@article {48009, title = {A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs.}, journal = {Nature Methods}, volume = {14}, year = {2017}, month = {2017 Jan 31}, pages = {45-8}, abstract = {

Many functional RNAs have an evolutionarily conserved secondary structure. Conservation of RNA base pairing induces pairwise covariations in sequence alignments. We developed a computational method, R-scape (RNA Structural Covariation Above Phylogenetic Expectation), that quantitatively tests whether covariation analysis supports the presence of a conserved RNA secondary structure. R-scape analysis finds no statistically significant support for proposed secondary structures of the long noncoding RNAs HOTAIR, SRA, and Xist.

}, author = {Rivas, Elena and Clements, Jody and Eddy, Sean R.} } @article {6272, title = {Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions.}, journal = {Nucleic Acids Research}, volume = {41}, year = {2013}, month = {2013 Jul}, pages = {e121}, abstract = {

Detection of protein homology via sequence similarity has important applications in biology, from protein structure and function prediction to reconstruction of phylogenies. Although current methods for aligning protein sequences are powerful, challenges remain, including problems with homologous overextension of alignments and with regions under convergent evolution. Here, we test the ability of the profile hidden Markov model method HMMER3 to correctly assign homologous sequences to \>13,000 manually curated families from the Pfam database. We identify problem families using protein regions that match two or more Pfam families not currently annotated as related in Pfam. We find that HMMER3 E-value estimates seem to be less accurate for families that feature periodic patterns of compositional bias, such as the ones typically observed in coiled-coils. These results support the continued use of manually curated inclusion thresholds in the Pfam database, especially on the subset of families that have been identified as problematic in experiments such as these. They also highlight the need for developing new methods that can correct for this particular type of compositional bias.

}, issn = {1362-4962}, doi = {10.1093/nar/gkt263}, author = {Mistry, Jaina and Finn, Robert D and Eddy, Sean R. and Bateman, Alex and Punta, Marco} } @article {6266, title = {Computational identification of functional RNA homologs in metagenomic data.}, journal = {RNA Biology}, volume = {10}, year = {2013}, month = {2013 Jul 1}, pages = {1170-9}, abstract = {

A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify because of their reduced alphabet size, lack of open reading frames, and short length. Infernal is a software package that implements "covariance models" (CMs) for RNA homology search, which harness both sequence and structural conservation when searching for RNA homologs. Thanks to the added statistical signal inherent in the secondary structure conservation of many RNA families, Infernal is more powerful than sequence-only based methods such as BLAST and profile HMMs. Together with the Rfam database of CMs, Infernal is a useful tool for identifying RNAs in metagenomics data sets.

}, issn = {1555-8584}, doi = {10.4161/rna.25038}, author = {Nawrocki, Eric P and Eddy, Sean R.} } @article {6285, title = {Dfam: a database of repetitive DNA based on profile hidden Markov models.}, journal = {Nucleic Acids Research}, volume = {41}, year = {2013}, month = {2013 Jan}, pages = {D70-82}, abstract = {

We present a database of repetitive DNA elements, called Dfam (http://dfam.janelia.org). Many genomes contain a large fraction of repetitive DNA, much of which is made up of remnants of transposable elements (TEs). Accurate annotation of TEs enables research into their biology and can shed light on the evolutionary processes that shape genomes. Identification and masking of TEs can also greatly simplify many downstream genome annotation and sequence analysis tasks. The commonly used TE annotation tools RepeatMasker and Censor depend on sequence homology search tools such as cross_match and BLAST variants, as well as Repbase, a collection of known TE families each represented by a single consensus sequence. Dfam contains entries corresponding to all Repbase TE entries for which instances have been found in the human genome. Each Dfam entry is represented by a profile hidden Markov model, built from alignments generated using RepeatMasker and Repbase. When used in conjunction with the hidden Markov model search tool nhmmer, Dfam produces a 2.9\% increase in coverage over consensus sequence search methods on a large human benchmark, while maintaining low false discovery rates, and coverage of the full human genome is 54.5\%. The website provides a collection of tools and data views to support improved TE curation and annotation efforts. Dfam is also available for download in flat file format or in the form of MySQL table dumps.

