@article {38529, title = {TIGRFAMs and Genome Properties in 2013}, journal = {Nucleic acids researchNucleic Acids Research}, volume = {41}, year = {2013}, note = {http://www.ncbi.nlm.nih.gov/pubmed/23197656?dopt=Abstract}, type = {10.1093/nar/gks1234}, abstract = {TIGRFAMs, available online at http://www.jcvi.org/tigrfams is a database of protein family definitions. Each entry features a seed alignment of trusted representative sequences, a hidden Markov model (HMM) built from that alignment, cutoff scores that let automated annotation pipelines decide which proteins are members, and annotations for transfer onto member proteins. Most TIGRFAMs models are designated equivalog, meaning they assign a specific name to proteins conserved in function from a common ancestral sequence. Models describing more functionally heterogeneous families are designated subfamily or domain, and assign less specific but more widely applicable annotations. The Genome Properties database, available at http://www.jcvi.org/genome-properties, specifies how computed evidence, including TIGRFAMs HMM results, should be used to judge whether an enzymatic pathway, a protein complex or another type of molecular subsystem is encoded in a genome. TIGRFAMs and Genome Properties content are developed in concert because subsystems reconstruction for large numbers of genomes guides selection of seed alignment sequences and cutoff values during protein family construction. Both databases specialize heavily in bacterial and archaeal subsystems. At present, 4284 models appear in TIGRFAMs, while 628 systems are described by Genome Properties. Content derives both from subsystem discovery work and from biocuration of the scientific literature.}, keywords = {Databases, Protein, Genome, Archaeal, Genome, Bacterial, Genomics, Internet, Markov chains, Molecular Sequence Annotation, Proteins, sequence alignment}, author = {Haft, Daniel H. and J. Selengut and Richter, Roland A. and Harkins, Derek and Basu, Malay K. and Beck, Erin} } @article {49764, title = {TIGRFAMs and Genome Properties in 2013.}, journal = {Nucleic Acids Res}, volume = {41}, year = {2013}, month = {2013 Jan}, pages = {D387-95}, abstract = {

TIGRFAMs, available online at http://www.jcvi.org/tigrfams is a database of protein family definitions. Each entry features a seed alignment of trusted representative sequences, a hidden Markov model (HMM) built from that alignment, cutoff scores that let automated annotation pipelines decide which proteins are members, and annotations for transfer onto member proteins. Most TIGRFAMs models are designated equivalog, meaning they assign a specific name to proteins conserved in function from a common ancestral sequence. Models describing more functionally heterogeneous families are designated subfamily or domain, and assign less specific but more widely applicable annotations. The Genome Properties database, available at http://www.jcvi.org/genome-properties, specifies how computed evidence, including TIGRFAMs HMM results, should be used to judge whether an enzymatic pathway, a protein complex or another type of molecular subsystem is encoded in a genome. TIGRFAMs and Genome Properties content are developed in concert because subsystems reconstruction for large numbers of genomes guides selection of seed alignment sequences and cutoff values during protein family construction. Both databases specialize heavily in bacterial and archaeal subsystems. At present, 4284 models appear in TIGRFAMs, while 628 systems are described by Genome Properties. Content derives both from subsystem discovery work and from biocuration of the scientific literature.

