Genomic and Transcriptomic Surveys for the Study of Ncrnas with a Focus on Tropical Parasites

Total Page:16

File Type:pdf, Size:1020Kb

Genomic and Transcriptomic Surveys for the Study of Ncrnas with a Focus on Tropical Parasites PhD Thesis PROGRAMA DE PÓS-GRADUAÇÃO EM BIOINFORMÁTICA UNIVERSIDADE FEDERAL DE MINAS GERAIS Genomic and transcriptomic surveys for the study of ncRNAs with a focus on tropical parasites Mainá Bitar Belo Horizonte February 2015 Universidade Federal de Minas Gerais PhD Thesis PROGRAMA DE PÓS-GRADUAÇÃO EM BIOINFORMÁTICA Genomic and transcriptomic surveys for the study of ncRNAs with a focus on tropical parasites PhD candidate: Mainá Bitar Advisor: Glória Regina Franco Co-advisor: Martin Alexander Smith Mainá Bitar Lourenço Genomic and transcriptomic surveys for the study of ncRNAs with a focus on tropical parasites Versão final Tese apresentada ao Programa Interunidades de Pós-Graduação em Bioinformática do Instituto de Ciências Biológicas da Universidade Federal de Minas Gerais como requisito parcial para a obtenção do título de Doutor em Bioinformática. Orientador: Profa. Dra. Glória Regina Franco BELO HORIZONTE 2015 043 Bitar, Mainá. Genomic and transcriptomic surveys for the study of ncRNAs with a focus on tropical parasites [manuscrito] / Mainá Bitar. – 2015. 134 f. : il. ; 29,5 cm. Orientador: Glória Regina Franco. Coorientador: Martin Alexander Smith. Tese (doutorado) – Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas. Programa de Pós-Graduação em Bioinformática. 1. Bioinformática - Teses. 2. Trypanosoma cruzi. 3. Schistosoma mansoni. 4. Genômica. 5. Transcriptoma. 6. Trans-Splicing. I. Franco, Glória Regina. II. Smith, Martin Alexander. III. Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. IV. Título. CDU: 573:004 Ficha catalográfica elaborada por Fabiane C. M. Reis – CRB 6/2680 Esta tese é dedicada à minha mãe, que me deu a liberdade para sonhar e a força para viver a realidade. AGRADECIMENTOS À Chefa, por ser meu exemplo de cientista, professora e de tantas outras coisas. Por me ensinar a ser melhor a cada dia em tudo que eu faço e pela nossa simbiose que vai além da pesquisa. Espero um dia poder retribuir tudo o que você fez por mim desde que essa Gloriosa história começou. À minha mãe e meus avós, por terem tido tanta dedicação a mim e me ensinado tanta coisa. Tudo em mim tem um pouquinho de vocês. À minha família, real e inventada, por ser o lugar para onde sempre quero voltar, seja lá de onde for. Às PriMas (Priscila, Marcela, Marcela e Marina), por terem sido minha casa em todos os sentidos, desde que cheguei em Belo Horizonte. À Pós-Graduação em Bioinformatica da UFMG, em especial à Sheila e ao Vasco, por terem ido muito além de suas obrigações para contribuir com a minha formação cientifica, acadêmica e pessoal. À CAPES, que através dos programas Reuni e Ciência sem Fronteiras abriu novas portas para a minha formação acadêmica e cientifica. Ao LGB, por ter sido a minha segunda casa durante os últimos anos, onde aprendi muito e fiz amigos para toda a vida. Obrigada a todos os que estão ou estiveram no Laboratório e fizeram parte desta tese. Obrigada em especial à Neuza e aos professores Andréa e Carlos Renato, por terem me ensinado tanto sobre as diversas faces da pesquisa e por todas as valiosas discussões. Aos alunos que passaram pelos cursos de Perl e Modelagem Molecular, por terem dividido comigo a experiência de aprender a ensinar, obrigada pela paciência! Obrigada ao Edgar e ao Diego em especial por toda a ajuda. À Ceres, à Maira, à Grazi, à Dani, ao Italo e ao André, por estarem sempre dispostos a me ajudar e pelas risadas e lições ao longo deste doutorado. Às meninas da JeMaDa, que dividiram comigo não somente a casa, mas também a vida, que me apoiaram e me acolheram sempre. À Família Franco, que me adotou com tanto carinho que eu não sei mais onde termina a minha família e começa a de vocês. Nem sei como agradecer. i Às irmãs que a vida me deu, Ana Laura, Yasmin e Laura, com vocês eu aprendi que alguns laços são pra sempre e que a saudade é amor com fuso horário. À Nicole, à Catarina, ao Diego e ao Ike, por serem meus anjos da guarda! Ao Cerne, aos ANJoS e ao Ritmo por terem feito parte da minha vida e ainda estarem comigo sempre. ACKNOWLEDGEMENTS To Dr. John Mattick, for listening to me, for taking the time to teach me and discuss with me and for making this year at the Garvan one of the best of my life in every way. To Dr. Martin Smith for the patience, trust and for being part