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Prediction of signal recognition particle RNA genes

BACKGROUND: The signal recognition particle (SRP) is a cytoplasmic ribonucleoprotein complex responsible for the cotranslational targeting of secretory and membrane proteins to the endoplasmic reticulum in eukaryotes or to the plasma membrane in prokaryotes. SRP RNA is a noncoding RNA component of SRP present in all living cells so far examined. We describe a method for prediction of genes that encode SRP RNA. A heuristic search for the strongly conserved helix 8 motif is combined with covariance models that are based on previously known SRP RNA sequences.
RESULTS: By screening available genomic sequences, we have identified a large number of novel SRP RNA genes and we can account for at least one gene in every genome that has been completely sequenced. Novel bacterial RNAs include that of Thermotoga maritima that, unlike all other non-gram positive eubacteria, is predicted to have an Alu domain. We have also found the RNAs of Lactococcus lactis and Staphylococcus to have an unusual UGAC tetraloop in helix 8 instead of the normal GNRA sequence. An investigation of yeast RNAs reveals conserved sequence elements of the Alu domain that aid in the analysis of these RNAs. Inspection of the human genome reveals only two likely genes, both on chromosome 14.
CONCLUSIONS: Our method for prediction of SRP RNA genes is the first convenient tool for this task that demonstrated to be efficient and accurate and it should be therefore helpful in genome annotation.

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- 5 - 1. Introduction Whereas there are many efficient tools for the identification of protein genes in genome sequences, we lack tools for the analysis of many noncoding RNAs. Therefore, most of the finished genome sequences published today lack annotation information about these RNAs. One difficulty with the identification of noncoding RNAs is that sequence tends to be poorly conserved and, therefore, standard tools such as BLAST [93] may be used only in the identification of orthologs in closely related organisms. On the other hand, many noncoding RNAs tend to have conserved secondary structure elements that may be used for their identification. Methods that have been used so far include the covariance models developed by Eddy and Durbin [94] as well as pattern matching, which is based on regular expression matching where also base-pairing schemes are taken into account. In the latter category, there is PatScan and PatSearch [95, 96, 97] as well as the rnabob tool of Sean Eddy [98]. In this work, we have studied methods to identify the RNA component of the signal recognition particle (SRP). The result is an automatic computer-based procedure that, starting from genomic data, finds genes coding for SRP RNA and hence scores, aligns and folds them. This procedure is available online as a public accessible web page [1]. The first part of this thesis will give a theoretical overview of both noncoding RNA and signal recognition particle structure and function. A brief description of the statistical algorithms on which the programs are based will be given too. This section is intended to provide all the biological and computer science background necessary for a complete understanding of the material discussed in the following chapters. It intentionally includes

Tesi di Dottorato

Dipartimento: Biologia

Autore: Marco Regalia Contatta »

Composta da 88 pagine.


Questa tesi ha raggiunto 687 click dal 20/03/2004.


Consultata integralmente una volta.

Disponibile in PDF, la consultazione è esclusivamente in formato digitale.