}, keywords = {Databases, Protein, Genome, Archaeal, Genome, Bacterial, Genomics, Internet, Markov chains, Molecular Sequence Annotation, Proteins, sequence alignment}, issn = {1362-4962}, doi = {10.1093/nar/gks1234}, author = {Haft, Daniel H and Selengut, Jeremy D and Richter, Roland A and Harkins, Derek and Basu, Malay K and Beck, Erin} } @article {38352, title = {InterPro in 2011: new developments in the family and domain prediction database}, journal = {Nucleic acids researchNucleic Acids Research}, volume = {40}, year = {2012}, note = {http://www.ncbi.nlm.nih.gov/pubmed/22096229?dopt=Abstract}, type = {10.1093/nar/gkr948}, abstract = {InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.}, keywords = {Databases, Protein, Protein Structure, Tertiary, Proteins, Sequence Analysis, Protein, software, Terminology as Topic, User-Computer Interface}, author = {Hunter, Sarah and Jones, Philip and Mitchell, Alex and Apweiler, Rolf and Attwood, Teresa K. and Bateman, Alex and Bernard, Thomas and Binns, David and Bork, Peer and Burge, Sarah and de Castro, Edouard and Coggill, Penny and Corbett, Matthew and Das, Ujjwal and Daugherty, Louise and Duquenne, Lauranne and Finn, Robert D. and Fraser, Matthew and Gough, Julian and Haft, Daniel and Hulo, Nicolas and Kahn, Daniel and Kelly, Elizabeth and Letunic, Ivica and Lonsdale, David and Lopez, Rodrigo and Madera, Martin and Maslen, John and McAnulla, Craig and McDowall, Jennifer and McMenamin, Conor and Mi, Huaiyu and Mutowo-Muellenet, Prudence and Mulder, Nicola and Natale, Darren and Orengo, Christine and Pesseat, Sebastien and Punta, Marco and Quinn, Antony F. and Rivoire, Catherine and Sangrador-Vegas, Amaia and J. Selengut and Sigrist, Christian J. A. and Scheremetjew, Maxim and Tate, John and Thimmajanarthanan, Manjulapramila and Thomas, Paul D. and Wu, Cathy H. and Yeats, Corin and Yong, Siew-Yit} } @article {49765, title = {InterPro in 2011: new developments in the family and domain prediction database.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Jan}, pages = {D306-12}, abstract = {

InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.

}, keywords = {Databases, Protein, Protein Structure, Tertiary, Proteins, Sequence Analysis, Protein, software, Terminology as Topic, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkr948}, author = {Hunter, Sarah and Jones, Philip and Mitchell, Alex and Apweiler, Rolf and Attwood, Teresa K and Bateman, Alex and Bernard, Thomas and Binns, David and Bork, Peer and Burge, Sarah and de Castro, Edouard and Coggill, Penny and Corbett, Matthew and Das, Ujjwal and Daugherty, Louise and Duquenne, Lauranne and Finn, Robert D and Fraser, Matthew and Gough, Julian and Haft, Daniel and Hulo, Nicolas and Kahn, Daniel and Kelly, Elizabeth and Letunic, Ivica and Lonsdale, David and Lopez, Rodrigo and Madera, Martin and Maslen, John and McAnulla, Craig and McDowall, Jennifer and McMenamin, Conor and Mi, Huaiyu and Mutowo-Muellenet, Prudence and Mulder, Nicola and Natale, Darren and Orengo, Christine and Pesseat, Sebastien and Punta, Marco and Quinn, Antony F and Rivoire, Catherine and Sangrador-Vegas, Amaia and Selengut, Jeremy D and Sigrist, Christian J A and Scheremetjew, Maxim and Tate, John and Thimmajanarthanan, Manjulapramila and Thomas, Paul D and Wu, Cathy H and Yeats, Corin and Yong, Siew-Yit} } @article {49779, title = {Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL): adapting the Partial Phylogenetic Profiling algorithm to scan sequences for signatures that predict protein function.}, journal = {BMC Bioinformatics}, volume = {11}, year = {2010}, month = {2010}, pages = {52}, abstract = {

BACKGROUND: Comparative genomics methods such as phylogenetic profiling can mine powerful inferences from inherently noisy biological data sets. We introduce Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL), a method that applies the Partial Phylogenetic Profiling (PPP) approach locally within a protein sequence to discover short sequence signatures associated with functional sites. The approach is based on the basic scoring mechanism employed by PPP, namely the use of binomial distribution statistics to optimize sequence similarity cutoffs during searches of partitioned training sets.

RESULTS: Here we illustrate and validate the ability of the SIMBAL method to find functionally relevant short sequence signatures by application to two well-characterized protein families. In the first example, we partitioned a family of ABC permeases using a metabolic background property (urea utilization). Thus, the TRUE set for this family comprised members whose genome of origin encoded a urea utilization system. By moving a sliding window across the sequence of a permease, and searching each subsequence in turn against the full set of partitioned proteins, the method found which local sequence signatures best correlated with the urea utilization trait. Mapping of SIMBAL "hot spots" onto crystal structures of homologous permeases reveals that the significant sites are gating determinants on the cytosolic face rather than, say, docking sites for the substrate-binding protein on the extracellular face. In the second example, we partitioned a protein methyltransferase family using gene proximity as a criterion. In this case, the TRUE set comprised those methyltransferases encoded near the gene for the substrate RF-1. SIMBAL identifies sequence regions that map onto the substrate-binding interface while ignoring regions involved in the methyltransferase reaction mechanism in general. Neither method for training set construction requires any prior experimental characterization.

