Computational Genomics Research Articles

Comparative Metabolic Pathways Analysis and Subtractive Genomics Profiling to Prioritize Potential Drug Targets Against Streptococcus pneumoniae.

Streptococcus pneumoniae is a notorious pathogen that affects ∼450 million people worldwide and causes up to four million deaths per annum. Despite availability of antibiotics (i.e., penicillin, doxycycline, or clarithromycin) and conjugate vaccines (e.g., PCVs), it is still challenging to treat because of its drug resistance ability. The rise of antibiotic resistance in S. pneumoniae is a major source of concern across the world. Computational subtractive genomics is one of the most applied techniques in which the whole proteome of the bacterial pathogen is gradually reduced to a limited number of potential therapeutic targets. Whole-genome sequencing has greatly reduced the time required and provides more opportunities for drug target identification. The goal of this work is to evaluate and analyze metabolic pathways in serotype 14 of S. pneumonia to identify potential drug targets. In the present study, 47 potent drug targets were identified against S. pneumonia by employing the computational subtractive genomics approach. Among these, two proteins are prioritized (i.e., 4-oxalocrotonate tautomerase and Sensor histidine kinase uniquely present in S. pneumonia) as novel drug targets and selected for further structure-based studies. The identified proteins may provide a platform for the discovery of a lead drug candidate that may be capable of inhibiting these proteins and, therefore, could be helpful in minimizing the associated risk related to the drug-resistant S. pneumoniae. Finally, these enzymatic proteins could be of prime interest against S. pneumoniae to design rational targeted therapy.

Potential Drug Targets Identification Against Clostridioides Difficile (CD) and Characterization of Indispensable Proteins by a Subtractive Genomics Approach Followed by Virtual Screening

Background:Clostridioides difficile (CD) is an enteric multi-drug resistant pathogenic bacterium. CD-associated infections are the leading cause of nosocomial diarrhea that can further lead to pseudomembranous colitis, toxic mega-colon or sepsis with greater mortality and morbidity risks. CD infection possesses higher rates of recurrence due to its greater resistance to antibiotics. Considering its higher rates of recurrence, it has become a major burden on healthcare facilities. Therefore, there is a dire need to identify novel drug targets to combat antibiotic resistance of Clostridioides difficile.Objective:To identify and propose new and novel drug targets against the Clostridioides difficile.Methods:In the current study, a computational subtractive genomics approach was applied to obtain a set of potential drug targets that exist in the multi-drug resistant strain of Clostridioides difficile. Here, the uncharacterized proteins were studied as potential drug targets. The methodology involved several bioinformatics databases and tools. The druggable proteins sequences were retrieved based on non-homology with host proteome and essentiality for the survival of the pathogen. The uncharacterized proteins were functionally characterized using different computational tools, and sub-cellular localization was also predicted. The metabolic pathways were analyzed using the KEGG database. Eventually, the druggable proteome has been fetched using sequence similarity with the already available drug targets present in the DrugBank database. These druggable proteins were further explored for the structural details to identify drug candidates.Results :A priority list of potential drug targets was provided with the help of the applied method on the complete proteome set of the C. difficile. Moreover, the drug-like compounds have been screened against the potential drug targets to prioritize potential drug candidates. To facilitate the need for drug targets and therapies, the study proposed five potential protein drug targets, out of which three proposed drug targets were subjected to homology modeling to explore their structural and functional activitiesConclusion:In conclusion, we proposed three unique, unexplored drug targets against C. difficile. The structure-based methods were applied and resulted in a list of top-scoring compounds as potential inhibitors to proposed drug targets.

