non - coding rna ( ncrna ) genes produce a functional rna product instead of a translated protein . these products are components of some of the most important cellular machines , such as the ribosome ( ribosomal rnas ) , the spliceosome ( u1 , u2 , u4 , u5 and u6 rnas ) and the telomerase ( telomerase rna ) . small nucleolar rnas ( snornas ) guide essential modifications of ribosomal and spliceosomal rnas [ reviewed in ( 1 ) ] . ribozymes catalyse a range of reactions , such as self - cleavage of hepatitis delta virus transcripts , and 5 maturation of transfer rnas ( trnas ) by the ubiquitous rnase p. a class of small rnas almost unknown before 2000 , the micrornas ( mirnas ) , are found to be involved in regulation of ever more processes in higher eukaryotes  including development , cell death and fat metabolism  by repressing the translation of mrna targets [ reviewed in ( 2 ) ] . similar mrna - binding regulatory roles in bacteria are fulfilled by distinct families of small rnas [ reviewed in ( 3 ) ] . like protein - coding genes , ncrna sequences can be grouped into families and much can be learnt about structure and function from multiple sequence alignments of such families . unlike proteins , ncrnas often conserve a base - paired secondary structure with low primary sequence similarity . the combined secondary structure and primary sequence profile of a multiple sequence alignment of ncrnas can be captured by statistical models , called profile stochastic context - free grammars ( scfgs ) , analogous to profile hidden markov models ( hmms ) of protein alignments . rfam is a database of ncrna families represented by multiple sequence alignments and profile scfgs , available via the web at http://www.sanger.ac.uk/software/rfam/ and http://rfam.wustl.edu/. all the data are also available for download , local installation and sequence searching using the infernal software package ( http://infernal.wustl.edu/ ) ( 4 ) . the rfam / infernal model is much like the pfam / hmmer system ( 5 ) , extended to deal with rna secondary structure consensus , and has been discussed previously ( 6 ) . here , we concentrate on recent improvements and discuss challenges that we expect to address through future development . the database has grown dramatically over the past two years : from 25 families annotating around 55  000 regions in the nucleotide sequences databases in release 1.0 , to 379 families annotating over 280  000 regions in release 6.1 . the evolution of some large gene families , such as mirnas and snornas , is constrained partially by inter - molecular base - pairing , and thus they do not conserve significant sequence or secondary structure . while we can not therefore represent all c / d box snornas , or all mirnas , with a single alignment and model , subfamilies are conserved and are now well represented in the database . rfam also now includes not only bona fide ncrna genes , but also structured regions of mrna transcripts . these fall into two broad classes : self - splicing introns and cis - regulatory elements in the untranslated regions ( utrs ) . the latter can be used as detectors for a wide range of environmental conditions [ e.g. bacterial riboswitches bind a range of metabolites as reviewed previously ( 7,8 ) , and the 5-utr of the prfa acts as a temperature - dependent switch ( 9 ) ] to regulate message stability or translational efficiency . this increased scope has led to the introduction of a limited type ontology , with the top - level types representing the three classes of structured rna discussed abovegene , intron and  cis - reg. the database currently contains 308 gene families , 69 cis - regulatory elements and two self - splicing introns . the type field provides one of the primary entry points for family browsing and searching , enabling the user to quickly identify all snorna gene families for instance , or to find all riboswitches in the database . one of the primary uses of the rfam database is to search for homologues of known rnas in a query sequence , including a complete genome . indeed , the profile scfg library has been used to annotate a number of newly sequenced genomes [ e.g. caenorhabditis briggsae ( 10 ) , chicken ( 11 ) and erwinia caratova ( 12 ) ] . these data are available through the web interface and are discussed briefly in the following section . rfam makes available annotation of over 13  400 candidate ncrna genes ( plus 172 self - splicing introns and 1285 cis - regulatory rna elements ) belonging to 172 families in 224 completed chromosomes and genomes . the average bacterial genome contains over 80 hits , dominated by the number of trnas . a total of 170 regions are annotated in escherichia coli , in which most experimental validation of computationally predicted ncrnas has been carried out . rfam annotated regions in bacillus genomes ( b.anthracis is shown in figure 1 ) include a number of recently described riboswitches ( 7,8 ) . these data provide the first comprehensive view of the distribution of ncrnas in the three kingdoms