of this thesis. To Nix for teaching me so much this year and for your friendship. I will carry both things with me forever. Thank you for the enthusiasm and support. To Boris, Dessi, Dom, Guy, Ira, Lotta and all the Mattick Lab group for having us and for everything I have learned this year. I will never forget you. To Luis for being, above all, the best friend I have had in a long time. For the inspiration, for the understanding and for making the most out of everything. You make me believe that time and distance are very relative. ii ABSTRACT Non-coding RNAs (ncRNAs) have been studied in great detail in the last decade, culminating in the current view of widespread molecules playing important roles in virtually all cell processes in all kingdoms of life. The tropical parasites Schistosoma mansoni (the causative agent of schistosomiasis) and Trypanosoma cruzi (the causative agent of Chagas disease) have complex life-cycles involving different hosts and environments and thus requiring a refined regulation of gene expression. In this sense, ncRNAs are known to be broad regulators of gene expression at multiple levels and more detailed surveys to identify such RNAs and the mechanisms in which they are involved are of great importance in the context of these parasites. To assess the roles and features of ncRNAs in tropical parasites, I have studied the mechanism of spliced-leader trans-splicing (SLTS) i n S. mansoni and further expanded the study to involve all species in which this mechanism is currently known and also performed a thorough survey to identify new ncRNAs in the genome of T. cruzi. The SLTS mechanism is known to be present in species of seven different phyla. The molecular characteristics of the spliced-leader (SL) RNA and the set of transcripts processed by the SLTS RNA were studied both in S. mansoni and further in ~150 species. Main findings include the sequence of the SL RNA exon being conserved among different species of a given phylum and more divergent when species from different phyla are compared, however the overall structure of the SL RNA is more conserved in all phyla than its sequence. Additionally, when analyzing the transcripts that receive the SL sequence, I have observed that these include genes from unrelated classes and have no clear bias to any specific function. The genome of T. cruzi was scanned in search of non-annotated ncRNAs, using both sequence-based and structure-based search algorithms and different databases. As iii a result, I report 1,595 unique ncRNA candidates, more than 700 of these representing new findings. Interestingly, the most abundant classes of new ncRNAs include snoRNAs, RNase P RNAs and miRNAs. The first are known to be widespread in the genome of Trypanosoma brucei (the causative agent of the sleeping sickness) and play important roles in the modification and putative stabilization of rRNAs. RNase P RNAs were not previously reported in trypanosomatids and to date only a protein-only RNase P complex was believed to be present in such organisms. The identification of miRNAs candidates in the genome of T. cruzi was surprising given that the parasite does not harbour a classic RNAi machinery and therefore further analyses should be conducted to prove this finding. Lastly, RNA candidates involved with regulation of gene expression that are believed to be exclusive of prokaryotes were also found, raising questions about how their function could be performed in this eukaryotic parasite. iv RESUMO RNAs não-codificadores têm sido estudados em detalhes na última década, culminando na visão recente de que estes são moléculas ubíquas que desempenham papel importante em virtualmente todos os processos biológicos em todos os reinos da vida. Os parasitos tropicais Schistosoma mansoni (agente causador da esquistosomose) e Trypanosoma cruzi (agente causador da Doença de Chagas) têm ciclos de vida complexos envolvendo diferentes hospedeiros e ambientes, necessitando portanto de uma refinada regulação da expressão gênica. Nesse sentido, os ncRNAs são reconhecidamente reguladores da expressão gênica em vários níveis e inspeções mais detalhadas para identificar tais RNAs e os mecanismos nos quais estes estão envolvidos são de grande importância no contexto destes parasitos. Para avaliar as funções e as características dos ncRNAs em parasitos tropicais, o mecanismo de spliced-leader trans- splicing (SLTS) foi estudado em S. mansoni e, posteriormente, o estudo foi expandido para incluir todas as espécies nas