SELEX_DB: an activated database on selected randomized DNA/RNA sequences addressed to genomic sequence annotation

SELEX_DB is a novel curated database on selected randomized DNA/RNA sequences designed for accumulation of experimental data on functional site sequences obtained by using SELEX and SELEX-like technologies from the pools of random sequences. This database also contains the programs for DNA/RNA functional site recognition within arbitrary nucleotide sequences. The first release of SELEX_DB has been installed under SRS and is available through the WWW at http://wwwmgs.bionet.nsc.ru/mgs/systems/selex/     

Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences

MOTIVATION: In 2001 and 2002, we published two papers (Bioinformatics, 17, 282-283, Bioinformatics, 18, 77-82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST.     

Pfold: RNA secondary structure prediction using stochastic context-free grammars

RNA secondary structures are important in many biological processes and efficient structure prediction can give vital directions for experimental investigations. Many available programs for RNA secondary structure prediction only use a single sequence at a time. This may be sufficient in some applications, but often it is possible to obtain related RNA sequences with conserved secondary structure. These should be included in structural analyses to give improved results. This work presents a practical way of predicting RNA secondary structure that is especially useful when related sequences can be obtained. The method improves a previous algorithm based on an explicit evolutionary model and a probabilistic model of structures. Predictions can be done on a web server at http://www.daimi.au.dk/~compbio/pfold.     

Small RNA database.

The small RNA database is a compilation of all the small size RNA sequences available to date from prokaryotic and eukaryotic organisms. About 500 small RNA sequences are in our database currently. The sources of individual RNAs and their GenBank accession numbers are also included. The small RNA database can be accessed through the World Wide Web(WWW). Our WWW URL is http://mbcr.bcm.tmc.edu/smallRNA/smallrna. html. The new small RNA sequences published since our last compilation are listed in this paper.     

Small RNA database.

The small RNA database is a compilation of all the small size RNA sequences available to date, including nuclear, nucleolar, cytoplasmic and mitochondria small RNAs from eukaryotic organisms and small RNAs from prokaryotic cells as well as viruses. Currently, approximately 600 small RNA sequences are in our database. It also gives the sources of individual RNAs and their GenBank accession numbers. The small RNA database can be accessed through the WWW (World Wide Web). Our WWW URL address is: http://mbcr.bcm.tmc. edu/smallRNA/smallrna.html . The new small RNA sequences published since our last compilation are listed in this paper (Table 1).     

Small RNA database.

The small RNA database is a compilation of all the small size RNA sequences available to date, including nuclear, nucleolar, cytoplasmic and mitochondrial small RNAs from eukaryotic organisms and small RNAs from prokaryotic cells as well as viruses. Currently, about 600 small RNA sequences are in our database. It also gives the sources of individual RNAs and their GenBank accession numbers. The small RNA database can be accessed through WWW(World Wide Web). Our WWW URL address is: http://mbcr.bcm.tmc.edu/smallRNA/smallrna. html . The new small RNA sequences published since our last compilation are listed in this paper.     

STRAL: progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time

MOTIVATION: Alignment of RNA has a wide range of applications, for example in phylogeny inference, consensus structure prediction and homology searches. Yet aligning structural or non-coding RNAs (ncRNAs) correctly is notoriously difficult as these RNA sequences may evolve by compensatory mutations, which maintain base pairing but destroy sequence homology. Ideally, alignment programs would take RNA structure into account. The Sankoff algorithm for the simultaneous solution of RNA structure prediction and RNA sequence alignment was proposed 20 years ago but suffers from its exponential complexity. A number of programs implement lightweight versions of the Sankoff algorithm by restricting its application to a limited type of structure and/or only pairwise alignment. Thus, despite recent advances, the proper alignment of multiple structural RNA sequences remains a problem. RESULTS: Here we present StrAl, a heuristic method for alignment of ncRNA that reduces sequence-structure alignment to a two-dimensional problem similar to standard multiple sequence alignment. The scoring function takes into account sequence similarity as well as up- and downstream pairing probability. To test the robustness of the algorithm and the performance of the program, we scored alignments produced by StrAl against a large set of published reference alignments. The quality of alignments predicted by StrAl is far better than that obtained by standard sequence alignment programs, especially when sequence homologies drop below approximately 65%; nevertheless StrAl's runtime is comparable to that of ClustalW.     

