Current Bioinformatics - Volume 18, Issue 2, 2023

The essentiality of a gene can be defined at different levels and is context-dependent. Essential protein-coding genes have been well studied. However, the essentiality of non-coding genes is not well characterized. Although experimental technologies, like CRISPR-Cas9, can provide insights into the essentiality of non-coding regions of the genome, scoring the essentiality of noncoding genes in different contexts is still challenging. With machine learning algorithms, the essentiality of protein-coding genes can be estimated well. But the development of these algorithms for non-coding genes was very early. Based on several recent studies, we believe the essentiality of noncoding genes will be a new and fertile ground in bioinformatics. We pointed out some possible research topics in this perspective article.

Background: Moth-flame optimization will meet the premature and stagnation phenomenon when encountering difficult optimization tasks. Objective: This paper presented a quasi-reflection moth-flame optimization algorithm with refraction learning called QRMFO to strengthen the property of ordinary MFO and apply it in various application fields to overcome shortcomings. Methods: In the proposed QRMFO, quasi-reflection-based learning increases the diversity of the population and expands the search space on the iteration jump phase; refraction learning improves the accuracy of the potential optimal solution. Results: Several experiments are conducted to evaluate the superiority of the proposed QRMFO in the paper; first of all, the CEC2017 benchmark suite is utilized to estimate the capability of QRMFO when dealing with the standard test sets compared with the state-of-the-art algorithms; afterward, QRMFO is adopted to deal with multilevel thresholding image segmentation problems and real medical diagnosis case. Conclusion: Simulation results and discussions show that the proposed optimizer is superior to the basic MFO and other advanced methods in terms of convergence rate and solution accuracy.

Background: Due to the rapid rise of allergies, anti-allergy medications are commonly being utilised to reduce inflammation; however, allergen-specific inhibitors may also be utilised. Objective: Our in silico study is aimed at finding out a promising anti-allergic compound that can act against a wide range of allergens. Methods: The inhibitory efficacies of potential anti-allergic compounds were investigated by ADMET studies and were followed by high throughput molecular docking. Binding energy was calculated by AUTODOCK, which led to the identification of binding sites between the allergens and antiallergic compounds. Each of the five anti-allergic compounds interacted with allergens at various levels. The docked poses showing significant binding energy were subjected to molecular docking simulation. Results: Marrubiin exhibits higher binding affinities to the catalytic pocket against allergens from chicken, European white birch plant, bacteria, fungus, and numerous food allergens. Conclusion: We propose Marrubiin, which appears to be a promising anti-allergic candidate and antiinflammatory agent against a wide spectrum of allergens. The future directions of this research are to analyze the effects of anti-allergic mechanisms in vivo.

Background: Due to overdispersion in the RNA-Seq data and its discrete structure, clustering samples based on gene expression profiles remains a challenging problem, and several clustering approaches have been developed so far. However, there is no “gold standard” strategy for clustering RNA-Seq data, so alternative approaches are needed. Objective: In this study, we presented a new clustering approach, which incorporates two powerful methods, i.e., voom and self-organizing maps, into the frequently used clustering algorithms such as kmeans, k-medoid and hierarchical clustering algorithms for RNA-seq data clustering. Methods: We first filter and normalize the raw RNA-seq count data. Then to transform counts into continuous data, we apply the voom method, which outputs the log-cpm matrix and sample quality weights. After the voom transformation, we apply the SOM algorithm to log-cpm values to get the codebook used in the downstream analysis. Next, we calculate the weighted distance matrices using the sample quality weights obtained from voom transformation and codebooks from the SOM algorithm. Finally, we apply k-means, k-medoid and hierarchical clustering algorithms to cluster samples. Results: The performances of the presented approach and existing methods are compared over simulated and real datasets. The results show that the new clustering approach performs similarly or better than other methods in the Rand index and adjusted Rand index. Since the voom method accurately models the observed mean-variance relationship of RNA-seq data and SOM is an efficient algorithm for modeling high dimensional data, integrating these two powerful methods into clustering algorithms increases the performance of clustering algorithms in overdispersed RNA-seq data. Conclusion: The proposed algorithm, voomSOM, is an efficient and novel clustering approach that can be applied to RNA-Seq data clustering problems.

Background: From the existing knowledge of viruses, those infecting plants and bacteria and affecting animals are particularly interesting. This is because such viruses have an ability to vertically transmit to other species, including humans, and therefore could represent a public health issue of significant proportions. Objective: This study aims to bioinformatically characterize the proteins from the DNA and RNA viruses capable of infecting plants and bacteria, and affecting animals, of which there is some evidence of contact with human beings. It follows up on our previous study “Characterization of Proteins from Putative Human DNA and RNA Viruses”. Methods: The Polarity Index Method profile (PIM), Intrinsic Disorder Predisposition (IDPD) profiles, and a Markov chains analysis of three DNA-viruses protein sequences and four RNA-viruses protein sequences that infect plants and bacteria and affect animals, extracted from the UniProt database, were calculated using a set of in-house computational programs. Results: Computational runs carried out in this work reveal relevant regularities at the level of the viral proteins' charge/polarity and IDPD profiles. These results enable the re-creation of the taxonomy known for the DNA- and RNA-virus protein sequences. In addition, an analysis of the entire set of proteins qualified as "reviewed" in the UniProt database was carried out for each protein viral group to discover proteins with similar PIM profiles. A significant number of proteins with such charge/polarity profiles were found. Conclusion: The bioinformatics results obtained at the level of the amino acid sequences, generated important information that contributes to the understanding of these protein groups.

Background: Meiotic recombination is an important source of genetic variation, but how recombination shapes the genome is not clearly understood yet. Objective: Here, we investigate the roles of recombination on human genome evolution from two aspects: How does recombination shape single nucleotide polymorphism (SNP)-related genomic variation features? Whether recombination drives genome evolution through a neighbor-dependent mutational bias? Methods: We analyzed the relationship of recombination rate with mutational bias and selection effect at SNP sites derived from the 1000 Genomes Project. Results: Our results show that SNP density, Ts/Tv, nucleotide diversity, and Tajima's D were positively correlated with the recombination rate, while Ka/Ks were negatively correlated with the recombination rate. Moreover, compared with non-coding regions, gene exonic regions have lower nucleotide diversity but higher Tajima's D, suggesting that coding regions are subject to stronger negative selection but have fewer rare alleles. Gene set enrichment analysis of the protein-coding genes with extreme Ka/Ks ratio implies that under the effect of high recombination rates, the genes involved in the cell cycle, RNA processing, and oocyte meiosis are subject to strong negative selection. Our data also support S (G or C) > W (A or T) mutational bias and W>S fixation bias in high recombination regions. In addition, the neighbor-dependent mutational bias was found to be stronger at high recombination regions. Conclusion: Our data suggest that genetic variation patterns, particularly the neighbor-dependent mutational bias at SNP sites in the human genome, are mediated by recombination.