Tag: Identification

iHMMune-align 20071126 – Hidden Markov model-based Alignment and Identification of Germline Genes

iHMMune-align 20071126

:: DESCRIPTION

 iHMMune-align is an alignment tool designed specifically for modelling the antibody generation process and identifying the constituent germline genes of a human heavy chain mature variable region sequence, in order to generate an alignment of the rearranged immunoglobulin sequence with its germline genes.

::DEVELOPER

Collins Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java

:: DOWNLOAD

 iHMMune-align

:: MORE INFORMATION

Citation

Gaeta BA, Malming HR, Jackson KJ, Bain ME, Wilson P, Collins AM (2007)
iHMMune-align: Hidden Markov model-based alignment and identification of germline genes in rearranged immunoglobulin gene sequences.
Bioinformatics 23:1580-1587

SLICSel 1.1 – Design Specific Oligonucleotide Probes for Microbial Detection and Identification

SLICSel 1.1

:: DESCRIPTION

SLICSel is a program for designing specific oligonucleotide probes for microbial detection and identification. To obtain maximal specificity of designed oligonucleotides, SLICSel uses the Nearest-Neighbor thermodynamics-based approach for probe design.

SLICSel Online Version

::DEVELOPER

Department of Bioinformatics, University of Tartu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows

:: DOWNLOAD

 SLICSel

:: MORE INFORMATION

Citation

BMC Biotechnol. 2011 Feb 28;11:17.
Detection of NASBA amplified bacterial tmRNA molecules on SLICSel designed microarray probes.
Scheler O, Kaplinski L, Glynn B, Palta P, Parkel S, Toome K, Maher M, Barry T, Remm M, Kurg A.

Oligomap 1.01 – Identification of nearly-perfect matches of small RNAs in Sequence databases

Oligomap 1.01

:: DESCRIPTION

Oligomap is a program for fast identification of nearly-perfect matches of small RNAs in sequence databases. It allows to exhaustively identify all the perfect and 1-error (where an error is defined to be a mismatch, insertion or deletion) matches of large sets of small RNAs to target sequences. Optimal performance is achieved at about 500000 query sequences

::DEVELOPER

Zavolan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Oligomap

:: MORE INFORMATION

Citation

Berninger P, Gaidatzis D, van Nimwegen E, Zavolan M.
“Computational analysis of small RNA cloning data“,
Methods, 44(13-21), 2008

Selecton 2.4 – Identification of Site-Specific Positive Selection & Purifying Selection

Selecton 2.4

:: DESCRIPTION

The Selecton server enables detecting the selective forces at a single amino-acid site. The ratio of non-synonymous (amino-acid altering) to synonymous (silent) substitutions, known as the Ka/Ks ratio, is used to estimate both positive and purifying selection at each amino-acid site.

::DEVELOPER

Selecton Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows /MacOsX

:: DOWNLOAD

 Selecton

:: MORE INFORMATION

Citation

Stern, A., Doron-Faigenboim, A., Erez, E., Martz, E., Bacharach, E., Pupko, T.
Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach.
Nucleic Acids Research. 35: W506-W511.

FastER 1.0 – Identification of Genes with Fast-evolving Regions in closely related Genomes

FastER 1.0

:: DESCRIPTION

FastER is a software for identifying orthologous gene pairs that appear to have fast-evolving regions between two closely related genomes. Its input can be either two closely related whole genome sequences or two closely related orthologous sequences. It consists of two steps: first, it will detect all regions with overrepresented nonsynonymous mutations; second, it will test the nonsynonymous evolution rate against the neutral evolution rate in that gene.

::DEVELOPER

Computational Genomics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 FastER

:: MORE INFORMATION

Citation:

Identification of genes with fast-evolving regions in microbial genomes
Yu Zheng, Richard J. Roberts and Simon Kasif
Nucl. Acids Res. (2004) 32 (21): 6347-6357.

MASPECTRAS 2.3 – Integrate MS protein Identification

MASPECTRAS 2.3

:: DESCRIPTION

 MASPECTRAS is a freely available platform for integrating MS protein identifications with information from the major bioinformatics databases (ontologies, domains, literature, etc.). It assists researchers in understanding their data and publishing through sample comparisons, targeted queries, summaries, and exports in multiple formats such as PRIDE XML . MASPECTRAS 2 also comprises mechanisms to facilitate its integration in a high-throughput infrastructure. MASPECTRAS 2 supports SEQUEST, Mascot, Spectrum Mill, X! Tandem, and OMSSA.

