Correction: A stomata classification and detection system in microscope images of maize cultivars.

[This corrects the article DOI: 10.1371/journal.pone.0258679.].

The rate and role of pseudogenes of the Mycobacterium tuberculosis complex.

Whole-genome sequence analyses have significantly contributed to the understanding of virulence and evolution of the Mycobacterium tuberculosis complex (MTBC), the causative pathogens of tuberculosis. Most MTBC evolutionary studies are focused on single nucleotide polymorphisms and deletions, but rare studies have evaluated gene content, whereas none has comprehensively evaluated pseudogenes. Accordingly, we describe an extensive study focused on quantifying and predicting possible functions of MTBC and Mycobacterium canettii pseudogenes. Using NCBI's PGAP-detected pseudogenes, we analysed 25 837 pseudogenes from 158 MTBC and M. canetii strains and combined transcriptomics and proteomics of M. tuberculosis H37Rv to gain insights about pseudogenes' expression. Our results indicate significant variability concerning rate and conservancy of in silico predicted pseudogenes among different ecotypes and lineages of tuberculous mycobacteria and pseudogenization of important virulence factors and genes of the metabolism and antimicrobial resistance/tolerance. We show that in silico predicted pseudogenes contribute considerably to MTBC genetic diversity at the population level. Moreover, the transcription machinery of M. tuberculosis can fully transcribe most pseudogenes, indicating intact promoters and recent pseudogene evolutionary emergence. Proteomics of M. tuberculosis and close evaluation of mutational lesions driving pseudogenization suggest that few in silico predicted pseudogenes are likely capable of neofunctionalization, nonsense mutation reversal, or phase variation, contradicting the classical definition of pseudogenes. Such findings indicate that genome annotation should be accompanied by proteomics and protein function assays to improve its accuracy. While indels and insertion sequences are the main drivers of the observed mutational lesions in these species, population bottlenecks and genetic drift are likely the evolutionary processes acting on pseudogenes' emergence over time. Our findings unveil a new perspective on MTBC's evolution and genetic diversity.

Trends in biological data integration for the selection of enzymes and transcription factors related to cellulose and hemicellulose degradation in fungi.

Fungi are key players in biotechnological applications. Although several studies focusing on fungal diversity and genetics have been performed, many details of fungal biology remain unknown, including how cellulolytic enzymes are modulated within these organisms to allow changes in main plant cell wall compounds, cellulose and hemicellulose, and subsequent biomass conversion. With the advent and consolidation of DNA/RNA sequencing technology, different types of information can be generated at the genomic, structural and functional levels, including the gene expression profiles and regulatory mechanisms of these organisms, during degradation-induced conditions. This increase in data generation made rapid computational development necessary to deal with the large amounts of data generated. In this context, the origination of bioinformatics, a hybrid science integrating biological data with various techniques for information storage, distribution and analysis, was a fundamental step toward the current state-of-the-art in the postgenomic era. The possibility of integrating biological big data has facilitated exciting discoveries, including identifying novel mechanisms and more efficient enzymes, increasing yields, reducing costs and expanding opportunities in the bioprocess field. In this review, we summarize the current status and trends of the integration of different types of biological data through bioinformatics approaches for biological data analysis and enzyme selection.

A stomata classification and detection system in microscope images of maize cultivars.

Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.