CONCLUSIONS: SIMBAL shows that, in functionally divergent protein families, selected short sequences often significantly outperform their full-length parent sequence for making functional predictions by sequence similarity, suggesting avenues for improved functional classifiers. When combined with structural data, SIMBAL affords the ability to localize and model functional sites.

}, keywords = {algorithms, Amino Acid Sequence, Gene Expression Profiling, Molecular Sequence Data, Phylogeny, Proteins, Sequence Analysis, Protein, Structure-Activity Relationship}, issn = {1471-2105}, doi = {10.1186/1471-2105-11-52}, author = {Selengut, Jeremy D and Rusch, Douglas B and Haft, Daniel H} } @article {38506, title = {Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL): adapting the Partial Phylogenetic Profiling algorithm to scan sequences for signatures that predict protein function}, journal = {BMC bioinformaticsBMC Bioinformatics}, volume = {11}, year = {2010}, note = {http://www.ncbi.nlm.nih.gov/pubmed/20102603?dopt=Abstract}, type = {10.1186/1471-2105-11-52}, abstract = {BACKGROUND: Comparative genomics methods such as phylogenetic profiling can mine powerful inferences from inherently noisy biological data sets. We introduce Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL), a method that applies the Partial Phylogenetic Profiling (PPP) approach locally within a protein sequence to discover short sequence signatures associated with functional sites. The approach is based on the basic scoring mechanism employed by PPP, namely the use of binomial distribution statistics to optimize sequence similarity cutoffs during searches of partitioned training sets. RESULTS: Here we illustrate and validate the ability of the SIMBAL method to find functionally relevant short sequence signatures by application to two well-characterized protein families. In the first example, we partitioned a family of ABC permeases using a metabolic background property (urea utilization). Thus, the TRUE set for this family comprised members whose genome of origin encoded a urea utilization system. By moving a sliding window across the sequence of a permease, and searching each subsequence in turn against the full set of partitioned proteins, the method found which local sequence signatures best correlated with the urea utilization trait. Mapping of SIMBAL "hot spots" onto crystal structures of homologous permeases reveals that the significant sites are gating determinants on the cytosolic face rather than, say, docking sites for the substrate-binding protein on the extracellular face. In the second example, we partitioned a protein methyltransferase family using gene proximity as a criterion. In this case, the TRUE set comprised those methyltransferases encoded near the gene for the substrate RF-1. SIMBAL identifies sequence regions that map onto the substrate-binding interface while ignoring regions involved in the methyltransferase reaction mechanism in general. Neither method for training set construction requires any prior experimental characterization. CONCLUSIONS: SIMBAL shows that, in functionally divergent protein families, selected short sequences often significantly outperform their full-length parent sequence for making functional predictions by sequence similarity, suggesting avenues for improved functional classifiers. When combined with structural data, SIMBAL affords the ability to localize and model functional sites.}, keywords = {algorithms, Amino Acid Sequence, Gene Expression Profiling, Molecular Sequence Data, Phylogeny, Proteins, Sequence Analysis, Protein, Structure-Activity Relationship}, author = {J. Selengut and Rusch, Douglas B. and Haft, Daniel H.