Tag: Discover

PhyloGibbs 1.2 – Discover Regulatory Sites in a Collection of DNA sequences

PhyloGibbs 1.2

:: DESCRIPTION

PhyloGibbs is an algorithm for discovering regulatory sites in a collection of DNA sequences, including multiple alignments of orthologous sequences from related organisms. Many existing approaches to either search for sequence-motifs that are overrepresented in the input data, or for sequence-segments that are more conserved evolutionary than expected. PhyloGibbs combines these two approaches and identifies significant sequence-motifs by taking both over-representation and conservation signals into account.

PhyloGibbs-MP is a significant enhancement.

::DEVELOPER

van Nimwegen Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX

:: DOWNLOAD

 PhyloGibbs

:: MORE INFORMATION

Citation:

Rahul Siddharthan, Eric D. Siggia, and Erik van Nimwegen
PhyloGibbs: A Gibbs Sampling Motif Finder That Incorporates Phylogeny
PLoS Comput Biol 1(7): e67 2005

bbq – Discovering Dis-regulatory modules

bbq

:: DESCRIPTION

bbq (Barbeques) is a command-line tool for discovering clusters of transcription factor binding sites that occur simultaneously in several genome sequences. Finding such clusters – which are sometimes also referred to as cis-regulatory modules – is done in a multiple-alignment-like fashion by solving a certain combinatorial and geometric optimization problem, the so-called best barbeque problem (explaining the name bbq). As opposed to classical, typically dynamic programming based, alignment procedures, the order of the binding sites’ occurences can be arbitrarily shuffled, so that bbq is the result of developing completely new algorithms.

::DEVELOPER

Bioinformatics Leipzig

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux /MacOsX

:: DOWNLOAD

 bbq

:: MORE INFORMATION

ATHENA 1.1.0 – Discover Epistasis among Quantitative Trait Loci

ATHENA 1.1.0

:: DESCRIPTION

ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) results in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications in silico using simulated datasets.

::DEVELOPER

Ritchie Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 ATHENA

:: MORE INFORMATION

Citation:

ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci.
Turner SD, Dudek SM, Ritchie MD.
BioData Min. 2010 Sep 27;3(1):5.

ViralFusionSeq 20160817 – Discover Viral Integration Events and Fusion Transcripts

ViralFusionSeq 20160817

:: DESCRIPTION

ViralFusionSeq (VFS) is a versatile high-throughput sequencing (HTS) tool for discovering viral integration events and reconstruct fusion transcripts at single-base resolution.

::DEVELOPER

ViralFusionSeq team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / WIndows/ MacOsX
  • Perl

:: DOWNLOAD

 ViralFusionSeq

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Mar 1;29(5):649-51. doi: 10.1093/bioinformatics/btt011. Epub 2013 Jan 12.
ViralFusionSeq: accurately discover viral integration events and reconstruct fusion transcripts at single-base resolution.
Li JW, Wan R, Yu CS, Co NN, Wong N, Chan TF.

DRIM – Discover Motifs in a list of ranked DNA sequences

DRIM

:: DESCRIPTION

DRIM (Discovery of Rank Imbalanced Motifs) is a tool for discovering short motifs in a list of nucleic acid sequences. DRIM was originally developed for DNA sequences and successfully applied on ChIP-chip and CpG methylation data. The current version has enhanced functionality and can be applied for both DNA and RNA. This new version was used to predict UTR motifs and Splicing Factor binding motifs based on RIP-Chip or CLIP data.
From a mathematical point of view, DRIM identifies subsequences that tend to appear at the top of the list more often than in the rest of the list. The definition of TOP in this context is flexible and driven by the data. Explicitly – DRIM identifies a threshold at which the statistical difference between top and rest is maximized. An exact p-value for the optimized event is also provided.

::DEVELOPER

Yakhini Lab and the Mandel-Gutfreund Lab, at the Technion.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 DRIM

:: MORE INFORMATION

Citation

E. Eden, D. Lipson, S. Yogev & Z. Yakhini.
Discovering Motifs in Ranked Lists of DNA Sequences,
PLoS Computational Biology, 2007.

