Binning de Sequências Anterior à Montagem em Metagenomas: um estudo de caso
Resumo
Este trabalho, por meio de um estudo empírico, procurou responder a seguinte questão: O binning sobre reads colabora com a produção de melhores montagens? Buscou-se verificar se o uso das abordagens quantitativa (binning genômico) e qualitativa (binning taxonômico) traz benefícios para a montagem de genomas em metagenomas utilizando estatísticas de avaliação que consideram tamanho e conteúdo das montagens.
Palavras-chave:
Sequence Analysis, Motifs, and Pattern Matching, Gene Identification, Regulation and Expression Analysis , Computational Systems Biology , Statistical Analysis of Molecular Sequences , Algorithms for Problems in Computational Biology , Applications in Molecular Biology, Biochemistry, Genetics, Medicine, Microbiology
Referências
Constantinescu, R.-I. (2015). A machine learning approach to dna shotgun sequence assembly. Master’s thesis, University of the Witwatersrand.
Girotto, S., Pizzi, C., and Comin, M. (2016). Metaprob: accurate metagenomic reads binning based on probabilistic sequence signatures. Bioinformatics.
Li, D., Liu, C.-M., Luo, R., Sadakane, K., and Lam, T.-W. (2015). Megahit: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de bruijn graph. Bioinformatics.
Mande, S. S. (2012). Classification of metagenomic sequences: methods and challenges. Briefings in Bioinformatics, 13:669–681.
Menzel, P., Ng, K. L., and Krogh, A. (2016). Fast and sensitive taxonomic classification for metagenomics with kaiju. Nature Communications.
Mikheenko, A., Saveliev, V., and Gurevich, A. (2015). Metaquast: evaluation of metagenome assemblies. Bioinformatics, 32:1088–1090.
Parks, D. H. (2015). Checkm: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome research, 25:1043–1055.
Rho, M., Tang, H., and Ye, Y. (2010). Fraggenescan: predicting genes in short and errorprone reads. Nucleic acids research, 38(20):191–191.
Rodriguez, L. M. and Konstantinidis, K. T. (2014). Nonpareil: a redundancy-based approach to assess the level of coverage in metagenomic datasets. Bioinformatics.
Schmieder, R. and Edwards, R. (2011). Quality control and preprocessing of metagenomic datasets. Bioinformatics, 27:863–864.
Sczyrba, A., Hofmann, P., and Belmann, P. (2017). Critical assessment of metagenome interpretation – a benchmark of computational metagenomics software. Nature methods, 14(11):1063–1071.
Sedlar, K. (2017). Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics. Computational and Structural Biotechnology Journal, 15:48–55.
Vervier, K., Mahe, P., and Vert, J.-P. (2018). MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification, pages 9–20. Springer New York, New York, NY.
Vollmers, J. (2017). Comparing and evaluating metagenome assembly tools from a microbiologist’s perspective - not only size matters! PLoS ONE 12.1.
Girotto, S., Pizzi, C., and Comin, M. (2016). Metaprob: accurate metagenomic reads binning based on probabilistic sequence signatures. Bioinformatics.
Li, D., Liu, C.-M., Luo, R., Sadakane, K., and Lam, T.-W. (2015). Megahit: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de bruijn graph. Bioinformatics.
Mande, S. S. (2012). Classification of metagenomic sequences: methods and challenges. Briefings in Bioinformatics, 13:669–681.
Menzel, P., Ng, K. L., and Krogh, A. (2016). Fast and sensitive taxonomic classification for metagenomics with kaiju. Nature Communications.
Mikheenko, A., Saveliev, V., and Gurevich, A. (2015). Metaquast: evaluation of metagenome assemblies. Bioinformatics, 32:1088–1090.
Parks, D. H. (2015). Checkm: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome research, 25:1043–1055.
Rho, M., Tang, H., and Ye, Y. (2010). Fraggenescan: predicting genes in short and errorprone reads. Nucleic acids research, 38(20):191–191.
Rodriguez, L. M. and Konstantinidis, K. T. (2014). Nonpareil: a redundancy-based approach to assess the level of coverage in metagenomic datasets. Bioinformatics.
Schmieder, R. and Edwards, R. (2011). Quality control and preprocessing of metagenomic datasets. Bioinformatics, 27:863–864.
Sczyrba, A., Hofmann, P., and Belmann, P. (2017). Critical assessment of metagenome interpretation – a benchmark of computational metagenomics software. Nature methods, 14(11):1063–1071.
Sedlar, K. (2017). Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics. Computational and Structural Biotechnology Journal, 15:48–55.
Vervier, K., Mahe, P., and Vert, J.-P. (2018). MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification, pages 9–20. Springer New York, New York, NY.
Vollmers, J. (2017). Comparing and evaluating metagenome assembly tools from a microbiologist’s perspective - not only size matters! PLoS ONE 12.1.
Publicado
30/10/2018
Como Citar
OLIVEIRA, Paulo; PADOVANI, Kleber; DA SILVA, Raíssa L.; ALVES, Ronnie.
Binning de Sequências Anterior à Montagem em Metagenomas: um estudo de caso. In: ARTIGOS CURTOS - SIMPÓSIO BRASILEIRO DE BIOINFORMÁTICA (BSB) , 2018, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 7-12.
DOI: https://doi.org/10.5753/bsb_estendido.2018.8797.