} } @proceedings {38179, title = {A cooperative combinatorial Particle Swarm Optimization algorithm for side-chain packing}, year = {2009}, month = {2009}, publisher = {IEEE}, type = {10.1109/SIS.2009.4937840}, abstract = {Particle Swarm Optimization (PSO) is a well-known, competitive technique for numerical optimization with real-parameter representation. This paper introduces CCPSO, a new Cooperative Particle Swarm Optimization algorithm for combinatorial problems. The cooperative strategy is achieved by splitting the candidate solution vector into components, where each component is optimized by a particle. Particles move throughout a continuous space, their movements based on the influences exerted by static particles that then get feedback based on the fitness of the candidate solution. Here, the application of this technique to side-chain packing (a proteomics optimization problem) is investigated. To verify the efficiency of the proposed CCPSO algorithm, we test our algorithm on three side-chain packing problems and compare our results with the provably optimal result. Computational results show that the proposed algorithm is very competitive, obtaining a conformation with an energy value within 1\% of the provably optimal solution in many proteins.}, keywords = {Algorithm design and analysis, Amino acids, combinatorial mathematics, cooperative combinatorial particle swarm optimization algorithm, Design optimization, Encoding, Feedback, numerical optimization, Optimization methods, particle swarm optimisation, Particle swarm optimization, Partitioning algorithms, Proteins, proteomics, proteomics optimization, Robustness, side-chain packing}, isbn = {978-1-4244-2762-8}, author = {Lapizco-Encinas, G. and Kingsford, Carl and Reggia, James A.} } @article {38353, title = {InterPro: the integrative protein signature database}, journal = {Nucleic acids researchNucleic Acids Research}, volume = {37}, year = {2009}, note = {http://www.ncbi.nlm.nih.gov/pubmed/18940856?dopt=Abstract}, type = {10.1093/nar/gkn785}, abstract = {The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or {\textquoteright}signatures{\textquoteright} representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. Integration is performed manually and approximately half of the total approximately 58,000 signatures available in the source databases belong to an InterPro entry. Recently, we have started to also display the remaining un-integrated signatures via our web interface. Other developments include the provision of non-signature data, such as structural data, in new XML files on our FTP site, as well as the inclusion of matchless UniProtKB proteins in the existing match XML files. The web interface has been extended and now links out to the ADAN predicted protein-protein interaction database and the SPICE and Dasty viewers. The latest public release (v18.0) covers 79.8\% of UniProtKB (v14.1) and consists of 16 549 entries. InterPro data may be accessed either via the web address above, via web services, by downloading files by anonymous FTP or by using the InterProScan search software (http://www.ebi.ac.uk/Tools/InterProScan/).}, keywords = {Databases, Protein, Proteins, Sequence Analysis, Protein, Systems Integration}, author = {Hunter, Sarah and Apweiler, Rolf and Attwood, Teresa K. and Bairoch, Amos and Bateman, Alex and Binns, David and Bork, Peer and Das, Ujjwal and Daugherty, Louise and Duquenne, Lauranne and Finn, Robert D. and Gough, Julian and Haft, Daniel and Hulo, Nicolas and Kahn, Daniel and Kelly, Elizabeth and Laugraud, Aur{\'e}lie and Letunic, Ivica and Lonsdale, David and Lopez, Rodrigo and Madera, Martin and Maslen, John and McAnulla, Craig and McDowall, Jennifer and Mistry, Jaina and Mitchell, Alex and Mulder, Nicola and Natale, Darren and Orengo, Christine and Quinn, Antony F. and J. Selengut and Sigrist, Christian J. A. and Thimma, Manjula and Thomas, Paul D. and Valentin, Franck and Wilson, Derek and Wu, Cathy H. and Yeats, Corin} } @article {49781, title = {InterPro: the integrative protein signature database.}, journal = {Nucleic Acids Res}, volume = {37}, year = {2009}, month = {2009 Jan}, pages = {D211-5}, abstract = {