}, keywords = {Databases, DNA Transposable Elements, Genome, Human, Humans, Internet, Markov Chains, Models, Molecular Sequence Annotation, Nucleic Acid, Statistical}, issn = {1362-4962}, doi = {10.1093/nar/gks1265}, author = {Wheeler, Travis J and Clements, Jody and Eddy, Sean R. and Hubley, Robert and Jones, Thomas A and Jurka, Jerzy and Smit, Arian F A and Finn, Robert D} } @article {6287, title = {The ENCODE project: missteps overshadowing a success.}, journal = {Current Biology}, volume = {23}, year = {2013}, month = {2013 Apr 8}, pages = {R259-61}, abstract = {

Two clich{\'e}s of science journalism have now played out around the ENCODE project. ENCODE{\textquoteright}s publicity first presented a misleading "all the textbooks are wrong" narrative about noncoding human DNA. Now several critiques of ENCODE{\textquoteright}s narrative have been published, and one was so vitriolic that it fueled "undignified academic squabble" stories that focused on tone more than substance. Neither story line does justice to our actual understanding of genomes, to ENCODE{\textquoteright}s results, or to the role of big science in biology.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2013.03.023}, author = {Eddy, Sean R.} } @article {6453, title = {Infernal 1.1: 100-fold faster RNA homology searches.}, journal = {Bioinformatics}, volume = {29}, year = {2013}, month = {2013 Sep 19}, pages = {2933-5}, abstract = {

SUMMARY: Infernal builds probabilistic profiles of the sequence and secondary structure of an RNA family called covariance models (CMs) from structurally annotated multiple sequence alignments given as input. Infernal uses CMs to search for new family members in sequence databases and to create potentially large multiple sequence alignments. Version 1.1 of Infernal introduces a new filter pipeline for RNA homology search based on accelerated profile hidden Markov model (HMM) methods and HMM-banded CM alignment methods. This enables \~{}100-fold acceleration over the previous version and \~{}10 000-fold acceleration over exhaustive non-filtered CM searches. AVAILABILITY: Source code, documentation and the benchmark are downloadable from http://infernal.janelia.org. Infernal is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X. Documentation includes a user{\textquoteright}s guide with a tutorial, a discussion of file formats and user options and additional details on methods implemented in the software. CONTACT: nawrockie@janelia.hhmi.org.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btt509}, author = {Nawrocki, Eric P and Eddy, Sean R.} } @article {6435, title = {nhmmer: DNA homology search with profile HMMs.}, journal = {Bioinformatics}, volume = {29}, year = {2013}, month = {2013 Oct 1}, pages = {2487-9}, abstract = {

SUMMARY: Sequence database searches are an essential part of molecular biology, providing information about the function and evolutionary history of proteins, RNA molecules and DNA sequence elements. We present a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search. This tool, called nhmmer, enables improved detection of remote DNA homologs, and has been used in combination with Dfam and RepeatMasker to improve annotation of transposable elements in the human genome. AVAILABILITY: nhmmer is a part of the new HMMER3.1 release. Source code and documentation can be downloaded from http://hmmer.org. HMMER3.1 is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X. CONTACT: wheelert@janelia.hhmi.org.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btt403}, author = {Wheeler, Travis J and Eddy, Sean R.} } @article {6526, title = {The Oxytricha trifallax macronuclear genome: a complex eukaryotic genome with 16,000 tiny chromosomes.}, journal = {PLoS Biology}, volume = {11}, year = {2013}, month = {2013 Jan}, pages = {e1001473}, abstract = {

