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.
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.
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.
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.
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.
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
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.
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.