The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or {\textquoteright}signatures{\textquoteright} representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. Integration is performed manually and approximately half of the total approximately 58,000 signatures available in the source databases belong to an InterPro entry. Recently, we have started to also display the remaining un-integrated signatures via our web interface. Other developments include the provision of non-signature data, such as structural data, in new XML files on our FTP site, as well as the inclusion of matchless UniProtKB proteins in the existing match XML files. The web interface has been extended and now links out to the ADAN predicted protein-protein interaction database and the SPICE and Dasty viewers. The latest public release (v18.0) covers 79.8\% of UniProtKB (v14.1) and consists of 16 549 entries. InterPro data may be accessed either via the web address above, via web services, by downloading files by anonymous FTP or by using the InterProScan search software (http://www.ebi.ac.uk/Tools/InterProScan/).

}, keywords = {Databases, Protein, Proteins, Sequence Analysis, Protein, Systems Integration}, issn = {1362-4962}, doi = {10.1093/nar/gkn785}, author = {Hunter, Sarah and Apweiler, Rolf and Attwood, Teresa K and Bairoch, Amos and Bateman, Alex and Binns, David and Bork, Peer and Das, Ujjwal and Daugherty, Louise and Duquenne, Lauranne and Finn, Robert D and Gough, Julian and Haft, Daniel and Hulo, Nicolas and Kahn, Daniel and Kelly, Elizabeth and Laugraud, Aur{\'e}lie and Letunic, Ivica and Lonsdale, David and Lopez, Rodrigo and Madera, Martin and Maslen, John and McAnulla, Craig and McDowall, Jennifer and Mistry, Jaina and Mitchell, Alex and Mulder, Nicola and Natale, Darren and Orengo, Christine and Quinn, Antony F and Selengut, Jeremy D and Sigrist, Christian J A and Thimma, Manjula and Thomas, Paul D and Valentin, Franck and Wilson, Derek and Wu, Cathy H and Yeats, Corin} } @article {49747, title = {Protein quantification across hundreds of experimental conditions.}, journal = {Proc Natl Acad Sci U S A}, volume = {106}, year = {2009}, month = {2009 Sep 15}, pages = {15544-8}, abstract = {

Quantitative studies of protein abundance rarely span more than a small number of experimental conditions and replicates. In contrast, quantitative studies of transcript abundance often span hundreds of experimental conditions and replicates. This situation exists, in part, because extracting quantitative data from large proteomics datasets is significantly more difficult than reading quantitative data from a gene expression microarray. To address this problem, we introduce two algorithmic advances in the processing of quantitative proteomics data. First, we use space-partitioning data structures to handle the large size of these datasets. Second, we introduce techniques that combine graph-theoretic algorithms with space-partitioning data structures to collect relative protein abundance data across hundreds of experimental conditions and replicates. We validate these algorithmic techniques by analyzing several datasets and computing both internal and external measures of quantification accuracy. We demonstrate the scalability of these techniques by applying them to a large dataset that comprises a total of 472 experimental conditions and replicates.

}, keywords = {algorithms, Animals, Automatic Data Processing, Chromatography, Liquid, Databases, Factual, Fungal Proteins, HUMANS, Isotopes, Mice, Proteins, proteomics, Tandem Mass Spectrometry}, issn = {1091-6490}, doi = {10.1073/pnas.0904100106}, author = {Khan, Zia and Bloom, Joshua S and Garcia, Benjamin A and Singh, Mona and Kruglyak, Leonid} } @article {49783, title = {New developments in the InterPro database.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jan}, pages = {D224-8}, abstract = {

InterPro is an integrated resource for protein families, domains and functional sites, which integrates the following protein signature databases: PROSITE, PRINTS, ProDom, Pfam, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D and PANTHER. The latter two new member databases have been integrated since the last publication in this journal. There have been several new developments in InterPro, including an additional reading field, new database links, extensions to the web interface and additional match XML files. InterPro has always provided matches to UniProtKB proteins on the website and in the match XML file on the FTP site. Additional matches to proteins in UniParc (UniProt archive) are now available for download in the new match XML files only. The latest InterPro release (13.0) contains more than 13 000 entries, covering over 78\% of all proteins in UniProtKB. The database is available for text- and sequence-based searches via a webserver (http://www.ebi.ac.uk/interpro), and for download by anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro). The InterProScan search tool is now also available via a web service at http://www.ebi.ac.uk/Tools/webservices/WSInterProScan.html.

