Triplet-SVM – Predict a query sequence with Hairpin Structure as a real miRNA precursor or not

Triplet-SVM

:: DESCRIPTION

Triplet-SVM is developed for predicting a query sequence with hairpin structure as a real miRNA precursor or not. The triplet-SVM classifier analyzes the triplet elements of the query and predicts it using a SVM classifier. The SVM classifier is previously trained based on the triplet element features of a set of real miRNA precursors and a set of pseudo-miRNA hairpins.

::DEVELOPER

Triplet-SVM team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  Triplet-SVM

:: MORE INFORMATION

Citation

Chenghai Xue, Fei Li, Tao He, Guoping Liu, Yanda Li, Xuegong Zhang,
Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine,
BMC Bioinformatics, 6: 310, 2005

nocoRNAc 1.23 – Predict & Characterise ncRNA Transcripts in Bacteria

nocoRNAc 1.23

:: DESCRIPTION

nocoRNAc (non-coding RNA characterization) is a Java program for the prediction and characterization of ncRNA transcripts in bacteria. nocoRNAc takes the coordinates of putative ncRNA loci as input and annotates them with transcriptional features to conduct strand-specific transcript predictions. Our approach is not limited to intergenic regions but also applied to predict cis-encoded asRNA transcripts. For the detection of the transcript’s 3′ end nocoRNAc integrates the program TransTermHP (Kingsford et al., 2007) to predict Rho-independent terminator signals. The 5′ start is predicted by the detection of destabilized regions in the genomic DNA. For this purpose we implemented the so-called SIDD model (Benham, Bi, 2004), which has been shown to be applicable to the detection of promoter regions in microbial genomes. Therefore, nocoRNAc does not have to rely on information about known TFBS. The putative transcriptional features are then combined to classify ncRNA loci into either being an ncRNA transcript or not. For ncRNAs that are classified as transcripts the strand is automatically specified, and its boundaries are derived from the SIDD sites and the Rho-independent transcription termination signal. Those loci that are classified not to be a transcript might be false positive predictions or they contain cis-regulatory motifs. For the latter, nocoRNAc incorporates other functionalities for the further analysis of the ncRNA loci such as the search for known RNA motifs from the Rfam database. Furthermore, nocoRNAc provides methods for the prediction of RNA-RNA interactions between ncRNAs and mRNAs. All results can be studied in detail in nocoRNAc’s integrated interactive R environment.

::DEVELOPER

Research Group “Integrative Transcriptomics” , Center for Bioinformatics Tübingen, University of Tübingen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

nocoRNAc

:: MORE INFORMATION

Citation

nocoRNAc: Characterization of non-coding RNAs in prokaryotes
Alexander Herbig, Kay Nieselt
BMC Bioinformatics. 2011 Jan 31;12:40. doi: 10.1186/1471-2105-12-40.

MultiMiTar – Predict mRNA Targets of a Given microRNA

MultiMiTar

:: DESCRIPTION

MultiMiTar is an enhancement of TargetMiner with multiobjective feature selection and ranking of the predicted mRNA targets of a given microRNA

::DEVELOPER

Bioinformatics Lab at Machine Intelligence Uni

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MultiMiTar

:: MORE INFORMATION

Citation

Ramkrishna Mitra and Sanghamitra Bandyopadhyay,
MultiMiTar: A Novel Multi Objective Optimization based miRNA-Target Prediction Method“,
PLoS ONE 6(9): e24583. doi:10.1371/journal.pone.0024583.

DriverNet 1.0.0 – Predict Functional Important Driver Genes in Cancer Genome

DriverNet 1.0.0

:: DESCRIPTION

DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values.

::DEVELOPER

Shah Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • R package

:: DOWNLOAD

  DriverNet

:: MORE INFORMATION

Citation

Genome Biol. 2012 Dec 22;13(12):R124.
DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.
Bashashati A, Haffari G, Ding J, Ha G, Lui K, Rosner J, Huntsman DG, Caldas C, Aparicio SA, Shah SP.

MPGAfold / MPGAfold Visualizer – Massively Parallel Genetic Algorithm that Predicts RNA Secondary Structure

MPGAfold / MPGAfold Visualizer

:: DESCRIPTION

MPGAfold is a massively parallel genetic algorithm that predicts RNA secondary structure.

