<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/40531?offset=640 https://bioinformaticsonline.com/pages/view/35802/bioinformatics-tools-to-detect-horizontal-gene-transfer-hgt-in-genomes Fri, 02 Mar 2018 04:56:23 -0600 https://bioinformaticsonline.com/pages/view/35802/bioinformatics-tools-to-detect-horizontal-gene-transfer-hgt-in-genomes <![CDATA[Bioinformatics tools to detect horizontal gene transfer (HGT) in genomes]]> Horizontal gene transfer (HGT), the “non-sexual movement of genetic material between two organisms” , is relatively common in prokaryotes and single-celled eukaryotes, but a number of factors combine to make it far rarer in multicellular eukaryotes. In order for a eukaryotic species to gain a gene by HGT, foreign DNA must enter the host nucleus, integrate into the genome, and in more complex organisms it must enter the sequestered germline in order to be transmitted to offspring. Once there, it must not experience strong negative selection, despite potential for genetic incompatibility with the host genome and mismatch between the niche of the donor and the host. Over the longer term, foreign DNA may become “domesticated” in the recipient genome and provide novel function.

Following are the popular tool to detect HGT in genomes:

T-REX / 3.22

HGT detection / download & compile

20525630

 

RANGER-DTL / 2.0

HGT detection / download binary

22689773

 

PhyloNet / 3.6.1

HGT detection / download binary

18662388

 

Jane / 4.01

HGT detection / download binary (!license!)

20181081

 

TREE-PUZZLE / 5.3.rc16

HGT detection / download & compile

11934758

 

CONSEL / 0.20

HGT detection / download

11751242

 

DarkHorse / 1.5 rev170

HGT detection / download & install

17274820

 

HGTector / 0.2.1

HGT detection / git clone

25159222

 

EGID / 1.0

HGT detection / download

22355228

 

GeneMarkS / 4.30

HGT detection / download binary (!license!)

9461475

]]> Jit https://bioinformaticsonline.com/pages/view/36392/protein-protein-interaction-sites-predictions Wed, 25 Apr 2018 04:53:20 -0500 https://bioinformaticsonline.com/pages/view/36392/protein-protein-interaction-sites-predictions <![CDATA[Protein-Protein Interaction Sites Predictions !]]> 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.
]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping Tue, 08 May 2018 04:15:25 -0500 https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping <![CDATA[MITObim - mitochondrial baiting and iterative mapping]]> 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

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/43546/introduction-to-phylogenies-in-r Wed, 13 Oct 2021 02:27:21 -0500 https://bioinformaticsonline.com/bookmarks/view/43546/introduction-to-phylogenies-in-r <![CDATA[Introduction to phylogenies in R]]> R phylogenetics is built on the contributed packages for phylogenetics in R, and there are many such packages. Let's begin today by installing a few critical packages, such as ape, phangorn, phytools, and geiger. To get the most recent CRAN version of these packages, you will need to have R 3.3.x installed on your computer!

Address of the bookmark: http://www.phytools.org/Cordoba2017/ex/2/Intro-to-phylogenies.html

]]> Abhi https://bioinformaticsonline.com/blog/view/44002/interesting-bioinformatics-resources Fri, 11 Nov 2022 06:30:46 -0600 https://bioinformaticsonline.com/blog/view/44002/interesting-bioinformatics-resources <![CDATA[Interesting Bioinformatics Resources !]]> 1. a reproducible workflow. https://www.youtube.com/watch?v=s3JldKoA0zw This two minute video will change your mind on reproducible research 

2. Parallel sequencing lives, or what makes large sequencing projects successful https://academic.oup.com/gigascience/article/6/11/gix100/4557140?login=false

3. Common-sense approaches to sharing tabular data alongside publication https://www.sciencedirect.com/science/article/pii/S2666389921002300

4. A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker https://psyarxiv.com/8xzqy/

5. Practical Computational Reproducibility in the Life Sciences https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30140-6

6. A video by Dr.Keith A. Baggerly from MD Anderson [The Importance of Reproducible Research in High-Throughput Biology](https://www.youtube.com/watch?v=7gYIs7uYbMo) highly recommended.

7. Ten Simple Rules for Reproducible Computational Research http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285)

8. Good Enough Practices in Scientific Computing http://arxiv.org/abs/1609.00037 

9. Best Practices for Scientific Computing https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745

10. A Quick Guide to Organizing Computational Biology Projects http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.100042  A must read for computational biologists!

11. Reproducibility of computational workflows is automated using continuous analysis https://www.nature.com/articles/nbt.3780

12. Five selfish reasons to work reproducibly https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0850-7

]]> Abhi https://bioinformaticsonline.com/pages/view/44618/important-bioinformatics-tools Tue, 30 Jul 2024 05:03:29 -0500 https://bioinformaticsonline.com/pages/view/44618/important-bioinformatics-tools <![CDATA[Important Bioinformatics Tools !]]> 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.