An optimal statistical and computational framework for generalized tensor estimation

This paper describes a flexible framework for generalized low-rank tensor estimation problems that includes many important instances arising from applications in computational imaging, genomics, and network analysis. The proposed estimator consists of finding a low-rank tensor fit to the data under generalized parametric models. To overcome the difficulty of nonconvexity in these problems, we introduce a unified approach of projected gradient descent that adapts to the underlying low-rank structure. Under mild conditions on the loss function, we establish both an upper bound on statistical error and the linear rate of computational convergence through a general deterministic analysis. Then we further consider a suite of generalized tensor estimation problems, including sub-Gaussian tensor PCA, tensor regression, and Poisson and binomial tensor PCA. We prove that the proposed algorithm achieves the minimax optimal rate of convergence in estimation error. Finally, we demonstrate the superiority of the proposed framework via extensive experiments on both simulated and real data.

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.

Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care.

ILoci: robust evaluation of genome content and organization for provisional and mature genome assemblies.

We introduce a new framework for genome analyses based on parsing an annotated genome assembly into distinct interval loci (iLoci), available as open-source software as part of the AEGeAn Toolkit (https://github.com/BrendelGroup/AEGeAn). We demonstrate that iLoci provide an alternative coordinate system that is robust to changes in assembly and annotation versions and facilitates granular quality control of genome data. We discuss how statistics computed on iLoci reflect various characteristics of genome content and organization and illustrate how these statistics can be used to establish a baseline for assessment of the completeness and accuracy of the data. We also introduce a well-defined measure of relative genome compactness and compute other iLocus statistics that reveal genome-wide characteristics of gene arrangements in the whole genome context. Given the fast pace of assembly/annotation updates, our AEGeAn Toolkit fills a niche in computational genomics based on deriving persistent and species-specific genome statistics. Gene structure model-centric iLoci provide a precisely defined coordinate system that can be used to store assembly/annotation updates that reflect either stable or changed assessments. Large-scale application of the approach revealed species- and clade-specific genome organization in precisely defined computational terms, promising intriguing forays into the forces of shaping genome structure as more and more genome assemblies are being deposited.

Teaching computational genomics and bioinformatics on a high performance computing cluster-a primer.

The burgeoning field of genomics as applied to personalized medicine, epidemiology, conservation, agriculture, forensics, drug development, and other fields comes with large computational and bioinformatics costs, which are often inaccessible to student trainees in classroom settings at universities. However, with increased availability of resources such as NSF XSEDE, Google Cloud, Amazon AWS, and other high-performance computing (HPC) clouds and clusters for educational purposes, a growing community of academicians are working on teaching the utility of HPC resources in genomics and big data analyses. Here, I describe the successful implementation of a semester-long (16 week) upper division undergraduate/graduate level course in Computational Genomics and Bioinformatics taught at San Diego State University in Spring 2022. Students were trained in the theory, algorithms and hands-on applications of genomic data quality control, assembly, annotation, multiple sequence alignment, variant calling, phylogenomic analyses, population genomics, genome-wide association studies, and differential gene expression analyses using RNAseq data on their own dedicated 6-CPU NSF XSEDE Jetstream virtual machines. All lesson plans, activities, examinations, tutorials, code, lectures, and notes are publicly available at https://github.com/arunsethuraman/biomi609spring2022.

Considering Computational Mathematics IGHG3 as Malignant Melanoma Is Associated with Immune Infiltration of Malignant Melanoma.

Malignant melanoma is one of the most threatening cancers to human health. Only 14% of patients with malignant melanoma have a remaining life span of 5 years. At present, there have been some studies looking for potential prognostic indicators of esophageal cancer from the level of genes and infiltrating immune cells, but there are still some problems that need to be resolved urgently. This paper proposes IGHG3 as the immune infiltration of malignant melanoma, which takes into account the computational mathematics. It aims to deduce the characteristics of immune cell infiltration in malignant melanoma and study the relationship between different immune cell infiltration characteristics and prognosis. The method in this article is to establish a computational mathematical model for the immunotherapy of melanoma, then study the method of identification of the affinity of the IGHG3 reagent, and finally obtain the gene expression of immune infiltration. The functions of these methods are, respectively, to predict the dynamic behavior of T cells with two different specificities through mathematical models and to test the matching degree of different concentrations of IGHG3 reagent with the human body. Then use the ssGSEA algorithm to obtain immune infiltration related data and calculate the difference between the weighted empirical cumulative distribution function of all genes in the effect of IGHG3 on melanoma that was carried out. The experimental results showed the computational mathematical method genome and all the remaining genes. In this study, a computational mathematical method to detect the IGHG3 gene expression had a significant inhibitory effect on A375 cells in the experimental group, and the knockdown efficiency reached 85.6%.

