In the last decades, omics technologies have been extensively used to explore information from the components of the central dogma of molecular biology. The term “omics” comprises genomics, transcriptomics, proteomics, metabolomics, as well as metagenomics and metatranscriptomics. Among the different omics branches, genomics was the first one to be developed, and refers to the study of genomes, their structure, function, and evolution. Transcriptomics studies the collection of RNA molecules in a cell, tissue or organism in a given condition, allowing the analysis of gene expression. Proteomics characterizes the whole protein content of a cell in terms of expression, structure, functions, interaction, and protein modifications. Metabolomics, in turn, involves the analysis of metabolites in an organism. Omics sciences have very diverse applications, ranging from environmental to clinical approaches. This includes the discovery of new genes, transcripts, proteins, metabolites, as well as the investigation of functions, structures, and pathways. Metagenomics, for instance, has been also revealing new species. In a general way, omics are fundamental to build scientific knowledge from genetic information. We propose to bring new discussions around these topics for people to update their information and make contributions to the area.
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract information and knowledge from structured and unstructured data. The field covers data preparation for analysis, data analysis, formulation of data science problems, development of data-based solutions and presentation of results. That is why it covers several areas such as mathematics, statistics, information science, computer science, complex systems, data integration, and information visualization. Data science is essential for any sector in the world today, due to the huge amounts of data that are produced daily. In bioinformatics and computational biology, this is not different.When sequencing and analyzing, we generate gigabyte or terabyte quantities from just one organism or a collection. The amount of metagenomic expression data sequence deposition is growing more and more. Learning how to implement data science techniques to expand the possibility of working with this data, as well as obtaining biological information is increasingly essential. The possibility of extracting this data from the results obtained, or even using a database, has been increasingly challenging. There are several methodologies and algorithms within data science that can support the scientific question or be the question itself in that biological data. We intend to present part of this proposal structure. To those who do not know, become familiar and add to those who work on new ideas, assisting in the production of knowledge as well as in the propagation of information.