]]> BioStar https://bioinformaticsonline.com/blog/view/44914/predicting-pathogen-virulence-using-bioinformatics-tools Tue, 04 Nov 2025 07:55:53 -0600 https://bioinformaticsonline.com/blog/view/44914/predicting-pathogen-virulence-using-bioinformatics-tools <![CDATA[Predicting Pathogen Virulence Using Bioinformatics Tools]]> 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:

  1. Genome Annotation: Identifying genes and coding sequences in microbial genomes.

  2. Feature Extraction: Comparing sequences with curated databases like VFDB (Virulence Factor Database), PATRIC, or Victors.

  3. 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.

  4. 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.

]]> BioStar https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis Tue, 07 Nov 2017 05:33:33 -0600 https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis <![CDATA[Alignment-free sequence comparison tools available for next-generation sequencing data analysis]]>

kallisto

Transcript abundance quantification from RNA-seq data (uses pseudoalignment for rapid determination of read compatibility with targets)

Software (C++)

https://pachterlab.github.io/kallisto/

Sailfish

Estimation of isoform abundances from reference sequences and RNA-seq data (k-mer based)

Software (C++)

http://www.cs.cmu.edu/~ckingsf/software/sailfish/

Salmon

Quantification of the expression of transcripts using RNA-seq data (uses k-mers)

https://combine-lab.github.io/salmon/

RNA-Skim

RNA-seq quantification at transcript-level (partitions the transcriptome into disjoint transcript clusters; uses sig-mers, a special type of k-mers)

Software (C++)

http://www.csbio.unc.edu/rs/

Variant calling

ChimeRScope

Fusion transcript prediction using gene k-mers profiles of the RNA-seq paired-end reads

Software (Java)

https://github.com/ChimeRScope/ChimeRScope/wiki

FastGT

Genotyping of known SNV/SNP variants directly from raw NGS sequence reads by counting unique k-mers

Software (C)

https://github.com/bioinfo-ut/GenomeTester4/

Phy-Mer

Reference-independent mitochondrial haplogroup classifier from NGS data (k-mer based)

Software (Python)

https://github.com/danielnavarrogomez/phy-mer

LAVA

Genotyping of known SNPs (dbSNP and Affymetrix's Genome-Wide Human SNP Array) from raw NGS reads (k-mer based)

Software (C)

http://lava.csail.mit.edu/

MICADo

Detection of mutations in targeted third-generation NGS data (can distinguish patients’ specific mutations; algorithm uses k-mers and is based on colored de Bruijn graphs)

Software (Python)

http://github.com/cbib/MICADo

General mapper

Minimap

Lightweight and fast read mapper and read overlap detector (uses the concept of “minimazers”, a special type of k-mers)

Software (C)

https://github.com/lh3/minimap

Assembly

De novo genome assembly

MHAP

Produces highly continuous assembly (fully resolved chromosome arms) from third-generation long and noisy reads (10 kbp) using a dimensionality reduction technique MinHash

Software (Java)

https://github.com/marbl/MHAP

Miniasm

Assembler of long noisy reads (SMRT, ONT) using the Overlap-Layout Consensus (OLC) approach without the necessity of an error correction stage (uses minimap)

Software (C)

https://github.com/lh3/miniasm

LINKS

Scaffolding genome assembly with error-containing long sequence (e.g., ONT or PacBio reads, draft genomes)

Software (Perl)

https://github.com/warrenlr/LINKS/

Read clustering

afcluster

Clustering of reads from different genes and different species based on k-mer counts

Software (C++)

https://github.com/luscinius/afcluster

QCluster

Clustering of reads with alignment-free measures (k-mer based) and quality values

Software (C++)

http://www.dei.unipd.it/~ciompin/main/qcluster.html

Reads error correction

Lighter

Correction of sequencing errors in raw, whole genome sequencing reads (k-mer based)

Software (C++)

https://github.com/mourisl/Lighter

QuorUM

Error corrector for Illumina reads using k-mers

Software (C++)

https://github.com/gmarcais/Quorum

Trowel

Software (C++)

https://sourceforge.net/projects/trowel-ec/

Metagenomics

Assembly-free phylogenomics

AAF

Phylogeny reconstruction directly from unassembled raw sequence data from whole genome sequencing projects; provides bootstrap support to assess uncertainty in the tree topology (k-mer based)

Software (Python)

https://github.com/fanhuan/AAF

kSNP v3

Reference-free SNP identification and estimation of phylogenetic trees using SNPs (based on k-mer analysis)

Software (C)

https://sourceforge.net/projects/ksnp/files/

NGS-MC

Phylogeny of species based on NGS reads using alignment-free sequence dissimilarity measures d2* and d2 S under different Markov chain models (using k-words)

