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The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.

Critical thinking involves the ability to properly assess statements and actions, and it also requires a permanent disposition to appropriately use cognitive skills in the evaluation of any claim. In the present paper, we discuss the characteristics of an ideal critical thinker, and apply them to a contemporary problem, namely anthropogenic global warming (AGW, a hypothesis that accounts for the increase of the average temperature of Earth as a consequence of human activities), in order to discuss what an ideal critical thinker is expected to conclude about the occurrence of this phenomenon. We assume that an ideal critical thinker is able to find out where the most reliable information regarding a certain issue is, is competent to reasonably evaluate it, and has an inclination to calibrate her beliefs according to the results of the inquiry. We thus conclude that an ideal critical thinker is expected to accept the current scientific consensus that AGW is occurring, albeit considering this issue, as well as virtually any other idea, open to revision. The fact that in many occasions people are selective in using critical thinking skills, such as when they reject the massive evidence of AGW, should be a cause of concern for educators, who should motivate their students to think critically about any problem and question they encounter; this, in turn, could help the students to develop a better understanding of the world, take more reasonable courses of action, and be protected against misinformation.

Many computational methods aim to improve the prediction and recognition of transcription elements in prokaryotes. Despite this, the natural features of those elements make their prediction and recognition remain as an open field of research. In this paper, we compared the open-access tools BacPP, BPROM, bTSSfinder, CNNPromoter_b, iPro70-PseZNC, NNPP2, PePPer, and PromPredict. First, we listed the overall functionalities of each tool and the resources available on their web pages. Later, we carried out a comparison of prediction results using 206 intergenic regions. When evaluating the prediction using intergenic regions containing a single promoter within each, NNPP2 and BacPP obtained >90% correct predictions, with NNPP2 obtaining the highest values of match between predicted promoter location and location indicated by RegulonDB. Overall, many discrepancies were observed among the results. They may be explained by the differences in the methodologies that each tool applies for promoter prediction, not excluding the natural features of promoters as a factor as well. In any case, the results highlight the necessity to continue the efforts to improve promoter prediction, perhaps combining multiple approaches. Through said efforts, some of the challenges of the postgenomic era may be tackled as well.

In the present postgenomic era, the capacity to generate big data has far exceeded the capacity to analyze, contextualize, and make sense of the data in clinical, biological, and ecological applica...

As técnicas computacionais têm importante papel na era pósgenômica, devido ao crescente número de dados biológicos gerados. Um campo bem desenvolvido envolve as questões relacionadas a regulação gênica. Com base no exposto, este trabalho dedica-se a uma análise in silico dos promotores relacionados aos fatores δ24 e δ32 da bactéria E. coli. Para isso, foi utilizada a técnica de Clusterização e a codificação dos dados nos valores de estabilidade. Os agrupamentos foram analisados com base na pureza obtida. A pureza média obtida foi de 63%, com destaque para os agrupamentos 7, 10 e 11, que obtiveram uma pureza média de 85%, 82% e 83% respectivamente. Foi possível observar diferentes graus de degeneração das sequências e as características que os grupos estudados apresentam. Deste modo, contribui-se para a compreensão dos diferentes perfis biológicos encontrados nos promotores bacterianos. Além disso, os resultados podem auxiliar na redução de falsos positivos em ferramentas de predição de promotores.

Estudar ciências significa mais do que ter contato com os seus produtos, isto é, com ideias, hipóteses, teorias e conclusões científicas. É, também, compreender os processos pelos quais os produtos científicos são propostos, justificados, avaliados, revisados, estabelecidos ou descartados. Uma das formas mais destacadas de tratar da ciência como um processo está no estudo de casos históricos. Neste artigo, argumentamos que a pandemia do novo coronavírus se constitui em um desses casos por oferecer um vislumbre interessante de pelos menos três elementos fundamentais dos processos científicos e, de modo mais geral, da natureza da ciência: o caráter social do empreendimento científico, o cuidado com alegações de autoridades e o falibilismo epistêmico. Identificamos e refletimos a respeito desses três elementos a partir do trabalho dos cientistas durante a pandemia do novo coronavírus e, por fim, oferecemos sugestões de como eles podem ser inseridos em aulas de ciências, em escolas e universidades.

In the present postgenomic era, the capacity to generate big data has far exceeded the capacity to analyze, contextualize, and make sense of the data in clinical, biological, and ecological applications. There is a great unmet need for automation and algorithms to aid in analyses of big data, in biology in particular. In this context, it is noteworthy that computational methods used to analyze the regulation of bacterial gene expression have in the past focused mainly on

Promoters are DNA sequences located upstream of the transcription start site of genes. In bacteria, the RNA polymerase enzyme requires additional subunits, called sigma factors (σ) to begin specific gene transcription in distinct environmental conditions. Currently, promoter prediction still poses many challenges due to the characteristics of these sequences. In this paper, the nucleotide content of Escherichia coli promoter sequences, related to five alternative σ factors, was analyzed by a machine learning technique in order to provide profiles according to the σ factor which recognizes them. For this, the clustering technique was applied since it is a viable method for finding hidden patterns on a data set. As a result, 20 groups of sequences were formed, and, aided by the Weblogo tool, it was possible to determine sequence profiles. These found patterns should be considered for implementing computational prediction tools. In addition, evidence was found of an overlap between the functions of the genes regulated by different σ factors, suggesting that DNA structural properties are also essential parameters for further studies.