Computational Genomic Analysis Research Articles

Exploring Indonesian actinomycete extracts for anti-tubercular compounds: Integrating inhibition assessment, genomic analysis, and prediction of its target by molecular docking

Tuberculosis (TB) is the foremost cause of infectious fatality globally. The primary global challenge in combatting TB lies in addressing the emergence of drug-resistant variants of the disease. However, the number of newly approved agents for treating TB has remained remarkably low over recent decades. Hence, research endeavors for discovering novel anti-TB agents are always needed. In the present study, we screened over 1,500 culture extracts from actinomycetes isolated in Indonesia for their inhibitory activity against Mycobacterium smegmatis used as a surrogate in the primary screening. The initial screening yielded approximately 6.2 % hit extracts, with a selection criterion of >80 % growth inhibition. The confirmed hit extracts were subsequently subjected to growth inhibition assay against Mycobacterium bovis and Mycobacterium tuberculosis. Approximately 20 % of the hit extracts that showed growth inhibition also exhibited efficacy against M. bovis BCG and M. tuberculosis H37Rv pathogenic strain. An active compound was successfully purified from a large-scale culture of the most potent representative extract by high-performance liquid chromatography and thin-layer chromatography. The structure of the active compound was elucidated by mass spectrometry and nuclear magnetic resonance. This compound displayed structural similarities to actinomycin group and exhibited robust inhibition, with IC50 values of 0.74, 0.02, and 0.07 μg/mL against M. smegmatis, M. bovis, and M. tuberculosis, respectively. The Actinomycetes strain A612, which produced the active compound, was taxonomically classified by phylogenetic analysis of 16s rRNA gene and whole genome sequencing data as Streptomyces parvus. Computational genome analysis utilizing anti-SMASH 7.0 unveiled that S. parvus A612 strain harbors 40 biosynthetic gene clusters with the potential to produce 16 known (with >70 % similarity) and 24 unknown compounds. A non-ribosomal peptide synthesis (NRPS) gene cluster associated with actinomycin D biosynthesis was also identified, boasting an 85 % similarity. Molecular docking analysis of actinomycin D and 21 potential M. tuberculosis targets revealed possible interactions with multiple targets. The purified active compound inhibited recombinant M. tuberculosis shikimate kinase (MtSK), which validated the results obtained from the docking analysis.

Trecode: A FAIR Eco-System for the Analysis and Archiving of Omics Data in a Combined Diagnostic and Research Setting

The increase in speed, reliability, and cost-effectiveness of high-throughput sequencing has led to the widespread clinical application of genome (WGS), exome (WXS), and transcriptome analysis. WXS and RNA sequencing is now being implemented as the standard of care for patients and for patients included in clinical studies. To keep track of sample relationships and analyses, a platform is needed that can unify metadata for diverse sequencing strategies with sample metadata whilst supporting automated and reproducible analyses, in essence ensuring that analyses are conducted consistently and data are Findable, Accessible, Interoperable, and Reusable (FAIR).We present “Trecode”, a framework that records both clinical and research sample (meta) data and manages computational genome analysis workflows executed for both settings, thereby achieving tight integration between analysis results and sample metadata. With complete, consistent, and FAIR (meta) data management in a single platform, stacked bioinformatic analyses are performed automatically and tracked by the database, ensuring data provenance, reproducibility, and reusability, which is key in worldwide collaborative translational research. The Trecode data model, codebooks, NGS workflows, and client programs are publicly available. In addition, the complete software stack is coded in an Ansible playbook to facilitate automated deployment and adoption of Trecode by other users.

Michael Waterman's Contributions to Computational Biology and Bioinformatics.

