DeepIDP-2L – Protein intrinsically Disordered Region Prediction

DeepIDP-2L

:: DESCRIPTION

DeepIDP-2L is a web server for protein intrinsically disordered region prediction by combining convolutional attention network and hierarchical attention network

::DEVELOPER

Liu Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Tang YJ, Pang YH, Liu B.
DeepIDP-2L: protein intrinsically disordered region prediction by combining convolutional attention network and hierarchical attention network.
Bioinformatics. 2021 Dec 2:btab810. doi: 10.1093/bioinformatics/btab810. Epub ahead of print. PMID: 34864847.

TRAP 3.05 – Transcription factor Affinity Prediction

TRAP 3.05

:: DESCRIPTION

TRAP (Transcription factor Affinity Prediction) calculates the affinity of transcription factors for DNA sequences on the basis of a biophysical model. This method has proven to be useful for several applications, including for determining the putative target genes of a given factor. This protocol covers two other applications: (i) determining which transcription factors have the highest affinity in a set of sequences (illustrated with chromatin immunoprecipitation–sequencing (ChIP-seq) peaks), and (ii) finding which factor is the most affected by a regulatory single-nucleotide polymorphism.

::DEVELOPER

TRAP Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ compiler
  • R Package

:: DOWNLOAD

  TRAP

:: MORE INFORMATION

Citation

Morgane Thomas-Chollier, Andrew Hufton, Matthias Heinig, Sean O’Keeffe, Nassim El Masri, Helge G Roider, Thomas Manke and Martin Vingron.
Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs.
Nature Protocols, 3;6(12):1860-9. (2011)

SPOCS 1.0.10 – Graph-based Ortholog/Paralog Prediction tool

SPOCS 1.0.10

:: DESCRIPTION

SPOCS (Species Paralogy and Orthology Clique Solver) is a graph-based ortholog/paralog prediction tool that will predict orthologs and paralogs given a set of prokaryotic proteomes (the set of proteins encoded by a genome). The software will take a set of protein fasta files (one per species genome), and an optional additional fasta to serve as an outgroup (a species that should be more distantly related to the species of interest than any of the species of interest are to each other).

::DEVELOPER

Computational Biology & Bioinformatics ,Pacific Northwest National Laboratory

:: SCREENSHOTS

SPOCS

:: REQUIREMENTS

  • Linux/ MacOsX
  • C++ Compiler
  • NCBI BLAST

:: DOWNLOAD

 SPOCS

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Oct 15;29(20):2641-2. doi: 10.1093/bioinformatics/btt454.
SPOCS: software for predicting and visualizing orthology/paralogy relationships among genomes.
Curtis DS, Phillips AR, Callister SJ, Conlan S, McCue LA.

CSS-Palm 4.0 – Palmitoylation Site Prediction with a Clustering and Scoring Strategy

CSS-Palm 4.0

:: DESCRIPTION

CSS-Palm is a computer program for palmitoylation site prediction, Clustering and Scoring Strategy for Palmitoylation Sites Prediction.The program’s prediction performance is encouraging with highly positive Jack-Knife validation results (sensitivity 82.16% and specificity 83.17% for cut-off score 2.6).

::DEVELOPER

The CUCKOO Workgroup

:: SCREENSHOTS

:: REQUIREMENTS

  • WIndows / Linux / MacOsX
  • Java

:: DOWNLOAD

 CSS-Palm

:: MORE INFORMATION

Citation

CSS-Palm 2.0: an updated software for palmitoylation sites prediction
Jian Ren, Longping Wen, Xinjiao Gao, Changjiang Jin, Yu Xue and Xuebiao Yao.
Protein Engineering, Design and Selection.2008 21(11):639-644

SABINE 1.2 – Prediction of the Binding Specificity of Transcription Factors using Support Vector Regression

SABINE 1.2

:: DESCRIPTION

SABINE (Stand-alone binding specificity estimator) is a tool to predict the binding specificity of a transcription factor (TF), given its amino acid sequence, species, structural superclass and DNA-binding domains. For convenience, the superclass and DNA-binding domains of a given TF can be predicted based on sequence homology with TFs in the training of SABINE.

::DEVELOPER

the Center for Bioinformatics Tübingen (Zentrum für Bioinformatik Tübingen, ZBIT).

