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Theoretical and practical metagenomic approaches to viral discovery
IBI 5071 - 2017
Syllabus
Janus - USP post-graduation system

Institute of Biomedical Sciences
 
Post-Graduate Program on Bioinformatics
 
Discipline:

IBI5071-1 - Theoretical and practical metagenomic approaches to virus detection

Class credits: 2
Homework credits : 0
Total Time: 30 hours
Concentration Area: 95131
Activation: 07/01/2017

Objective
  • This course aims to present fundamental concepts of experimental design and analysis of metagenomic data using next generation sequencing data. Topics covered include sequencing technologies, application theoretical databases, public databases, and examples of advancing the discovery of new viruses from metagenomics studies, some traditional approaches, and a hands-on sessions using crAss and GenSeed-HMM programs.
 
Instructors
Arthur Gruber (university of São Paulo, Brazil)
Bas E. Dutilh (Utrecht University, Netherlands)
 
Course description

The advent of next-generation sequencing has brought the possibility of sequencing not only a single genome but the genomes of a whole community of microorganisms of a biome. Metagenomics allows estimating the biological diversity of a sample, the whole set of enzymes and pathways present in the community, as well as to detect unknown organisms. We currently know only a small fraction of the viral diversity. The use of metagenomic data and the identification of emerging viruses represent a major challenge in terms of bioinformatics. In this discipline we intend to cover some methods and tools for processing metagenomic data and their use for the viral discovery, including theoretical concepts and practical sessions.


This course introduces fundamental concepts of metagenomics experimental design and data analysis using next-generation sequencing data. Topics covered include rational for experimental design of metagenomics experiments, theoretical basis of the most common bioinformatics and statistical tools used for metagenome analysis, and practical approaches for data analysis using web-based and command line tools and commands. Theoretical and practical classes will be provided. The course will also exemplify some of the advances in microbial ecology and viral discovery derived from recent metagenomic studies, including some mainstream approaches and practical sessions using crAss and GenSeed-HMM programs.

 
Content
  1. Metagenomic data generation.
  2. Data processing (data formats, quality evalutation, trimming, genome binning).
  3. Shotgun metagenomics (general concepts, experimental design, absolute analysis vs relative analysis).
  4. Measuring biological diversity with metagenomic data.
  5. Innovative methods for viral metagenomics and viral discovery.
  6. Reference-independent comparative metagenomics using the cross-assembly tool (crass).
  7. Viral orthologous groups and profile HMMs: vFam, pVOGs (formerly known as POGs) viral section of eggnog (viral OGs).
  8. Viral genome reconstruction using profile HMMs as seeds and progressive assembly with GenSeed-HMM tool.
  9.  Functional annotation of viral sequences.
 
Evaluation Method
  • Theory test at the end of the course.
Bibliography
     
  • No textbook is required for this course. Some papers covering the main topics are listed below. Additional papers will be assigned and made available on the course’s web site in advance.
  1. Alves, J.M., de Oliveira, A.L., Sandberg, T.O., Moreno-Gallego, J.L., de Toledo, M.A., de Moura, E.M., Oliveira, L.S., Durham, A.M., Mehnert, D.U., Zanotto, P.M., Reyes, A., and Gruber, A. (2016). GenSeed-HMM: A Tool for Progressive Assembly Using Profile HMMs as Seeds and its Application in Alpavirinae Viral Discovery from Metagenomic Data. Front Microbiol. 7, 269.
  2. Bexfield, N., and Kellam, P. (2011). Metagenomics and the molecular identification of novel viruses. Vet J 190, 191-198.
  3. Bibby, K., and Peccia, J. (2013). Identification of viral pathogen diversity in sewage sludge by metagenome analysis. Environ Sci Technol 47, 1945-1951.
  4. Dutilh, B.E., Schmieder, R., Nulton, J., Felts, B., Salamon, P., Edwards, R.A., and Mokili, J.L. (2012). Reference-independent comparative metagenomics using cross-assembly: crAss. Bioinformatics 28, 3225-3231.
  5. Edwards RA, McNair K, Faust K, Raes J, Dutilh BE. (2016). Computational approaches to predict bacteriophage-host relationships. FEMS Microbiol Rev. 40, 258-272.
  6. Fancello, L., Raoult, D., and Desnues, C. (2012). Computational tools for viral metagenomics and their application in clinical research. Virology 434, 162-174.
  7. Grazziotin, A.L., Koonin, E.V., Kristensen, D.M. (2016). Prokaryotic Virus Orthologous Groups (pVOGs): a resource for comparative genomics and protein family annotation. Nucleic Acids Res 45(D1):D491-D498.
  8. Huerta-Cepas, J., Szklarczyk, D., Forslund, K., Cook, H., Heller, D., Walter, M.C., Rattei, T., Mende, D.R., Sunagawa, S., Kuhn, M., Jensen, L.J., Von Mering, C., and Bork, P. (2015). eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res.
  9. Kristensen, D.M., Waller, A.S., Yamada, T., Bork, P., Mushegian, A.R., and Koonin, E.V. (2013). Orthologous gene clusters and taxon signature genes for viruses of prokaryotes. J Bacteriol 195, 941-950.
  10. Mokili, J.L., Rohwer, F., and Dutilh, B.E. (2012). Metagenomics and future perspectives in virus discovery. Curr Opin Virol 2, 63-77.
  11. Reyes, A., Haynes, M., Hanson, N., Angly, F.E., Heath, A.C., Rohwer, F., and Gordon, J.I. (2010). Viruses in the faecal microbiota of monozygotic twins and their mothers. Nature 466, 334-338.
  12. Roux, S., Enault, F., Hurwitz, B.L., and Sullivan, M.B. (2015). VirSorter: mining viral signal from microbial genomic data. PeerJ 3, e985.
  13. Sharma, D., Priyadarshini, P., and Vrati, S. (2015). Unraveling the web of viroinformatics: computational tools and databases in virus research. J Virol 89, 1489-1501.
  14. Skewes-Cox, P., Sharpton, T.J., Pollard, K.S., and Derisi, J.L. (2014). Profile hidden Markov models for the detection of viruses within metagenomic sequence data. PLoS One 9, e105067.
  15. Smits, S.L., Bodewes, R., Ruiz-Gonzalez, A., Baumgartner, W., Koopmans, M.P., Osterhaus, A.D., and Schurch, A.C. (2015). Recovering full-length viral genomes from metagenomes. Front Microbiol 6, 1069.
  16. Tang, P., and Chiu, C. (2010). Metagenomics for the discovery of novel human viruses. Future Microbiol 5, 177-189.
  17. Yutin, N., Wolf, Y.I., Raoult, D., and Koonin, E.V. (2009). Eukaryotic large nucleo-cytoplasmic DNA viruses: clusters of orthologous genes and reconstruction of viral genome evolution. Virol J 6, 223.

© 2017 Arthur Gruber

Instituto de Ciências Biomédicas - Av. Prof. Lineu Prestes, 1374 - Cidade Universitária - SP

Last update: March 24, 2017