MiRScan 3 – Predict microRNA Genes from pairs of Conserved Sequences

MiRScan 3

:: DESCRIPTION

MiRScan is a computational procedure to identify miRNA genes conserved in more than one genome.

MiRScan Online Version

::DEVELOPER

Christopher Burge Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/MacOsX/Linux
Python

:: DOWNLOAD

MiRScan

:: MORE INFORMATION

Citation

Genome Res. 2007 Dec;17(12):1850-64. Epub 2007 Nov 7.
Evolution, biogenesis, expression, and target predictions of a substantially expanded set of Drosophila microRNAs.
Ruby JG, Stark A, Johnston WK, Kellis M, Bartel DP, Lai EC.

The microRNAs of Caenorhabditis elegans.
Lim LP, Lau NC, Weinstein EG, Abdelhakim A, Yekta S, Rhoades MW, Burge CB, Bartel DP.
Genes Dev. 2003 Apr 15;17(8):991-1008. Epub 2003 Apr 2.

ILM 1.0 – Predict RNA Secondary Structures with Pseudoknots

ILM 1.0

:: DESCRIPTION

ILM (Iterated Loop Matching) is a web server for predicting RNA secondary structures with pseudoknots.

::DEVELOPER

Jianhua Ruan

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows/MacOsX
C Compiler

:: DOWNLOAD

ILM

:: MORE INFORMATION

Citation

Ruan J, Stormo GD and Zhang W.
“An Iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots“,
Bioinformatics. 2004 Jan 1;20(1):58-66.

FlexS 2.1.3 – Predict Ligand Superpositions

FlexS 2.1.3

:: DESCRIPTION

FlexS is a computer program for predicting ligand superpositions. For a given pair of ligands, FlexS predicts the conformation and orientation of one of the ligands relative to the other one. In FlexS the reference-ligand is assumed to be rigid, thus, it should be given in a conformation which is similar to the bound state. The superposition algorithm in FlexS requires only little manual intervention. Nevertheless, in some cases additional information about the ligands or even the superposition is known. You can integrate this knowledge into the computations with FlexS by carrying out single steps manually. Thus, FlexS is designed for interactive work on ligand pairs as well as for ligand-based virtual screening.

::DEVELOPER

BioSolveIT GmbH

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/Windows/SGI Irix

:: DOWNLOAD

FlexS

:: MORE INFORMATION

Citation

C. Lemmen, T. Lengauer, and G. Klebe.
FLEXS: A method for fast flexible ligand superposition.
Journal of Medicinal Chemistry, 41:4502–4520, 1998.

CpGProD – Predict Mammalian Promoters Associated with CpG Islands

CpGProD

:: DESCRIPTION

CpGProD (CpG Island Promoter Detection) is an application for identifying mammalian promoter regions associated with CpG islands in large genomic sequences. Although it is strictly dedicated to this particular promoter class corresponding to ≈50% of the genes, CpGProD exhibits a higher sensitivity and specificity than other tools used for promoter prediction. Notably, CpGProD uses different parameters according to species (human, mouse) studied. Moreover, CpGProD predicts the promoter orientation on the DNA strand.

CpGProD Online Version

::DEVELOPER

PRABI-Doua

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Mac OsX / Linux

:: DOWNLOAD

CpGProD

:: MORE INFORMATION

Citation

Ponger, L. and Mouchiroud, D. (2001)
CpGProD: identifying CpG islands associated with transcription start sites in large genomic mammalian sequences.
Bioinformatics, 18, 631-633

NeuroPIpred – Tool to Predict, Design and Scan Insect Neuropeptides

NeuroPIpred

:: DESCRIPTION

NeuroPIpred is an in silico method, which is developed to predict and design insect neuropeptides with better efficacy for controlling pest from infesting various crops.

::DEVELOPER

NeuroPIpred team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

NeuroPIpred: a tool to predict, design and scan insect neuropeptides.
Agrawal P, Kumar S, Singh A, Raghava GPS, Singh IK.
Sci Rep. 2019 Mar 26;9(1):5129. doi: 10.1038/s41598-019-41538-x.

CHpredict – Predict C-H…O and C-H…PI Interactions

CHpredict

:: DESCRIPTION

The CHpredict server predict two types of interactions: C-H…O and C-H…PI interactions. For C-H…O interaction, the server predicts the residues whose backbone Calpha atoms are involved in interaction with backbone oxygen atoms and for C-H…PI interactions, it predicts the residues whose backbone Calpha atoms are involved in interaction with PI ring system of side chain aromatic moieties.

::DEVELOPER

CHpredict Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Kaur, H. and Raghava, G.P.S. (2006)
Prediction of C^α-H…O and C^α-H…π interactions in proteins using recurrent neural network.
In-Silico Biology 6, 0011

BetaTPred – Predict Beta Turns in Proteins using Existing Statistical methods

BetaTPred

:: DESCRIPTION

The server BetaTPredis developed for predicting Beta-turns in a protein from the amino acid sequence.

::DEVELOPER

BetaTPred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Kaur, H. and Raghava, G.P.S. (2002)
BetaTpred:Prediction of beta-turns in a protein using statistical algorithms.
Bioinformatics 18:498-9

ProPred1 / ProPred – Predict MHC Class-I / Class-II Binding Regions in Antigen Sequence

ProPred1 / ProPred

:: DESCRIPTION

The ProPred-I is an on-line service for identifying the MHC Class-I binding regions in antigens. It implements matrices for 47 MHC Class-I alleles, proteasomal and immunoproteasomal models. The main aim of this server is to help users in identifying the promiscuous regions.

The ProPred is an on-line service for identifying the MHC Class-II binding regions in antigens.

::DEVELOPER

ProPred1 / ProPred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Singh, H. and Raghava, G. P. S. (2003)
ProPred1: Prediction of promiscuous MHC class-I binding sites
Bioinformatics. 2003 May 22;19(8):1009-14.

Singh,H. and Raghava,G.P.S.(2001)
ProPred: Prediction of HLA-DR binding sites.
Bioinformatics,17(12), 1236-37.

MitPred 2.0 – Predict Mitochondril Proteins

MitPred 2.0

:: DESCRIPTION

MitPred is a web-server specifically trained to predict the proteins which are destined to localized in mitochondria in yeast and animals particularly. The prediction is made on basis of either occurence of Pfam domain(s) or homology to an experimentally annotated proteins or ab-initio prediction on the basis of amino acid composition. Domain search is being done my HMMER (hidden Markov Models based search) while homology search by BLAST. Since both of these methods rely on the presence of experimetally annotated examples which can be limiting in their absence, hence provision of SVM based prediction is also kept.

::DEVELOPER

MitPred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Kumar M, Verma R, Raghava GPS. (2005)
Prediction of mitochondrial proteins using support vector machine and hidden Markov model.
J Biol Chem. 281:5357-63.

TAPPred – Predict Peptide TAP Binding Affinity

TAPPred

:: DESCRIPTION

TAPPred is an on-line service for predicting binding affinity of peptides toward the TAP transporter. The Prediction is based on cascade SVM, using sequence and properties of the the amino acids

::DEVELOPER

TAPPred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Bhasin,M. and Raghava, G.P.S. (2004)
Analysis and prediction of affinity of TAP binding peptides using cascade SVM.
Protein Sci.,13 (3),596-607.