The macronuclear genome of the ciliate Oxytricha trifallax displays an extreme and unique eukaryotic genome architecture with extensive genomic variation. During sexual genome development, the expressed, somatic macronuclear genome is whittled down to the genic portion of a small fraction (\~{}5\%) of its precursor "silent" germline micronuclear genome by a process of "unscrambling" and fragmentation. The tiny macronuclear "nanochromosomes" typically encode single, protein-coding genes (a small portion, 10\%, encode 2-8 genes), have minimal noncoding regions, and are differentially amplified to an average of \~{}2,000 copies. We report the high-quality genome assembly of \~{}16,000 complete nanochromosomes (\~{}50 Mb haploid genome size) that vary from 469 bp to 66 kb long (mean \~{}3.2 kb) and encode \~{}18,500 genes. Alternative DNA fragmentation processes \~{}10\% of the nanochromosomes into multiple isoforms that usually encode complete genes. Nucleotide diversity in the macronucleus is very high (SNP heterozygosity is \~{}4.0\%), suggesting that Oxytricha trifallax may have one of the largest known effective population sizes of eukaryotes. Comparison to other ciliates with nonscrambled genomes and long macronuclear chromosomes (on the order of 100 kb) suggests several candidate proteins that could be involved in genome rearrangement, including domesticated MULE and IS1595-like DDE transposases. The assembly of the highly fragmented Oxytricha macronuclear genome is the first completed genome with such an unusual architecture. This genome sequence provides tantalizing glimpses into novel molecular biology and evolution. For example, Oxytricha maintains tens of millions of telomeres per cell and has also evolved an intriguing expansion of telomere end-binding proteins. In conjunction with the micronuclear genome in progress, the O. trifallax macronuclear genome will provide an invaluable resource for investigating programmed genome rearrangements, complementing studies of rearrangements arising during evolution and disease.

}, keywords = {Base Sequence, DNA, DNA Copy Number Variations, DNA Fragmentation, Gene Amplification, Gene Rearrangement, Genes, Genetic Variation, Genome, Macronucleus, Messenger, Molecular Sequence Data, Oxytricha, Protein Binding, Protozoan, RNA, Sequence Analysis, Telomere}, issn = {1545-7885}, doi = {10.1371/journal.pbio.1001473}, author = {Swart, Estienne C and Bracht, John R and Magrini, Vincent and Minx, Patrick and Chen, Xiao and Zhou, Yi and Khurana, Jaspreet S and Goldman, Aaron D and Nowacki, Mariusz and Schotanus, Klaas and Jung, Seolkyoung and Fulton, Robert S and Ly, Amy and McGrath, Sean and Haub, Kevin and Wiggins, Jessica L and Storton, Donna and Matese, John C and Parsons, Lance and Chang, Wei-Jen and Bowen, Michael S and Stover, Nicholas A and Jones, Thomas A and Eddy, Sean R. and Herrick, Glenn A and Doak, Thomas G and Wilson, Richard K and Mardis, Elaine R and Landweber, Laura F} } @article {6298, title = {Rfam 11.0: 10 years of RNA families.}, journal = {Nucleic Acids Research}, volume = {41}, year = {2013}, month = {2013 Jan}, pages = {D226-32}, abstract = {

The Rfam database (available via the website at http://rfam.sanger.ac.uk and through our mirror at http://rfam.janelia.org) is a collection of non-coding RNA families, primarily RNAs with a conserved RNA secondary structure, including both RNA genes and mRNA cis-regulatory elements. Each family is represented by a multiple sequence alignment, predicted secondary structure and covariance model. Here we discuss updates to the database in the latest release, Rfam 11.0, including the introduction of genome-based alignments for large families, the introduction of the Rfam Biomart as well as other user interface improvements. Rfam is available under the Creative Commons Zero license.