On the occasion of Dr. Michael Waterman's 80th birthday, we review his major contributions to the field of computational biology and bioinformatics including the famous Smith-Waterman algorithm for sequence alignment, the probability and statistics theory related to sequence alignment, algorithms for sequence assembly, the Lander-Waterman model for genome physical mapping, combinatorics and predictions of ribonucleic acid structures, word counting statistics in molecular sequences, alignment-free sequence comparison, and algorithms for haplotype block partition and tagSNP selection related to the International HapMap Project. His books Introduction to Computational Biology: Maps, Sequences and Genomes for graduate students and Computational Genome Analysis: An Introduction geared toward undergraduate students played key roles in computational biology and bioinformatics education. We also highlight his efforts of building the computational biology and bioinformatics community as the founding editor of the Journal of Computational Biology and a founding member of the International Conference on Research in Computational Molecular Biology (RECOMB).

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.

Coronaviruses caused epidemics for millennia

Inside modern human DNA lie traces of ancient coronavirus infections from many, many years ago ( Curr. Biol. 2021, DOI: 10.1016/j.cub.2021.05.067 ). Around 25,000 years ago, in fact. That’s the conclusion of a team of researchers led by David Enard of the University of Arizona. The scientists used a large database of individual human genomes known as 1000 Genomes to compare the genes coding for proteins that interact with coronaviruses, including SARS-CoV-2. They found evidence in the DNA of people whose families came from East Asia that a coronavirus epidemic raged in that region around 25,000 years ago. In addition, the virus had changed the DNA of the survivors and their descendants. After completing the computational genomic analysis, the researchers synthesized human and coronavirus proteins and confirmed that they interact, as suggested by the team’s earlier results. The researchers also identified previously unknown drug targets that they say could be

Study of lipase producing gene in wheat – an in silico approach

BackgroundLipases (EC 3.1.1.3) catalyze the hydrolysis of oil into free fatty acids and glycerol forming the 3rd largest group of commercialized enzymes. Plant lipases grab attention recently because of their specificity, less production and purified cost, and easy availability. In silico approach is the first step to identify different genes coding for lipase in a most common indigenous plant, wheat, to explore the possibility of this plant as an alternative source for commercial lipase production. As the hierarchy organization of genes reflects an ancient process of gene duplication and divergence, many of the theoretical and analytical tools of the phylogenetic systematics can be utilized for comparative genomic studies. Also, in addition to experimental identification and characterization of genes, for computational genomic analysis, Arabidopsis has become a popular strategy to identify crop genes which are economically important, as Arabidopsis genes had been well identified and characterized for lipase. A number of articles had been reported in which genes of wheat have shown strong homology with Arabidopsis. The complete genome sequences of rice and Arabidopsis constitute a valuable resource for comparative genome analysis as they are representatives of the two major evolutionary lineages within the angiosperms. Here, in this in silico approach, Arabidopsis and Oryza sativa serve as models for dicotyledonous and monocotyledonous species, respectively, and the genomic sequence data available was used to identify the lipase genes in wheat. ResultsIn this present study, Ensembl Plants database was explored for lipase producing gene present in wheat genome and 21 genes were screened down as they contain specific domain and motif for lipase (GXSXG). According to the evolutionary analysis, it was found that the gene TraesCS5B02G157100, located in 5B chromosome, has 58.35% sequence similarity with the reported lipase gene of Arabidopsis thaliana and gene TraesCS3A02G463500 located in the 3A chromosome has 51.74% sequence similarity with the reported lipase gene of Oryza sativa. Homology modeling was performed using protein sequences coded by aforementioned genes and optimized by molecular dynamic simulations. Further with the help of molecular docking of modeled structures with tributyrin, binding efficiency was checked, and the difference in energies (DE) was −9.83 kcal/mol and −6.67 kcal/mol, respectively. ConclusionsThe present work provides a basic understanding of the gene-encoding lipase in wheat, which could be easily accessible and used as a potent industrial enzyme. The study enlightens another direction which can be used further to explore plant lipases.

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.

Computational Genomic Analysis of Oral Squamous Cell Carcinoma - A New Diagnostic Approach

Computational Genomic Analysis of Oral Squamous Cell Carcinoma - A New Diagnostic Approach

A connectivity map-based drug repurposing study and integrative analysis of transcriptomic profiling of SARS-CoV-2 infection.

