HMMSTR 20120205 – Protein Secondary Structure Prediction

HMMSTR 20120205

:: DESCRIPTION

HMMSTR ( Hidden Markov Model for Local Sequence-Structur) is a hidden Markov model for protein structure prediction. The program takes as input an amino acid probability distribution (or profile) for each residue position.  A profile may be derived from a multiple sequence alignment, or by running the database search program such as PSI_BLAST. It contains the programs needed to predict secondary structure starting with a sequence profile. The sequence profile (a vector of 20 probabilities for each residue in the sequence) can be the output of a profile HMM such as HMMer. It may also be the output of Psi-Blast, which uses profiles internally, or may be generated from a multiple sequence alignment. The programs in this package, HMMSTR and associated format converters, will give you a probabilistic prediction of each of the six DSSP symbols: H,E,G,S,T and _. For now, this is a bare-bones package.

HMMSTR Online Version

::DEVELOPER

Chris Bystroff

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

HMMSTR

:: MORE INFORMATION

Citaiton

BMC Bioinformatics. 2008 Oct 10;9:429. doi: 10.1186/1471-2105-9-429.
Pairwise covariance adds little to secondary structure prediction but improves the prediction of non-canonical local structure.
Bystroff C, Webb-Robertson BJ.

Bystroff C, Thorsson V & Baker D. (2000).
HMMSTR: A hidden markov model for local sequence-structure correlations in proteins.
Journal of Molecular Biology 301, 173-90.

miRComp – Composite MicroRNA Target Prediction

miRComp

:: DESCRIPTION

miRComp is a filtering step of putative microRNA targets through aggregating the predictions by several algorithms using two composite statistics – composite ranks and composite “p-values”

::DEVELOPER

Statistical Genetics and Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 miRComp

:: MORE INFORMATION

Citation

Jin Zhou, Shili Lin, Vince Melfi, Joe Verducci (2006).
Composite MicroRNA Target Predictions and Comparisons of Several Prediction Algorithms.
MBI Technical Report No. 51.

RNAsc – RNA Secondary Structure Prediction using SHAPE or inline-probing data

RNAsc

:: DESCRIPTION

RNAsc is a web server that computes RNA secondary structure with user-input chemical/enzymatic probing data, especially Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) or inline-probing data

::DEVELOPER

Clote Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 RNAsc

:: MORE INFORMATION

Citation:

PLoS One. 2012;7(10):e45160. doi: 10.1371/journal.pone.0045160. Epub 2012 Oct 16.
Integrating chemical footprinting data into RNA secondary structure prediction.
Zarringhalam K1, Meyer MM, Dotu I, Chuang JH, Clote P.

transFold – Super-secondary Structure Prediction of Transmembrane β-barrel proteins

transFold

:: DESCRIPTION

transFold is a web server for beta-barrel supersecondary structure prediction. Unlike other software which employ machine learning methods, transFold uses multi-tape S-attribute grammars to describe the space of all possible supersecondary structures, then applies dynamic programming to compute the global energy minimum structure.

::DEVELOPER

Clote Lab 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

J. Waldispühl, B. Berger, P. Clote, J.-M. Steyaert,
TransFold: a Web Server for predicting the structure and residue contacts of transmembrane beta-barrels,
Nucleic Acids Res. 34(Web Server Issue):189-193 (2006).

GimmeMotifs 0.14.2 – Systematic de novo Motif Prediction pipeline

GimmeMotifs 0.14.2

:: DESCRIPTION

GimmeMotifs is a de novo motif prediction pipeline, especially suited for ChIP-seq datasets. It incorporates several existing motif prediction algorithms in an ensemble method to predict motifs and clusters these motifs using the WIC similarity scoring metric.

::DEVELOPER

van Heeringen Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 GimmeMotifs

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Jan 15;27(2):270-1. doi: 10.1093/bioinformatics/btq636. Epub 2010 Nov 15.
GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments.
van Heeringen SJ, Veenstra GJ.

Repeatoire – ab inito Prediction of Repeat Families

Repeatoire

:: DESCRIPTION

Repeatoire is a software on a hybrid data set of real genomic DNA with simulated interspersed repeats.

::DEVELOPER

Treangen Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Repeatoire

:: MORE INFORMATION

Citation:

IEEE/ACM Trans Comput Biol Bioinform. 2009 Apr-Jun;6(2):180-9. doi: 10.1109/TCBB.2009.9.
A novel heuristic for local multiple alignment of interspersed DNA repeats.
Treangen TJ, Darling AE, Achaz G, Ragan MA, Messeguer X, Rocha EP.

homoTarget – Prediction of microRNA targets in Homo Sapiens

homoTarget

:: DESCRIPTION

HomoTarget is a new algorithm for prediction of microRNA targets in Homo sapiens.

::DEVELOPER

Laboratory of Systems Biology & Bioinformatics (LBB)

:: SCREENSHOTS

HomoTarget

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

 HomoTarget

:: MORE INFORMATION

Citation:

Genomics. 2012 Nov 19. pii: S0888-7543(12)00213-3. doi: 10.1016/j.ygeno.2012.11.005. [Epub ahead of print]
HomoTarget: A new algorithm for prediction of microRNA targets in Homo sapiens.
Ahmadi H1, Ahmadi A, Azimzadeh-Jamalkandi S, Shoorehdeli MA, Salehzadeh-Yazdi A, Bidkhori G, Masoudi-Nejad A

PCP 1.0 – Protein Complex Prediction

PCP 1.0

:: DESCRIPTION

PCP is a sofware that searches for cliques in the modified network, and merge cliques to form clusters using a “partial clique merging” method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, the would be very useful for protein complex prediction, especially for prediction of novel protein complexes.

::DEVELOPER

Limsoon Wong Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  •  Linux
  • C++ Compiler
  • Perl

:: DOWNLOAD

 PCP

:: MORE INFORMATION

Citation:

J Bioinform Comput Biol. 2008 Jun;6(3):435-66.
Using indirect protein-protein interactions for protein complex prediction.
Chua HN, Ning K, Sung WK, Leong HW, Wong L.

PhyloPFP – Gene Ontology Prediction Using Phylogenomics

PhyloPFP

:: DESCRIPTION

PhyloPFP is a phylogenomics based protein function prediction server

::DEVELOPER

Kihara Bioinformatics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2019 Mar 1;35(5):753-759. doi: 10.1093/bioinformatics/bty704.
Phylo-PFP: improved automated protein function prediction using phylogenetic distance of distantly related sequences.
Jain A, Kihara D.

SUPRB 1.0 – Threading Strucuture Prediction

SUPRB 1.0

:: DESCRIPTION

SUPRB (threading with Suboptimal alignment-based PRoBabilistic residue contact information) is a threading strucuture prediction algorithm which employs suboptimal alignments.

::DEVELOPER

Kihara Bioinformatics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 SUPRB

:: MORE INFORMATION

Citation

Proteins. 2011 Jan;79(1):315-34. doi: 10.1002/prot.22885.
Effect of using suboptimal alignments in template-based protein structure prediction.
Chen H1, Kihara D.