}, keywords = {Databases, Protein, Internet, Protein Structure, Tertiary, Proteins, Sequence Analysis, Protein, Systems Integration, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkl841}, author = {Mulder, Nicola J and Apweiler, Rolf and Attwood, Teresa K and Bairoch, Amos and Bateman, Alex and Binns, David and Bork, Peer and Buillard, Virginie and Cerutti, Lorenzo and Copley, Richard and Courcelle, Emmanuel and Das, Ujjwal and Daugherty, Louise and Dibley, Mark and Finn, Robert and Fleischmann, Wolfgang and Gough, Julian and Haft, Daniel and Hulo, Nicolas and Hunter, Sarah and Kahn, Daniel and Kanapin, Alexander and Kejariwal, Anish and Labarga, Alberto and Langendijk-Genevaux, Petra S and Lonsdale, David and Lopez, Rodrigo and Letunic, Ivica and Madera, Martin and Maslen, John and McAnulla, Craig and McDowall, Jennifer and Mistry, Jaina and Mitchell, Alex and Nikolskaya, Anastasia N and Orchard, Sandra and Orengo, Christine and Petryszak, Robert and Selengut, Jeremy D and Sigrist, Christian J A and Thomas, Paul D and Valentin, Franck and Wilson, Derek and Wu, Cathy H and Yeats, Corin} } @article {38325, title = {A guild of 45 CRISPR-associated (Cas) protein families and multiple CRISPR/Cas subtypes exist in prokaryotic genomes}, journal = {PLoS computational biologyPLOS Computational Biology}, volume = {1}, year = {2005}, note = {http://www.ncbi.nlm.nih.gov/pubmed/16292354?dopt=Abstract}, type = {10.1371/journal.pcbi.0010060}, abstract = {Clustered regularly interspaced short palindromic repeats (CRISPRs) are a family of DNA direct repeats found in many prokaryotic genomes. Repeats of 21-37 bp typically show weak dyad symmetry and are separated by regularly sized, nonrepetitive spacer sequences. Four CRISPR-associated (Cas) protein families, designated Cas1 to Cas4, are strictly associated with CRISPR elements and always occur near a repeat cluster. Some spacers originate from mobile genetic elements and are thought to confer "immunity" against the elements that harbor these sequences. In the present study, we have systematically investigated uncharacterized proteins encoded in the vicinity of these CRISPRs and found many additional protein families that are strictly associated with CRISPR loci across multiple prokaryotic species. Multiple sequence alignments and hidden Markov models have been built for 45 Cas protein families. These models identify family members with high sensitivity and selectivity and classify key regulators of development, DevR and DevS, in Myxococcus xanthus as Cas proteins. These identifications show that CRISPR/cas gene regions can be quite large, with up to 20 different, tandem-arranged cas genes next to a repeat cluster or filling the region between two repeat clusters. Distinctive subsets of the collection of Cas proteins recur in phylogenetically distant species and correlate with characteristic repeat periodicity. The analyses presented here support initial proposals of mobility of these units, along with the likelihood that loci of different subtypes interact with one another as well as with host cell defensive, replicative, and regulatory systems. It is evident from this analysis that CRISPR/cas loci are larger, more complex, and more heterogeneous than previously appreciated.}, keywords = {Genes, Archaeal, Genes, Bacterial, Genes, Fungal, Genome, Genome, Bacterial, Haloarcula marismortui, Markov chains, Multigene Family, Oligonucleotide Array Sequence Analysis, Phylogeny, Prokaryotic Cells, Proteins, Repetitive Sequences, Nucleic Acid, Yersinia pestis}, author = {Haft, Daniel H. and J. Selengut and Mongodin, Emmanuel F. and Nelson, Karen E.} } @article {38531, title = {The TIGRFAMs database of protein families}, journal = {Nucleic acids researchNucleic Acids Research}, volume = {31}, year = {2003}, note = {http://www.ncbi.nlm.nih.gov/pubmed/12520025?dopt=Abstract}, abstract = {TIGRFAMs is a collection of manually curated protein families consisting of hidden Markov models (HMMs), multiple sequence alignments, commentary, Gene Ontology (GO) assignments, literature references and pointers to related TIGRFAMs, Pfam and InterPro models. These models are designed to support both automated and manually curated annotation of genomes. TIGRFAMs contains models of full-length proteins and shorter regions at the levels of superfamilies, subfamilies and equivalogs, where equivalogs are sets of homologous proteins conserved with respect to function since their last common ancestor. The scope of each model is set by raising or lowering cutoff scores and choosing members of the seed alignment to group proteins sharing specific function (equivalog) or more general properties. The overall goal is to provide information with maximum utility for the annotation process. TIGRFAMs is thus complementary to Pfam, whose models typically achieve broad coverage across distant homologs but end at the boundaries of conserved structural domains. The database currently contains over 1600 protein families. TIGRFAMs is available for searching or downloading at www.tigr.org/TIGRFAMs.}, keywords = {Animals, Databases, Protein, Markov chains, Mixed Function Oxygenases, Phylogeny, Proteins, Pyruvate Carboxylase, Sequence Homology, Amino Acid}, author = {Haft, Daniel H. and J. Selengut and White, Owen} } @article {49687, title = {The draft genome of Ciona intestinalis: insights into chordate and vertebrate origins.}, journal = {Science}, volume = {298}, year = {2002}, month = {2002 Dec 13}, pages = {2157-67}, abstract = {

