GenoMiner: a tool for genome-wide search of coding and non-coding conserved sequence tags

Abstract

GenoMiner is a software tool that searches for regions of similarity between user-submitted genome or transcript sequences and user-specified whole genome assemblies. The program then identifies conserved sequence tags (CSTs) in these homologous regions and provides a prediction of their coding or non-coding nature. The analysis is carried out through three steps: (1) definition of sequence regions homologous to the query sequence in the selected target genomes by a fast BLAT alignment; (2) identification of CSTs by a more sensitive BLAST-like alignment between the query and the homologous regions in the target genomes and (3) assessment of the coding or non-coding nature of detected CSTs through the computation of a suitable coding potential score. GenoMiner allows the user to search the query sequence against a number of vertebrate genome assemblies in a single run providing a user-friendly graphical output.

Statistical assessment of discriminative features for protein-coding and non coding cross-species conserved sequence elements

“…Thus the problem is to reliably discriminate between coding and non-coding conserved sequences, as the first, when falling outside current annotations, may represent novel exons of alternative splicing variants or of unknown protein coding genes, while the latter are likely involved in some regulatory activity. We previously developed an heuristic method to measure the protein coding potential that resulted quite well performing [ 18 , 39 ]. However, we did not evaluate the specific contributions of different features on the discriminatory efficiency.…”

Genome-wide identification of coding and non-coding conserved sequence tags in human and mouse genomes

“…The CSTminer algorithm has been described in [ 6 ] and slightly modified in [ 7 ] where a web interface to run CSTminer was made available. A further automatic web system to compare a single sequence to several genomes has also been implemented in [ 25 ].…”

Genetic Association Studies: An Information Content Perspective

“…Schneidman [44] systematically examined the interrelated framework of the three measures of independence and correlation. Varadan [45] later on extended the synergy definition to a set of multiple SNPs { X 1 ; X 2 ... X n } and a phenotype Z as, $\begin{array}{c}\mathit{Syn}\left(X_1,X_2..., X_n;Z\right)\\ =I\left(X_1,X_2..., X_n\right)\\ -\underset{\mathit{all\; partitions\; into}{S}_i}{\mathrm{max }}\sum _{i}I\left(S_i,Z\right)\end{array}$ where the partition is denoted as U i S i = { X 1 , X 2 ... X n } and ∩ i S i = ø. The generalization from bivariate synergy to multivariate synergy is consistent with the intuitive idea that synergy is the extra information conveyed to the phenotype by comparing the information contribution between the entire set and the maximum information partition of the entire set.…”