@article {49606, title = {Orchestrating high-throughput genomic analysis with Bioconductor.}, volume = {12}, year = {2015}, month = {2015 Feb}, pages = {115-21}, abstract = {

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.

}, keywords = {Computational Biology, Gene Expression Profiling, Genomics, High-Throughput Screening Assays, Programming Languages, software, User-Computer Interface}, issn = {1548-7105}, doi = {10.1038/nmeth.3252}, author = {Huber, Wolfgang and Carey, Vincent J and Gentleman, Robert and Anders, Simon and Carlson, Marc and Carvalho, Benilton S and Bravo, H{\'e}ctor Corrada and Davis, Sean and Gatto, Laurent and Girke, Thomas and Gottardo, Raphael and Hahne, Florian and Hansen, Kasper D and Irizarry, Rafael A and Lawrence, Michael and Love, Michael I and MacDonald, James and Obenchain, Valerie and Ole{\'s}, Andrzej K and Pag{\`e}s, Herv{\'e} and Reyes, Alejandro and Shannon, Paul and Smyth, Gordon K and Tenenbaum, Dan and Waldron, Levi and Morgan, Martin} } @article {45867, title = {Automated ensemble assembly and validation of microbial genomes.}, journal = {BMC Bioinformatics}, volume = {15}, year = {2014}, month = {2014}, pages = {126}, abstract = {

BACKGROUND: The continued democratization of DNA sequencing has sparked a new wave of development of genome assembly and assembly validation methods. As individual research labs, rather than centralized centers, begin to sequence the majority of new genomes, it is important to establish best practices for genome assembly. However, recent evaluations such as GAGE and the Assemblathon have concluded that there is no single best approach to genome assembly. Instead, it is preferable to generate multiple assemblies and validate them to determine which is most useful for the desired analysis; this is a labor-intensive process that is often impossible or unfeasible.

RESULTS: To encourage best practices supported by the community, we present iMetAMOS, an automated ensemble assembly pipeline; iMetAMOS encapsulates the process of running, validating, and selecting a single assembly from multiple assemblies. iMetAMOS packages several leading open-source tools into a single binary that automates parameter selection and execution of multiple assemblers, scores the resulting assemblies based on multiple validation metrics, and annotates the assemblies for genes and contaminants. We demonstrate the utility of the ensemble process on 225 previously unassembled Mycobacterium tuberculosis genomes as well as a Rhodobacter sphaeroides benchmark dataset. On these real data, iMetAMOS reliably produces validated assemblies and identifies potential contamination without user intervention. In addition, intelligent parameter selection produces assemblies of R. sphaeroides comparable to or exceeding the quality of those from the GAGE-B evaluation, affecting the relative ranking of some assemblers.

CONCLUSIONS: Ensemble assembly with iMetAMOS provides users with multiple, validated assemblies for each genome. Although computationally limited to small or mid-sized genomes, this approach is the most effective and reproducible means for generating high-quality assemblies and enables users to select an assembly best tailored to their specific needs.

}, keywords = {Genome, Bacterial, Genome, Microbial, Genomics, Mycobacterium tuberculosis, Rhodobacter sphaeroides, Sequence Analysis, DNA, software}, issn = {1471-2105}, doi = {10.1186/1471-2105-15-126}, author = {Koren, Sergey and Todd Treangen and Hill, Christopher M and Pop, Mihai and Phillippy, Adam M} } @article {49599, title = {BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution.}, volume = {30}, year = {2014}, month = {2014 May 1}, pages = {1214-9}, abstract = {

MOTIVATION: Base-calling of sequencing data produced by high-throughput sequencing platforms is a fundamental process in current bioinformatics analysis. However, existing third-party probabilistic or machine-learning methods that significantly improve the accuracy of base-calls on these platforms are impractical for production use due to their computational inefficiency.