}, keywords = {Base Sequence, Databases, Genomics, Internet, Molecular Sequence Annotation, Nucleic Acid, Nucleic Acid Conformation, RNA, Sequence Alignment, Untranslated, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gks1005}, author = {Burge, Sarah W and Daub, Jennifer and Eberhardt, Ruth and Tate, John and Barquist, Lars and Nawrocki, Eric P and Eddy, Sean R. and Gardner, Paul P and Bateman, Alex} } @article {6265, title = {Transcription factors that convert adult cell identity are differentially polycomb repressed.}, journal = {PLoS One}, volume = {8}, year = {2013}, month = {2013 May}, pages = {e63407}, abstract = {

Transcription factors that can convert adult cells of one type to another are usually discovered empirically by testing factors with a known developmental role in the target cell. Here we show that standard genomic methods (RNA-seq and ChIP-seq) can help identify these factors, as most are more strongly Polycomb repressed in the source cell and more highly expressed in the target cell. This criterion is an effective genome-wide screen that significantly enriches for factors that can transdifferentiate several mammalian cell types including neural stem cells, neurons, pancreatic islets, and hepatocytes. These results suggest that barriers between adult cell types, as depicted in Waddington{\textquoteright}s "epigenetic landscape", consist in part of differentially Polycomb-repressed transcription factors. This genomic model of cell identity helps rationalize a growing number of transdifferentiation protocols and may help facilitate the engineering of cell identity for regenerative medicine.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0063407}, author = {Davis, Fred P and Eddy, Sean R.} } @article {6283, title = {Cell type-specific genomics of Drosophila neurons.}, journal = {Nucleic Acids Research}, volume = {40}, year = {2012}, month = {2012 Oct}, pages = {9691-704}, abstract = {

Many tools are available to analyse genomes but are often challenging to use in a cell type-specific context. We have developed a method similar to the isolation of nuclei tagged in a specific cell type (INTACT) technique [Deal,R.B. and Henikoff,S. (2010) A simple method for gene expression and chromatin profiling of individual cell types within a tissue. Dev. Cell, 18, 1030-1040; Steiner,F.A., Talbert,P.B., Kasinathan,S., Deal,R.B. and Henikoff,S. (2012) Cell-type-specific nuclei purification from whole animals for genome-wide expression and chromatin profiling. Genome Res., doi:10.1101/gr.131748.111], first developed in plants, for use in Drosophila neurons. We profile gene expression and histone modifications in Kenyon cells and octopaminergic neurons in the adult brain. In addition to recovering known gene expression differences, we also observe significant cell type-specific chromatin modifications. In particular, a small subset of differentially expressed genes exhibits a striking anti-correlation between repressive and activating histone modifications. These genes are enriched for transcription factors, recovering those known to regulate mushroom body identity and predicting analogous regulators of octopaminergic neurons. Our results suggest that applying INTACT to specific neuronal populations can illuminate the transcriptional regulatory networks that underlie neuronal cell identity.

}, keywords = {Animals, Cell Fractionation, Cell Nucleus, Chromatin, Drosophila, Gene Expression Profiling, Gene Silencing, Genomics, Histones, Luminescent Proteins, Neurons, Transcription Factors}, issn = {1362-4962}, doi = {10.1093/nar/gks671}, author = {Henry, Gilbert L and Davis, Fred P and Picard, Serge and Eddy, Sean R.} } @article {6280, title = {The C-value paradox, junk DNA and ENCODE.}, journal = {Current Biology}, volume = {22}, year = {2012}, month = {2012 Nov 6}, pages = {R898-9}, abstract = {

paradox? You might expect more complex organisms to have progressively larger genomes, but eukaryotic genome size fails to correlate well with apparent complexity, and instead varies wildly over more than a 100,000-fold range. Single-celled amoebae have some of the largest genomes, up to 100-fold larger than the human genome. This variation suggested that genomes can contain a substantial fraction of DNA other than for genes and their regulatory sequences. C.A. Thomas Jr dubbed it the {\textquoteleft}C-value paradox{\textquoteright} in 1971.

