Frontiers in bioinformatics最新文献

Neotropical Freshwater Fish (NFF) fauna exhibits the greatest phenotypic disparity and species richness among all continental aquatic vertebrate faunas, with more than 6,345 species distributed across the mostly tropical regions of Central and South America. The last two decades have seen a proliferation of molecular phylogenies, often at the species level, covering almost all 875 valid NFF genera. This study presents the most comprehensive genome-wide, time-calibrated phylogenetic hypothesis of NFF species to date, based on DNA sequences generated over decades through the collaborative efforts of the multinational ichthyological research community. Our purpose is to build and curate an extensive molecular dataset allowing researchers to evaluate macroevolutionary hypotheses in the NFF while facilitating continuous refinement and expansion. Using thousands of DNA sequences from dozens of studies, we compiled a supermatrix of 51 markers for 5,984 taxa, representing 3,167 NFF species. Based on this dataset, we built the most species-rich time-calibrated phylogeny of the NFF taxa to date, summarizing the collective efforts of the ichthyological research community since the midpoint of the last century. We provide a summary review of this remarkable evolutionary history and hope this dataset provides a framework for forthcoming studies of the NFF fauna, documenting compelling, emergent patterns in the world's most diverse continental vertebrate fauna.

The implementation of Hi-C reads in the de novo genome assembly process allows the ordering of large regions of the genome in scaffolds and the generation of chromosome-level assemblies. Several bioinformatics tools have been developed for genome scaffolding with Hi-C, and each tool has advantages and disadvantages that need to be carefully evaluated before their adoption. We generated two de novo assemblies of Arabidopsis thaliana obtained from the same raw PacBio HiFi and Oxford Nanopore Technologies data. We scaffolded the assemblies implementing Hi-C reads with the scaffolders 3D-DNA, SALSA2, and YaHS, with the aim of identifying the tool providing the most accurate assembly. The scaffolded assemblies were evaluated according to contiguity, completeness, accuracy, and structural correctness. In our analysis, YaHS proved to be the best-performing bioinformatics tool for scaffolding de novo genome assemblies in Arabidopsis thaliana.

Climate can shape plant genetic diversity and genetic structure, and genetic diversity and genetic structure can reflect the adaptation of plants to climate change. We used rbcl and trnL-trnF sequences to analyze the genetic diversity and genetic structure of C. squarrosa under the influence of different environmental factors in Inner Mongolia grassland. The results showed that the genetic diversity of this species was low. (The trnL-trnF sequences have higher genetic diversity than rbcl sequences.) C. squarrosa had low genetic diversity compared to other prairie plants, but had a more pronounced genetic structure. The haplotype network diagram of the combined sequences could be divided into two categories, and the results of the NJ, MP, and ML trees also showed that the haplotypes were divided into two branches. The results of genetic structure analysis showed that that the populations located in the desert steppe fall into exactly one cluster, and the populations located in the typical steppe fall into exactly another cluster. The neutrality tests were all negative and the mismatch distribution also showed a single peak across the population, suggesting that C. squarrosa had undergone population expansion and was well adapted to the local environment. The results of the mantel test showed that climate had a greater influence on the genetic distance of C. squarrosa, with annual precipitation having a higher influence than mean annual temperature. This study provided basic genetic information on the genetic structure of C. squarrosa and contributes to the study of genetic adaptation mechanisms in grassland plants.

Background: Microscopy of regenerated tissue shows different morphologies between the healing of acute wounds and chronic wounds. This difference can be seen manually by biologists, but computational methods are needed to automate the characterization of morphology and regenerative quality in regenerated muscle tissue.

Results: From the detected edge segments, we computed several imaging biomarkers of interest, such as median tortuosity, number of edge segments normalized by area, median edge segment distance and interquartile range of orientation angles of edge segments of the microscope images of successful and unsuccessful muscle regeneration. We observed that muscle fibers in saline-treated pressure ulcers had a larger interquartile range of orientation angles of the edge segments (p = 0.05) and shorter edge segment distances (p = 0.003) compared to those of acute cardiotoxin injuries.

Conclusion: Our edge detection method was able to identify statistically significant differences in some of the imaging biomarkers between saline-treated pressure ulcers and cardiotoxin injuries, suggesting that chronic pressure ulcers have increased muscle fiber malformations compared to cardiotoxin injuries.

The dihedral angle of the protein backbone can describe the main structure of the protein, which is of great significance for determining the protein structure. Many computational methods have been proposed to predict this critically important protein structure, including deep learning. However, these heavyweight methods require more computational resources, and the training time becomes intolerable. In this article, we introduce a novel lightweight method, named dilated convolution and multi-head attention (DCMA), that predicts protein backbone torsion dihedral angles $\left(\varphi ,\psi \right)$ . DCMA is stacked by five layers of two hybrid inception blocks and one multi-head attention block (I2A1) module. The hybrid inception blocks consisting of multi-scale convolutional neural networks and dilated convolutional neural networks are designed for capturing local and long-range sequence-based features. The multi-head attention block supplementally strengthens this operation. The proposed DCMA is validated on public critical assessment of protein structure prediction (CASP) benchmark datasets. Experimental results show that DCMA obtains better or comparable generalization performance. Compared to best-so-far methods, which are mostly ensemble models and constructed of recurrent neural networks, DCMA is an individual model that is more lightweight and has a shorter training time. The proposed model could be applied as an alternative method for predicting other protein structural features.