RESULTS: We directly formulate base-calling as a blind deconvolution problem and implemented BlindCall as an efficient solver to this inverse problem. BlindCall produced base-calls at accuracy comparable to state-of-the-art probabilistic methods while processing data at rates 10 times faster in most cases. The computational complexity of BlindCall scales linearly with read length making it better suited for new long-read sequencing technologies.

}, keywords = {algorithms, High-Throughput Nucleotide Sequencing, HUMANS, Probability, Reproducibility of Results, Sequence Analysis, DNA, software, Time factors}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btu010}, author = {Ye, Chengxi and Hsiao, Chiaowen and Corrada Bravo, Hector} } @article {49602, title = {Epiviz: interactive visual analytics for functional genomics data.}, volume = {11}, year = {2014}, month = {2014 Sep}, pages = {938-40}, abstract = {

Visualization is an integral aspect of genomics data analysis. Algorithmic-statistical analysis and interactive visualization are most effective when used iteratively. Epiviz (http://epiviz.cbcb.umd.edu/), a web-based genome browser, and the Epivizr Bioconductor package allow interactive, extensible and reproducible visualization within a state-of-the-art data-analysis platform.

}, keywords = {algorithms, Chromosome mapping, Data Mining, database management systems, Databases, Genetic, Genomics, Internet, software, User-Computer Interface}, issn = {1548-7105}, doi = {10.1038/nmeth.3038}, author = {Chelaru, Florin and Smith, Llewellyn and Goldstein, Naomi and Bravo, H{\'e}ctor Corrada} } @article {49605, title = {Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays.}, volume = {30}, year = {2014}, month = {2014 May 15}, pages = {1363-9}, abstract = {

MOTIVATION: The recently released Infinium HumanMethylation450 array (the {\textquoteright}450k{\textquoteright} array) provides a high-throughput assay to quantify DNA methylation (DNAm) at \~{}450 000 loci across a range of genomic features. Although less comprehensive than high-throughput sequencing-based techniques, this product is more cost-effective and promises to be the most widely used DNAm high-throughput measurement technology over the next several years.

RESULTS: Here we describe a suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. The software is structured to easily adapt to future versions of the technology. We include methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. We show how our software provides a powerful and flexible development platform for future methods. We also illustrate how our methods empower the technology to make discoveries previously thought to be possible only with sequencing-based methods.

AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/release/bioc/html/minfi.html.

CONTACT: khansen@jhsph.edu; rafa@jimmy.harvard.edu

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Aged, algorithms, Colonic Neoplasms, DNA Methylation, Genome, High-Throughput Nucleotide Sequencing, HUMANS, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btu049}, author = {Aryee, Martin J and Jaffe, Andrew E and Corrada-Bravo, Hector and Ladd-Acosta, Christine and Feinberg, Andrew P and Hansen, Kasper D and Irizarry, Rafael A} } @article {49735, title = {Quantitative 4D analyses of epithelial folding during Drosophila gastrulation.}, journal = {Development}, volume = {141}, year = {2014}, month = {2014 Jul}, pages = {2895-900}, abstract = {

Understanding the cellular and mechanical processes that underlie the shape changes of individual cells and their collective behaviors in a tissue during dynamic and complex morphogenetic events is currently one of the major frontiers in developmental biology. The advent of high-speed time-lapse microscopy and its use in monitoring the cellular events in fluorescently labeled developing organisms demonstrate tremendous promise in establishing detailed descriptions of these events and could potentially provide a foundation for subsequent hypothesis-driven research strategies. However, obtaining quantitative measurements of dynamic shapes and behaviors of cells and tissues in a rapidly developing metazoan embryo using time-lapse 3D microscopy remains technically challenging, with the main hurdle being the shortage of robust imaging processing and analysis tools. We have developed EDGE4D, a software tool for segmenting and tracking membrane-labeled cells using multi-photon microscopy data. Our results demonstrate that EDGE4D enables quantification of the dynamics of cell shape changes, cell interfaces and neighbor relations at single-cell resolution during a complex epithelial folding event in the early Drosophila embryo. We expect this tool to be broadly useful for the analysis of epithelial cell geometries and movements in a wide variety of developmental contexts.