:: SCREENSHOTS

SABINE

:: REQUIREMENTS

  • Linux
  • Java

:: DOWNLOAD

  SABINE

:: MORE INFORMATION

Citation

PLoS One. 2013 Dec 12;8(12):e82238. doi: 10.1371/journal.pone.0082238. eCollection 2013.
TFpredict and SABINE: Sequence-Based Prediction of Structural and Functional Characteristics of Transcription Factors.
Eichner J, Topf F1, Dräger A, Wrzodek C, Wanke D, Zell A.

BERT-Kcr – Prediction of Protein Lysine Crotonylation sites

BERT-Kcr

:: DESCRIPTION

BERT-Kcr is a novel predictor for protein Kcr sites prediction, which was developed by using a transfer learning method with pre-trained bidirectional encoder representations from transformers (BERT) models.

::DEVELOPER

Zhu Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux

:: DOWNLOAD

BERT-Kcr

:: MORE INFORMATION

Citation

Qiao Y, Zhu X, Gong H.
BERT-Kcr: Prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models.
Bioinformatics. 2021 Oct 13:btab712. doi: 10.1093/bioinformatics/btab712. Epub ahead of print. PMID: 34643684.

ViennaRNA 2.5.0 – RNA Secondary Structure Prediction & Comparison

ViennaRNA 2.5.0

:: DESCRIPTION

ViennaRNA Package consists of a C code library and several stand-alone programs for the prediction and comparison of RNA secondary structures.

Vienna RNA WebServers

::DEVELOPER

Theoretical Biochemistry Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Mac OsX/ Windows

:: DOWNLOAD

Vienna RNA

:: MORE INFORMATION

Citation

Methods Mol Biol. 2015;1269:307-26. doi: 10.1007/978-1-4939-2291-8_19.
The ViennaRNA web services.
Gruber AR1, Bernhart SH, Lorenz R.

Ivo L. Hofacker
Vienna RNA secondary structure serverNucl.
Acids Res. (2003) 31 (13): 3429-3431

Lorenz, Ronny and Bernhart, Stephan H. and Höner zu Siederdissen, Christian and Tafer, Hakim and Flamm, Christoph and Stadler, Peter F. and Hofacker, Ivo L.
ViennaRNA Package 2.0
Algorithms for Molecular Biology, 6:1 26, 2011, doi:10.1186/1748-7188-6-26

PASTA 2.0 – Protein Aggregation Prediction

PASTA 2.0

:: DESCRIPTION

PASTA (Prediction of amyloid structure aggregation) is a web server for the analysis of amino acid sequences. It predicts which portions of a given input sequence are more likely to stabilize the cross-beta core of fibrillar aggregates.

::DEVELOPER

The BioComputing UP lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 PASTA

:: MORE INFORMATION

Citation:

PASTA 2.0: an improved server for protein aggregation prediction.
Walsh I, Seno F, Tosatto SC, Trovato A.
Nucleic Acids Res. 2014 Jul;42(Web Server issue):W301-7. doi: 10.1093/nar/gku399.

Antonio Trovato, Flavio Seno and Silvio C.E. Tosatto.
The PASTA server for protein aggregation prediction
Protein Engineering Design & Selection, 20(10):521-523. (2007)

RNAmigos – RNA Small Molecule Ligand Prediction

RNAmigos

:: DESCRIPTION

RNAmigos is a Graph Neural Network for predicting RNA small molecule ligands.

::DEVELOPER

Carlos Oliver

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Python

:: DOWNLOAD

RNAmigos

:: MORE INFORMATION

Citation:

Oliver C, Mallet V, Gendron RS, Reinharz V, Hamilton WL, Moitessier N, Waldispühl J.
Augmented base pairing networks encode RNA-small molecule binding preferences.
Nucleic Acids Res. 2020 Aug 20;48(14):7690-7699. doi: 10.1093/nar/gkaa583. PMID: 32652015; PMCID: PMC7430648.

TSMDA – Target and Symptom-based computational model for miRNA-disease Association Prediction

TSMDA

:: DESCRIPTION

TSMDA is a novel machine learning method that leverages target and symptom information and negative sample selection to predict miRNA-disease association.

::DEVELOPER

Biosig Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Uthayopas K, de Sá AGC, Alavi A, Pires DEV, Ascher DB.
TSMDA: Target and symptom-based computational model for miRNA-disease-association prediction.
Mol Ther Nucleic Acids. 2021 Aug 26;26:536-546. doi: 10.1016/j.omtn.2021.08.016. PMID: 34631283; PMCID: PMC8479276.