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

:: 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.

DeNovo Pipeline 1.5 – Protein Identification by de novo Interpretation

DeNovo Pipeline 1.5

:: DESCRIPTION

DeNovo Pipeline performes protein identification by de novo interpretation by coupling the PepNovo software for sequencing and the Fasta tool for homology searching.

::DEVELOPER

pappso (Plate-forme d’analyses protéomiques de Paris Sud-Ouest)

:: SCREENSHOTS

:: REQUIREMENTS

Windows/ MacOsX / Linux
Java

:: DOWNLOAD

DeNovo Pipeline

:: MORE INFORMATION

NucImport v2 – Nuclear Protein Import and Localisation Signals Predictor

NucImport v2

:: DESCRIPTION

NucImport is a protein sequence and interaction prediction service designed to determine the nuclear transport of proteins in mouse and yeast. NucImport uses Bayesian networks, recognizes NLSs, links interactions to localization signals and incorporates sequence similarity. NucImport accepts amino acid sequences from user, presented in the FASTA format, and determines the probability of nuclear import as well as the location of nuclear localization signal in the query protein.

::DEVELOPER

Institute for Molecular Bioscience, The University of Queensland

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Mac
Java

:: DOWNLOAD

NucImport

:: MORE INFORMATION

Citation

Bioinformatics. 2011 May 1;27(9):1239-46. Epub 2011 Mar 3.
A probabilistic model of nuclear import of proteins.
Mehdi AM, Sehgal MS, Kobe B, Bailey TL, Bodén M.

Mosaic 1.05 – a visual framework for Sequence Analysis using N-grams and Spectral rearrangement

Mosaic 1.05

:: DESCRIPTION

Mosaic is a software application to visually analyze sequence relationships. The similarities between the sequences of a given set are displayed within a matrix (mosaic plot), which enables the visual identification of clusters of related sequences, outliers or other sequences with special properties.

::DEVELOPER

Mosaic team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/MacOsX
Python
matplotlib

:: DOWNLOAD

Mosaic

:: MORE INFORMATION

Citation

Stefan R. Maetschke, Karin S. Kassahn, Jasmyn A. Dunn, Siew P. Han, Eva Z. Curley, Katryn J. Stacey, Mark A. Ragan
A visual framework for sequence analysis using n-grams and spectral rearrangement
Bioinformatics. 2010 Mar 15;26(6):737-44. doi: 10.1093/bioinformatics/btq042

PSAAM – Protein Sequence Analysis And Modelling

PSAAM

:: DESCRIPTION

PSAAM (Protein Sequence Analysis And Modelling) includes many useful features for analysis of structural information from sequence data, cartoons for graphical representation of secondary structures, prediction programs, and prediction aids, a ribosome function for generation of coordinates sets in PDB format (including Header information) for viewing by molecular graphics packages (examples, see also PDVWIN), and a SeqPlot function for plotting secondary structural models (helical cylinders and wheels, coils, etc., with each residue indicated by a circle, color-coded (or coded by line thickness) according to the current physico-chemical index.

::DEVELOPER

A.R. CROFTS

:: SCREENSHOTS

:: REQUIREMENTS

Windows

:: DOWNLOAD

PSAAM

:: MORE INFORMATION

ProteoVision – Advanced Visualization of (Ribosomal) Proteins

ProteoVision

:: DESCRIPTION

ProteoVision is a web server designed to explore protein structure and evolution through simultaneous visualization of multiple sequence alignments, topology diagrams and 3D structures.

::DEVELOPER

ProteoVision team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Penev PI, McCann HM, Meade CD, Alvarez-Carreño C, Maddala A, Bernier CR, Chivukula VL, Ahmad M, Gulen B, Sharma A, Williams LD, Petrov AS.
ProteoVision: web server for advanced visualization of ribosomal proteins.
Nucleic Acids Res. 2021 Jul 2;49(W1):W578-W588. doi: 10.1093/nar/gkab351. PMID: 33999189; PMCID: PMC8265156.

DEME 1.0 – Motif Discovery using Positive & Negative Examples

DEME 1.0

:: DESCRIPTION

DEME (Discriminatively Enhanced Motif Elicitation) is a discriminative motif discovery algorithm for use with protein and DNA sequences. Using a probabilistic framework, DEME discovers motifs that are overrepresented in a set of “positive” sequences relative to a “negative” set.

::DEVELOPER

ARC Centre of Excellence in Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/MacOsX
C compiler

:: DOWNLOAD

DEME

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2007 Oct 15;8:385.
Discriminative motif discovery in DNA and protein sequences using the DEME algorithm.
Redhead E, Bailey TL.

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.

MobiDB-lite 3.8.4 – Long Disorder Consensus Predictor

MobiDB-lite 3.8.4

:: DESCRIPTION

MobiDB-lite is an optimized method for highly specific predictions of long intrinsically disordered regions (IDRs). The method uses 8 different predictors to derive a consensus which is filtered for spurious short predictions in a second step.

::DEVELOPER

The BioComputing UP lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Java

:: DOWNLOAD

MobiDB-lite

:: MORE INFORMATION

Citation:

Necci M, Piovesan D, Clementel D, Dosztányi Z, Tosatto SCE.
MobiDB-lite 3.0: fast consensus annotation of intrinsic disorder flavours in proteins.
Bioinformatics. 2020 Dec 16:btaa1045. doi: 10.1093/bioinformatics/btaa1045. Epub ahead of print. PMID: 33325498.

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)