}, keywords = {Animals, DNA, DNA Transposable Elements, Genetic, Genome, Genome Size, Human, Humans, Intergenic, Models}, issn = {1879-0445}, doi = {10.1016/j.cub.2012.10.002}, author = {Eddy, Sean R.} } @article {4597, title = {A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.}, journal = {RNA}, volume = {18}, year = {2012}, month = {2012 Feb}, pages = {193-212}, abstract = {

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

}, issn = {1469-9001}, doi = {10.1261/rna.030049.111}, author = {Rivas, Elena and Lang, Raymond and Eddy, Sean R.} } @article {5271, title = {Accelerated profile HMM searches.}, journal = {PLoS Computational Biology}, volume = {7}, year = {2011}, month = {2011 Oct}, pages = {e1002195}, abstract = {

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the "multiple segment Viterbi" (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call "sparse rescaling". These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.

}, keywords = {Algorithms, Computational Biology, Databases, Information Storage and Retrieval, Markov Chains, Protein, Sequence Alignment, Software}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1002195}, author = {Eddy, Sean R.} } @article {6507, title = {Exploiting Oxytricha trifallax nanochromosomes to screen for non-coding RNA genes.}, journal = {Nucleic Acids Research}, volume = {39}, year = {2011}, month = {2011 Sep 1}, pages = {7529-47}, abstract = {

We took advantage of the unusual genomic organization of the ciliate Oxytricha trifallax to screen for eukaryotic non-coding RNA (ncRNA) genes. Ciliates have two types of nuclei: a germ line micronucleus that is usually transcriptionally inactive, and a somatic macronucleus that contains a reduced, fragmented and rearranged genome that expresses all genes required for growth and asexual reproduction. In some ciliates including Oxytricha, the macronuclear genome is particularly extreme, consisting of thousands of tiny {\textquoteright}nanochromosomes{\textquoteright}, each of which usually contains only a single gene. Because the organism itself identifies and isolates most of its genes on single-gene nanochromosomes, nanochromosome structure could facilitate the discovery of unusual genes or gene classes, such as ncRNA genes. Using a draft Oxytricha genome assembly and a custom-written protein-coding genefinding program, we identified a subset of nanochromosomes that lack any detectable protein-coding gene, thereby strongly enriching for nanochromosomes that carry ncRNA genes. We found only a small proportion of non-coding nanochromosomes, suggesting that Oxytricha has few independent ncRNA genes besides homologs of already known RNAs. Other than new members of known ncRNA classes including C/D and H/ACA snoRNAs, our screen identified one new family of small RNA genes, named the Arisong RNAs, which share some of the features of small nuclear RNAs.

}, keywords = {Base Sequence, Chromosomes, Conserved Sequence, Genes, Genome, Genomics, Introns, Oxytricha, Protozoan, RNA, Small Nuclear, Small Nucleolar, Untranslated}, issn = {1362-4962}, doi = {10.1093/nar/gkr501}, author = {Jung, Seolkyoung and Swart, Estienne C and Minx, Patrick J and Magrini, Vincent and Mardis, Elaine R and Landweber, Laura F and Eddy, Sean R.} } @article {5280, title = {Fast filtering for RNA homology search.}, journal = {Bioinformatics}, volume = {27}, year = {2011}, month = {2011 Nov 15}, pages = {3102-9}, abstract = {

MOTIVATION: Homology search for RNAs can use secondary structure information to increase power by modeling base pairs, as in covariance models, but the resulting computational costs are high. Typical acceleration strategies rely on at least one filtering stage using sequence-only search. RESULTS: Here we present the multi-segment CYK (MSCYK) filter, which implements a heuristic of ungapped structural alignment for RNA homology search. Compared to gapped alignment, this approximation has lower computation time requirements (O(N$^{4}$) reduced to O(N{\textthreesuperior}), and space requirements (O(N{\textthreesuperior}) reduced to O(N{\texttwosuperior}). A vector-parallel implementation of this method gives up to 100-fold speed-up; vector-parallel implementations of standard gapped alignment at two levels of precision give 3- and 6-fold speed-ups. These approaches are combined to create a filtering pipeline that scores RNA secondary structure at all stages, with results that are synergistic with existing methods.