of life . there are a small number of very large families representing some of the best - understood rnas . figure 2 shows that these few large families are the only rnas that are ubiquitous between all three domains of life  only the essential translation components , trna and ribosomal rna , together with rnase p ( trna maturation ) and srp rna ( protein export ) are found in eukaryotes , bacteria and archaea . it is tempting to believe that very few families will be added to the catalogue of universally conserved rnas . however , it is clear that members of some families are highly divergent so as to be computationally almost unrecognizable . for example , although most eukaryotes would be expected to have a telomerase rna , current computational techniques are unable to identify homologues in even well - studied model organisms such as caenorhabditis elegans . only snornas are found in eukaryotes and archaea and not in bacteria , but rna families have not yet been identified that are common to bacteria and archaea but not eukaryotes , or eukaryotes and bacteria but not archaea . the vast majority of rfam families are small , and are often specific to one taxonomic group , and in some cases to one organism , suggesting relatively recent evolution of function or divergence beyond our ability to recognize homologues . many novel bacterial ncrnas have been identified by a number of recent computational screens in e.coli [ reviewed in ( 13 ) ] , but comparatively few have been experimentally verified . few large - scale studies have been conducted in archaea or eukaryotes , and it is clear that such efforts will identify many more small families . rfam at present uses a blast - based heuristic ( 14 ) as described previously ( 6 ) , reducing the search space with an inevitable sensitivity cost . this allows us to search a 5 mb bacterial genome against the entire rfam library in 24 h. annotation of large eukaryotic genomes is just feasible using this approach . recent advances allow the speed of profile scfgs to be increased by a factor of 100 for most families , and provably do not reduce the sensitivity of the full scfg search ( 15 ) . we also recognize that the current approach is restricted to rnas with defined secondary structures , precluding inclusion of important families of essentially unstructured rnas like xist ( x - inactive specific transcript ) , rox ( rna on x ) and ipw ( imprinted in prader we plan to evaluate how the use of profile hmms may allow the detection of homologues of unstructured rnas , and investigate the propagation of structure annotation at the sequence level . perhaps the biggest challenge for annotation of higher eukaryotic genomes is the problem of ncrna - derived pseudogenes and repeats . for example , the b2 repeat in mouse is evolutionarily related to a trna , and alu repeats in human derive from srp rna ( 16 ) . over 10% of the draft human genome sequence is made up of 1.1 million alu sequences ( 17 ) , and there are over 350  000 b2 repeat sequences in mouse ( 18 ) . the human genome also contains over 1000 sequences that are closely related to u6 spliceosomal rna , yet sensible estimates of the u6 gene count suggest that < 50 are functional . distinguishing the functional copies from the large numbers of pseudogenes is an unsolved problem and presents a significant challenge to rna computational biologists . it seems likely that computational and experimental screens will continue to identify numerous novel ncrnas . most of these genes are predicted to fall into small families with narrow taxonomic ranges . in contrast , we believe that very few universally conserved rnas will be found , and the large , well - studied and ubiquitous families will continue to make up the large majority of ncrnas in a single genome . rfam will continue to translate novel discoveries of ncrna genes into alignments and models that are immediately useful for genome annotation and phylogenetic analysis . we thank all those who have contributed data and annotation and developed tools and algorithms for ncrna detection , alignment and structure prediction . are supported by the howard hughes medical institute , the nih national human genome research institute and alvin goldfarb .
rfam is a comprehensive collection of non - coding rna ( ncrna ) families , represented by multiple sequence alignments and profile stochastic context - free grammars . 
 rfam aims to facilitate the identification and classification of new members of known sequence families , and distributes annotation of ncrnas in over 200 complete genome sequences . 
 the data provide the first glimpses of conservation of multiple ncrna families across a wide taxonomic range . 
 a small number of large families are essential in all three kingdoms of life , with large numbers of smaller families specific to certain taxa . 
 recent improvements in the database are discussed , together with challenges for the future . 
 rfam is available on the web at http://www.sanger.ac.uk/software/rfam/ and http://rfam.wustl.edu/.