MPGAfold Visualizer is a Java application that allows the user to visually see an MPGAfold run.

::DEVELOPER

Shapiro Group

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/SGI/Irix

:: DOWNLOAD

MPGAfold / MPGAfold Visualizer

Citation

Kasprzak WK, Shapiro BA.
MPGAfold in dengue secondary structure prediction.
Methods Mol Biol. 2014;1138:199-224. doi: 10.1007/978-1-4939-0348-1_13. PMID: 24696339; PMCID: PMC6354254.

tCONCOORD 1.0 – Predict Protein Conformational Flexibility

tCONCOORD 1.0

:: DESCRIPTION

tCONCOORD predicts protein conformational flexibility based on geometrical considerations. In a first step, the protein structure is analyzed and turned into a set of constraints, mostly distance constraints but also angle, chiral and planarity constraints with upper and lower bounds. This set of constraints serves as a kind of construction plan for the protein.

::DEVELOPER

Daniel Seeliger

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  tCONCOORD

:: MORE INFORMATION

Citation

Daniel Seeliger and Bert L. de Groot.
tCONCOORD-GUI: Visually supported conformational sampling of bioactive molecules.
J. Comp. Chem. 30:1160-1166 (2009)

LTRsift 1.0.2 – Postprocessing of de novo predicted LTR Retrotransposon Annotations

LTRsift 1.0.2

:: DESCRIPTION

LTRsift is a graphical desktop tool for semi-automatic postprocessing of de novo predicted LTR retrotransposon annotations, such as the ones generated by LTRharvest and LTRdigest.

::DEVELOPER

RESEARCH GROUP FOR GENOME INFORMATICS ,Center for Bioinformatics, University of Hamburg

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

LTRsift

:: MORE INFORMATION

Citation:

S. Steinbiss, S. Kastens and S. Kurtz:
LTRsift: a graphical user interface for semi-automatic classification and postprocessing of de novo detected LTR retrotransposons.
Mobile DNA, 3:18 (2012)

FSFinder 2.0 – Predict Frameshifting in Genomic Sequences

FSFinder 2.0

:: DESCRIPTION

FSFinder (Frameshift Signal Finder) is a software that searches the genomic sequences or mRNA sequences for frameshifting sites.FSFinder is capable of finding -1 frameshift sites for most known genes and +1 frameshift sites for two genes: protein chain release factor (prfB ) and ornithine decarboxylase antizyme (oaz ).

::DEVELOPER

Biocomputing Lab. School of Computer Science and Engineering Inha University, Inchon

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

FSFinder

:: MORE INFORMATION

Citation

Byun Y, Moon S, Han K.
A general computational model for predicting ribosomal frameshifts in genome sequences.
Comput Biol Med. 2007 Dec;37(12):1796-801. Epub 2007 Aug 2.

Carnac 0.98 – Predict Secondary Structure for a set of Homologous RNA Sequences

Carnac 0.98

:: DESCRIPTION

Carnac is a software tool for analysing the hypothetical secondary structure of a family of homologous RNA. It aims at predicting if the sequences actually share a common secondary structure. When this structure exists, Carnac is then able to correctly recover a large amount of the folded stems. The input is a set of single-stranded RNA sequences that need not to be aligned. The folding strategy relies on a thermodynamic model with energy minimization. It combines information coming from locally conserved elements of the primary structure and mutual information between sequences with covariations too.

::DEVELOPER

Bonsai Bioinformatics – INRIA

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows /  Linux / MacOsX
  • C Complier

:: DOWNLOAD

Carnac

:: MORE INFORMATION

Citation

CARNAC: folding families of related RNAs
Touzet H. and Perriquet O.
Nucl. Acids Res. (2004) 32 (suppl 2): W142-W145

BSpred – Predict Binding Site of Proteins

BSpred

:: DESCRIPTION

BSpred is a neural network based algorithm for predicting binding site of proteins from amino acid sequences. The algorithm was extensively trained on the sequence-based features including protein sequence profile, secondary structure prediction, and hydrophobicity scales of amino acids.

::DEVELOPER

Yang Zhang’s Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
:: DOWNLOAD

  BSpred

:: MORE INFORMATION

Citation

S. Mukherjee, Y. Zhang.
Protein-protein complex structure predictions by multimetic threading and template recombination.
Structure. 2011 Jul 13;19(7):955-66.