}, keywords = {Algorithms, Base Sequence, Consensus Sequence, Humans, Nucleic Acid, Nucleic Acid Conformation, RNA, Sequence Alignment, Sequence Analysis, Sequence Homology, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btr545}, author = {Kolbe, Diana L and Eddy, Sean R.} } @article {4302, title = {HMMER web server: interactive sequence similarity searching.}, journal = {Nucleic Acids Research}, volume = {39}, year = {2011}, month = {2011 Jul}, pages = {W29-37}, abstract = {

HMMER is a software suite for protein sequence similarity searches using probabilistic methods. Previously, HMMER has mainly been available only as a computationally intensive UNIX command-line tool, restricting its use. Recent advances in the software, HMMER3, have resulted in a 100-fold speed gain relative to previous versions. It is now feasible to make efficient profile hidden Markov model (profile HMM) searches via the web. A HMMER web server (http://hmmer.janelia.org) has been designed and implemented such that most protein database searches return within a few seconds. Methods are available for searching either a single protein sequence, multiple protein sequence alignment or profile HMM against a target sequence database, and for searching a protein sequence against Pfam. The web server is designed to cater to a range of different user expertise and accepts batch uploading of multiple queries at once. All search methods are also available as RESTful web services, thereby allowing them to be readily integrated as remotely executed tasks in locally scripted workflows. We have focused on minimizing search times and the ability to rapidly display tabular results, regardless of the number of matches found, developing graphical summaries of the search results to provide quick, intuitive appraisement of them.

}, keywords = {Internet, Protein, Sequence Alignment, Sequence Analysis, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkr367}, author = {Finn, Robert D and Clements, Jody and Eddy, Sean R.} } @article {1927, title = {Rfam: Wikipedia, clans and the "decimal" release.}, journal = {Nucleic Acids Research}, volume = {39}, year = {2011}, month = {2011 Jan}, pages = {D141-5}, abstract = {

The Rfam database aims to catalogue non-coding RNAs through the use of sequence alignments and statistical profile models known as covariance models. In this contribution, we discuss the pros and cons of using the online encyclopedia, Wikipedia, as a source of community-derived annotation. We discuss the addition of groupings of related RNA families into clans and new developments to the website. Rfam is available on the Web at http://rfam.sanger.ac.uk.

}, issn = {1362-4962}, doi = {10.1093/nar/gkq1129}, author = {Gardner, Paul P and Daub, Jennifer and Tate, John and Moore, Benjamin L and Osuch, Isabelle H and Griffiths-Jones, Sam and Finn, Robert D and Nawrocki, Eric P and Kolbe, Diana L and Eddy, Sean R. and Bateman, Alex} } @article {4286, title = {Hidden Markov model speed heuristic and iterative HMM search procedure.}, journal = {BMC Bioinformatics}, volume = {11}, year = {2010}, month = {2010}, pages = {431}, abstract = {

Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases.

}, keywords = {Algorithms, Artificial Intelligence, Base Sequence, Databases, Markov Chains, Protein, Proteins, Sequence Alignment, Software}, issn = {1471-2105}, doi = {10.1186/1471-2105-11-431}, author = {Johnson, L Steven and Eddy, Sean R. and Portugaly, Elon} } @article {4253, title = {The Pfam protein families database.}, journal = {Nucleic Acids Research}, volume = {38}, year = {2010}, month = {2010 Jan}, pages = {D211-22}, abstract = {

Pfam is a widely used database of protein families and domains. This article describes a set of major updates that we have implemented in the latest release (version 24.0). The most important change is that we now use HMMER3, the latest version of the popular profile hidden Markov model package. This software is approximately 100 times faster than HMMER2 and is more sensitive due to the routine use of the forward algorithm. The move to HMMER3 has necessitated numerous changes to Pfam that are described in detail. Pfam release 24.0 contains 11,912 families, of which a large number have been significantly updated during the past two years. Pfam is available via servers in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/).

