Genomic landscape of the SARS-CoV-2 pandemic in Brazil suggests an external P.1 variant origin

What are we learning with Yoga? Mapping the scientific literature on Yoga using a vector-text-mining approach

The techniques used in yoga have roots in traditions that precede modern science. Research shows that yoga enhances quality of life and well-being, positively impacting physical and mental health. As yoga gains acceptance in Western countries, scientific studies on the subject increase exponentially. However, many of these studies are considered inconsistent due to the diverse methodologies and focuses in the field, which creates challenges for researchers and hampers progress. This study aims to develop a comprehensive framework for existing literature on yoga, facilitating multidisciplinary collaboration and bringing new light to relevant aspects. Given the complexity of the subject, advanced modeling techniques are necessary. Contemporary artificial intelligence methods have advanced Bioinformatics, including text mining (TM), allowing us to employ vector representations of texts to derive semantic insights and organize literature effectively. Based on TM resources, we provided a better general understanding of yoga and highlighted the relationships between yoga practice and various domains, including biochemical parameters and neuroscience. It also reveals that practitioners can learn to engage with their bodies and environments actively, enhancing their quality of life. However, there is a lack of research exploring the mechanisms behind this learning and its potential for further enhancement. Vector TM has made it possible to bolster and improve human analysis. The set of resources developed allowed us to determine the mapping of the literature, the analysis of which revealed 4 dimensions (exercise, physiology, theory and therapeutic) divided into 9 cohesive groups, representing the trends in the literature. The resulting platforms are available to Yoga researchers to evaluate our findings and make their forays into the existing literature.

GRAMEP: an alignment-free method based on the maximum entropy principle for identifying SNPs

BACKGROUND

Advances in high throughput sequencing technologies provide a huge number of genomes to be analyzed. Thus, computational methods play a crucial role in analyzing and extracting knowledge from the data generated. Investigating genomic mutations is critical because of their impact on chromosomal evolution, genetic disorders, and diseases. It is common to adopt aligning sequences for analyzing genomic variations. However, this approach can be computationally expensive and restrictive in scenarios with large datasets.

RESULTS

We present a novel method for identifying single nucleotide polymorphisms (SNPs) in DNA sequences from assembled genomes. This study proposes GRAMEP, an alignment-free approach that adopts the principle of maximum entropy to discover the most informative k-mers specific to a genome or set of sequences under investigation. The informative k-mers enable the detection of variant-specific mutations in comparison to a reference genome or other set of sequences. In addition, our method offers the possibility of classifying novel sequences with no need for organism-specific information. GRAMEP demonstrated high accuracy in both in silico simulations and analyses of viral genomes, including Dengue, HIV, and SARS-CoV-2. Our approach maintained accurate SARS-CoV-2 variant identification while demonstrating a lower computational cost compared to methods with the same purpose.

CONCLUSIONS

GRAMEP is an open and user-friendly software based on maximum entropy that provides an efficient alignment-free approach to identifying and classifying unique genomic subsequences and SNPs with high accuracy, offering advantages over comparative methods. The instructions for use, applicability, and usability of GRAMEP are open access at https://github.com/omatheuspimenta/GRAMEP .

Exploring diazotrophic diversity: unveiling Nif core distribution and evolutionary patterns in nitrogen-fixing organisms

BACKGROUND

Diazotrophs carry out biological nitrogen fixation (BNF) using the nitrogenase enzyme complex (NEC), which relies on nitrogenase encoded by nif genes. Horizontal gene transfer (HGT) and gene duplications have created significant diversity among these genes, making it challenging to identify potential diazotrophs. Previous studies have established a minimal set of Nif proteins, known as the Nif core, which includes NifH, NifD, NifK, NifE, NifN, and NifB. This study aimed to identify potential diazotroph groups based on the Nif core and to analyze the inheritance patterns of accessory Nif proteins related to Mo-nitrogenase, along with their impact on N2 fixation maintenance.

RESULTS

In a systematic study, 118 diazotrophs were identified, resulting in a database of 2,156 Nif protein sequences obtained with RAFTS³G. Using this Nif database and a data mining strategy, we extended our analysis to 711 species and found that 544 contain the Nif core. A partial Nif core set was observed in eight species in this study. Finally, we cataloged 662 species with Nif core, of which 52 were novel. Our analysis generated 10,076 Nif proteins from these species and revealed some Nif core duplications. Additionally, we determined the optimal cluster value (k = 10) for analyzing diazotrophic diversity. Combining synteny and phylogenetic analyses revealed distinct syntenies in the nif gene composition across ten groups.

CONCLUSIONS

This study advances our understanding of the distribution of nif genes, aiding in the prediction and classification of N₂-fixing organisms. Furthermore, we present a comprehensive overview of the diversity, distribution, and evolutionary relationships among diazotrophic organisms associated with the Nif core. The analysis revealed the phylogenetic and functional organization of different groups, identifying synteny patterns and new nif gene arrangements across various bacterial and archaeal species.The identified groups serve as a valuable framework for further exploration of the molecular mechanisms underlying biological nitrogen fixation and its evolutionary significance across different bacterial lineages.