Tag: Model

SOMPNN – An Efficient Non-Parametric Model for Predicting Transmembrane Helices

SOMPNN

:: DESCRIPTION

SOMPNN is a novel model for prediction of TMH that features by minimal parameter assumptions requirement and high computational efficiency. In the SOMPNN model, a self-organizing map (SOM) is used to adaptively learn the helices distribution knowledge hidden in the training data, and then a probabilistic neural network (PNN) is adopted to predict TMH segments based on the knowledge learned by SOM.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

 N/A

:: REQUIREMENTS

  • Windows
  • Matlab

:: DOWNLOAD

  SOMPNN 

 :: MORE INFORMATION

Citation:

Amino Acids. 2012 Jun;42(6):2195-205. doi: 10.1007/s00726-011-0959-2. Epub 2011 Jun 22.
SOMPNN: an efficient non-parametric model for predicting transmembrane helices.
Yu DJ1, Shen HB, Yang JY.

BMIQ 1.4 – Beta Mixture Quantile Model

BMIQ 1.4

:: DESCRIPTION

BMIQ (Beta Mixture Quantile) is a method for normalisation of Illumina Infinium 450k data

::DEVELOPER

Teschendorff Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R package

:: DOWNLOAD

 BMIQ 

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Jan 15;29(2):189-96. doi: 10.1093/bioinformatics/bts680. Epub 2012 Nov 21.
A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data.
Teschendorff AE1, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, Beck S.

SHM 0.1 – Models of Somatic Hypermutation

SHM 0.1

:: DESCRIPTION

SHM (somatic hypermutation) is a model of targeting and nucleotide substitution constructed from high-throughput B cell immunoglobulin (Ig) sequencing data.

::DEVELOPER

Kleinstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R

:: DOWNLOAD

 SHM

:: MORE INFORMATION

Citation

Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data.
Yaari G, Vander Heiden JA, Uduman M, Gadala-Maria D, Gupta N, Stern JN, O’Connor KC, Hafler DA, Laserson U, Vigneault F, Kleinstein SH.
Front Immunol. 2013 Nov 15;4:358. doi: 10.3389/fimmu.2013.00358. eCollection 2013.

lDDT – Comparing Protein Structures and Models using Distance Difference Tests

lDDT

:: DESCRIPTION

The lDDT (local Distance Difference Test) is a superposition-free score which evaluates local distance differences in a model compared to a reference structure.

::DEVELOPER 

lDDT team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Bioinformatics. 2013 Nov 1;29(21):2722-8. doi: 10.1093/bioinformatics/btt473. Epub 2013 Aug 27.
lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests.
Mariani V1, Biasini M, Barbato A, Schwede T.

ModFOLD 6 – Model Quality Assessment Server

ModFOLD 6

:: DESCRIPTION

The ModFOLD server is the latest version of server for the estimation of both the global and local (per-residue) quality of 3D protein models.

::DEVELOPER

McGuffin Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 ModFOLD

:: MORE INFORMATION

Citation

Maghrabi, A.H.A. & McGuffin, L.J. (2017)
ModFOLD6: an accurate web server for the global and local quality estimation of 3D models of proteins,
Nucleic Acids Res. 2017 Jul 3;45(W1):W416-W421. doi: 10.1093/nar/gkx332.

Nucleic Acids Res. 2013 Jul;41(Web Server issue):W368-72. doi: 10.1093/nar/gkt294. Epub 2013 Apr 25.
The ModFOLD4 server for the quality assessment of 3D protein models.
McGuffin LJ1, Buenavista MT, Roche DB.

RFMapp 20121217 – Ribosome Flow Model Application

RFMapp 20121217

:: DESCRIPTION

The RFMapp is a graphical user interface application, enabling the prediction of fundamental features of the gene translation process, including translation rates, ribosomal densities, protein abundance levels, and the relation between all these variables.

::DEVELOPER

Tamir Tuller’s Group

:: SCREENSHOTS

RFMapp

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

 RFMapp

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Jun 15;28(12):1663-4. doi: 10.1093/bioinformatics/bts185. Epub 2012 Apr 11.
RFMapp: ribosome flow model application.
Zur H1, Tuller T.

MACML 1.1.2 – Model Averaging Clustering by Maximum Likelihood

MACML 1.1.2

:: DESCRIPTION

MACML is a program that clusters sequences into heterogeneous regions with specific site types, without requiring any prior knowledge.

::DEVELOPER

the Townsend Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • C++Compiler

:: DOWNLOAD

 MACML

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2009 Jun;5(6):e1000421. Epub 2009 Jun 26.
Maximum-likelihood model averaging to profile clustering of site types across discrete linear sequences.
Zhang Z, Townsend JP.

HHMMiR 1.2 – Prediction of microRNAs using Hierarchical Hidden Markov models

HHMMiR 1.2

:: DESCRIPTION

HHMMiR is a novel approach for de novo miRNA hairpin prediction in the absence of evolutionary conservation. HHMMiR implements a Hierarchical Hidden Markov Model (HHMM) that utilizes region-based structural as well as sequence information of miRNA precursors.

:: DEVELOPER

Benos Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 HHMMiR

:: MORE INFORMATION

Citation:

S. Kadri, V. Hinman, P.V. Benos,
“HHMMiR: Efficient de novo Prediction of MicroRNAs using Hierarchical Hidden Markov Models“,
BMC Bioinformatics (Proc APBC 2009) (2009) 10 (Suppl 1):S35.

MBCluster.Seq 1.0 – Model-Based Clustering for RNA-seq Data

MBCluster.Seq 1.0

:: DESCRIPTION

MBCluster.Seq : Cluster genes based on Poisson or Negative-Binomial model for RNA-Seq or other digital gene expression (DGE) data

::DEVELOPER

Yaqing Si <siyaqing at gmail.com>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R

:: DOWNLOAD

 MBCluster.Seq

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Jan 15;30(2):197-205. doi: 10.1093/bioinformatics/btt632. Epub 2013 Nov 4.
Model-based clustering for RNA-seq data.
Si Y1, Liu P, Li P, Brutnell TP.