quais o mecanismo é atualmente conhecido. Além disso, uma inspeção minuciosa foi realizada para identificar novos ncRNAs no genoma de T. cruzi. O mecanismo de SLTS já foi descrito em espécies de sete diferentes filos. As características moleculares do RNA spliced-leader (SL) e o conjunto de transcritos processados pelo mecanismo de SLTS foram estudados em ambos em S. mansoni e posteriormente em ~150 espécies. Os principais achados incluem a sequência do éxon do SL RNA, a qual é conservada dentre diferentes espécies de um mesmo filo e mais divergentes quando espécies de diferentes filos são comparadas. No entanto, a estrutura tridimensional geral do SL RNA é mais conservada em todos os filos do que sua sequência. Adicionalmente, quando os transcritos que recebem a sequência SL foram analisados, observou-se que estes incluem genes de classes não relacionadas e não v parecem estar enviesados para nenhuma função especifica.
Recommended publications
  • RDA COVID-19 Recommendations and Guidelines on Data Sharing
    RDA COVID-19 Recommendations and Guidelines on Data Sharing DOI: 10.15497/RDA00052 Authors: RDA COVID-19 Working Group Published: 30th June 2020 Abstract: This is the final version of the Recommendations and Guidelines from the RDA COVID19 Working Group, and has been endorsed through the official RDA process. Keywords: RDA; Recommendations; COVID-19. Language: English License: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication RDA webpage: https://www.rd-alliance.org/group/rda-covid19-rda-covid19-omics-rda-covid19- epidemiology-rda-covid19-clinical-rda-covid19-1 Related resources: - RDA COVID-19 Guidelines and Recommendations – preliminary version, https://doi.org/10.15497/RDA00046 - Data Sharing in Epidemiology, https://doi.org/10.15497/RDA00049 - RDA COVID-19 Zotero Library, https://doi.org/10.15497/RDA00051 Citation and Download: RDA COVID-19 Working Group. Recommendations and Guidelines on data sharing. Research Data Alliance. 2020. DOI: https://doi.org/10.15497/RDA00052 RDA COVID-19 Recommendations and Guidelines on Data Sharing RDA Recommendation (FINAL Release) Produced by: RDA COVID-19 Working Group, 2020 Document Metadata Identifier DOI: https://doi.org/10.15497/rda00052 Citation To cite this document please use: RDA COVID-19 Working Group. Recommendations and Guidelines on data sharing. Research Data Alliance. 2020. DOI: https://doi.org/10.15497/rda00052 Title RDA COVID-19; Recommendations and Guidelines on Data Sharing, Final release 30 June 2020 Description This is the final version of the Recommendations and Guidelines
    [Show full text]
  • Downloaded Were Considered to Be True Positive While Those from the from UCSC Databases on 14Th September 2011 [70,71]
    Basu et al. BMC Bioinformatics 2013, 14(Suppl 7):S14 http://www.biomedcentral.com/1471-2105/14/S7/S14 RESEARCH Open Access Examples of sequence conservation analyses capture a subset of mouse long non-coding RNAs sharing homology with fish conserved genomic elements Swaraj Basu1, Ferenc Müller2, Remo Sanges1* From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS) Catania, Sicily. 2-4 May 2012 Abstract Background: Long non-coding RNAs (lncRNA) are a major class of non-coding RNAs. They are involved in diverse intra-cellular mechanisms like molecular scaffolding, splicing and DNA methylation. Through these mechanisms they are reported to play a role in cellular differentiation and development. They show an enriched expression in the brain where they are implicated in maintaining cellular identity, homeostasis, stress responses and plasticity. Low sequence conservation and lack of functional annotations make it difficult to identify homologs of mammalian lncRNAs in other vertebrates. A computational evaluation of the lncRNAs through systematic conservation analyses of both sequences as well as their genomic architecture is required. Results: Our results show that a subset of mouse candidate lncRNAs could be distinguished from random sequences based on their alignment with zebrafish phastCons elements. Using ROC analyses we were able to define a measure to select significantly conserved lncRNAs. Indeed, starting from ~2,800 mouse lncRNAs we could predict that between 4 and 11% present conserved sequence fragments in fish genomes. Gene ontology (GO) enrichment analyses of protein coding genes, proximal to the region of conservation, in both organisms highlighted similar GO classes like regulation of transcription and central nervous system development.
