Dataset Information

Searching for non-coding RNAs in genomic sequences using ncRNAscout.

ABSTRACT: Recently non-coding RNA (ncRNA) genes have been found to serve many important functions in the cell such as regulation of gene expression at the transcriptional level. Potentially there are more ncRNA molecules yet to be found and their possible functions are to be revealed. The discovery of ncRNAs is a difficult task because they lack sequence indicators such as the start and stop codons displayed by protein-coding RNAs. Current methods utilize either sequence motifs or structural parameters to detect novel ncRNAs within genomes. Here, we present an ab initio ncRNA finder, named ncRNAscout, by utilizing both sequence motifs and structural parameters. Specifically, our method has three components: (i) a measure of the frequency of a sequence, (ii) a measure of the structural stability of a sequence contained in a t-score, and (iii) a measure of the frequency of certain patterns within a sequence that may indicate the presence of ncRNA. Experimental results show that, given a genome and a set of known ncRNAs, our method is able to accurately identify and locate a significant number of ncRNA sequences in the genome. The ncRNAscout tool is available for downloading at http://bioinformatics.njit.edu/ncRNAscout.

SUBMITTER: Bao M

PROVIDER: S-EPMC5054157 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Searching for non-coding RNAs in genomic sequences using ncRNAscout.

Bao Michael M Cervantes Cervantes Miguel M Zhong Ling L Wang Jason T L JT

Genomics, proteomics & bioinformatics 20120401 2

Recently non-coding RNA (ncRNA) genes have been found to serve many important functions in the cell such as regulation of gene expression at the transcriptional level. Potentially there are more ncRNA molecules yet to be found and their possible functions are to be revealed. The discovery of ncRNAs is a difficult task because they lack sequence indicators such as the start and stop codons displayed by protein-coding RNAs. Current methods utilize either sequence motifs or structural parameters to ...[more]

PMID: 22768985

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Project description:BackgroundIdentification of RNA homologs within genomic stretches is difficult when pairwise sequence identity is low or unalignable flanking residues are present. In both cases structure-sequence or profile/family-sequence alignment programs become difficult to apply because of unreliable RNA structures or family alignments. As such, local sequence-sequence alignment programs are frequently used instead. We have recently demonstrated that maximal expected accuracy alignments using partition function match probabilities (implemented in Probalign) are significantly better than contemporary methods on heterogeneous length protein sequence datasets, thus suggesting an affinity for local alignment.ResultsWe create a pairwise RNA-genome alignment benchmark from RFAM families with average pairwise sequence identity up to 60%. Each dataset contains a query RNA aligned to a target RNA (of the same family) embedded in a genomic sequence at least 5K nucleotides long. To simulate common conditions when exact ends of an ncRNA are unknown, each query RNA has 5' and 3' genomic flanks of size 50, 100, and 150 nucleotides. We subsequently compare the error of the Probalign program (adjusted for local alignment) to the commonly used local alignment programs HMMER, SSEARCH, and BLAST, and the popular ClustalW program with zero end-gap penalties. Parameters were optimized for each program on a small subset of the benchmark. Probalign has overall highest accuracies on the full benchmark. It leads by 10% accuracy over SSEARCH (the next best method) on 5 out of 22 families. On datasets restricted to maximum of 30% sequence identity, Probalign's overall median error is 71.2% vs. 83.4% for SSEARCH (P-value < 0.05). Furthermore, on these datasets Probalign leads SSEARCH by at least 10% on five families; SSEARCH leads Probalign by the same margin on two of the fourteen families. We also demonstrate that the Probalign mean posterior probability, compared to the normalized SSEARCH Z-score, is a better discriminator of alignment quality. All datasets and software are available online.ConclusionWe demonstrate, for the first time, that partition function match probabilities used for expected accuracy alignment, as done in Probalign, provide statistically significant improvement over current approaches for identifying distantly related RNA sequences in larger genomic segments.

Dataset Information

Searching for non-coding RNAs in genomic sequences using ncRNAscout.

Publications

Searching for non-coding RNAs in genomic sequences using ncRNAscout.

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