O uso de ferramentas de bioinformática para análise de dados genéticos: uma revisão
DOI:
https://doi.org/10.36560/17120241872Palavras-chave:
Ferramentas genômicas, softwares e algoritmos, dados biológicosResumo
A bioinformática vem revolucionando o modo como os cientistas analisam e interpretam os dados genéticos. Dessa forma, esta revisão destaca o papel fundamental das ferramentas de bioinformática na compreensão dos dados genômicos. O artigo explora a diversidade de softwares e algoritmos disponíveis para processar, analisar e interpretar dados genéticos. Aborda-se a relevância dessas ferramentas na identificação de genes, variações genéticas, predição de estruturas proteicas e estudos de evolução e filogenia. Além disso, são apresentados os desafios enfrentados na bioinformática, incluindo a integração de dados de diferentes fontes, padronização e interpretação dos resultados. É disponibilizado no artigo informações sobre alinhamento de sequências, limpeza de dados de sequenciamento, que são dados importantes quando se trabalha com conjunto de dados genéticos. Destaca-se ainda que discussões como estas são importantes, pois as ferramentas de bioinformática estão constantemente evoluindo, o que requer atualização constante de conhecimento e habilidades por parte dos pesquisadores.
Referências
ABDELKRIM, R. Bioinformatics: An Exciting Field of Science-Importance and Applications. Journal of Concepts in Structural Biology & Bioinformatics (JSBB), v. 1, n. 4, 2023.
ARONSON, S. J.; REHM, H. L. Building the foundation for genomics in precision medicine. Nature, v. 526, n. 7573, p. 336-342, 2015. DOI: https://doi.org/10.1038/nature15816
BELKADI, A. et al. Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. Proceedings of the National Academy of Sciences, v. 112, n. 17, p. 5473-5478, 2015. DOI: https://doi.org/10.1073/pnas.1418631112
BOCK, C. Analysing and interpreting DNA methylation data. Nature Reviews Genetics, v. 13, n. 10, p. 705-719, 2012. DOI: https://doi.org/10.1038/nrg3273
BOUCKAERT, R. et al. BEAST 2: A Software Platform for Bayesian Evolutionary Analysis. PLOS Computational Biology, v. 10, n. 4, p. e1003537, 2014. DOI: https://doi.org/10.1371/journal.pcbi.1003537
CERVANTES-PÉREZ, S. A. et al. Challenges and perspectives in applying single nuclei RNA-seq technology in plant biology. Plant Science, v. 325, p. 111486, 2022. DOI: https://doi.org/10.1016/j.plantsci.2022.111486
GUINDON, S. et al. Estimating Maximum Likelihood Phylogenies with PhyML. Em: POSADA, D. (Ed.). Bioinformatics for DNA Sequence Analysis. Methods in Molecular Biology. Totowa, NJ: Humana Press, p. 113–137, 2009. DOI: https://doi.org/10.1007/978-1-59745-251-9_6
IQBAL, N.; KUMAR, P. From Data Science to Bioscience: Emerging era of bioinformatics applications, tools and challenges. Procedia Computer Science, v. 218, p. 1516-1528, 2023. DOI: https://doi.org/10.1016/j.procs.2023.01.130
JO, H.; KOH, G. Faster single-end alignment generation utilizing multi-thread for BWA. Bio-medical materials and engineering, v. 26, n. s1, p. S1791-S1796, 2015. DOI: https://doi.org/10.3233/BME-151480
KANEHISA, M. The KEGG database. In: ‘In silico’simulation of biological processes: Novartis Foundation Symposium 247. Chichester, UK: John Wiley & Sons, Ltd, 2002. p. 91-103. DOI: https://doi.org/10.1002/0470857897.ch8
KANZI, A. M. et al. Next generation sequencing and bioinformatics analysis of family genetic inheritance. Frontiers in Genetics, v. 11, p. 544162, 2020. DOI: https://doi.org/10.3389/fgene.2020.544162
KATOH, K.; ASIMENOS, G.; TOH, H. Multiple alignment of DNA sequences with MAFFT. Bioinformatics for DNA sequence analysis, p. 39-64, 2009. DOI: https://doi.org/10.1007/978-1-59745-251-9_3
LAM, H. Y. K. et al. Detecting and annotating genetic variations using the HugeSeq pipeline. Nature biotechnology, v. 30, n. 3, p. 226-229, 2012. DOI: https://doi.org/10.1038/nbt.2134
LAM, H-M. et al. Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nat Genet 42, 1053–1059, 2010. DOI: https://doi.org/10.1038/ng.715
LAN, K. et al. A survey of data mining and deep learning in bioinformatics. Journal of medical systems, v. 42, p. 1-20, 2018. DOI: https://doi.org/10.1007/s10916-018-1003-9
LARSSON, O.; WAHLESTEDT, C.; TIMMONS, J. A. Considerations when using the significance analysis of microarrays (SAM) algorithm. BMC bioinformatics, v. 6, n. 1, p. 1-6, 2005. DOI: https://doi.org/10.1186/1471-2105-6-129
LARTILLOT, N. PhyloBayes: Bayesian Phylogenetics Using Site-heterogeneous Models. IN: SCORNAVACCA, C.; DELSUC, F.; GALTIER, N. (Eds.). Phylogenetics in the Genomic Era. p. 1.5:1-1.5:16, 2020.
