Binning de Sequências Anterior à Montagem em Metagenomas: um estudo de caso
Abstract
This work, through an empirical study, aimed to answer the following question: Does binning over reads contribute to the production of better assemblies? We evaluated whether quantitative (genome binning) and qualitative (taxonomic binning) approaches bring benefits to the assembly of genomes from metagenome data through statistics which evaluate assemblies considering their sizes and qualities.
Keywords:
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
References
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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.
Published
2018-10-30
How to Cite
OLIVEIRA, Paulo; PADOVANI, Kleber; DA SILVA, Raíssa L.; ALVES, Ronnie.
Binning de Sequências Anterior à Montagem em Metagenomas: um estudo de caso. In: SHORT PAPERS - BRAZILIAN SYMPOSIUM ON BIOINFORMATICS (BSB) , 2018, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 7-12.
DOI: https://doi.org/10.5753/bsb_estendido.2018.8797.