    [Show full text]
  • Biological Sequence Analysis Probabilistic Models of Proteins and Nucleic Acids
    This page intentionally left blank Biological sequence analysis Probabilistic models of proteins and nucleic acids The face of biology has been changed by the emergence of modern molecular genetics. Among the most exciting advances are large-scale DNA sequencing efforts such as the Human Genome Project which are producing an immense amount of data. The need to understand the data is becoming ever more pressing. Demands for sophisticated analyses of biological sequences are driving forward the newly-created and explosively expanding research area of computational molecular biology, or bioinformatics. Many of the most powerful sequence analysis methods are now based on principles of probabilistic modelling. Examples of such methods include the use of probabilistically derived score matrices to determine the significance of sequence alignments, the use of hidden Markov models as the basis for profile searches to identify distant members of sequence families, and the inference of phylogenetic trees using maximum likelihood approaches. This book provides the first unified, up-to-date, and tutorial-level overview of sequence analysis methods, with particular emphasis on probabilistic modelling. Pairwise alignment, hidden Markov models, multiple alignment, profile searches, RNA secondary structure analysis, and phylogenetic inference are treated at length. Written by an interdisciplinary team of authors, the book is accessible to molecular biologists, computer scientists and mathematicians with no formal knowledge of each others’ fields. It presents the state-of-the-art in this important, new and rapidly developing discipline. Richard Durbin is Head of the Informatics Division at the Sanger Centre in Cambridge, England. Sean Eddy is Assistant Professor at Washington University’s School of Medicine and also one of the Principle Investigators at the Washington University Genome Sequencing Center.
    [Show full text]
  • On the Necessity of Dissecting Sequence Similarity Scores Into
    Wong et al. BMC Bioinformatics 2014, 15:166 http://www.biomedcentral.com/1471-2105/15/166 METHODOLOGY ARTICLE Open Access On the necessity of dissecting sequence similarity scores into segment-specific contributions for inferring protein homology, function prediction and annotation Wing-Cheong Wong1*, Sebastian Maurer-Stroh1,2, Birgit Eisenhaber1 and Frank Eisenhaber1,3,4* Abstract Background: Protein sequence similarities to any types of non-globular segments (coiled coils, low complexity regions, transmembrane regions, long loops, etc. where either positional sequence conservation is the result of a very simple, physically induced pattern or rather integral sequence properties are critical) are pertinent sources for mistaken homologies. Regretfully, these considerations regularly escape attention in large-scale annotation studies since, often, there is no substitute to manual handling of these cases. Quantitative criteria are required to suppress events of function annotation transfer as a result of false homology assignments. Results: The sequence homology concept is based on the similarity comparison between the structural elements, the basic building blocks for conferring the overall fold of a protein. We propose to dissect the total similarity score into fold-critical and other, remaining contributions and suggest that, for a valid homology statement, the fold-relevant score contribution should at least be significant on its own. As part of the article, we provide the DissectHMMER software program for dissecting HMMER2/3 scores into segment-specific contributions. We show that DissectHMMER reproduces HMMER2/3 scores with sufficient accuracy and that it is useful in automated decisions about homology for instructive sequence examples. To generalize the dissection concept for cases without 3D structural information, we find that a dissection based on alignment quality is an appropriate surrogate.
