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      The genome and transcriptome of the zoonotic hookworm Ancylostoma ceylanicum identify infection-specific gene families

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          Abstract

          Hookworms infect over 400 million people, stunting and impoverishing them 13 . Sequencing hookworm genomes and finding which genes they express during infection should help in devising new drugs or vaccines against hookworms 4, 5 . Unlike other hookworms, Ancylostoma ceylanicum infects both humans and other mammals, providing a laboratory model for hookworm disease 6, 7 . We determined an A. ceylanicum genome sequence of 313 Mb, with transcriptomic data throughout infection showing expression of 30,738 genes. Approximately 900 genes were upregulated during early infection in vivo, including ASPRs, a cryptic subfamily of activation-associated secreted proteins (ASPs) 8 . Genes downregulated during early infection included ion channels and G protein–coupled receptors; this downregulation was observed in both parasitic and free-living nematodes. Later, at the onset of heavy blood feeding, C-lectin genes were upregulated along with genes for secreted clade V proteins (SCVPs), encoding a previously undescribed protein family. These findings provide new drug and vaccine targets and should help elucidate hookworm pathogenesis.

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          Activities at the Universal Protein Resource (UniProt)

          The mission of the Universal Protein Resource (UniProt) (http://www.uniprot.org) is to provide the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequences and functional annotation. It integrates, interprets and standardizes data from literature and numerous resources to achieve the most comprehensive catalog possible of protein information. The central activities are the biocuration of the UniProt Knowledgebase and the dissemination of these data through our Web site and web services. UniProt is produced by the UniProt Consortium, which consists of groups from the European Bioinformatics Institute (EBI), the SIB Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is updated and distributed every 4 weeks and can be accessed online for searches or downloads.
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            A new generation of homology search tools based on probabilistic inference.

            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'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'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.
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              Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project.

              We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor-binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor-binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                15 April 2015
                02 March 2015
                April 2015
                23 October 2015
                : 47
                : 4
                : 416-422
                Affiliations
                [1 ]Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
                [2 ]Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA
                [3 ]Section of Cell and Developmental Biology, University of California, San Diego, La Jolla, California, USA
                [4 ]Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
                [5 ]Howard Hughes Medical Institute, California Institute of Technology, Pasadena, California, USA
                Author notes
                Correspondence should be addressed to E.M.S. ( ems394@ 123456cornell.edu )
                Article
                NIHMS681025
                10.1038/ng.3237
                4617383
                25730766
                0e20556e-85ef-4529-84d9-661f6f4468d5

                Reprints and permissions information is available online at http://www.nature.com/reprints/index.html

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