}, keywords = {Amino Acid, Amino Acid Sequence, Animals, Archaeal, Computational Biology, Databases, Fungal, Genome, Humans, Information Storage and Retrieval, Internet, Molecular Sequence Data, Nucleic Acid, Protein, Protein Structure, Sequence Alignment, Sequence Homology, Software, Tertiary}, issn = {1362-4962}, doi = {10.1093/nar/gkp985}, author = {Finn, Robert D and Mistry, Jaina and Tate, John and Coggill, Penny and Heger, Andreas and Pollington, Joanne E and Gavin, O Luke and Gunasekaran, Prasad and Ceric, Goran and Forslund, Kristoffer and Holm, Liisa and Sonnhammer, Erik L L and Eddy, Sean R. and Bateman, Alex} } @article {4277, title = {Infernal 1.0: inference of RNA alignments.}, journal = {Bioinformatics}, volume = {25}, year = {2009}, month = {2009 May 15}, pages = {1335-7}, abstract = {

SUMMARY: INFERNAL builds consensus RNA secondary structure profiles called covariance models (CMs), and uses them to search nucleic acid sequence databases for homologous RNAs, or to create new sequence- and structure-based multiple sequence alignments. AVAILABILITY: Source code, documentation and benchmark downloadable from http://infernal.janelia.org. INFERNAL is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X.

}, keywords = {Databases, Nucleic Acid, Nucleic Acid Conformation, RNA, Sequence Alignment, Sequence Analysis, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btp157}, author = {Nawrocki, Eric P and Kolbe, Diana L and Eddy, Sean R.} } @article {5243, title = {Local RNA structure alignment with incomplete sequence.}, journal = {Bioinformatics}, volume = {25}, year = {2009}, month = {2009 May 15}, pages = {1236-43}, abstract = {

Accuracy of automated structural RNA alignment is improved by using models that consider not only primary sequence but also secondary structure information. However, current RNA structural alignment approaches tend to perform poorly on incomplete sequence fragments, such as single reads from metagenomic environmental surveys, because nucleotides that are expected to be base paired are missing.

}, keywords = {Algorithms, Databases, Genetic, Models, Molecular, Nucleic Acid Conformation, RNA, Sequence Alignment, Sequence Analysis}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btp154}, author = {Kolbe, Diana L and Eddy, Sean R.} } @article {4265, title = {A new generation of homology search tools based on probabilistic inference.}, journal = {Genome Informatics. International Conference on Genome Informatics}, volume = {23}, year = {2009}, month = {2009 Oct}, pages = {205-11}, abstract = {

Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST{\textquoteright}s programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST{\textquoteright}s speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.

}, keywords = {Markov Chains, Probability, Sequence Alignment}, issn = {0919-9454}, author = {Eddy, Sean R.} } @article {4266, title = {Rfam: updates to the RNA families database.}, journal = {Nucleic Acids Research}, volume = {37}, year = {2009}, month = {2009 Jan}, pages = {D136-40}, abstract = {

Rfam is a collection of RNA sequence families, represented by multiple sequence alignments and covariance models (CMs). The primary aim of Rfam is to annotate new members of known RNA families on nucleotide sequences, particularly complete genomes, using sensitive BLAST filters in combination with CMs. A minority of families with a very broad taxonomic range (e.g. tRNA and rRNA) provide the majority of the sequence annotations, whilst the majority of Rfam families (e.g. snoRNAs and miRNAs) have a limited taxonomic range and provide a limited number of annotations. Recent improvements to the website, methodologies and data used by Rfam are discussed. Rfam is freely available on the Web at http://rfam.sanger.ac.uk/and http://rfam.janelia.org/.