Alignment of RNA base pairing probability matrices

MOTIVATION: Many classes of functional RNA molecules are characterized by highly conserved secondary structures but little detectable sequence similarity. Reliable multiple alignments can therefore be constructed only when the shared structural features are taken into account. Since multiple alignments are used as input for many subsequent methods of data analysis, structure-based alignments are an indispensable necessity in RNA bioinformatics. RESULTS: We present here a method to compute pairwise and progressive multiple alignments from the direct comparison of base pairing probability matrices. Instead of attempting to solve the folding and the alignment problem simultaneously as in the classical Sankoff's algorithm, we use McCaskill's approach to compute base pairing probability matrices which effectively incorporate the information on the energetics of each sequences. A novel, simplified variant of Sankoff's algorithms can then be employed to extract the maximum-weight common secondary structure and an associated alignment. AVAILABILITY: The programs pmcomp and pmmulti described in this contribution are implemented in Perl and can be downloaded together with the example datasets from http://www.tbi.univie.ac.at/RNA/PMcomp/. A web server is available at http://rna.tbi.univie.ac.at/cgi-bin/pmcgi.pl     

Murlet: a practical multiple alignment tool for structural RNA sequences

MOTIVATION: Structural RNA genes exhibit unique evolutionary patterns that are designed to conserve their secondary structures; these patterns should be taken into account while constructing accurate multiple alignments of RNA genes. The Sankoff algorithm is a natural alignment algorithm that includes the effect of base-pair covariation in the alignment model. However, the extremely high computational cost of the Sankoff algorithm precludes its application to most RNA sequences. RESULTS: We propose an efficient algorithm for the multiple alignment of structural RNA sequences. Our algorithm is a variant of the Sankoff algorithm, and it uses an efficient scoring system that reduces the time and space requirements considerably without compromising on the alignment quality. First, our algorithm computes the match probability matrix that measures the alignability of each position pair between sequences as well as the base pairing probability matrix for each sequence. These probabilities are then combined to score the alignment using the Sankoff algorithm. By itself, our algorithm does not predict the consensus secondary structure of the alignment but uses external programs for the prediction. We demonstrate that both the alignment quality and the accuracy of the consensus secondary structure prediction from our alignment are the highest among the other programs examined. We also demonstrate that our algorithm can align relatively long RNA sequences such as the eukaryotic-type signal recognition particle RNA that is approximately 300 nt in length; multiple alignment of such sequences has not been possible by using other Sankoff-based algorithms. The algorithm is implemented in the software named 'Murlet'. AVAILABILITY: The C++ source code of the Murlet software and the test dataset used in this study are available at http://www.ncrna.org/papers/Murlet/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Predicting RNA Structure Using Mutual Information

BACKGROUND: With the ever-increasing number of sequenced RNAs and the establishment of new RNA databases, such as the Comparative RNA Web Site and Rfam, there is a growing need for accurately and automatically predicting RNA structures from multiple alignments. Since RNA secondary structure is often conserved in evolution, the well known, but underused, mutual information measure for identifying covarying sites in an alignment can be useful for identifying structural elements. This article presents MIfold, a MATLAB toolbox that employs mutual information, or a related covariation measure, to display and predict conserved RNA secondary structure (including pseudoknots) from an alignment. RESULTS: We show that MIfold can be used to predict simple pseudoknots, and that the performance can be adjusted to make it either more sensitive or more selective. We also demonstrate that the overall performance of MIfold improves with the number of aligned sequences for certain types of RNA sequences. In addition, we show that, for these sequences, MIfold is more sensitive but less selective than the related RNAalifold structure prediction program and is comparable with the COVE structure prediction package. CONCLUSION: MIfold provides a useful supplementary tool to programs such as RNA Structure Logo, RNAalifold and COVE, and should be useful for automatically generating structural predictions for databases such as Rfam.     