}, keywords = {Animals, Body Patterning, Cell Shape, Cell Tracking, Drosophila melanogaster, Epithelial Cells, Epithelium, Gastrulation, Image Processing, Computer-Assisted, software}, issn = {1477-9129}, doi = {10.1242/dev.107730}, author = {Khan, Zia and Wang, Yu-Chiun and Wieschaus, Eric F and Kaschube, Matthias} } @article {49572, title = {Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms.}, volume = {32}, year = {2014}, month = {2014 May}, pages = {462-4}, abstract = {

We introduce Sailfish, a computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data. Because Sailfish entirely avoids mapping reads, a time-consuming step in all current methods, it provides quantification estimates much faster than do existing approaches (typically 20 times faster) without loss of accuracy. By facilitating frequent reanalysis of data and reducing the need to optimize parameters, Sailfish exemplifies the potential of lightweight algorithms for efficiently processing sequencing reads.

}, keywords = {algorithms, Brain Chemistry, Computational Biology, HUMANS, Models, Biological, RNA Isoforms, Sequence Analysis, RNA, software}, issn = {1546-1696}, doi = {10.1038/nbt.2862}, author = {Patro, Rob and Mount, Stephen M and Kingsford, Carl} } @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 {49744, title = {Accurate proteome-wide protein quantification from high-resolution 15N mass spectra.}, journal = {Genome Biol}, volume = {12}, year = {2011}, month = {2011}, pages = {R122}, abstract = {

In quantitative mass spectrometry-based proteomics, the metabolic incorporation of a single source of 15N-labeled nitrogen has many advantages over using stable isotope-labeled amino acids. However, the lack of a robust computational framework for analyzing the resulting spectra has impeded wide use of this approach. We have addressed this challenge by introducing a new computational methodology for analyzing 15N spectra in which quantification is integrated with identification. Application of this method to an Escherichia coli growth transition reveals significant improvement in quantification accuracy over previous methods.

}, keywords = {algorithms, Amino Acid Sequence, Bacterial Proteins, Escherichia coli, Isotope Labeling, Mass Spectrometry, Molecular Sequence Data, Nitrogen Isotopes, Proteome, proteomics, Sensitivity and Specificity, software}, issn = {1474-760X}, doi = {10.1186/gb-2011-12-12-r122}, author = {Khan, Zia and Amini, Sasan and Bloom, Joshua S and Ruse, Cristian and Caudy, Amy A and Kruglyak, Leonid and Singh, Mona and Perlman, David H and Tavazoie, Saeed} } @proceedings {38176, title = {Computing the Tree of Life: Leveraging the Power of Desktop and Service Grids}, year = {2011}, month = {2011}, type = {10.1109/IPDPS.2011.344}, abstract = {The trend in life sciences research, particularly in molecular evolutionary systematics, is toward larger data sets and ever-more detailed evolutionary models, which can generate substantial computational loads. Over the past several years we have developed a grid computing system aimed at providing researchers the computational power needed to complete such analyses in a timely manner. Our grid system, known as The Lattice Project, was the first to combine two models of grid computing - the service model, which mainly federates large institutional HPC resources, and the desktop model, which harnesses the power of PCs volunteered by the general public. Recently we have developed a "science portal" style web interface that makes it easier than ever for phylogenetic analyses to be completed using GARLI, a popular program that uses a maximum likelihood method to infer the evolutionary history of organisms on the basis of genetic sequence data. This paper describes our approach to scheduling thousands of GARLI jobs with diverse requirements to heterogeneous grid resources, which include volunteer computers running BOINC software. A key component of this system provides a priori GARLI runtime estimates using machine learning with random forests.}, keywords = {(artificial, (mathematics), analysis, BOINC, COMPUTATION, computational, computing, data, Estimation, evolutionary, GARLI, genetic, Grid, GRIDS, handling, heterogeneous, History, HPC, information, intelligence), interface, interfaces, Internet, jobs, lattice, learning, life, likelihood, load, machine, maximum, method, model, molecular, phylogenetic, portal, Portals, power, project, resource, Science, sequence, service, services, sets, software, substantial, system, systematics, tree, TREES, user, Web}, author = {Adam L. Bazinet and Michael P. Cummings} } @article {49777, title = {ProPhylo: partial phylogenetic profiling to guide protein family construction and assignment of biological process.}, journal = {BMC Bioinformatics}, volume = {12}, year = {2011}, month = {2011}, pages = {434}, abstract = {