The first chordates appear in the fossil record at the time of the Cambrian explosion, nearly 550 million years ago. The modern ascidian tadpole represents a plausible approximation to these ancestral chordates. To illuminate the origins of chordate and vertebrates, we generated a draft of the protein-coding portion of the genome of the most studied ascidian, Ciona intestinalis. The Ciona genome contains approximately 16,000 protein-coding genes, similar to the number in other invertebrates, but only half that found in vertebrates. Vertebrate gene families are typically found in simplified form in Ciona, suggesting that ascidians contain the basic ancestral complement of genes involved in cell signaling and development. The ascidian genome has also acquired a number of lineage-specific innovations, including a group of genes engaged in cellulose metabolism that are related to those in bacteria and fungi.

}, keywords = {Alleles, Animals, Apoptosis, Base Sequence, Cellulose, Central Nervous System, Ciona intestinalis, Computational Biology, Endocrine System, Gene Dosage, Gene Duplication, genes, Genes, Homeobox, Genome, Heart, Immunity, Molecular Sequence Data, Multigene Family, Muscle Proteins, Organizers, Embryonic, Phylogeny, Polymorphism, Genetic, Proteins, Sequence Analysis, DNA, Sequence Homology, Nucleic Acid, Species Specificity, Thyroid Gland, Urochordata, Vertebrates}, issn = {1095-9203}, doi = {10.1126/science.1080049}, author = {Dehal, Paramvir and Satou, Yutaka and Campbell, Robert K and Chapman, Jarrod and Degnan, Bernard and De Tomaso, Anthony and Davidson, Brad and Di Gregorio, Anna and Gelpke, Maarten and Goodstein, David M and Harafuji, Naoe and Hastings, Kenneth E M and Ho, Isaac and Hotta, Kohji and Huang, Wayne and Kawashima, Takeshi and Lemaire, Patrick and Martinez, Diego and Meinertzhagen, Ian A and Necula, Simona and Nonaka, Masaru and Putnam, Nik and Rash, Sam and Saiga, Hidetoshi and Satake, Masanobu and Terry, Astrid and Yamada, Lixy and Wang, Hong-Gang and Awazu, Satoko and Azumi, Kaoru and Boore, Jeffrey and Branno, Margherita and Chin-Bow, Stephen and DeSantis, Rosaria and Doyle, Sharon and Francino, Pilar and Keys, David N and Haga, Shinobu and Hayashi, Hiroko and Hino, Kyosuke and Imai, Kaoru S and Inaba, Kazuo and Kano, Shungo and Kobayashi, Kenji and Kobayashi, Mari and Lee, Byung-In and Makabe, Kazuhiro W and Manohar, Chitra and Matassi, Giorgio and Medina, Monica and Mochizuki, Yasuaki and Mount, Steve and Morishita, Tomomi and Miura, Sachiko and Nakayama, Akie and Nishizaka, Satoko and Nomoto, Hisayo and Ohta, Fumiko and Oishi, Kazuko and Rigoutsos, Isidore and Sano, Masako and Sasaki, Akane and Sasakura, Yasunori and Shoguchi, Eiichi and Shin-i, Tadasu and Spagnuolo, Antoinetta and Stainier, Didier and Suzuki, Miho M and Tassy, Olivier and Takatori, Naohito and Tokuoka, Miki and Yagi, Kasumi and Yoshizaki, Fumiko and Wada, Shuichi and Zhang, Cindy and Hyatt, P Douglas and Larimer, Frank and Detter, Chris and Doggett, Norman and Glavina, Tijana and Hawkins, Trevor and Richardson, Paul and Lucas, Susan and Kohara, Yuji and Levine, Michael and Satoh, Nori and Rokhsar, Daniel S} } @article {49696, title = {Genetic enhancement of RNA-processing defects by a dominant mutation in B52, the Drosophila gene for an SR protein splicing factor.}, journal = {Mol Cell Biol}, volume = {15}, year = {1995}, month = {1995 Nov}, pages = {6273-82}, abstract = {