Perspectives in the new era of materials intelligent design

The launching integrated computational materials engineering (ICME) and materials genome engineering (MGE) has led the transformation of empirical and theoretical design paradigm into the rational computational one that further provides the basis for the data-driven design paradigm by integrating the high-throughput techniques in experiments and computations, the big data science with general principles, the informatics with knowledge discovery based on data mining and machine learning, and ultimately enabling the possibility of materials intelligence design (MID) via artificial intelligence. In this perspective article, we highlight the intelligent solution to acquire the property-structure-process-performance relationship of multilevel-structured materials by emphasizing modularization, automation, standardization, integration and intelligence, following the hierarchical relationship of data, information, knowledge and wisdom, which is essentially different from the past empirical, theoretical and computational paradigms. The new era of MID is expected to fundamentally reform the material innovation mode through an integrated infrastructure guided by novel concepts that is radically distinguished from the way of thinking and doing in the past, providing a perspective scientific vision and direction for future materials design.

Polygenomic Interrogation of Drug Resistance Genes.

Understanding drug resistance in cancer is paramount to improving patient outcomes, quality of life and reducing toxicities in patients receiving chemotherapy. Pharmacogenomic methods seek to understand the interaction of genomic variation and response to chemotherapeutic treatment. This chapter presents a workflow to interrogate multiple genomic inputs and individually assess their relationship with the phenotype of drug resistance using hierarchical clustering to determine the set of features that can best describe what features are associated with drug resistance. Then in a gene-centric manner regulatory features such as miRNAs, SNPs, or DNA methylation can be related back to the differential expression of genes to give understanding to the mechanism underlying resistance. In this chapter, we describe a computational method that can be adapted to a number of different diseases and phenotypes in which there are multiple genomic data types available with concordant phenotypic drug resistance information.

Analysing the Protein-DNA Binding Sites in Arabidopsis thaliana from ChIP-seq Experiments

Computational genomics aim at supporting the discovery of how the functionality of the genome of the organism under study is affected both by its own sequence and structure, and by the network of interaction between this genome and different biological or physical factors. In this work, we focus on the analysis of ChIP-seq data, for which many methods have been proposed in the recent years. However, to the best of our knowledge, those methods lack an appropriate mathematical formalism. We have developed a method based on multivariate models for the analysis of the set of peaks obtained from a ChIP-seq experiment. This method can be used to characterize an individual experiment and to compare different experiments regardless of where and when they were conducted. The method is based on a multivariate hypergeometric distribution, which fits the complexity of the biological data and is better suited to deal with the uncertainty generated in this type of experiments than the dichotomous models used by the state of the art methods. We have validated this method with Arabidopsis thaliana datasets obtained from the Remap2020 database, obtaining results in accordance with the original study of these samples. Our work shows a novel way for analyzing ChIP-seq data.

Exposome-wide Association Study for Metabolic Syndrome.

OPINION article Front. Genet., 07 December 2021Sec. Computational Genomics https://doi.org/10.3389/fgene.2021.783930

Computational Structural Genomics Unravels Common Folds and Novel Families in the Secretome of Fungal Phytopathogen Magnaporthe oryzae.

