Tag: Predictor

iPSORT / caml-iPSORT 20100316 – Subcellular Localization Site Predictor for N-terminal Sorting Signals

iPSORT / caml-iPSORT 20100316

:: DESCRIPTION

iPSORT is a subcellular localization site predictor for N-terminal sorting signals. Given a protein sequence , it will predict whether it contains a Signal Peptide (SP), Mitochondrial Targeting Peptide (mTP), or Chloroplast Transit Peptide (cTP).

caml-iPSORT is the command line version of iPSORT

::DEVELOPER

Hideo Bannai 

:: REQUIREMENTS

:: DOWNLOAD

  caml-iPSORT

:: MORE INFORMATION

Citation

Bannai, H., Tamada, Y., Maruyama, O., Nakai, K., and Miyano, S.,
Extensive feature detection of N-terminal protein sorting signals,
Bioinformatics, 18(2) 298–305, 2002.

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.

K-Fold – Predictor of the Protein Folding Mechanism and Rate

K-Fold

:: DESCRIPTION

K-Fold is a tool for the automatic prediction of the protein folding kinetic order and rate. The tool is based on a support vector machine (SVM) that was trained on a data set of 63 proteins, whose 3D structure and folding mechanism are known from experiments already described in the literature.

::DEVELOPER

Bologna Biocomputing Group

:: SCREENSHOTS

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 No, Only Web Service

:: MORE INFORMATION

Citation

Bioinformatics. 2007 Feb 1;23(3):385-6. Epub 2006 Nov 30.
K-Fold: a tool for the prediction of the protein folding kinetic order and rate.
Capriotti E, Casadio R.

MetalDetector 2.0 – Cysteines and Histidines Binding State Predictor

MetalDetector 2.0

:: DESCRIPTION

MetalDetector identifies cysteines and histidines involved in transition metal protein binding sites, starting from the protein sequence alone.

::DEVELOPER

MetalDetector team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
:: DOWNLOAD

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2011 Jul;39(Web Server issue):W288-92. doi: 10.1093/nar/gkr365. Epub 2011 May 16.
MetalDetector v2.0: predicting the geometry of metal binding sites from protein sequence.
Passerini A, Lippi M, Frasconi P.

DeepDRBP-2L – Genome Annotation predictor for identifying DNA-binding proteins and RNA-binding proteins

DeepDRBP-2L

:: DESCRIPTION

DeepDRBP-2L is a new computational predictor for identifying DBPs, RBPs and DRBPs by combining Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM).

::DEVELOPER

Liu Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Zhang J, Chen Q, Liu B.
DeepDRBP-2L: A New Genome Annotation Predictor for Identifying DNA-Binding Proteins and RNA-Binding Proteins Using Convolutional Neural Network and Long Short-Term Memory.
IEEE/ACM Trans Comput Biol Bioinform. 2021 Jul-Aug;18(4):1451-1463. doi: 10.1109/TCBB.2019.2952338. Epub 2021 Aug 6. PMID: 31722485.

iEnhancer-2L – Two-layer predictor for Identifying Enhancers

iEnhancer-2L

:: DESCRIPTION

iEnhancer-2L is a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition

::DEVELOPER

Liu Lab, Harbin Institute of Technology Shenzhen Graduate School.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition.
Liu B, Fang L, Long R, Lan X, Chou KC.
Bioinformatics. 2015 Oct 17. pii: btv604.

TMKink – Transmembrane Kink Predictor

TMKink

:: DESCRIPTION

TMKink is a method to predict transmembrane helix kinks.

::DEVELOPER

BOWIE LAB

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Protein Sci. 2011 Jul;20(7):1256-64. doi: 10.1002/pro.653. Epub 2011 Jun 2.
TMKink: a method to predict transmembrane helix kinks.
Meruelo AD1, Samish I, Bowie JU.

GAP 0.0.1 – Gene functional Association Predictor

GAP 0.0.1

:: DESCRIPTION

GAP is an integrative, general-purpose framework for deriving a quantitative measure of gene similarity, which is relevant to a wide range of bioinformatics applications from gene clustering and phenotype and protein interactions predictions to interaction network modeling, and pharmacology analysis.

::DEVELOPER

Jurisica Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Syst Biol. 2013 Mar 14;7:22. doi: 10.1186/1752-0509-7-22.
Novel semantic similarity measure improves an integrative approach to predicting gene functional associations.
Vafaee F, Rosu D, Broackes-Carter F, Jurisica I.

CCTop 1.0.0 – CRISPR/Cas9 Target Online Predictor

CCTop 1.0.0

:: DESCRIPTION

CCTop is a CRISPR/Cas9 target online predictor

::DEVELOPER

The Centre for Organismal Studies (COS) Heidelberg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

CCTop

:: MORE INFORMATION

Citation

CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool.
Stemmer M, Thumberger T, Del Sol Keyer M, Wittbrodt J, Mateo JL.
PLoS One. 2015 Apr 24;10(4):e0124633. doi: 10.1371/journal.pone.0124633.

MDLoc – A Dependency-Based Protein Subcellular Location Predictor

MDLoc

:: DESCRIPTION

MDLoc is a Dependency-Based Protein Subcellular Location Predictor

::DEVELOPER

Computational Biomedicine Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Protein (multi-)location prediction: utilizing interdependencies via a generative model.
Simha R, Briesemeister S, Kohlbacher O, Shatkay H.
Bioinformatics. 2015 Jun 15;31(12):i365-i374. doi: 10.1093/bioinformatics/btv264.