RSEARCH: Finding homologs of single structured RNA sequences

Background

For many RNA molecules, secondary structure rather than primary sequence is the evolutionarily conserved feature. No programs have yet been published that allow searching a sequence database for homologs of a single RNA molecule on the basis of secondary structure.

Results

We have developed a program, RSEARCH, that takes a single RNA sequence with its secondary structure and utilizes a local alignment algorithm to search a database for homologous RNAs. For this purpose, we have developed a series of base pair and single nucleotide substitution matrices for RNA sequences called RIBOSUM matrices. RSEARCH reports the statistical confidence for each hit as well as the structural alignment of the hit. We show several examples in which RSEARCH outperforms the primary sequence search programs BLAST and SSEARCH. The primary drawback of the program is that it is slow. The C code for RSEARCH is freely available from our lab's website.

Conclusion

RSEARCH outperforms primary sequence programs in finding homologs of structured RNA sequences.

     

RNA Sampler: a new sampling based algorithm for common RNA secondary structure prediction and structural alignment

MOTIVATION: Non-coding RNA genes and RNA structural regulatory motifs play important roles in gene regulation and other cellular functions. They are often characterized by specific secondary structures that are critical to their functions and are often conserved in phylogenetically or functionally related sequences. Predicting common RNA secondary structures in multiple unaligned sequences remains a challenge in bioinformatics research. Methods and RESULTS: We present a new sampling based algorithm to predict common RNA secondary structures in multiple unaligned sequences. Our algorithm finds the common structure between two sequences by probabilistically sampling aligned stems based on stem conservation calculated from intrasequence base pairing probabilities and intersequence base alignment probabilities. It iteratively updates these probabilities based on sampled structures and subsequently recalculates stem conservation using the updated probabilities. The iterative process terminates upon convergence of the sampled structures. We extend the algorithm to multiple sequences by a consistency-based method, which iteratively incorporates and reinforces consistent structure information from pairwise comparisons into consensus structures. The algorithm has no limitation on predicting pseudoknots. In extensive testing on real sequence data, our algorithm outperformed other leading RNA structure prediction methods in both sensitivity and specificity with a reasonably fast speed. It also generated better structural alignments than other programs in sequences of a wide range of identities, which more accurately represent the RNA secondary structure conservations. AVAILABILITY: The algorithm is implemented in a C program, RNA Sampler, which is available at http://ural.wustl.edu/software.html     

Database on the structure of large ribosomal subunit RNA.

The latest release of the large ribosomal subunit RNA database contains 429 sequences. All these sequences are aligned, and incorporate secondary structure information. The rRNA WWW Server at URL http://rrna.uia.ac.be/ provides researchers with an easily accessible resource to obtain the data in this database in a number of computer-readable formats. A new query interface has been added to the server. If necessary, the data can also be obtained by anonymous ftp from the same site.     

Sequence search algorithm assessment and testing toolkit (SAT)

MOTIVATION: The Sequence Search Algorithm Assessment and Testing Toolkit (SAT) aims to be a complete package for the comparison of different protein homology search algorithms. The structural classification of proteins can provide us with a clear criterion for judgment in homology detection. There have been several assessments based on structural sequences with classifications but a good deal of similar work is now being repeated with locally developed procedures and programs. The SAT will provide developers with a complete package which will save time and produce more comparable performance assessments for search algorithms. The package is complete in the sense that it provides a non-redundant large sequence resource database, a well-characterized query database of proteins domains, all the parsers and some previous results from PSI-BLAST and a hidden markov model algorithm. RESULTS: An analysis on two different data sets was carried out using the SAT package. It compared the performance of a full protein sequence database (RSDB100) with a non-redundant representative sequence database derived from it (RSDB50). The performance measurement indicated that the full database is sub-optimal for a homology search. This result justifies the use of much smaller and faster RSDB50 than RSDB100 for the SAT. AVAILABILITY: A web site is up. The whole packa ge is accessible via www and ftp. ftp://ftp.ebi.ac.uk/pub/contrib/jong/SAT http://cyrah.ebi.ac.uk:1111/Proj/Bio/SAT http://www.mrc-lmb.cam.ac.uk/genomes/SAT In the package, some previous assessment results produced by the package can also be found for reference. CONTACT: jong@ebi.ac.uk     

