HomologMiner 1.00 – Find Homologous Genomic Groups in Whole Genomes

HomologMiner 1.00

:: DESCRIPTION

HomologMiner is a software to identify homologous groups applicable to genome sequences that have been properly marked for low-complexity repeats and annotated interspersed repeats.

::DEVELOPER

Minmei Hou

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 HomologMiner

:: MORE INFORMATION

Citation

Bioinformatics. 2007 Apr 15;23(8):917-25. Epub 2007 Feb 18.
HomologMiner: looking for homologous genomic groups in whole genomes.
Hou M, Berman P, Hsu CH, Harris RS.

BASELINe 1.3 – Bayesian Estimation of Antigen-Driven Selection in Immunoglobulin Sequences

BASELINe 1.3

:: DESCRIPTION

BASELINe, a new computational framework for Bayesian estimation of Antigen-driven selection in Immunoglobulin sequences, provides a more intuitive means of analyzing selection by actually quantifying it.

::DEVELOPER

Kleinstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R

:: DOWNLOAD

 BASELINe

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2012 Sep 1;40(17):e134. Epub 2012 May 27.
Quantifying selection in high-throughput Immunoglobulin sequencing data sets.
Yaari G1, Uduman M, Kleinstein SH.

pRESTO 0.5.13 – Processing raw reads from high-throughput Sequencing of Lymphocyte Repertoires

pRESTO 0.5.13

:: DESCRIPTION

pRESTO (REpertoire Sequencing TOolkit) is an integrated collection of platform-independent Python modules for processing raw reads from high-throughput (next-generation) sequencing of lymphocyte repertoires.

::DEVELOPER

Kleinstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Python
  • Biopython
  • MUSCLE

:: DOWNLOAD

 pRESTO

:: MORE INFORMATION

Citation

pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires.
Vander Heiden JA, Yaari G, Uduman M, Stern JN, O’Connor KC, Hafler DA, Vigneault F, Kleinstein SH.
Bioinformatics. 2014 Mar 26.

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.

MaCS 0.5d – Markovian Coalescent Simulator

MaCS 0.5d

:: DESCRIPTION

MaCS is a simulator of the coalescent process that simulates geneologies spatially across chromosomes as a Markovian process. The algorithm is similar to the SMC algorithm (McVean and Cardin, Phil Trans Soc R B 2005) in that the algorithm scales linearly in time with respect to sample size and sequence length. However, it more accurately models the true coalescent, while supporting all demographic scenarios found in the popular program MS (Hudson, Bioinformatics 2002) making this program appropriate for simulating data for structured populations in genome wide association studies.

::DEVELOPER

Gary K. Chen, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  MaCS

:: MORE INFORMATION

Citation

Genome Res. 2009 Jan;19(1):136-42. Epub 2008 Nov 24.
Fast and flexible simulation of DNA sequence data.
Chen GK, Marjoram P, Wall JD.

CAT 1.3 – Composition Analysis Toolkit

CAT 1.3

:: DESCRIPTION

CAT (Composition Analysis Toolkit) is a software package that includes a novel measure of codon usage bias, Codon Deviation Coefficient (CDC). Unlike previous measures, CDC effectively accounts for background nucleotide composition in estimating codon usage bias and utilizes a bootstrap assessment of the statistical significance of codon usage bias.

::DEVELOPER

The Zhang Lab — Computational Biology and Bioinformatics

:: SCREENSHOTS

:: REQUIREMENTS

  • WIndows/ Linux / MacOsX

:: DOWNLOAD

 CAT

:: MORE INFORMATION

Citation

Zhang, Z., Li, J., Cui, P., Ding, F., Li, A., Townsend, J.P., and Yu, J. (2011)
Codon Deviation Coefficient: a novel measure for estimating codon usage bias and its statistical significance
BMC Bioinformatics. 2012 Mar 22;13:43.

STAMP 1.1 – Tool-kit for DNA Motif Comparison

STAMP 1.1

:: DESCRIPTION

STAMP is a newly developed web server that is designed to support the study of DNA-binding motifs. STAMP may be used to query motifs against databases of known motifs; the software aligns input motifs against the chosen database (or alternatively against a user-provided dataset), and lists of the highest-scoring matches are returned. Such similarity-search functionality is expected to facilitate the identification of transcription factors that potentially interact with newly discovered motifs.

:: DEVELOPER

Benos Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 STAMP

:: MORE INFORMATION

Citation:

S Mahony, PV Benos,
STAMP: a web tool for exploring DNA-binding motif similarities“,
Nucleic Acids Research (2007) 35(Web Server issue):W253-W258.

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.

FOOTER 2.0 – Find Ammalian Transcription Factor Binding Sites using Phylogenetic Footprinting

FOOTER 2.0

:: DESCRIPTION

FOOTER analyses a pair of homologous mammalian DNA sequences (i.e. human and mouse/rat) for high probability binding sites of known transcription factors. A set of Position-Specific Scoring Matrices (PSSM) has been carefully constructed from mammalian transcription factor binding sites deposited in TRANSFAC database.

::DEVELOPER

Benos Lab

 SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

FOOTER: a web tool for finding mammalian DNA regulatory regions using phylogenetic footprinting.
Corcoran DL, Feingold E, Benos PV.
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W442-6.