Dataset Information

RegExpBlasting (REB), a Regular Expression Blasting algorithm based on multiply aligned sequences.

ABSTRACT:

Background

One of the most frequent uses of bioinformatics tools concerns functional characterization of a newly produced nucleotide sequence (a query sequence) by applying Blast or FASTA against a set of sequences (the subject sequences). However, in some specific contexts, it is useful to compare the query sequence against a cluster such as a MultiAlignment (MA). We present here the RegExpBlasting (REB) algorithm, which compares an unclassified sequence with a dataset of patterns defined by application of Regular Expression rules to a given-as-input MA datasets. The REB algorithm workflow consists in i. the definition of a dataset of multialignments ii. the association of each MA to a pattern, defined by application of regular expression rules; iii. automatic characterization of a submitted biosequence according to the function of the sequences described by the pattern best matching the query sequence.

Results

An application of this algorithm is used in the "characterize your sequence" tool available in the PPNEMA resource. PPNEMA is a resource of Ribosomal Cistron sequences from various species, grouped according to nematode genera. It allows the retrieval of plant nematode multialigned sequences or the classification of new nematode rDNA sequences by applying REB. The same algorithm also supports automatic updating of the PPNEMA database. The present paper gives examples of the use of REB within PPNEMA.

Conclusion

The use of REB in PPNEMA updating, the PPNEMA "characterize your sequence" option clearly demonstrates the power of the method. Using REB can also rapidly solve any other bioinformatics problem, where the addition of a new sequence to a pre-existing cluster is required. The statistical tests carried out here show the powerful flexibility of the method.

SUBMITTER: Rubino F

PROVIDER: S-EPMC2697652 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Publications

RegExpBlasting (REB), a Regular Expression Blasting algorithm based on multiply aligned sequences.

Rubino Francesco F Attimonelli Marcella M

BMC bioinformatics 20090616

<h4>Background</h4>One of the most frequent uses of bioinformatics tools concerns functional characterization of a newly produced nucleotide sequence (a query sequence) by applying Blast or FASTA against a set of sequences (the subject sequences). However, in some specific contexts, it is useful to compare the query sequence against a cluster such as a MultiAlignment (MA). We present here the RegExpBlasting (REB) algorithm, which compares an unclassified sequence with a dataset of patterns defin ...[more]

PMID: 19534754

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Project description:BackgroundComputational prediction of noncoding RNAs (ncRNAs) is an important task in the post-genomic era. One common approach is to utilize the profile information contained in alignment data rather than single sequences. However, this strategy involves the possibility that the quality of input alignments can influence the performance of prediction methods. Therefore, the evaluation of the robustness against alignment errors is necessary as well as the development of accurate prediction methods.ResultsWe describe a new method, called Profile BPLA kernel, which predicts ncRNAs from alignment data in combination with support vector machines (SVMs). Profile BPLA kernel is an extension of base-pairing profile local alignment (BPLA) kernel which we previously developed for the prediction from single sequences. By utilizing the profile information of alignment data, the proposed kernel can achieve better accuracy than the original BPLA kernel. We show that Profile BPLA kernel outperforms the existing prediction methods which also utilize the profile information using the high-quality structural alignment dataset. In addition to these standard benchmark tests, we extensively evaluate the robustness of Profile BPLA kernel against errors in input alignments. We consider two different types of error: first, that all sequences in an alignment are actually ncRNAs but are aligned ignoring their secondary structures; second, that an alignment contains unrelated sequences which are not ncRNAs but still aligned. In both cases, the effects on the performance of Profile BPLA kernel are surprisingly small. Especially for the latter case, we demonstrate that Profile BPLA kernel is more robust compared to the existing prediction methods.ConclusionsProfile BPLA kernel provides a promising way for identifying ncRNAs from alignment data. It is more accurate than the existing prediction methods, and can keep its performance under the practical situations in which the quality of input alignments is not necessarily high.

Dataset Information

RegExpBlasting (REB), a Regular Expression Blasting algorithm based on multiply aligned sequences.

Background

Results

Conclusion

Publications

RegExpBlasting (REB), a Regular Expression Blasting algorithm based on multiply aligned sequences.

Similar Datasets

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