TSNI assumes that the gene network can be modeled by the following system of ordinary differential equation to represent the rate of synthesis of a transcript as a function of the concentrations of every other transcript in a cell and the external perturbation.
StarORF facilitates the identification of the protein(s) encoded within a DNA sequence. Using StarORF, the DNA sequence is first transcribed into RNA and then translated into all the potential ORFs (Open Reading Frame) encoded within each of the six translation frames (3 in the forward direction and 3 in the reverse direction).
TSRchitect allows the user to efficiently identify the putative promoter (the transcription start region, or TSR) from a variety of TSS profiling data types, including both single-end (e.g. CAGE) as well as paired-end (RAMPAGE, PEAT, STRIPE-seq).
MetWAMer currently implements five distinct methods for TIS prediction, the most accurate of which is a routine that combines weighted, signal-based translation initiation site scores and the contrast in coding potential of sequences flanking TISs using a perceptron. Also, our program implements clustering capabilities through use of the k-medoids algorithm, thereby enabling cluster-specific TIS parameter utilization. In practice, our static weight array matrix-based indexing method for parameter set lookup can be used with good results in data sets exhibiting moderate levels of 5′-complete coverage.
Marina is an OS-independent GUI tool for computing TFBS abundance given two sets of promoter sequences. Marina performs such computations by harnessing 7 knowledge-discovery statistical metrics and the hypergeometric distribution so as to infer magnitude of TFBS over-representation. A standardization algorithm known as Iterative Proportional Fitting (IPF) enables “agreement” across these various metrics as to which TFBSs are the most over-represented and which are not.
LcaMap is a software for simultaneous identification of duplication, losses and lateral gene transfers. LcaMap takes a gene tree G, a species tree S, and the costs of a lateral gene transfer, a gene duplication, and a gene loss as its input. It outputs all minimum-cost LCA-reconciliations between G and S.
SEGID is a bioinformatics webtool for conserved SEGment IDentification. The software is a sequence analysis tool designed to identify conserved segments in a (multiple) sequence alignment. Conserved segments are high-scoring substrings in a long alignment which are probably biologically meaningful. SEGID accepts an alignment, converts the alignment into a sequence of numbers, one for each column, identifies its conserved segments, and generates graphical output. (It can also directly accept a sequence of numbers as input.)