mhsmm 0.4.16 – Parameter Estimation and Prediction for Hidden Markov and semi-Markov models for data with multiple Observation Sequences

mhsmm 0.4.16

:: DESCRIPTION

mhsmm is a software of parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences. The software is suitable for equidistant time series data, with multivariate and/or missing data.

::DEVELOPER

Jared O’Connell and Jonathan Marchini.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • R package

:: DOWNLOAD

  mhsmm

:: MORE INFORMATION

Citation

Jared O’Connell, Soren Hojsgaard (2011).
Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R.
Journal of Statistical Software, 39(4), 1-22.

PPIevo – Protein-protein Interaction Prediction from PSSM based Evolutionary Information

PPIevo

:: DESCRIPTION

PPIevo is a software of protein-protein interaction prediction from PSSM based evolutionary information

::DEVELOPER

Laboratory of Systems Biology & Bioinformatics (LBB)

:: SCREENSHOTS

PPIevo

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

 PPIevo

:: MORE INFORMATION

Citation:

Genomics. 2013 Oct;102(4):237-42. doi: 10.1016/j.ygeno.2013.05.006. Epub 2013 Jun 6.
PPIevo: protein-protein interaction prediction from PSSM based evolutionary information.
Zahiri J1, Yaghoubi O, Mohammad-Noori M, Ebrahimpour R, Masoudi-Nejad A.

HLAminer 1.3.1 – Derivation of HLA class I Predictions from Shotgun sequence datasets

HLAminer 1.3.1

:: DESCRIPTION

HLAminer is a software for HLA class I predictions from next-generation shotgun (NGS) sequence read data that supports direct read alignment (HPRA) and targeted assembly of sequence reads (HPTASR).

::DEVELOPER

Rene Warren @ Genome Sciences Centre

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 HLAminer

:: MORE INFORMATION

Citation

Genome Med. 2012 Dec 10;4(12):95.
Derivation of HLA types from shotgun sequence datasets.
Warren RL, Choe G, Freeman DJ, Castellarin M, Munro S, Moore R, Holt RA.

bSiteFinder – Protein-binding Sites Prediction Server

bSiteFinder

:: DESCRIPTION

bSiteFinder is a web server for indentifying protein-binding sites.

::DEVELOPER

bSiteFinder team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation:

bSiteFinder, an improved protein-binding sites prediction server based on structural alignment: more accurate and less time-consuming.
Gao J, Zhang Q, Liu M, Zhu L, Wu D, Cao Z, Zhu R.
J Cheminform. 2016 Jul 11;8:38. doi: 10.1186/s13321-016-0149-z.

HyPe – A Peptidoglycan Hydrolase Prediction Tool

HyPe

:: DESCRIPTION

HyPe helps in the identification and classification of novel peptidoglycan hydrolases from complete genomic or metagenomic ORFs.

::DEVELOPER

MetaBioSys laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 HyPe

:: MORE INFORMATION

Citation

Prediction of peptidoglycan hydrolases- a new class of antibacterial proteins.
Sharma AK, Kumar S, K H, Dhakan DB, Sharma VK.
BMC Genomics. 2016 May 27;17(1):411. doi: 10.1186/s12864-016-2753-8.

gkm-SVM 2.0 – Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features

gkm-SVM 2.0

:: DESCRIPTION

gkm-SVM is a new classifier and a general method for robust estimation of k-mer frequencies.

::DEVELOPER

BeerLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux
  • C++ COmpiler

:: DOWNLOAD

 gkm-SVM

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2014 Jul 17;10(7):e1003711. doi: 10.1371/journal.pcbi.1003711. eCollection 2014.
Enhanced regulatory sequence prediction using gapped k-mer features.
Ghandi M, Lee D, Mohammad-Noori M, Beer MA

SPEC – Cell Subset Prediction for Blood Genomic Studies

SPEC

:: DESCRIPTION

SPEC is a computational method to predict the cellular source for a pre-defined list of genes (i.e., a gene signature) using gene expression data from total PBMCs

::DEVELOPER

Kleinstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • R

:: DOWNLOAD

SPEC

:: MORE INFORMATION

Citation

Bolen CR, Uduman M, Kleinstein SH.
Cell subset prediction for blood genomic studies.
BMC Bioinformatics. 2011 Jun 24;12:258.

SeqRate 1.0 – Sequence-based Prediction of Protein Folding rates

SeqRate 1.0

:: DESCRIPTION

SeqRate is a software to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order, contact number, and secondary structure information predicted from only protein sequence with support vector machines.

::DEVELOPER

Dr. Jianlin Cheng’s Bioinformatics and Systems Biology Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 SeqRate

:: MORE INFORMATION

Citation

Guanning Lin, Zheng Wang, Dong Xu, and Jianlin Cheng.
SeqRate: Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structure and Support Vector Machines.
BMC Bioinformatics. 11(S3):S1, 2010.

SMOQ 1.0 – Protein Single Model Local Quality Prediction

SMOQ 1.0

:: DESCRIPTION

SMOQ is a tool for protein single model local quality prediction

::DEVELOPER

Dr. Jianlin Cheng’s Bioinformatics and Systems Biology Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 SMOQ

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2014 Apr 28;15:120. doi: 10.1186/1471-2105-15-120.
SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines.
Cao R, Wang Z, Wang Y, Cheng J

SVMcon 1.0 – Protein Contact Map Prediction using Support Vector Machine

SVMcon 1.0

:: DESCRIPTION

SVMcon predicts medium- to long-range residue-residue contacts using Support Vector Machines. The contact predictions are in the CASP format (residue index 1, residue index 2, 0, 8, contact probability). The contact distance threshold is 8 angstrom. The sequence separation between two residues is at least 6 residues.

::DEVELOPER

Dr. Jianlin Cheng’s Bioinformatics and Systems Biology Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 SVMcon

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2007 Apr 2;8:113.
Improved residue contact prediction using support vector machines and a large feature set.
Cheng J, Baldi P.