Structural biology has the potential to illuminate the evolution of pathogen effectors and their commonalities that cannot be readily detected at the primary sequence level. Recent breakthroughs in protein structure modeling have demonstrated the feasibility to predict the protein folds without depending on homologous templates. These advances enabled a genome-wide computational structural biology approach to help understand proteins based on their predicted folds. In this study, we employed structure prediction methods on the secretome of the destructive fungal pathogen Magnaporthe oryzae. Out of 1,854 secreted proteins, we predicted the folds of 1,295 proteins (70%). We showed that template-free modeling by TrRosetta captured 514 folds missed by homology modeling, including many known effectors and virulence factors, and that TrRosetta generally produced higher quality models for secreted proteins. Along with sensitive homology search, we employed structure-based clustering, defining not only homologous groups with divergent members but also sequence-unrelated structurally analogous groups. We demonstrate that this approach can reveal new putative members of structurally similar MAX effectors and novel analogous effector families present in M. oryzae and possibly in other phytopathogens. We also investigated the evolution of expanded putative ADP-ribose transferases with predicted structures. We suggest that the loss of catalytic activities of the enzymes might have led them to new evolutionary trajectories to be specialized as protein binders. Collectively, we propose that computational structural genomics approaches can be an integral part of studying effector biology and provide valuable resources that were inaccessible before the advent of machine learning-based structure prediction.

Genome mining of biosynthetic gene clusters intended for secondary metabolites conservation in actinobacteria

Evolution of genome sequencing technology, on the one hand, and advancement of computational genome mining tools, on the other hand, paves way for improvement in predicting secondary metabolites. In past, numerous efforts were made concerning genome mining for recognizing secondary metabolites within the genus, but only a negligible quantity of comparative genomic reports had carried out among species of different genera. In this study, we explored potential of 24 actinobacteria species belonging to the genera, including Streptomyces, Nocardia, Micromonospora, and Saccharomonospora, to traverse diversity and distribution of Biosynthetic Gene Clusters (BGCs). Investigating results obtained from antiSMASH (Antibiotics and Secondary Metabolites Analysis Shell), NaPDoS (Natural Product Domain Seeker), and NP.searcher revealed conservation of genus-specific gene clusters among various species. E.g., NAGGN (n-acetyl glutaminyl glutamine amide) is present in Micromonospora, furan in Nocardia, melanin, and lassopeptide occur in Streptomyces. Bioactive compounds like alkyl-O-dihydro geranyl methoxy hydroquinone, SapB, desferrioxamine E, 2-Methylisoborneol, mayamycin, cyclodipeptide synthase, diisonitrile, salinichelin, hopene, ectoine and isorenieratene are highly conserved among diverse genera. Furthermore, pharmacological activity of actinobacterial derived metabolites against bacterial and fungal pathogens were illustrated. We need to accomplish large-scale analysis of natural products, including various genera of actinobacteria to deliver comprehensive intuition to overcome antibiotic resistance.

Chromosome-specific polymorphic SSR markers in tropical eucalypt species using low coverage whole genome sequences: systematic characterization and validation.

Eucalyptus is one of the major plantation species with wide variety of industrial uses. Polymorphic and informative simple sequence repeats (SSRs) have broad range of applications in genetic analysis. In this study, two individuals of Eucalyptus tereticornis (ET217 and ET86), one individual each from E. camaldulensis (EC17) and E. grandis (EG9) were subjected to whole genome resequencing. Low coverage (10×) genome sequencing was used to find polymorphic SSRs between the individuals. Average number of SSR loci identified was 95,513 and the density of SSRs per Mb was from 157.39 in EG9 to 155.08 in EC17. Among all the SSRs detected, the most abundant repeat motifs were di-nucleotide (59.6%–62.5%), followed by tri- (23.7%–27.2%), tetra- (5.2%–5.6%), penta- (5.0%–5.3%), and hexa-nucleotide (2.7%–2.9%). The predominant SSR motif units were AG/CT and AAG/TTC. Computational genome analysis predicted the SSR length variations between the individuals and identified the gene functions of SSR containing sequences. Selected subset of polymorphic markers was validated in a full-sib family of eucalypts. Additionally, genome-wide characterization of single nucleotide polymorphisms, InDels and transcriptional regulators were carried out. These variations will find their utility in genome-wide association studies as well as understanding of molecular mechanisms involved in key economic traits. The genomic resources generated in this study would provide an impetus to integrate genomics in marker-trait associations and breeding of tropical eucalypts.