::DEVELOPER

Genomics & Bioinformatics Graz, Graz University of Technology

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java
  • Oracle / PostgreSQL / MySQL

:: DOWNLOAD

 MASPECTRAS

:: MORE INFORMATION

Citation

Mohien CU, Hartler J, Rix U, Rix LR, Winter GE, Thallinger GG, Bennet KL, Superti-Furga G, Trajanoski Z, and Colinge J.
MASPECTRAS 2: An integration and analysis platform for proteomic data.
Proteomics 2010, 10, 2719-2722

IRiS – Identification of Recombinations in Sequences

IRiS

:: DESCRIPTION

IRiS produces a subARG in two phases by given a collection of haplotypes. A combinatorial algorithm called the DSR is a model-based approach to detecting recombinations in haplotypes (with a guaranteed approximation factor). The algorithm is based on iteratively classifying sets of lineages as dominant, subdominant or recombinant (DSR). In the first phase, DSR is run multiple times with different sets of parameters and statistical consensus is derived from them to produce a matrix of recombination information called the recomatrix. This encodes the local topology information of only the high confidence recombination events detected in the first phase. The subARG is constructed from the recomatrix in the second phase

::DEVELOPER

IBM Computational Biology Center 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows

:: DOWNLOAD

 IRiS

:: MORE INFORMATION

Citation

Javed, A., Pybus, M., Melé, M., Utro, F., Bertranpetit, J., Calafell, F., and Parida, L.,
IRiS: Construction of ARG network at genomic scales,
Bioinformatics (2011) 27 (17): 2448-2450.

 

SCOPE 2.1.0 – Suite for Computational identification Of Promoter Elements

SCOPE 2.1.0

:: DESCRIPTION

SCOPE (Suite for Computational identification Of Promoter Elements) is an ensemble of programs aimed at identifying novel cis-regulatory elements from groups of upstream sequences.The SCOPE motif finder is designed to identify candidate regulatory DNA motifs from sets of genes that are coordinately regulated. SCOPE motif finder uses an ensemble of three programs behind the scenes to identify different kinds of motifs – BEAM identifies nondegenerate motifs (e.g. ACGTGC), PRISM identifies degenerate motifs (e.g. AWCGRYH), and SPACER identifies bipartite motifs (e.g. ACCNNNNNNNNNGTT). All parameters are automatically set to find the optimal length motif and degree of degeneracy in the reported motifs.

:: DEVELOPER

the lab of Prof. Robert H. Gross

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • Java

:: DOWNLOAD

 SCOPE

:: MORE INFORMATION

Citation:

Chakravarty, A, Carlson, JM, Khetani, RS, and Gross, RH,
“A novel ensemble learning method for de novo computational identification of DNA binding sites.”
BMC Bioinformatics 8: 249 (2007)

 

MISA – MIcroSAtellite Identification Tool

MISA

:: DESCRIPTION

MISA (MIcroSAtellite Identification Tool) allows the identification and localization of perfect microsatellites as well as compound microsatellites which are interrupted by a certain number of bases.

::DEVELOPER

Thomas Thiel @ the Plant Genome Resources Center (PGRC)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux/  MacOSX
  • Perl

:: DOWNLOAD

 MISA

:: MORE INFORMATION

Citation

Theor Appl Genet. 2003 Feb;106(3):411-22. Epub 2002 Sep 14.
Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L.).
Thiel T, Michalek W, Varshney RK, Graner A.

SPAM 3.7b – Genetic Stock Identification Software

SPAM 3.7b

:: DESCRIPTION

SPAM (Statistics Program for Analysing Mixtures) estimates the relative contributions of discrete populations to a mixture sample, solving what is commonly referred to in fisheries as the mixed stock analysis or genetic stock identification problem.

::DEVELOPER

Gene Conservation Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 SPAM

:: MORE INFORMATION

Citation

Debevec, Gates, Masuda, Pella, Reynolds, Seeb (2000),
“SPAM (version 3.2): statistics program for analyzing mixtures“,
Journal of Heredity, 91:509-511.