Biomedical subjects

André Lambert

Publications and source records attributed to André Lambert.

4 recordsLinked to original sources

PubMed2005

Computing expectation values for RNA motifs using discrete convolutions.

BACKGROUND: Computational biologists use Expectation values (E-values) to estimate the number of solutions that can be expected by chance during a database scan. Here we focus on computing Expectation values for RNA motifs defined by single-strand and helix lod-score profiles with variable helix spans. Such E-values cannot be computed assuming a normal score distribution and their estimation previously required lengthy simulations. RESULTS: We introduce discrete convolutions as an accurate and fast mean to estimate score distributions of lod-score profiles. This method provides excellent score estimations for all single-strand or helical elements tested and also applies to the combination of elements into larger, complex, motifs. Further, the estimated distributions remain accurate even when pseudocounts are introduced into the lod-score profiles. Estimated score distributions are then easily converted into E-values. CONCLUSION: A good agreement was observed between computed E-values and simulations for a number of complete RNA motifs. This method is now implemented into the ERPIN software, but it can be applied as well to any search procedure based on ungapped profiles with statistically independent columns.

Algorithms↗

PubMed2004

Profile-based detection of microRNA precursors in animal genomes.

MOTIVATION: MicroRNAs (miRNA) are essential 21-22 nt regulatory RNAs produced from larger hairpin-like precursors. Local sequence alignment tools such as BLAST are able to identify new members of known miRNA families, but not all of them. We set out to estimate how many new miRNAs could be recovered using a profile-based strategy such as that implemented in the ERPIN program. RESULTS: We constructed alignments for 18 miRNA families and performed ERPIN searches on animal genomes. Results were compared to those of a WU-BLAST search at the same E-value cutoff. The two combined approaches produced 265 new miRNA candidates that were not found in miRNA databases. About 17% of hits were ERPIN specific. They showed better structural characteristics than BLAST-specific hits and included interesting candidates such as members of the miR-17 cluster in Tetraodon. Profile-based RNA detection will be an important complement of similarity search programs in the completion of miRNA collections.

Algorithms↗

PubMed2004

The ERPIN server: an interface to profile-based RNA motif identification.

ERPIN is an RNA motif identification program that takes an RNA sequence alignment as an input and identifies related sequences using a profile-based dynamic programming algorithm. ERPIN differs from other RNA motif search programs in its ability to capture subtle biases in the training set and produce highly specific and sensitive searches, while keeping CPU requirements at a practical level. In its latest version, ERPIN also computes E-values, which tell biologists how likely they are to encounter a specific sequence match by chance-a useful indication of biological significance. We present here the ERPIN online search interface (http://tagc.univ-mrs.fr/erpin/). This web server automatically performs ERPIN searches for different RNA genes or motifs, using predefined training sets and search parameters. With a couple of clicks, users can analyze an entire bacterial genome or a genomic segment of up to 5Mb for the presence of tRNAs, 5S rRNAs, SRP RNA, C/D box snoRNAs, hammerhead motifs, miRNAs and other motifs. Search results are displayed with sequence, score, position, E-value and secondary structure graphics. An example of a complete genome scan is provided, as well as an evaluation of run times and specificity/sensitivity information for all available motifs.

Internet↗

PubMed2002

A survey of metazoan selenocysteine insertion sequences.

The computational detection of novel selenoproteins in genomic sequences is usually achieved through identification of SECIS, a conserved secondary structure element found in the 3' UTR of animal selenoprotein mRNAs. Previous studies have used "descriptors" specifying the number of base pairs and the conserved nucleotides in SECIS to identify this element. A major drawback of the "descriptor" approach is that the number of detections in current genomic or transcript databases largely exceeds the number of true selenoproteins. In this study, we use instead the ERPIN program to detect SECIS elements. ERPIN is based on a lod-score profile algorithm that uses a training-set of aligned RNA sequences as input. From an initial alignment of 44 animal SECIS sequences, we performed a series of iterative searches in which the training set was progressively enriched up to 117 confirmed SECIS elements, from a large collection of metazoan species. About 200 high-scoring candidates were also detected. We show that ERPIN scores for these candidates can be converted into expect values, thus enabling their statistical evaluation. The most interesting SECIS candidates are presented.

Animals↗