BACKGROUND: Phylogenetic profiling is a technique of scoring co-occurrence between a protein family and some other trait, usually another protein family, across a set of taxonomic groups. In spite of several refinements in recent years, the technique still invites significant improvement. To be its most effective, a phylogenetic profiling algorithm must be able to examine co-occurrences among protein families whose boundaries are uncertain within large homologous protein superfamilies.

RESULTS: Partial Phylogenetic Profiling (PPP) is an iterative algorithm that scores a given taxonomic profile against the taxonomic distribution of families for all proteins in a genome. The method works through optimizing the boundary of each protein family, rather than by relying on prebuilt protein families or fixed sequence similarity thresholds. Double Partial Phylogenetic Profiling (DPPP) is a related procedure that begins with a single sequence and searches for optimal granularities for its surrounding protein family in order to generate the best query profiles for PPP. We present ProPhylo, a high-performance software package for phylogenetic profiling studies through creating individually optimized protein family boundaries. ProPhylo provides precomputed databases for immediate use and tools for manipulating the taxonomic profiles used as queries.

CONCLUSION: ProPhylo results show universal markers of methanogenesis, a new DNA phosphorothioation-dependent restriction enzyme, and efficacy in guiding protein family construction. The software and the associated databases are freely available under the open source Perl Artistic License from ftp://ftp.jcvi.org/pub/data/ppp/.

