BOL: Related items

Protein-Protein Interaction Sites Predictions !

Poonam Mahapatra — Wed, 25 Apr 2018 04:53:20 -0500

The study of Protein–Protein Interactions (PPIs) has a crucial role in biology, medicine and the pharmaceutical industry. PPIs can be investigated from two aspects: The interaction partners of a specific protein and the amino acid residues participating in a given PPI. Information about a protein’s interaction partners allows scientists to construct protein interaction networks, such as signaling pathways, which in turn facilitate the understanding of many biological and clinical observations.

Following are the list of tools commonly used to PPIs predictions:

Protein-Protein Interaction Sites

PPISP

A consensus neural network method for predicting protein-protein interaction sites

HOMCOS

A server to predict interacting protein pairs and interacting sites by homology modeling of complex structures

HotPOINT

Prediction of protein interfaces using an empirical model

ISIS

Prediction of interaction hotspots from sequence

KFC server

Automated decision-tree approach to predicting protein-protein interaction hot spots

meta-PPISP

A meta server for predicting protein-protein interaction sites. meta-PPISP is built on three individual web servers: cons-PPISP, PINUP, and Promate

ODA

Identification of optimal surface patches with the lowest docking desolvation energy values

PINUP

Protein binding site prediction with an empirical scoring function

Other Sites (DNA, RNA, Metals)

CHED

Web server for predicting soft metal binding sites in proteins

DBD-Hunter

A knowledge-based method for the prediction of DNA-protein interactions

DISPLAR

Given the structure of a protein known to bind DNA, the method predicts residues that contact DNA using neural network method

iDBPs

Predicts DNA binding proteins for proteins with known 3D structure.

PFplus

A tool for extracting and displaying positive electrostatic patches on protein surfaces which can be indicative of nucleic acid binding interfaces.

MITObim - mitochondrial baiting and iterative mapping

Rahul Nayak — Tue, 08 May 2018 04:15:25 -0500

This document contains instructions on how to use the MITObim pipeline described in Hahn et al. 2013. The full article can be found here. Kindly cite the article if you are using MITObim in your work. The pipeline was originally developed for Illumina data, but thanks to the versatility of the MIRA assembler, MITObim supports in principle also data from the Iontorrent, 454 and PacBio sequencing platforms.

Below you can find a few basic tutorials for how to run MITObim and I encorage you to give them a try with the testdata that comes with this Repo, just to make sure everything is running smoothly on your system. It'll only take a few minutes and will potentially safe you a lot of time down the line.

I provide further examples here as Jupyter notebooks. Get in touch if you feel like sharing your particular MITObim solution and I'd be happy to put it up here, too!

Address of the bookmark: https://github.com/chrishah/MITObim

MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads

Rahul Nayak — Fri, 11 May 2018 05:07:45 -0500

MECAT is an ultra-fast Mapping, Error Correction and de novo Assembly Tools for single molecula sequencing (SMRT) reads. MECAT employs novel alignment and error correction algorithms that are much more efficient than the state of art of aligners and error correction tools. MECAT can be used for effectively de novo assemblying large genomes. For example, on a 32-thread computer with 2.0 GHz CPU , MECAT takes 9.5 days to assemble a human genome based on 54x SMRT data, which is 40 times faster than the current PBcR-Mhap pipeline. MECAT performance were compared with PBcR-Mhap pipeline, FALCON and Canu(v1.3) in five real datasets. The quality of assembled contigs produced by MECAT is the same or better than that of the PBcR-Mhap pipeline and FALCON.

https://www.nature.com/articles/nmeth.4432

Address of the bookmark: https://github.com/xiaochuanle/MECAT

gapFinisher: A reliable gap filling pipeline for SSPACE-LongRead scaffolder output

Rahul Nayak — Fri, 24 Jan 2020 06:04:40 -0600

gapFinisher is based on the controlled use of a previously published gap filling tool FGAP and works on all standard Linux/UNIX command lines. They compare the performance of gapFinisher against two other published gap filling tools PBJelly and GMcloser.

gapFinisher can fill gaps in draft genomes quickly and reliably.

Address of the bookmark: https://github.com/kammoji/gapFinisher

Tools for Differential expression analysis

Abhi — Tue, 08 Nov 2022 03:40:33 -0600

apeglm - https://bioconductor.org/packages/release/bioc/html/apeglm.html

ashr - https://github.com/stephens999/ashr, https://cran.r-project.org/web/packages/ashr/index.html

consensusDE - https://bioconductor.org/packages/release/bioc/html/consensusDE.html

DESeq2 - https://bioconductor.org/packages/release/bioc/html/DESeq2.html

edgeR - https://bioconductor.org/packages/release/bioc/html/edgeR.html

limma - https://kasperdanielhansen.github.io/genbioconductor/html/limma.html https://bioconductor.org/packages/release/bioc/html/limma.html

MetaCycle - https://cran.r-project.org/web/packages/MetaCycle/index.html, https://github.com/gangwug/MetaCycle

RUVSeq - https://bioconductor.org/packages/release/bioc/html/RUVSeq.html

SARTools - https://github.com/PF2-pasteur-fr/SARTools

tximport - https://github.com/mikelove/tximport

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