SR proteins are essential for pre-mRNA splicing in vitro, act early in the splicing pathway, and can influence alternative splice site choice. Here we describe the isolation of both dominant and loss-of-function alleles of B52, the gene for a Drosophila SR protein. The allele B52ED was identified as a dominant second-site enhancer of white-apricot (wa), a retrotransposon insertion in the second intron of the eye pigmentation gene white with a complex RNA-processing defect. B52ED also exaggerates the mutant phenotype of a distinct white allele carrying a 5{\textquoteright} splice site mutation (wDR18), and alters the pattern of sex-specific splicing at doublesex under sensitized conditions, so that the male-specific splice is favored. In addition to being a dominant enhancer of these RNA-processing defects, B52ED is a recessive lethal allele that fails to complement other lethal alleles of B52. Comparison of B52ED with the B52+ allele from which it was derived revealed a single change in a conserved amino acid in the beta 4 strand of the first RNA-binding domain of B52, which suggests that altered RNA binding is responsible for the dominant phenotype. Reversion of the B52ED dominant allele with X rays led to the isolation of a B52 null allele. Together, these results indicate a critical role for the SR protein B52 in pre-mRNA splicing in vivo.

}, keywords = {Alleles, Amino Acid Sequence, Animals, Base Sequence, DNA Primers, Drosophila melanogaster, Drosophila Proteins, Frameshift Mutation, Genes, Dominant, Genes, Insect, Molecular Sequence Data, Nuclear Proteins, Phosphoproteins, Point Mutation, Protein Structure, Tertiary, Proteins, RNA Splicing, RNA-Binding Proteins, Sequence Deletion, Sex Determination Analysis}, issn = {0270-7306}, author = {Peng, X and Mount, S M} } @article {49701, title = {Species-specific signals for the splicing of a short Drosophila intron in vitro.}, journal = {Mol Cell Biol}, volume = {13}, year = {1993}, month = {1993 Feb}, pages = {1104-18}, abstract = {

The effects of branchpoint sequence, the pyrimidine stretch, and intron size on the splicing efficiency of the Drosophila white gene second intron were examined in nuclear extracts from Drosophila and human cells. This 74-nucleotide intron is typical of many Drosophila introns in that it lacks a significant pyrimidine stretch and is below the minimum size required for splicing in human nuclear extracts. Alteration of sequences of adjacent to the 3{\textquoteright} splice site to create a pyrimidine stretch was necessary for splicing in human, but not Drosophila, extracts. Increasing the size of this intron with insertions between the 5{\textquoteright} splice site and the branchpoint greatly reduced the efficiency of splicing of introns longer than 79 nucleotides in Drosophila extracts but had an opposite effect in human extracts, in which introns longer than 78 nucleotides were spliced with much greater efficiency. The white-apricot copia insertion is immediately adjacent to the branchpoint normally used in the splicing of this intron, and a copia long terminal repeat insertion prevents splicing in Drosophila, but not human, extracts. However, a consensus branchpoint does not restore the splicing of introns containing the copia long terminal repeat, and alteration of the wild-type branchpoint sequence alone does not eliminate splicing. These results demonstrate species specificity of splicing signals, particularly pyrimidine stretch and size requirements, and raise the possibility that variant mechanisms not found in mammals may operate in the splicing of small introns in Drosophila and possibly other species.

}, keywords = {Animals, Base Sequence, Cell Nucleus, Consensus Sequence, DNA, DNA Transposable Elements, Drosophila, Drosophila Proteins, Electrophoresis, Polyacrylamide Gel, HeLa Cells, HUMANS, Introns, Molecular Sequence Data, Mutation, Peptide Hydrolases, Proteins, Regulatory Sequences, Nucleic Acid, Retroelements, RNA Splicing, Species Specificity}, issn = {0270-7306}, author = {Guo, M and Lo, P C and Mount, S M} }