}, keywords = {algorithms, Archaea, Archaeal Proteins, DNA, Methane, Phylogeny, software}, issn = {1471-2105}, doi = {10.1186/1471-2105-12-434}, author = {Basu, Malay K and Selengut, Jeremy D and Haft, Daniel H} } @article {38452, title = {ProPhylo: partial phylogenetic profiling to guide protein family construction and assignment of biological process}, journal = {BMC bioinformaticsBMC Bioinformatics}, volume = {12}, year = {2011}, note = {http://www.ncbi.nlm.nih.gov/pubmed/22070167?dopt=Abstract}, type = {10.1186/1471-2105-12-434}, abstract = {BACKGROUND: Phylogenetic profiling is a technique of scoring co-occurrence between a protein family and some other trait, usually another protein family, across a set of taxonomic groups. In spite of several refinements in recent years, the technique still invites significant improvement. To be its most effective, a phylogenetic profiling algorithm must be able to examine co-occurrences among protein families whose boundaries are uncertain within large homologous protein superfamilies. RESULTS: Partial Phylogenetic Profiling (PPP) is an iterative algorithm that scores a given taxonomic profile against the taxonomic distribution of families for all proteins in a genome. The method works through optimizing the boundary of each protein family, rather than by relying on prebuilt protein families or fixed sequence similarity thresholds. Double Partial Phylogenetic Profiling (DPPP) is a related procedure that begins with a single sequence and searches for optimal granularities for its surrounding protein family in order to generate the best query profiles for PPP. We present ProPhylo, a high-performance software package for phylogenetic profiling studies through creating individually optimized protein family boundaries. ProPhylo provides precomputed databases for immediate use and tools for manipulating the taxonomic profiles used as queries. CONCLUSION: ProPhylo results show universal markers of methanogenesis, a new DNA phosphorothioation-dependent restriction enzyme, and efficacy in guiding protein family construction. The software and the associated databases are freely available under the open source Perl Artistic License from ftp://ftp.jcvi.org/pub/data/ppp/.}, keywords = {algorithms, Archaea, Archaeal Proteins, DNA, Methane, Phylogeny, software}, author = {Basu, Malay K. and J. Selengut and Haft, Daniel H.} } @article {38530, title = {TIGRFAMs and Genome Properties: tools for the assignment of molecular function and biological process in prokaryotic genomes}, journal = {Nucleic acids researchNucleic Acids Research}, volume = {35}, year = {2007}, note = {http://www.ncbi.nlm.nih.gov/pubmed/17151080?dopt=Abstract}, type = {10.1093/nar/gkl1043}, abstract = {TIGRFAMs is a collection of protein family definitions built to aid in high-throughput annotation of specific protein functions. Each family is based on a hidden Markov model (HMM), where both cutoff scores and membership in the seed alignment are chosen so that the HMMs can classify numerous proteins according to their specific molecular functions. Most TIGRFAMs models describe {\textquoteright}equivalog{\textquoteright} families, where both orthology and lateral gene transfer may be part of the evolutionary history, but where a single molecular function has been conserved. The Genome Properties system contains a queriable set of metabolic reconstructions, genome metrics and extractions of information from the scientific literature. Its genome-by-genome assertions of whether or not specific structures, pathways or systems are present provide high-level conceptual descriptions of genomic content. These assertions enable comparative genomics, provide a meaningful biological context to aid in manual annotation, support assignments of Gene Ontology (GO) biological process terms and help validate HMM-based predictions of protein function. The Genome Properties system is particularly useful as a generator of phylogenetic profiles, through which new protein family functions may be discovered. The TIGRFAMs and Genome Properties systems can be accessed at http://www.tigr.org/TIGRFAMs and http://www.tigr.org/Genome_Properties.}, keywords = {Archaeal Proteins, Bacterial Proteins, Databases, Protein, Genome, Bacterial, Genomics, Internet, Phylogeny, software, User-Computer Interface}, author = {J. Selengut and Haft, Daniel H. and Davidsen, Tanja and Ganapathy, Anurhada and Gwinn-Giglio, Michelle and Nelson, William C. and Richter, R. Alexander and White, Owen} } @article {38294, title = {Genome Properties: a system for the investigation of prokaryotic genetic content for microbiology, genome annotation and comparative genomics}, journal = {Bioinformatics (Oxford, England)Bioinformatics (Oxford, England)}, volume = {21}, year = {2005}, note = {http://www.ncbi.nlm.nih.gov/pubmed/15347579?dopt=Abstract}, type = {10.1093/bioinformatics/bti015}, abstract = {MOTIVATION: The presence or absence of metabolic pathways and structures provide a context that makes protein annotation far more reliable. Compiling such information across microbial genomes improves the functional classification of proteins and provides a valuable resource for comparative genomics. RESULTS: We have created a Genome Properties system to present key aspects of prokaryotic biology using standardized computational methods and controlled vocabularies. Properties reflect gene content, phenotype, phylogeny and computational analyses. The results of searches using hidden Markov models allow many properties to be deduced automatically, especially for families of proteins (equivalogs) conserved in function since their last common ancestor. Additional properties are derived from curation, published reports and other forms of evidence. Genome Properties system was applied to 156 complete prokaryotic genomes, and is easily mined to find differences between species, correlations between metabolic features and families of uncharacterized proteins, or relationships among properties. AVAILABILITY: Genome Properties can be found at http://www.tigr.org/Genome_Properties SUPPLEMENTARY INFORMATION: http://www.tigr.org/tigr-scripts/CMR2/genome_properties_references.spl.}, keywords = {Chromosome mapping, database management systems, Databases, Genetic, documentation, Gene Expression Profiling, Gene Expression Regulation, Genomics, Information Storage and Retrieval, Microbiological Techniques, natural language processing, Prokaryotic Cells, Proteome, signal transduction, software, User-Computer Interface, Vocabulary, Controlled}, author = {Haft, Daniel H. and J. Selengut and Brinkac, Lauren M. and Zafar, Nikhat and White, Owen} } @article {49685, title = {Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies.}, journal = {Nucleic Acids Res}, volume = {31}, year = {2003}, month = {2003 Oct 1}, pages = {5654-66}, abstract = {