}, keywords = {Computer Graphics, Databases, Internet, Nucleic Acid, RNA, Sequence Alignment, Sequence Analysis}, issn = {1362-4962}, doi = {10.1093/nar/gkn766}, author = {Gardner, Paul P and Daub, Jennifer and Tate, John G and Nawrocki, Eric P and Kolbe, Diana L and Lindgreen, Stinus and Wilkinson, Adam C and Finn, Robert D and Griffiths-Jones, Sam and Eddy, Sean R. and Bateman, Alex} } @article {4268, title = {A survey of nematode SmY RNAs.}, journal = {RNA Biology}, volume = {6}, year = {2009}, month = {2009 Jan-Mar}, pages = {5-8}, abstract = {

SmY RNAs are a family of approximately 70-90 nt small nuclear RNAs found in nematodes. In C. elegans, SmY RNAs copurify in a small ribonucleoprotein (snRNP) complex related to the SL1 and SL2 snRNPs that are involved in nematode mRNA trans-splicing. Here we describe a comprehensive computational analysis of SmY RNA homologs found in the currently available genome sequences. We identify homologs in all sequenced nematode genomes in class Chromadorea. We are unable to identify homologs in a more distantly related nematode species, Trichinella spiralis (class: Dorylaimia), and in representatives of non-nematode phyla that use trans-splicing. Using comparative RNA sequence analysis, we infer a conserved consensus SmY RNA secondary structure consisting of two stems flanking a consensus Sm protein binding site. A representative seed alignment of the SmY RNA family, annotated with the inferred consensus secondary structure, has been deposited with the Rfam RNA families database.

}, keywords = {Animals, Base Sequence, Binding Sites, Caenorhabditis elegans, Genome, Molecular Sequence Data, Nematoda, Nucleic Acid Conformation, Phylogeny, RNA, Small Nuclear, snRNP Core Proteins, Trichinella spiralis}, issn = {1555-8584}, author = {Jones, Thomas A and Otto, Wolfgang and Marz, Manja and Eddy, Sean R. and Stadler, Peter F} } @article {4264, title = {A tool for identification of genes expressed in patterns of interest using the Allen Brain Atlas.}, journal = {Bioinformatics}, volume = {25}, year = {2009}, month = {2009 Jul 1}, pages = {1647-54}, abstract = {

Gene expression patterns can be useful in understanding the structural organization of the brain and the regulatory logic that governs its myriad cell types. A particularly rich source of spatial expression data is the Allen Brain Atlas (ABA), a comprehensive genome-wide in situ hybridization study of the adult mouse brain. Here, we present an open-source program, ALLENMINER, that searches the ABA for genes that are expressed, enriched, patterned or graded in a user-specified region of interest.

}, keywords = {Animals, Brain, Computer Graphics, Gene Expression, Gene Expression Profiling, Genome, Mice, Oligonucleotide Array Sequence Analysis}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btp288}, author = {Davis, Fred P and Eddy, Sean R.} } @article {4224, title = {Query-dependent banding (QDB) for faster RNA similarity searches.}, journal = {PLoS Computational Biology}, volume = {3}, year = {2007}, month = {2007 Mar 30}, pages = {e56}, abstract = {

When searching sequence databases for RNAs, it is desirable to score both primary sequence and RNA secondary structure similarity. Covariance models (CMs) are probabilistic models well-suited for RNA similarity search applications. However, the computational complexity of CM dynamic programming alignment algorithms has limited their practical application. Here we describe an acceleration method called query-dependent banding (QDB), which uses the probabilistic query CM to precalculate regions of the dynamic programming lattice that have negligible probability, independently of the target database. We have implemented QDB in the freely available Infernal software package. QDB reduces the average case time complexity of CM alignment from LN(2.4) to LN(1.3) for a query RNA of N residues and a target database of L residues, resulting in a 4-fold speedup for typical RNA queries. Combined with other improvements to Infernal, including informative mixture Dirichlet priors on model parameters, benchmarks also show increased sensitivity and specificity resulting from improved parameterization.

}, keywords = {Base Sequence, Database Management Systems, Databases, Genetic, Information Storage and Retrieval, Molecular Sequence Data, Nucleic Acid, RNA, Sequence Alignment, Sequence Analysis, Sequence Homology}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.0030056}, author = {Nawrocki, Eric P and Eddy, Sean R.} }