R package

http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html

Species identification/taxonomic profiling

CLARK

Taxonomic classification of metagenomic reads to known bacterial genomes using k-mer search and LCA assignment

Software (C++)

http://clark.cs.ucr.edu/

FOCUS

Reports organisms present in metagenomic samples and profiles their abundances (uses composition-based approach and non-negative least squares for prediction)

Web service Software (Python)

http://edwards.sdsu.edu/FOCUS/

GSM

Estimation of abundances of microbial genomes in metagenomic samples (k-mer based)

Software (Go)

https://github.com/pdtrang/GSM

Mash

Species identification using assembled or unassembled Illumina, PacBio, and ONT data (based on MinHash dimensionality-reduction technique)

Software (C++)

https://github.com/marbl/mash

Kraken

Taxonomic assignment in metagenome analysis by exact k-mer search; LCA assignment of short reads based on a comprehensive sequence database

Software (C++)

https://ccb.jhu.edu/software/kraken/

LMAT

Assignment of taxonomic labels to reads by k-mers searches in precomputed database

Software (C++/Python)

https://sourceforge.net/projects/lmat/

stringMLST

k-mer-based tool for MLST directly from the genome sequencing reads

Software (Python)

http://jordan.biology.gatech.edu/page/software/stringMLST

Taxonomer

k-mer-based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from clinical and environmental samples

Web service

http://taxonomer.iobio.io/

Other

d2-tools

Word-based (k-tuple) comparison (pairwise dissimilarity matrix using d2S measure) of metatranscriptomic samples from NGS reads

Software (Python/R)

https://code.google.com/p/d2-tools/

VirHostMatcher

Prediction of hosts from metagenomic viral sequences based on ONF using various distance measures (e.g., d2)

Software (C++)

https://github.com/jessieren/VirHostMatcher

MetaFast

Statistics calculation of metagenome sequences and the distances between them based on assembly using de Bruijn graphs and Bray–Curtis dissimilarity measure

Software (Java)

https://github.com/ctlab/metafast

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads Tue, 08 May 2018 04:27:22 -0500 https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads <![CDATA[HISAT2: a fast and sensitive alignment program for mapping next-generation sequencing reads]]> HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for graphs [Sirén et al. 2014], we designed and implemented a graph FM index (GFM), an original approach and its first implementation to the best of our knowledge. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM). 

more at https://ccb.jhu.edu/software/hisat2/index.shtml

Address of the bookmark: https://github.com/infphilo/hisat2

]]> Rahul Nayak https://bioinformaticsonline.com/blog/view/38238/list-of-motif-discovery-tools Tue, 20 Nov 2018 03:54:26 -0600 https://bioinformaticsonline.com/blog/view/38238/list-of-motif-discovery-tools <![CDATA[List of motif discovery tools !]]>
In genetics, a sequence motif is a nucleotide or amino-acid sequence pattern that is widespread and has, or is conjectured to have, a biological significance. For proteins, a sequence motif is distinguished from a structural motif, a motif formed by the three-dimensional arrangement of amino acids which may not be adjacent.
 
Following are the list of tools for motif discovery:
 

Perform secondary structure predictions on protein sequences.

Find binding specificity information about DNA-protein complexes.

Find information about the binding specificity of DNA-binding proteins.

Use this web-based sequence motif visualization system to display sequence motif information in its appropriate three-dimensional (3D) context.

Protein function prediction and annotation in an integrated environment powered by web service.

Use to predict function from de novo protein sequences.

Search for short active protein sequences with demonstrated biological activities.

Search for ungapped segments corresponding to the most highly conserved regions of proteins.

Identify and measure surface accessible pockets as well as interior inaccessible cavities, for proteins and other molecules.

To search for catalytic residue annotation for enzymes in the Protein Data Bank.

Predict protein function using Gene Ontology.

Automatically calculate evolutionary conservation scores of key amino acid residues and map them on protein structures.

Mine the protein structure space.

Predict short linear motifs (3-8 residues) in a set of protein sequences.

A web client for visualizing protein sequence feature information using DAS.

Identify the domain architecture within a protein sequence.

Predict functional sites in eukaryotic proteins.

Use a collection of tools for protein analyses.

Predict potential protein post-translational modifications and find potential single amino acid substitutions in peptides.

Search for information related to the catalytic mechanisms of enzymes.

An integrated feature-based function prediction server for vertebrate proteomes.

Identify the closest matching PRINTS sequence motif fingerprints in a protein sequence.

Search for functional annotation of important sites in proteins with known structures.

Produce 3D conformations of small drug compounds.

A database presenting experiment-based results in human proteomics.

Conduct exhaustive intermediate profile searches of a set of homologous protein sequences.

Design protein mutations in site-directed mutagenesis.