Searching RNA motifs and their intermolecular contacts with constraint networks

MOTIVATION: Searching RNA gene occurrences in genomic sequences is a task whose importance has been renewed by the recent discovery of numerous functional RNA, often interacting with other ligands. Even if several programs exist for RNA motif search, none exists that can represent and solve the problem of searching for occurrences of RNA motifs in interaction with other molecules. RESULTS: We present a constraint network formulation of this problem. RNA are represented as structured motifs that can occur on more than one sequence and which are related together by possible hybridization. The implemented tool MilPat is used to search for several sRNA families in genomic sequences. Results show that MilPat allows to efficiently search for interacting motifs in large genomic sequences and offers a simple and extensible framework to solve such problems. New and known sRNA are identified as H/ACA candidates in Methanocaldococcus jannaschii. AVAILABILITY: http://carlit.toulouse.inra.fr/MilPaT/MilPat.pl.     

The guide RNA database (3.0).

The RNA editing process within the mitochondria of kinetoplastid organisms is controlled by small, trans -acting RNA molecules referred to as guide RNAs. The guide RNA database is a compilation of published guide RNA sequences, currently containing 254 entries from 11 different organisms. Additional information includes RNA secondary and tertiary structure models, information on the gene localisation, literature citations and other relevant facts. The database can be accessed through the World Wide Web (WWW) at http://www.biochem.mpg.de/ goeringe/     

A graph theoretical approach for predicting common RNA secondary structure motifs including pseudoknots in unaligned sequences

MOTIVATION: RNA structure motifs contained in mRNAs have been found to play important roles in regulating gene expression. However, identification of novel RNA regulatory motifs using computational methods has not been widely explored. Effective tools for predicting novel RNA regulatory motifs based on genomic sequences are needed. RESULTS: We present a new method for predicting common RNA secondary structure motifs in a set of functionally or evolutionarily related RNA sequences. This method is based on comparison of stems (palindromic helices) between sequences and is implemented by applying graph-theoretical approaches. It first finds all possible stable stems in each sequence and compares stems pairwise between sequences by some defined features to find stems conserved across any two sequences. Then by applying a maximum clique finding algorithm, it finds all significant stems conserved across at least k sequences. Finally, it assembles in topological order all possible compatible conserved stems shared by at least k sequences and reports a number of the best assembled stem sets as the best candidate common structure motifs. This method does not require prior structural alignment of the sequences and is able to detect pseudoknot structures. We have tested this approach on some RNA sequences with known secondary structures, in which it is capable of detecting the real structures completely or partially correctly and outperforms other existing programs for similar purposes. AVAILABILITY: The algorithm has been implemented in C++ in a program called comRNA, which is available at http://ural.wustl.edu/softwares.html     

Visualization of nucleic acid sequence structural information

Several interactive Pascal programs have been written for theanalysis and display of structural information in nucleic acidsequences. Layout procedures were developed to display the homologyand repeat matrices of a sequence and to predict and displaythe secondary structure of RNA/DNA molecules free of overlapand to predict and display internal repeats. No special plottingdevices are required because the output is adapted to line printers.Sequences from several DNA database systems can be used as input.These programs are part of a general nucleic acid sequence analysispackage. Received on December 9, 1984; accepted on January 11, 1985     

Methylation blockage and other improvements to a comprehensive DNA analysis program.

A comprehensive DNA analysis computer program was described in the second special issue of Nucleic Acids Research on the applications of computers to research on nucleic acids by Stone and Potter (1). Criteria used in designing the program were user friendliness, ability to handle large DNA sequences, low storage requirement, migratability to other computers and comprehensive analysis capability. The program has been used extensively in an industrial-research environment. This paper talks about improvements to that program. These improvements include testing for methylation blockage of restriction enzyme recognition sites, homology analysis, RNA folding analysis, integration of a large DNA database (GenBank), a site specific mutagenesis analysis, a protein database and protein searching programs. The original design of the DNA analysis program using a command executive from which any analytical programs can be called, has proven to be extremely versatile in integrating both developed and outside programs to the file management system employed.