Journal of Bioinformatics and Computational Biology

Glioma is one particular type of brain malignancy which is highly complex and usually has a poor prognosis. Despite the limited diagnostic level of glioma, the survival time of affected patients broadly varies. Here, we conducted a detailed analysis, regarding the differences in patient survival time, to discover potential survival-related genes in glioma as well as their putative regulatory mechanisms. To contextualize the acquisition of these potential prognosis markers in large populations, particularly in China, we combined CGGA and The Cancer Genome Atlas (TCGA) databases to properly identify genes that are significantly related to survival. Our workflow combined a series of analytical approaches, including differential analysis, survival time, co-expression, clinical correlation analysis, ROC curve evaluation and prediction ability. Our results indicate that the four particular genes - PLAT, IGFBP2, BCAT1, SERPINH1 could be used as independent prognostic marker genes. These genes have also shown good prognostic ability in distinct populations, reiterating the robustness and value of these prognostic markers.

The pandemic caused by SARS-CoV-2 has had a significant impact on the whole world. In a theory of the origin of SARS-CoV-2, pangolins are considered as a potential intermediate host. To assemble the genome of suspicious coronavirus (CoV) found in pangolins, SARS-CoV-2 was used as a reference in most of the previous studies, implicitly assuming the pangolin CoV and SARS-CoV-2 are the closest neighbors in evolution. However, this assumption may not be true. We investigated how the choice of reference genome affected the resulting CoV genome assembly. We explored various representative CoVs as the reference genome, and found significant differences in the resulting assemblies. The assembly obtained using RaTG13 as a reference showed better statistics in total length, N50, and pairwise distance reconstruction (PDR) scores than the assembly guided by SARS-CoV-2, indicating that RaTG13 may be a better reference. Therefore, RaTG13 should also be considered as a reference for assembling suspicious CoV found in pangolins and other potential intermediate hosts.

The application of graph theory in structural biology offers an alternative means of studying 3D models of large macromolecules such as proteins. The radius of gyration, which scales with exponent $\sim 0.4$, provides quantitative information about the compactness of the protein structure. In this study, we combine two proven methods, the graph-theoretical and the fundamental scaling laws, to study 3D protein models. This study shows that the mean node degree (MND) of the protein graphs, which scales with exponent 0.038, is scale-invariant. In addition, proteins that differ in size have a highly similar node degree distribution. Linear regression analysis showed that the graph parameters (radius, diameter, and mean eccentricity) can explain up to 90% of the total radius of gyration variance. Thus, the graph parameters of radius, diameter, and mean eccentricity scale match along with the same exponent as the radius of gyration. The main advantage of graph eccentricity compared to the radius of gyration is that it can be used to analyze the distribution of the central and peripheral amino acids/nodes of the macromolecular structure.

Large amounts of research efforts have been focused on learning gene regulatory networks (GRNs) based on gene expression data to understand the functional basis of a living organism. Under the assumption that the joint distribution of the gene expressions of interest is a multivariate normal distribution, such networks can be constructed by assessing the nonzero elements of the inverse covariance matrix, the so-called precision matrix or concentration matrix. This may not reflect the true connectivity between genes by considering just pairwise linear correlations. To relax this limitative constraint, we employ Gaussian process (GP) model which is well known as computationally efficient non-parametric Bayesian machine learning technique. GPs are among a class of methods known as kernel machines which can be used to approximate complex problems by tuning their hyperparameters. In fact, GP creates the ability to use the capacity and potential of different kernels in constructing precision matrix and GRNs. In this paper, in the first step, we choose the GP with appropriate kernel to learn the considered GRNs from the observed genetic data, and then we estimate kernel hyperparameters using rule-of-thumb technique. Using these hyperparameters, we can also control the degree of sparseness in the precision matrix. Then we obtain kernel-based precision matrix similar to GLASSO to construct kernel-based GRN. The findings of our research are used to construct GRNs with high performance, for different species of Drosophila fly rather than simply using the assumption of multivariate normal distribution, and the GPs, despite the use of the kernels capacity, have a much better performance than the multivariate Gaussian distribution assumption.

Understanding the molecular mechanisms that correlate pathologies with missense mutations is of critical importance for disease risk estimations and for devising personalized therapies. Thus, we have performed a bioinformatic survey of ClinVar, a database of human genomic variations, to find signals that can account for missense mutation pathogenicity. Arginine resulted as the most frequently replaced amino acid both in benign and pathogenic mutations. By adding the structural dimension to this investigation to increase its resolution, we found that arginine mutations occurring at the protein–DNA interface increase pathogenicity 6.5 times with respect to benign variants. Glycine is the second amino acid among all the pathological missense mutations. Necessarily replaced by larger amino acids, glycine substitutions perturb the structural stability of proteins and, therefore, their functions, being mostly located in buried protein moieties. Arginine and glycine appear as representative of missense mutations causing respective changes in interaction processes and protein structural features, the two main molecular mechanisms of genome-induced pathologies.

Defining protein complexes in the cell is important for learning about cellular processes mechanisms as they perform many of the molecular functions in these processes. Most of the proposed algorithms predict a complex as a dense area in a Protein–Protein Interaction (PPI) network. Others, on the other hand, weight the network using gene expression or geneontology (GO). These approaches, however, eliminate the proteins and their edges that offer no gene expression data. This can lead to the loss of important topological relations. Therefore, in this study, a method based on the Gene Expression and Core-Attachment (GECA) approach was proposed for addressing these limitations. GECA is a new technique to identify core proteins using common neighbor techniques and biological information. Moreover, GECA improves the attachment technique by adding the proteins that have low closeness but high similarity to the gene expression of the core proteins. GECA has been compared with several existing methods and proved in most datasets to be able to achieve the highest F-measure. The evaluation of complexes predicted by GECA shows high biological significance.

Discovering motifs and repeats in data sequences is of great importance in biology and a large number of efficient tools for their finding have been developed. As the number of results found can be very large, our goal is to provide a tool that, on a mathematical basis, can precisely find all motifs and repeats, filter them according to input arguments and output the results in a convenient way. RepeatsPlus is a program that provides statistical filtering according to input sequence length and number of repeat occurrences, motif mask filtering and filtering related to ambiguous letters in input sequence and a large number of other options. RepeatPlus is implemented in Python and C$++$. It is freely available for public use. The user manual and examples of usage are also available.