    [Show full text]
  • Download PDF of This Story
    B NY RA DY BARRETT ILLUSTRATION BY MIKE PERRY TE H NEW JANELIA COMPUTING CLUSTER PUTS A PREMIUM ON EXPANDABILITY AND SPEED. ple—it’s pretty obvious to anyone which words are basically the same. That would be like two genes from humans and apes.” But in organisms that are more diver- gent, Eddy needs to understand how DNA sequences tend to change over time. “And it becomes a difficult specialty, with seri- ous statistical analysis,” he says. From a computational standpoint, that means churning through a lot of opera- tions. Comparing two typical-sized protein sequences, to take a simple example, would require a whopping 10200 opera- Computational biologists have a need for to help investigators conduct genome tions. Classic algorithms, available since speed. The computing cluster at HHMI’s searches and catalog the inner workings the 1960s, can trim that search to 160,000 Janelia Farm Research Campus delivers and structures of the brain. computations—a task that would take the performance they require—at a mind- only a millisecond or so on any modern boggling 36 trillion operations per second. F ASTER Answers processor. But in the genome business, In the course of their work, Janelia A group leader at Janelia Farm, Eddy deals people routinely do enormous numbers researchers generate millions of digitized in the realm of millions of computations of these sequence comparisons—trillions images and gigabytes of data files, and they daily as he compares sequences of DNA. and trillions of them. These “routine” cal- run algorithms daily that demand robust He is a rare breed, both biologist and code culations could take years if they had to be computational horsepower.
    [Show full text]
  • Clawhmmer: a Streaming Hmmer-Search Implementation
    ClawHMMER: A Streaming HMMer-Search Implementation Daniel Reiter Horn Mike Houston Pat Hanrahan Stanford University Abstract To mitigate the problem of choosing an ad-hoc gap penalty for a given BLAST search, Krogh et al. [1994] The proliferation of biological sequence data has motivated proposed bringing the probabilistic techniques of hidden the need for an extremely fast probabilistic sequence search. Markov models(HMMs) to bear on the problem of fuzzy pro- One method for performing this search involves evaluating tein sequence matching. HMMer [Eddy 2003a] is an open the Viterbi probability of a hidden Markov model (HMM) source implementation of hidden Markov algorithms for use of a desired sequence family for each sequence in a protein with protein databases. One of the more widely used algo- database. However, one of the difficulties with current im- rithms, hmmsearch, works as follows: a user provides an plementations is the time required to search large databases. HMM modeling a desired protein family and hmmsearch Many current and upcoming architectures offering large processes each protein sequence in a large database, eval- amounts of compute power are designed with data-parallel uating the probability that the most likely path through the execution and streaming in mind. We present a streaming query HMM could generate that database protein sequence. algorithm for evaluating an HMM’s Viterbi probability and This search requires a computationally intensive procedure, refine it for the specific HMM used in biological sequence known as the Viterbi [1967; 1973] algorithm. The search search. We implement our streaming algorithm in the Brook could take hours or even days depending on the size of the language, allowing us to execute the algorithm on graphics database, query model, and the processor used.
    [Show full text]
  • HMMER User's Guide
    HMMER User’s Guide Biological sequence analysis using profile hidden Markov models http://hmmer.wustl.edu/ Version 2.2; August 2001 Sean Eddy Howard Hughes Medical Institute and Dept. of Genetics Washington University School of Medicine 660 South Euclid Avenue, Box 8232 Saint Louis, Missouri 63110, USA [email protected] With contributions by Ewan Birney ([email protected]) Copyright (C) 1992-2001, Washington University in St. Louis. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are retained on all copies. The HMMER software package is a copyrighted work that may be freely distributed and modified under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. Some versions of HMMER may have been obtained under specialized commercial licenses from Washington University; for details, see the files COPYING and LICENSE that came with your copy of the HMMER software. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Appendix for a copy of the full text of the GNU General Public License. 1 Contents 1 Tutorial 6 1.1 The programs in HMMER . 6 1.2 Files used in the tutorial . 7 1.3 Searching a sequence database with a single profile HMM . 7 HMM construction with hmmbuild ............................. 7 HMM calibration with hmmcalibrate ........................... 8 Sequence database search with hmmsearch ........................
