Noncoding RNA gene detection using comparative sequence analysis

By Elena Rivas, Sean R. Eddy. BMC Bioinformatics, 2001

549 citations recorded by the source corpus.

BackgroundNoncoding RNA genes produce transcripts that exert their function without ever producing proteins. Noncoding RNA gene sequences do not have strong statistical signals, unlike protein coding genes. A reliable general purpose computational genefinder for noncoding RNA genes has been elusive.ResultsWe describe a comparative sequence analysis algorithm for detecting novel structural RNA genes. The key idea is to test the pattern of substitutions observed in a pairwise alignment of two homologous sequences. A conserved coding region tends to show a pattern of synonymous substitutions, whereas a conserved structural RNA tends to show a pattern of compensatory mutations consistent with some base-paired secondary structure. We formalize this intuition using three probabilistic "pair-grammars": a pair stochastic context free grammar modeling alignments constrained by structural RNA evolution, a pair hidden Markov model modeling alignments constrained by coding sequence evolution, and a pair hidden Markov model modeling a null hypothesis of position-independent evolution. Given an input pairwise sequence alignment (e.g. from a BLASTN comparison of two related genomes) we classify the alignment into the coding, RNA, or null class according to the posterior probability of each class.ConclusionsWe have implemented this approach as a program, QRNA, which we consider to be a prototype structural noncoding RNA genefinder. Tests suggest that this approach detects noncoding RNA genes with a fair degree of reliability.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/548ad22350ef9824fa77819e9febf16840494a9e

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Citations in the corpus

Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones (2004, unknown): “Out of 296 annotated putative ncRNAs, we identified 47 ncRNAs with conserved RNA secondary structure motifs (Rivas and Eddy 2001), and nearly 60% of these were found expressed in up to eight human tissues (data not shown), indicating that the manual curation strategy employed in this study may facil”
Differentiating Protein-Coding and Noncoding RNA: Challenges and Ambiguities (2008, methodology): “These include the programs QRNA [38], RNAz [39], and EvoFOLD [40].”
Identification and Classification of Conserved RNA Secondary Structures in the Human Genome (2006, background): “This evolutionary signal can be exploited for comparative identification of fRNAs [6–12].”
The Diploid Genome Sequence of an Individual Human (2007, background): “These genomic datasets have also enabled the identification of additional functional sequence such as cis-regulatory DNA [24–29] as well as both noncoding and microRNA [30–34] .”
Highly Conserved Non-Coding Sequences Are Associated with Vertebrate Development (2004, unknown): “We used the program QRNA [75] to see whether any of the BLAST matches had a pattern of mutation consistent with RNA secondary structure.”
Rfam: updates to the RNA families database (2008, background): “The rate of discovery of new RNA families is accelerating rapidly, facilitated by advancements in new sequencing technologies (12, 13) and targeted computational screens (14) (15) (16) (17) .”
Non–coding RNA genes and the modern RNA world (2001, background): “Elena Rivas and co-workers have developed a general ncRNA gene-finding algorith”
LocARNA-P: accurate boundary prediction and improved detection of structural RNAs. (2012, background): “Whereas EvoFold applies stochastic context-free grammars (SCFGs), an approach pioneered by qrna (Rivas and Eddy 2001), RNAz is based on the evaluation of folding thermodynamics and covariance.”
Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction (2004, methodology): “These include an SCFG-based mirror of the standard Zuker algorithm for single-sequence structure prediction [35], and two "pair-SCFG" approaches for simultaneous folding and alignment of two homologous RNAs [31,36].”
Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering (2007, methodology): “WhileEvoFold uses the SCFG approach pioneered by qrna [7], RNAz is based on eval-”
A benchmark of multiple sequence alignment programs upon structural RNAs (2005, methodology): “RNA structure analysis [1–5], RNA homology search [6, 7], ncRNA detection [8, 9] and RNA based phylogenetic inference [10, 11]).”
lincRNAs: Genomics, Evolution, and Mechanisms (2013, unknown): “, 2011), QRNA (Rivas and Eddy, 2001; Rivas et al., 2001), or RNAcode (Washietl et al.”
Cluster-Buster: finding dense clusters of motifs in DNA sequences (2003, background): “Unfortunately, the algorithm for finding these subsequences requires time proportional to the sequence length squared and is not feasible for sequences longer than a few kb (15).”
Fast and reliable prediction of noncoding RNAs (2005, unknown): “The most widely used program QRNA [22], which has been successfully used to identify ncRNAs in bacteria [23] and yeast [24], is not suitable for screens of large genomes: QRNA is limited to pairwise alignments and its reliability is low especially if the evolutionary distance of the two sequences li”
Pfold: RNA secondary structure prediction using stochastic context-free grammars (2003, methodology): “This method was successfully used in the RNA part of the non-coding RNA gene finding algorithm by Rivas and Eddy (18).”
MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. (2007, background): “S. mediterranea is an emerging model organism for regeneration studies following demonstrations that it is amenable to modern cell (Robb and Sánchez Alvarado 2002), molecular (Sánchez Alvarado et al. 2002), and RNAi (Sánchez Alvarado and Newmark 1999) techniques.”