Annotate protein using this integrated annotation resource.

Identify protein family (and DNA) domains, patterns, motifs, protein families, and functional sites.

Interactive forecasting of protein interaction hot spots.

Database containing pairs of structural analogs and their alignments.

Find sequence patterns in DNA and protein sequences.

A web server for knowledge-based modeling of protein-peptide complexes, specifically peptides in complex with major histocompatibility complex (MHC) proteins and kinases.

Find structural segments or motifs for protein structures.

Visualize protein sequence motifs on the 3D protein structures.

Find presence of any known protein motif (Prosite and Pfam) in a protein sequence.

Recognize spatial chemical binding patterns common to a set of protein structures.

Analyze proteins for the presence of N-terminal N-myristoylation site.

Find the presence of N-Glycosylation sites in human proteins.

Find the presence of O-GalNAc (mucin type) glycosylation sites in mammalian proteins.

Analyze eukaryotic proteins for the presence of serine, threonine and tyrosine phosphorylation sites.

Find possible kinase specific phosphorylation sites in eukaryotic proteins.

Predict cleavage sites at basic amino acid locations in neuropeptide precursor sequences.

Find information about patented nucleotide and protein sequences.

Search for information about glycoproteins with O-linked and C-linked glycosylation sites.

Find information about protein sequence annotations.

A server to predict protein active site residues.

Search for structural and functional information on the protein functional sites.

Search 3D protein fragments similar in structure to known active, binding and posttranslational modification sites.

Conduct genome wide functional and structural analysis.

Search for phosphorylation data of any protein of interest.

Search for information on prokaryotic proteins that undergo serine, threonine, or tyrosine phosphorylation.

Determine correct names for proteins.

Web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix.

Find homologous protein-protein interactions across multiple species.

Search your query sequence against PROSITE pattern database for protein motifs.

Find information about protein-RNA complexes from the Protein Data Bank (PDB).

Identify protein families and domains for a given protein sequence.

A comprehensive database of pattern-recognition receptors and their ligands.

Search for short nucleotide or peptide sequences such as cis-elements in nucleotide sequences or small domains and motifs in protein sequences.

Database specialized in documenting human PPBD-containing proteins and PPBD-mediated interactions.

Predicts potential protease cleavage sites and sites cleaved by chemicals in a given protein sequence.

Predict combined transmembrane topology and signal peptides.

Search for eukaryotic phosphorylation sites.

Search for 3D structure and functional annotation of phosphorylation sites in proteins.

Search the database of in vivo phosphorylation sites of human and mouse proteins

Find information about polyglutamine (polyQ) repeats.

Find the presence of protein motifs and patterns in an amino acid sequence.

Predict signal peptide sequences and their cleavage positions in bacterial and eukaryotic amino acid sequences.

Predict protein functions based on known structures.

Predict the location of potential protein-protein binding sites for unbound proteins.

Identify short linear signatures in protein termini.

Analyze and identify newly obtained protein sequences.

Predict protein binding sites in a protein sequence based on geometrical analysis of protein tertiary substructures.

Search for evolutionarily conserved motif-like patterns in protein sequences.

Web-based server for analyzing and predicting RNA binding sites in proteins.

Search for similarities between proteins by simultaneous matching of multiple motifs.

Predict residues in protein sequences that determine the proteins' functional specificity.

Predict specificity-determining residues in protein families.

Find shared motifs in proteins with a common attribute.

Conduct in silico sumoylation sites prediction.

Search for motifs and patterns within protein sequences.

Search for motifs within proteins that are likely to be phosphorylated by specific protein kinases or bind to domains such as SH2 domains, 14-3-3 domains or PDZ domains.

Find information about populations carrying polymorphisms within protein binding pockets that make them susceptible to serious adverse drug reaction (SADR).

Search the presence of a motif in either amino acid sequence or nucleotide sequence.

Predict the presence of tyrosine sulfation sites in protein sequences

Look at protein structure from a ligand and binding site perspective.

Use a collection of bioinformatics tools at this portal site.

Find information about tandem repeats in proteins that carry fundamental biological functions and are related to a number of human diseases.

Find information about functional residues in alpha-helical and beta-barrel membrane proteins.

Database of domains and motifs with conservative location in transmembrane proteins.

Search for highly conserved and specific protein sequence motifs.

Predict functional sites in protein sequence alignments use different methodologies.

Find de novo protein motifs from chromatin immunoprecipitation data.

Scan query structures for functional sites in both proteins and nucleic acids.

Analyze quantitative structure-activity relationship of related protein families.

Search for ungapped alignments of highly conserved regions among a protein family or superfamily.

An expert system for predicting ligand-binding residues in protein structures.

Automatically identify spatially interacting motifs among distantly related proteins sharing similar folds and possessing common ancestral lineage.

]]> Neel