LEE, T.-H. et al. SNPhylo: a pipeline to construct a phylogenetic tree from huge SNP data. BMC Genomics 15, 162, 2014. DOI: https://doi.org/10.1186/1471-2164-15-162
LI, L.; STOECKERT, C. J.; ROOS, D. S. OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes. Genome Research, v. 13, n. 9, p. 2178–2189, 2003. DOI: https://doi.org/10.1101/gr.1224503
LIU, C. et al. The community coevolution model with application to the study of evolutionary relationships between genes based on phylogenetic profiles. Systematic Biology, v. 72, n. 3, p. 559-574, 2023. DOI: https://doi.org/10.1093/sysbio/syac052
LIU, S. et al. Three differential expression analysis methods for RNA sequencing: limma, EdgeR, DESeq2. JoVE (Journal of Visualized Experiments), n. 175, p. e62528, 2021. DOI: https://doi.org/10.3791/62528-v
MITRA, K. et al. Integrative approaches for finding modular structure in biological networks. Nature Reviews Genetics, v. 14, n. 10, p. 719-732, 2013. DOI: https://doi.org/10.1038/nrg3552
PABINGER, S. et al. A survey of tools for variant analysis of next-generation genome sequencing data. Briefings in bioinformatics, v. 15, n. 2, p. 256-278, 2014. DOI: https://doi.org/10.1093/bib/bbs086
PEARSON, W. R. BLAST and FASTA similarity searching for multiple sequence alignment. Multiple sequence alignment methods, p. 75-101, 2014. DOI: https://doi.org/10.1007/978-1-62703-646-7_5
PEREIRA, R.; OLIVEIRA, J.; SOUSA, M. Bioinformatics and computational tools for next-generation sequencing analysis in clinical genetics. Journal of clinical medicine, v. 9, n. 1, p. 132, 2020. DOI: https://doi.org/10.3390/jcm9010132
POND, S. L. K.; FROST, S. D. W.; MUSE, S. V. HyPhy: hypothesis testing using phylogenies. Bioinformatics, v. 21, n. 5, p. 676–679, 2005. DOI: https://doi.org/10.1093/bioinformatics/bti079
RAO, M. S. et al. Comparison of RNA-Seq and microarray gene expression platforms for the toxicogenomic evaluation of liver from short-term rat toxicity studies. Frontiers in genetics, v. 9, p. 636, 2019. DOI: https://doi.org/10.3389/fgene.2018.00636
SHAKYA, M. et al. Standardized phylogenetic and molecular evolutionary analysis applied to species across the microbial tree of life. Sci Rep 10, 1723, 2020. DOI: https://doi.org/10.1038/s41598-020-58356-1
SPENCER, D. H. et al. Performance of common analysis methods for detecting low-frequency single nucleotide variants in targeted next-generation sequence data. The Journal of Molecular Diagnostics, v. 16, n. 1, p. 75-88, 2014. DOI: https://doi.org/10.1016/j.jmoldx.2013.09.003
THOMPSON, D.; REGEV, A.; ROY, S. Comparative analysis of gene regulatory networks: from network reconstruction to evolution. Annual review of cell and developmental biology, v. 31, p. 399-428, 2015. DOI: https://doi.org/10.1146/annurev-cellbio-100913-012908
TRAPNELL, C. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nature biotechnology, v. 31, n. 1, p. 46-53, 2013. DOI: https://doi.org/10.1038/nbt.2450
VARET, H. et al. SARTools: a DESeq2-and EdgeR-based R pipeline for comprehensive differential analysis of RNA-Seq data. PloS one, v. 11, n. 6, p. e0157022, 2016. DOI: https://doi.org/10.1371/journal.pone.0157022
WHITE, M. H.; ADAMS, D. A.; BU, J. On the go with SONOS. IEEE Circuits and Devices Magazine, v. 16, n. 4, p. 22-31, 2000. DOI: https://doi.org/10.1109/101.857747
YANG, Z. PAML 4: Phylogenetic Analysis by Maximum Likelihood. Molecular Biology and Evolution, v. 24, n. 8, p. 1586–1591, 2007. DOI: https://doi.org/10.1093/molbev/msm088
YAO, Z. et al. Evaluation of variant calling tools for large plant genome re-sequencing. BMC bioinformatics, v. 21, n. 1, p. 1-16, 2020. DOI: https://doi.org/10.1186/s12859-020-03704-1
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