Background: The fungus Beauveria bassiana is widely used for agronomical applications, mainly in biological control. B. bassiana uses chitinase enzymes to degrade chitin, a major chemical component found in insect exoskeletons and fungal cell walls. However, until recently, genomic information on neotropical isolates, as well as their metabolic and biotechnological potential, has been limited.

Methods: Eight complete B. bassiana genomes of Neotropical origin and three references were studied to identify chitinase genes and its corresponding proteins, which were curated and characterized using manual curation and computational tools. We conducted a computational study to highlight functional differences and similarities for chitinase proteins in these Neotropical isolates.

Results: Eleven chitinase 1 genes were identified, categorized as chitinase 1.1 and chitinase 1.2. Five chitinase 2 genes were identified but presented a higher sequence conservation across all sequences. Interestingly, physicochemical parameters were more similar between chitinase 1.1 and chitinase 2 than between chitinase 1.1 and 1.2.

Conclusion: Chitinases 1 and 2 demonstrated variations, especially within chitinase 1, which presented a potential paralog. These differences were observed in their physical parameters. Additionally, CHIT2 completely lacks a signal peptide. This implies that CHIT1 might be associated with infection processes, while CHIT2 could be involved in morphogenesis and cellular growth. Therefore, our work highlights the importance of computational studies on local isolates, providing valuable resources for further experimental validation. Intrinsic changes within local species can significantly impact our understanding of complex pathogen-host interactions and offer practical applications, such as biological control.

Background: Helicobacter pylori, a bacterium associated with severe gastrointestinal diseases and malignancies, poses a significant challenge because of its increasing antibiotic resistance rates. This study aimed to identify potential drug targets and inhibitors against H. pylori using a structure-based virtual screening (SBVS) approach.

Methods: Core-proteome analysis of 132 H. pylori genomes was performed using the EDGAR database. Essential genes were identified and human and gut microbiota homolog proteins were excluded. The DAH7PS protein involved in the shikimate pathway was selected for the structure-based virtual screening (SBVS) approach. The tertiary structure of the protein was predicted through homology modeling (based on PDB ID: 5UXM). Molecular docking was performed to identify potential inhibitors of DAH7PS among StreptomeDB compounds using the AutoDock Vina tool. Molecular dynamics (MD) simulations assessed the stability of DAH7PS-ligand complexes. The complexes were further evaluated in terms of their binding affinity, Lipinski's Rule of Five, and ADMET properties.

Results: A total of 54 novel drug targets with desirable properties were identified. DAH7PS was selected for further investigation, and virtual screening of StreptomeDB compounds yielded 36 high-affinity binding of the ligands. Two small molecules, 6,8-Dihydroxyisocoumarin-3-carboxylic acid and Epicatechin, also showed favorable RO5 and ADMET properties. MD simulations confirmed the stability and reliability of DAH7PS-ligand complexes, indicating their potential as inhibitors.

Conclusion: This study identified 54 novel drug targets against H. pylori. The DAH7PS protein as a promising drug target was evaluated using a computer-aided drug design. 6,8-Dihydroxyisocoumarin-3-carboxylic acid and Epicatechin demonstrated desirable properties and stable interactions, highlighting their potential to inhibit DAH7PS as an essential protein. Undoubtedly, more experimental validations are needed to advance these findings into practical therapies for treating drug-resistant H. pylori.

Nucleic acid amplification assays represent a pivotal category of methodologies for targeted sequence detection within contemporary biological research, boasting diverse utility in diagnostics, identification, and DNA sequencing. The foundational principles of these assays have been extrapolated to various simple and intricate nucleic acid amplification technologies. Concurrently, a burgeoning trend toward computational or virtual methodologies is exemplified by in silico PCR analysis. In silico PCR analysis is a valuable and productive adjunctive approach for ensuring primer or probe specificity across a broad spectrum of PCR applications encompassing gene discovery through homology analysis, molecular diagnostics, DNA profiling, and repeat sequence identification. The prediction of primer and probe sensitivity and specificity necessitates thorough database searches, accounting for an optimal balance of mismatch tolerance, sequence similarity, and thermal stability. This software facilitates in silico PCR analyses of both linear and circular DNA templates, including bisulfited treatment DNA, enabling multiple primer or probe searches within databases of varying scales alongside advanced search functionalities. This tool is suitable for processing batch files and is essential for automation when working with large amounts of data.

Introduction: Blood-based biomarkers are quantitative, non-invasive diagnostic tools. This study aimed to identify candidate biomarkers for Alzheimer's disease (AD) using publicly available omics datasets, using the hypothesis that with blood-brain barrier dysfunction in AD, brain-synthesized proteins can leak into plasma for detection.

Methods: Differential abundance results of plasma and brain proteomic datasets were integrated to obtain a list of potential biomarkers. Biological validity was investigated with intercellular communication and gene regulatory analyses on brain single-cell transcriptomics data.

Results: Five proteins (APOD, B2M, CFH, CLU, and C3) fit biomarker criteria. 4 corresponding transcripts (APOD, B2M, CLU, and C3) were overexpressed in AD astrocytes, mediated by AD-related dysregulations in transcription factors regulating neuroinflammation. Additionally, CLU specifically induced downstream expression of neuronal death genes.

Discussion: In conclusion, a 5-protein panel is shown to effectively identify AD patients, with evidence of disease specificity and biological validity. Future research should investigate the mechanism of protein leakage through the blood-brain barrier.