The spliced alignment of expressed sequence data to genomic sequence has proven a key tool in the comprehensive annotation of genes in eukaryotic genomes. A novel algorithm was developed to assemble clusters of overlapping transcript alignments (ESTs and full-length cDNAs) into maximal alignment assemblies, thereby comprehensively incorporating all available transcript data and capturing subtle splicing variations. Complete and partial gene structures identified by this method were used to improve The Institute for Genomic Research Arabidopsis genome annotation (TIGR release v.4.0). The alignment assemblies permitted the automated modeling of several novel genes and >1000 alternative splicing variations as well as updates (including UTR annotations) to nearly half of the approximately 27 000 annotated protein coding genes. The algorithm of the Program to Assemble Spliced Alignments (PASA) tool is described, as well as the results of automated updates to Arabidopsis gene annotations.

}, keywords = {algorithms, Alternative Splicing, Arabidopsis, DNA, Complementary, Expressed Sequence Tags, Genome, Plant, Introns, Plant Proteins, RNA, Plant, sequence alignment, software, Transcription, Genetic, Untranslated Regions}, issn = {1362-4962}, author = {Haas, Brian J and Delcher, Arthur L and Mount, Stephen M and Wortman, Jennifer R and Smith, Roger K and Hannick, Linda I and Maiti, Rama and Ronning, Catherine M and Rusch, Douglas B and Town, Christopher D and Salzberg, Steven L and White, Owen} } @article {49702, title = {Splicing signals in Drosophila: intron size, information content, and consensus sequences.}, journal = {Nucleic Acids Res}, volume = {20}, year = {1992}, month = {1992 Aug 25}, pages = {4255-62}, abstract = {

A database of 209 Drosophila introns was extracted from Genbank (release number 64.0) and examined by a number of methods in order to characterize features that might serve as signals for messenger RNA splicing. A tight distribution of sizes was observed: while the smallest introns in the database are 51 nucleotides, more than half are less than 80 nucleotides in length, and most of these have lengths in the range of 59-67 nucleotides. Drosophila splice sites found in large and small introns differ in only minor ways from each other and from those found in vertebrate introns. However, larger introns have greater pyrimidine-richness in the region between 11 and 21 nucleotides upstream of 3{\textquoteright} splice sites. The Drosophila branchpoint consensus matrix resembles C T A A T (in which branch formation occurs at the underlined A), and differs from the corresponding mammalian signal in the absence of G at the position immediately preceding the branchpoint. The distribution of occurrences of this sequence suggests a minimum distance between 5{\textquoteright} splice sites and branchpoints of about 38 nucleotides, and a minimum distance between 3{\textquoteright} splice sites and branchpoints of 15 nucleotides. The methods we have used detect no information in exon sequences other than in the few nucleotides immediately adjacent to the splice sites. However, Drosophila resembles many other species in that there is a discontinuity in A + T content between exons and introns, which are A + T rich.

}, keywords = {Animals, Base Sequence, Consensus Sequence, Databases, Factual, Drosophila, Introns, Molecular Sequence Data, RNA Splicing, RNA, Messenger, software}, issn = {0305-1048}, author = {Mount, S M and Burks, C and Hertz, G and Stormo, G D and White, O and Fields, C} }