SNPidentifier 1.10 – Discover SNPs based on CAP3 Alignments

SNPidentifier 1.10

:: DESCRIPTION

SNPidentifier is designed to predict the location of SNPs from clusters of ESTs produced by the program CAP3. SNPIDENTIFIER is designed for ESTs without accompanying chromatogram sequence quality information, and therefore it performs quality control checks on all data.

::DEVELOPER

NAGRP Bioinformatics Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/MacOsX/Linux
  • Perl

:: DOWNLOAD

 SNPidentifier

:: MORE INFORMATION

Citation

Gorbach, Danielle M., Zhi-Liang Hu, Zhi-Qiang Du and M. F. Rothschild (2009)
“SNP discovery in Litopenaeus vannamei with a new computational pipeline“.
Animal Genetics, 40(1):106-109.

Magallanes – Multi-Architecture Resources Discovering

Magallanes

:: DESCRIPTION

Magallanes is a versatile and platform-independent Java library of algorithms to built-up search engines to help in the discovery of services and datatypes specially oriented to deal with repositories of web-services and associated datatypes. A service or data-type discovery process aims to identify the set of services or data-types that satisfy a given number of constraints from the pool of all the available

Magallanes Online Version

::DEVELOPER

Bioinformatics and Information Technologies LaboratoryUniversity of Malaga

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • Java

:: DOWNLOAD

 Magallanes

:: MORE INFORMATION

Citation

Magallanes: a web services discovery and automatic workflow composition tool.
Ríos J, Karlsson J, Trelles O.
BMC Bioinformatics. 2009 Oct 15;10:334.

AVID – Discover Functional Relationships among Proteins

AVID

:: DESCRIPTION

AVID (Annotation via Integration of Data) is a computational method for predicting Gene Ontology (link to GO site) annotation terms using high-throughput experimental and sequence data. The method works by constructing functional correlation networks in which proteins are linked if they are likely to share a common GO descriptor. The networks are used to assign very specific functional annotations to individual proteins.

::DEVELOPER

Keating Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation:

“AVID: An integrative framework for discovering functional relationships among proteins”
Taijiao Jiang & Amy E Keating
BMC Bioinformatics 2005, 6:136 (1 Jun 2005)

DLocalMotif – Discover Local Motifs in Protein Sequences

DLocalMotif

:: DESCRIPTION

DLocalMotif is a discriminitive motif discovery web service specifically designed to discover local motifs in protein sequences that are aligned relative to a defined sequence landmark. It uses three discriminitive scoring features, motif spatial confinement (MSC), motif over-representation (MOR) and motif relative entropy (MRE). These features establish if a motif is positioned in a constrained sequence interval in positive data set and absent in negative data set.

::DEVELOPER

Bioinformatics group,The University of Queensland

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux / MacOSX
  • Java

:: DOWNLOAD

   DLocalMotif

:: MORE INFORMATION

Citation

Ahmed M. Mehdi, Muhammad Shoaib B. Sehgal, Bostjan Kobe, Timothy L. Bailey and Mikael Bodén
“DLocalMotif: A discriminative approach for discovering local motifs in protein sequences“,
Bioinformatics (2013) 29 (1): 39-46.

Voting 4 – Discover Patterns, Motif, in a set of DNA sequences

Voting 4

:: DESCRIPTION

Voting is a software package that discovers common patterns, motif, in a set of DNA sequences. Voting is efficient to solve the planted (l,d) motif problem which discover a hidden length-l string motif appear in each input DNA sequence with at most d Hamming distance. Our package guarantees discovering all motifs in a short time.

::DEVELOPER

Bioinformatics Research Group of Hong Kong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  Voting

:: MORE INFORMATION

Citation:

Francis Y.L. Chin and Henry C.M. Leung.
Voting Algorithms for Discovering Long Motifs (2005)
In Proceedings of Asia-Pacific Bioinformatics Conference (APBC) pages 261 – 271