Identifying potential human and medicinal plant microRNAs against SARS-CoV-2 3′UTR region: A computational genomics assessment

The coronavirus disease of 2019 (COVID-19) began as an outbreak and has taken a toll on human lives. The current pandemic requires scientific attention; hence we designed a systematic computational workflow to identify the cellular microRNAs (miRNAs) from human host possessing the capability to target and silence 3′UTR of SARS-CoV-2 genome. Based on this viewpoint, we extended our miRNA search to medicinal plants like Ocimum tenuiflorum, Zingiber officinale and Piper nigrum, which are well-known to possess antiviral properties, and are often consumed raw or as herbal decoctions. Such an approach, that makes use of miRNA of one species to interact and silence genes of another species including viruses is broadly categorized as cross-kingdom interactions. As a part of our genomics study on host-virus-plant interaction, we identified one unique 3′UTR conserved site ‘GGAAGAG’ amongst 5024 globally submitted SARS-CoV-2 complete genomes, which can be targeted by the human miRNA ‘hsa-miR-1236–3p’ and by Z. officinale miRNA ‘zof-miR2673b’. Additionally, we also predicted that the members of miR477 family commonly found in these three plant genomes possess an inherent potential to silence viral genome RNA and facilitate antiviral defense against SARS-CoV-2 infection. In conclusion, this study reveals a universal site in the SARS-CoV-2 genome that may be crucial for targeted therapeutics to cure COVID-19.

A Python-based programming language for high-performance computational genomics

A Python-based programming language for high-performance computational genomics

Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis.

Interactions of drugs with the classical epigenetic mechanism of DNA methylation or histone modification are increasingly being elucidated mechanistically and used to develop novel classes of epigenetic therapeutics. A data science approach is used to synthesize current knowledge on the pharmacological implications of epigenetic regulation of gene expression. Computer-aided knowledge discovery for epigenetic implications of current approved or investigational drugs was performed by querying information from multiple publicly available gold-standard sources to (i) identify enzymes involved in classical epigenetic processes, (ii) screen original biomedical scientific publications including bibliometric analyses, (iii) identify drugs that interact with epigenetic enzymes, including their additional non-epigenetic targets, and (iv) analyze computational functional genomics of drugs with epigenetic interactions. PubMed database search yielded 3051 hits on epigenetics and drugs, starting in 1992 and peaking in 2016. Annual citations increased to a plateau in 2000 and show a downward trend since 2008. Approved and investigational drugs in the DrugBank database included 122 compounds that interacted with 68 unique epigenetic enzymes. Additional molecular functions modulated by these drugs included other enzyme interactions, whereas modulation of ion channels or G-protein-coupled receptors were underrepresented. Epigenetic interactions included (i) drug-induced modulation of DNA methylation, (ii) drug-induced modulation of histone conformations, and (iii) epigenetic modulation of drug effects by interference with pharmacokinetics or pharmacodynamics. Interactions of epigenetic molecular functions and drugs are mutual. Recent research activities on the discovery and development of novel epigenetic therapeutics have passed successfully, whereas epigenetic effects of non-epigenetic drugs or epigenetically induced changes in the targets of common drugs have not yet received the necessary systematic attention in the context of pharmacological plasticity.

