2001
DOI: 10.1093/nar/29.19.3928
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A computational approach to identify genes for functional RNAs in genomic sequences

Abstract: Currently there is no successful computational approach for identification of genes encoding novel functional RNAs (fRNAs) in genomic sequences. We have developed a machine learning approach using neural networks and support vector machines to extract common features among known RNAs for prediction of new RNA genes in the unannotated regions of prokaryotic and archaeal genomes. The Escherichia coli genome was used for development, but we have applied this method to several other bacterial and archaeal genomes.… Show more

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Cited by 167 publications
(124 citation statements)
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“…A QRNA screen of E. coli resulted in an estimate of about 200 structural ncRNA genes in this organism (11), a number that is roughly consistent with the results of three other screens (8)(9)(10). Thirty-four different loci identified in these screens have been experimentally shown to express small stable RNAs thus far.…”
Section: Discussionsupporting
confidence: 67%
See 1 more Smart Citation
“…A QRNA screen of E. coli resulted in an estimate of about 200 structural ncRNA genes in this organism (11), a number that is roughly consistent with the results of three other screens (8)(9)(10). Thirty-four different loci identified in these screens have been experimentally shown to express small stable RNAs thus far.…”
Section: Discussionsupporting
confidence: 67%
“…This approach obviously requires the genome sequence of an organism for which transcriptional regulation is well understood. Carter et al (9) used a neural network to classify genomic sequences based on several features, including GC composition. Two other approaches used a comparative genomics approach, requiring genomic sequence from related organisms as well as that of E. coli.…”
mentioning
confidence: 99%
“…Major findings of this analysis are: (1) As many as 2481 pairs of sense-antisense transcript were identified; (2) there is a strong bias in the frequency of the mapping patterns of the sense-antisense transcript pairs (few only on the X chromosome); (3) , predicting noncoding antisense transcripts on the genome sequences by computer programs is almost impossible because virtually all the existing programs are designed to predict protein-coding regions (exons). Recently, trials to predict ncRNA were attempted using computational algorithms (Argaman et al 2001;Carter et al 2001;Rivas et al, 2001;Wassarman et al 2001), but these attempts were possible only for ncRNA that shares structural similarities. The antisense RNA is expected to function through sequence complementarity to the sense strand.…”
Section: Discussionmentioning
confidence: 99%
“…Most identified CNSs have no experimentally defined function (16). There are now several web-based tools that can be used to identify CNSs (17,18) and functional RNAs (19). Very few examples of CNSs have been reported in plants (20,21).…”
mentioning
confidence: 99%