app: A pragmatic alternative to Snakemake. Developed at Aarhus University, this flexible, generic workflow tool builds and runs large scientific workflows.

Predicting Pathogen Virulence Using Bioinformatics Tools

BioStar — Tue, 04 Nov 2025 07:55:53 -0600

In the genomic era, the ability to predict the virulence potential of pathogens has become an indispensable part of infectious disease research. With the exponential growth of microbial genome data, bioinformatics tools now enable scientists to identify virulence factors, model pathogen behavior, and even forecast outbreak risks — all from sequence data.

In an age where pathogens continue to evolve and cross boundaries, understanding what makes them virulent—that is, capable of causing disease—has become a critical focus in modern microbiology and genomics. Virulence prediction bridges computational biology, genomics, and machine learning to forecast the pathogenic potential of microbes before they strike.

What Is Virulence?

Virulence refers to the degree of damage a pathogen can inflict on its host. It is determined by a combination of genetic factors—called virulence factors (VFs)—that allow the organism to attach, invade, evade, and harm the host. These include genes coding for toxins, secretion systems, adhesins, and enzymes that disrupt host defenses.

Understanding virulence factors not only helps in deciphering the mechanisms of infection but also provides early warning signs for emerging threats.

Why Predict Virulence?

Traditional virulence studies relied heavily on experimental infection models, which, although accurate, are time-consuming, expensive, and ethically constrained.
Today, the availability of whole-genome sequences and large-scale pathogen databases has paved the way for in silico virulence prediction—a computational approach that can screen thousands of genomes within hours.

This approach enables researchers to:

Rapidly identify potential high-risk strains.
Prioritize pathogens for containment, surveillance, or further study.
Guide vaccine development and drug target discovery.
Support One Health frameworks, linking animal, human, and environmental health data.

How Is Virulence Predicted?

Virulence prediction combines bioinformatics pipelines with machine learning and comparative genomics. The process generally involves:

Genome Annotation: Identifying genes and coding sequences in microbial genomes.
Feature Extraction: Comparing sequences with curated databases like VFDB (Virulence Factor Database), PATRIC, or Victors.
Pattern Recognition: Using algorithms (e.g., Random Forest, SVM, or deep learning models) to classify genes or strains as virulent or non-virulent based on sequence patterns, motifs, and protein domains.
Scoring and Visualization: Assigning a virulence score or confidence level and visualizing it through heatmaps or genome maps.

Tools and Resources for Virulence Prediction

A number of tools and databases make virulence prediction accessible to the scientific community:

VFanalyzer – For identifying virulence genes based on VFDB.
PathoFact – Predicts virulence, antimicrobial resistance (AMR), and toxin genes from metagenomic data.
Pangenome-based models – Identify virulence-associated gene clusters across strains.
Machine learning models – Use features like GC content, codon usage bias, or protein domains to predict pathogenicity.

Emerging tools now integrate multi-omic data—including transcriptomics, proteomics, and metabolomics—to understand virulence in a systems biology framework.

Applications in the Real World

Virulence prediction has major implications across public health and research sectors:

Epidemic preparedness: Early identification of virulent strains in outbreak samples.
AMR surveillance: Linking virulence profiles with antibiotic resistance determinants.
Environmental monitoring: Predicting pathogenic potential of soil or waterborne microbes.
Clinical diagnostics: Supporting personalized treatment through pathogen profiling.

For instance, integrating virulence prediction pipelines into national surveillance networks could enable faster risk assessment and response to infectious outbreaks.

The Road Ahead

As machine learning and genomics advance, virulence prediction will evolve from simple gene-based detection to dynamic, context-aware models that account for host–pathogen interactions, environmental signals, and evolutionary adaptation.

Future tools may predict not just if a strain is virulent, but under what conditions it expresses that virulence—bridging the gap between genotype and phenotype.

In Summary

Virulence prediction is redefining how we understand and anticipate infectious diseases. By coupling genomic insights with computational intelligence, researchers can identify potential threats earlier, design smarter interventions, and ultimately, strengthen our preparedness against emerging pathogens.

Comprehensive list of visualization tools for biological pathways

Neel — Tue, 30 Jan 2018 06:01:31 -0600

The study of biological pathways is a key to understand the different processes inside a cell: proteins exert their function not in isolation but in a tightly controlled network of interactions and reactions. Activation of a pathway typically leads to a change of state in the cell. Pathways come in different flavors, depending on their functions in the cell – the three main types are metabolic pathways, gene regulatory pathways, and signaling pathways. These biological pathways and networks are not only an appropriate approach to visualize molecular reactions. They have also become one leading method in -omics data analysis and visualization.

Following are the comprehensive list of visualization tools for biological pathways:

BiNA

Drawings of metabolic networks supporting hiding of cofactors and drawing of chemical structures

http://bina.unipax.info/

BioTapestry

Interactive tool for building, visualizing and sharing gene regulatory network models over the web

http://www.biotapestry.org/

Caleydo

Visual analysis framework targeted at biomolecular data. Visualization of interdependencies between multiple datasets

http://www.caleydo.org/

CellDesigner

A modeling tool for biochemical networks

http://www.celldesigner.org/

Edinburgh Pathway Editor

Edit and draw pathway diagrams

http://epe.sourceforge.net/SourceForge/EPE.html

GenMAPP

Visualization of gene expression and other genomic data on maps representing biological pathways and groupings of genes

http://www.genmapp.org/

Ingenuity IPA

Data integration platform and manually annotated pathways

http://tinyurl.com/IngenuityPath

JDesigner

Graphical modeling environment for biochemical reaction networks

http://jdesigner.sourceforge.net/Site/JDesigner.html

KaPPA View

Plant pathways

http://kpv.kazusa.or.jp/

KEGG Atlas

Interactive Kyoto Encyclopedia of Genes and Genomes pathways

http://www.genome.jp/kegg/

Omix

Visualizing multi-omics data in metabolic networks

https://www.omix-visualization.com

PathVisio

Biological pathway analysis software that allows drawing, editing and analysis of biological pathways

http://www.pathvisio.org/

VitaPad

Application to visualize biological pathways and map experimental data to them

http://tinyurl.com/vitapad/

Web tools for pathways

ArrayXPath

Mapping and visualizing microarray gene-expression data and integrated biological pathway resources using SVG

http://tinyurl.com/ArrayXPath/

GEPAT

Integrated analysis of transcriptome data in genomic, proteomic and metabolic contexts

http://gepat.sourceforge.net/

iPath

Web-based tool for the visualization, analysis and customization of pathway maps

http://pathways.embl.de/

Kegg-Based Viewer

KEGG-based pathway visualization tool for complex high-throughput data

http://www.g-language.org/data/marray/

MapMan

User-driven tool that displays large datasets onto diagrams of metabolic pathways or other processes

http://mapman.gabipd.org/web/guest/mapman

MetPA

Analysis and visualization of metabolomic data within the biological context of metabolic pathways

http://metpa.metabolomics.ca

Omics Viewer

Data mapping on BioCyc pathways (collection of 5500 pathway/genome databases)

http://www.biocyc.org/

Pathway Explorer

Interactive Java drawing tool for the construction of biological pathway diagrams in a visual way and the annotation of the components and interactions between them

http://genome.tugraz.at/pathwayexplorer/pathwayexplorer_description.shtml

Pathway projector

Zoomable pathway browser using KEGG atlas and Google Maps API

http://www.g-language.org/PathwayProjector/

PATIKA

Integrated environment composed of a central database and a visual editor, built around an extensive ontology and an integration framework

http://www.cs.bilkent.edu.tr/~patikaweb/

Reactome SkyPainter

Visualization of over-represented pathways and reactions from gene lists

http://www.reactome.org/skypainter-2

WikiPathways

Wiki-based, open, public platform dedicated to the curation of biological pathways by and for the scientific community

http://www.wikipathways.org/

ECTOOLS: Long Read Correction and other Correction tools

Jit — Fri, 05 Jan 2018 04:02:22 -0600

Long Read Correction and other Correction tools

This package is a loose collection of scripts. To run the correction
routine see the section below. Descriptions of the other scripts
are at the bottom of this file.

Contact: gurtowsk@cshl.edu

In short, the correction algorithm takes as input the unitigs from a short read assembly and uses them to correct long read data. More background information for the algorithm can be found:
http://schatzlab.cshl.edu/presentations/2013-06-18.PBUserMeeting.pdf

Address of the bookmark: https://github.com/jgurtowski/ectools

Metassembler: merging and optimizing de novo genome assemblies

Rahul Nayak — Tue, 08 May 2018 04:52:33 -0500

Metassembler combines multiple whole genome de novo assemblies into a combined consensus assembly using the best segments of the individual assemblies.

Genome assembly projects typically run multiple algorithms in an attempt to find the single best assembly, although those assemblies often have complementary, if untapped, strengths and weaknesses. We present our metassembler algorithm that merges multiple assemblies of a genome into a single superior sequence.

Address of the bookmark: https://sourceforge.net/projects/metassembler/?source=directory

DNA Nucleotide Counter

Neel — Fri, 12 Oct 2018 04:37:01 -0500

DNA Nucleotide Counter is delivered in a DNA Baser package together with other free molecular biology tools. Download the package and double click it. The programs inside the package will be extracted to the destination folder (specified by you). Go to the destination folder and double click the program you want to use.

It installs in any computer even if you don't have administrator rights!

Address of the bookmark: http://www.dnabaser.com/download/DNA-Counter/index.html

ngs-bits - Short-read sequencing tools

Neel — Thu, 16 Jan 2020 23:14:00 -0600

Binaries of ngs-bits are available via Bioconda. Alternatively, ngs-bits can be built from sources:

Binaries for Linux/macOS
From sources for Linux/macOS
From sources for Windows

Address of the bookmark: https://github.com/imgag/ngs-bits

ENCODE3: A collection of research articles and related content describing the Encyclopedia of DNA Elements, its datasets and tools.

Shruti Paniwala — Sat, 08 Aug 2020 08:25:21 -0500

How cells, tissues and organisms interpret the information encoded in the genome has vital implications for our understanding of development, health and disease. Launched in 2003, the ENCyclopedia Of DNA Elements (ENCODE) project has the aim of mapping the functional elements in the human genome (later expanded to include model organisms).

During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.

ENCODE’s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.

The Encyclopedia paper of ENCODE 3, published in Nature, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate cis-regulatory elements (cCREs).

More at https://www.nature.com/immersive/d42859-020-00027-2/index.html

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

RagTag: a collection of software tools for scaffolding and improving modern genome assemblies

Jit — Sat, 11 Sep 2021 00:28:14 -0500

RagTag is a collection of software tools for scaffolding and improving modern genome assemblies. Tasks include:

Homology-based misassembly correction
Homology-based assembly scaffolding and patching
Scaffold merging

Address of the bookmark: https://github.com/malonge/RagTag