Editorial: Computational Genomics and Molecular Medicine for Emerging COVID-19

The papers in this special section focus on computational genomics and molecular medicine for emerging COVID-19. In 2020, World Health Organization announced Coronavirus disease (COVID)-19 is a pandemic disease, which is devastated the socio-economic life around the world. The disease caused by the zoonotic single-strand RNA virus known as “SARS-CoV-2”. To overcome the pandemic, the diagnosis and therapeutics products needs to be developed in short term. Developing therapeutics for infectious diseases, especially viral diseases always a challenging task for the scientific community. However, the utility of high-performance computational resources, artificial intelligence, and machine-learning algorithms can make the process in an affordable way through the usage of genomics, proteomics, pharmacogenomics, and chemical data. Thus, the special section received potential research articles related to computational genomics, molecular medicine, and COVID-19 from reputed scientist around the world. Different articles were employed machine learning, molecular dynamics, computer aided drug design techniques, and emphasizing viral genomics, mutation, drug target, drug candidates, and patient data, were included in this special section.

Using the longest run subsequence problem within homology-based scaffolding

Genome assembly is one of the most important problems in computational genomics. Here, we suggest addressing an issue that arises in homology-based scaffolding, that is, when linking and ordering contigs to obtain larger pseudo-chromosomes by means of a second incomplete assembly of a related species. The idea is to use alignments of binned regions in one contig to find the most homologous contig in the other assembly. We show that ordering the contigs of the other assembly can be expressed by a new string problem, the longest run subsequence problem (LRS). We show that LRS is NP-hard and present reduction rules and two algorithmic approaches that, together, are able to solve large instances of LRS to provable optimality. All data used in the experiments as well as our source code are freely available. We demonstrate its usefulness within an existing larger scaffolding approach by solving realistic instances resulting from partial Arabidopsis thaliana assemblies in short computation time.

Identification of Low‐Resolution Translocations via Novel Computational Genomics Analysis

Background and Objective Identification of chromosomal translocations has been historically important in elucidating evolutionary distinctions and diagnosing numerous genetic disorders. Current methods of translocation identification center on multi-color fluorescence in-situ hybridization (FISH). This provides a cost-effective, genome-wide method to screen translocations. However, FISH is limited by its inability to detect low-resolution translocations with high specificity and sensitivity, and its time efficiency is restricted by the researcher having prior knowledge of the abnormality. Computational approaches comparing sequenced genomes have shown promise in identifying low-resolution chromosomal translocations. Nevertheless, the practicality of such techniques are limited by the time and resource requirements of a high computational burden. Methods The authors developed a novel computational approach to detect translocations between members of the primate order via genomic examination of Short Interspersed Nuclear Elements (SINEs). Phylogenetic literature has validated SINEs as an effective tool for making accurate evolutionary distinctions. Specific SINEs, such as the Alu family of transposable elements, are widely conserved among primate species and exhibit unidirectional and homoplasy-free evolution. This investigation employs Smith-Waterman string distances to determine conserved SINEs across species, allowing their analysis to serve as a robust measure of loci-specific chromosome sequence homology between related species. Results The technique allowed for clear and quantitative detection of patterns displaying chromosomal translocations. Translocation events were analyzed between the Chimpanzee chromosome 3 (panTro4) and the Green Monkey (chlSab2) reference genome. In total, previously published literature utilizing chromosomal painting in conjunction with comparison of whole genome assemblies had identified 8 distinct discontinuities greater than 5 megabase pairs (Mbp), each corresponding to a translocation or change in orientation of the synteny. These translocations were validated using the novel SINE-based methodology, and each reported translocation or change in orientation was detected with a minimum z-score of 2.5 (p < 0.007) and regularly above 4 (p < 1e-4). The authors further tested the novel method by expanding to include translocations between the panTro4 chromosome 3 and the gorGor4 and Marmoset (calJac3) genomes as well. The novel method additionally identified every reported translocation or change in orientation greater than 5 Mbp in this set with a similar p value threshold. Conclusion These results show that in a limited sample, the novel methodology analyzing string distances can detect relatively low-resolution translocations with the same specificity and sensitivity of chromosomal painting in conjunction with comparison of whole genome assemblies. Further work needs to be done to determine whether this accuracy is maintained when comparing other species in the order Primates and to ascertain the exact resolution at which this technique can no longer reliably detect translocations as accurately as more resource-intensive techniques.