    [Show full text]
  • Computational Identification of Functional RNA Homologs in Metagenomic Data
    Computational identification of functional RNA homologs in metagenomic data Eric P. Nawrocki and Sean R. Eddy* HHMI Janelia Farm Research Campus Ashburn, VA 20147 Phone: 571-209-3112 (EPN) 571-209-4163 (SRE) Fax: 571-209-4094 E-mail: [email protected] [email protected] * Corresponding author. Send proofs to: Sean Eddy 19700 Janelia Farm Blvd HHMI Janelia Farm Research Campus Ashburn, VA 20147 [email protected] An important step in analyzing a metagenomic sequence dataset is identifying functional sequence elements. This is a prerequisite for determining important properties of the environment the sequence data were sampled from, such as the metabolic processes and organismal diversity present there. At least initially, functional sequence element identification is addressed computationally. One class of elements, functional noncoding RNA elements, are especially difficult to identify because they tend to be short, lack open reading frames, and sometimes evolve rapidly at the sequence level even while conserving structure integral to their function (Eddy, 2001, Szymanski, 2003, Backofen, 2007, Hammann, 2007, Jossinet, 2007, Machado-Lima, 2008). Functional RNA elements include both RNA genes (genes transcribed into functional untranslated RNA) and cis-regulatory mRNA structures. RNA elements play many roles. Ribosomal RNAs (rRNAs) and transfer RNAs (tRNAs) are well known and universally present in all cellular life. Bacteria, archaea, and viruses, the organisms predominantly targeted by current metagenomics studies, also use numerous small RNA (sRNA) genes for translational and posttranslational regulation (Gottesman, 2005), as well as many cis-regulatory RNAs such as riboswitches (structural RNAs that respond to binding small molecule metabolites and control expression of nearby genes (Winkler, 2005, Tucker, 2005)).
    [Show full text]
  • INFERNAL User's Guide
    INFERNAL User’s Guide Sequence analysis using profiles of RNA sequence and secondary structure consensus http://eddylab.org/infernal Version 1.1.4; Dec 2020 Eric Nawrocki and Sean Eddy for the INFERNAL development team https://github.com/EddyRivasLab/infernal/ Copyright (C) 2020 Howard Hughes Medical Institute. Infernal and its documentation are freely distributed under the 3-Clause BSD open source license. For a copy of the license, see http://opensource.org/licenses/BSD-3-Clause. Infernal development is supported by the Intramural Research Program of the National Library of Medicine at the US National Institutes of Health, and also by the National Human Genome Research Institute of the US National Institutes of Health under grant number R01HG009116. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 1 Contents 1 Introduction 6 How to avoid reading this manual . 6 What covariance models are . 6 Applications of covariance models . 7 Infernal and HMMER, CMs and profile HMMs . 7 What’s new in Infernal 1.1 . 8 How to learn more about CMs and profile HMMs . 8 2 Installation 10 Quick installation instructions . 10 System requirements . 10 Multithreaded parallelization for multicores is the default . 11 MPI parallelization for clusters is optional . 11 Using build directories . 12 Makefile targets . 12 Why is the output of ’make’ so clean? . 12 What gets installed by ’make install’, and where? . 12 Staged installations in a buildroot, for a packaging system . 13 Workarounds for some unusual configure/compilation problems . 13 3 Tutorial 15 The programs in Infernal .
    [Show full text]
  • Reading Genomes Bit by Bit
    Reading genomes bit by bit Sean Eddy HHMI Janelia Farm Ashburn, Virginia Why did we sequence so many different flies? the power of comparative genome sequence analysis Why did we sequence a single-celled pond protozooan? exploiting unusual adaptations and unusual genomes Oxytricha trifallax Symbolic texts can be cracked by statistical analysis “Cryptography has contributed a new weapon to the student of unknown scripts.... the basic principle is the analysis and indexing of coded texts, so that underlying patterns and regularities can be discovered. If a number of instances can be collected, it may appear that a certain group of signs in the coded text has a particular function....” John Chadwick, The Decipherment of Linear B Cambridge University Press, 1958 Linear B, from Mycenae ca. 1500-1200 BC deciphered by Michael Ventris and John Chadwick, 1953 How much data are we talking about, really? STORAGE TIME TO COST/YEAR DOWNLOAD raw images 30 TB $36,000 20 days unassembled reads 100 GB $120 1 hr mapped reads 100 GB $120 1 hr assembled genome 6 GB $7 5 min differences 4 MB $0.005 0.2 sec my coffee coaster selab:/misc/data0/genomes 3 GB 450 GB JFRC computing, available disk ~ 1 petabyte (1000 TB) 1000 Genomes Project pilot 5 TB (30 GB/genome) selab:~eddys/Music/iTunes 128 albums 3 MB 15 GB NCBI Short Read Archive 200 TB + 10-20 TB/mo GAGTTTTATCGCTTCCATGACGCAGAAGTTAACACTTTCGGATATTTCTGATGAGTCGAAAAATTATCTTGATAAAGCAGGAATTACTACTGCTTGTTTACGAATTAAATCGAAGTGGACTGCTGG CGGAAAATGAGAAAATTCGACCTATCCTTGCGCAGCTCGAGAAGCTCTTACTTTGCGACCTTTCGCCATCAACTAACGATTCTGTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGCTTAATATG
    [Show full text]
  • The Janus-Faced E-Values of Hmmer2: Extreme Value Distribution Or Logistic Function?
    January 28, 2011 15:38 WSPC/185-JBCB S0219720011005264 Journal of Bioinformatics and Computational Biology Vol. 9, No. 1 (2011) 179–206 c The Authors DOI: 10.1142/S0219720011005264 THE JANUS-FACED E-VALUES OF HMMER2: EXTREME VALUE DISTRIBUTION OR LOGISTIC FUNCTION? WING-CHEONG WONG∗,¶, SEBASTIAN MAURER-STROH∗,†, and FRANK EISENHABER∗,‡,§,∗∗ ∗Bioinformatics Institute (BII) Agency for Science, Technology and Research (A*STAR) 30 Biopolis Street, #07-01, Matrix, Singapore 138671 †School of Biological Sciences (SBS) Nanyang Technological University (NTU) 60 Nanyang Drive, Singapore 63755 ‡Department of Biological Sciences (DBS) National University of Singapore (NUS) 8 Medical Drive, Singapore 117597 §School of Computer Engineering (SCE) Nanyang Technological University (NTU) 50 Nanyang Drive, Singapore 637553 ¶[email protected] [email protected] ∗∗[email protected] Received 16 July 2010 Revised 11 October 2010 Accepted 11 October 2010 E-value guided extrapolation of protein domain annotation from libraries such as Pfam with the HMMER suite is indispensable for hypothesizing about the function of exper- imentally uncharacterized protein sequences. Since the recent release of HMMER3 does not supersede all functions of HMMER2, the latter will remain relevant for ongoing research as well as for the evaluation of annotations that reside in databases and in the literature. In HMMER2, the E-value is computed from the score via a logistic function or via a domain model-specific extreme value distribution (EVD); the lower of the two is returned as E-value for the domain hit in the query sequence. We find that, for thousands of domain models, this treatment results in switching from the EVD to the statistical model with the logistic function when scores grow (for Pfam release 23, 99% in the global mode and 75% in the fragment mode).
    [Show full text]
  • Enhanced Protein Domain Discovery by Using Language Modeling Techniques from Speech Recognition
    Enhanced protein domain discovery by using language modeling techniques from speech recognition Lachlan Coin, Alex Bateman, and Richard Durbin* Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, United Kingdom Edited by Michael Levitt, Stanford University School of Medicine, Stanford, CA, and approved February 12, 2003 (received for review December 10, 2002) Most modern speech recognition uses probabilistic models to equation and introduce the terms P(Di) to represent the prior interpret a sequence of sounds. Hidden Markov models, in partic- probability of the ith domain: ular, are used to recognize words. The same techniques have been P͑A ͉D ͒ adapted to find domains in protein sequences of amino acids. To ͑ ͉ ͒ϰ ͹ i i ͑ ͒ P D A, M ͩ ͑ ͉ ͒ P Di ͪ increase word accuracy in speech recognition, language models are P Ai R used to capture the information that certain word combinations i are more likely than others, thus improving detection based on P͑D ͉D Ϫ ...D , M͒ ϫ ͹ i i 1 1 context. However, to date, these context techniques have not been ͩ ͑ ͒ ͪ. [2] P Di applied to protein domain discovery. Here we show that the i application of statistical language modeling methods can signifi- cantly enhance domain recognition in protein sequences. As an Note that we have replaced P(AԽM), which is a constant given the example, we discover an unannotated Tf࿝Otx Pfam domain on the signal, independent of our interpretation of the sequence, by cone rod homeobox protein, which suggests a possible mechanism another constant, P(AԽR): the probability of the sequence being for how the V242M mutation on this protein causes cone-rod generated independently residue by residue according to a dystrophy.
    [Show full text]