AimsThe recent outbreak of COVID-19 has become a global health concern. There are currently no effective treatment strategies and vaccines for the treatment or prevention of this fatal disease. The current study aims to determine promising treatment options for the COVID-19 through a computational drug repurposing approach.Materials and methodsIn this study, we focus on differentially expressed genes (DEGs), detected in SARS-CoV-2 infected cell lines including “the primary human lung epithelial cell line NHBE” and “the transformed lung alveolar cell line A549”. Next, the identified DEGs are used in the connectivity map (CMap) analysis to identify similarly acting therapeutic candidates. Furthermore, to interpret lists of DEGs, pathway enrichment and protein network analysis are performed. Genes are categorized into easily interpretable pathways based on their biological functions, and overrepresentation of each pathway is tested in comparison to what is expected randomly.Key findingsThe results suggest the effectiveness of lansoprazole, folic acid, sulfamonomethoxine, tolnaftate, diclofenamide, halcinonide, saquinavir, metronidazole, ebselen, lidocaine and benzocaine, histone deacetylase (HDAC) inhibitors, heat shock protein 90 (HSP90) inhibitors, and many other clinically approved drugs as potent drugs against COVID-19 outbreak.SignificanceMaking new drugs remain a lengthy process, so the drug repurposing approach provides an insight into the therapeutics that might be helpful in this pandemic. In this study, pathway enrichment and protein network analysis are also performed, and the effectiveness of some drugs obtained from the CMap analysis has been investigated according to previous researches.

A computational subtractive genome analysis for the characterization of novel drug targets in Klebsiella pneumonia strain PittNDM01

The emergence of carbapenem-resistant Klebsiella Pneumoniae had been reported previously, which needs rapid attention. Currently, Pittsburgh University Hospital reported a new strain of carbapenem-resistant Klebsiella pneumoniae that was co-producing OXA-232 and NDM-1 named as PittNDM01. This strain is resistant to almost all beta-lactam antibiotics such as Carbapenem as well as to fluoroquinolones and aminoglycosides. Globally, failure to the wide-spread pathogenic strains had been observed due to the increased and antibiotic resistance, which leads to less antimicrobial drug efficacy. Since last decades, computational genomic approaches have been introduced to fight against resistant pathogens, which is an advanced approach for novel drug targets investigation. The current study emphasizes the utilization of the available genomic and proteomic data of Klebsiella pneumoniae PittNDM01 for the identification of novel drug targets for future drug developments. Comparative genomic analysis and molecular biological tools were applied, results in observing 582 non-human homologous-essential proteins of Klebsiella pneumoniae. Among the total 582 proteins, 66 were closely related to the pathogen-specific pathway. Out of all 66-targeted proteins, ten non-homologous essential proteins were found to have druggability potential. The subcellular localization of these proteins revealed; 6 proteins in the cytoplasm, 2 in the inner membrane, and one each in periplasmic space and outer membrane. All the above 10 proteins were compared to the proteins sequences of gut flora to eliminate the homologous proteins. In total, 6-novel non-human and non-gut flora essential drug targets of Klebsiella pneumoniae PittNDM01 strain were identified. Further, the 3D structures of the identified drug target proteins were developed, and protein-protein interaction network analysis was performed to know the functional annotation of the desire proteins. Therefore, these non-homologous essential targets ensure the survival of the pathogen and hence can be targeted for drug discovery.

Computational genomic discovery of diverse gene clusters harbouring Fe-S flavoenzymes in anaerobic gut microbiota

The gut contains an enormous diversity of simple as well as complex molecules from highly diverse food sources, together with host-secreted molecules. This presents a large metabolic opportunity for the gut microbiota, but little is known about how gut microbes are able to catabolize this large chemical diversity. Recently, Fe-S flavoenzymes were found to be key in the transformation of bile acids, catalysing the key step in the 7α-dehydroxylation pathway that allows gut bacteria to transform cholic acid into deoxycholic acid, an exclusively microbe-derived molecule with major implications for human health. While this enzyme family has also been implicated in a limited number of other catalytic transformations, little is known about the extent to which it is of more global importance in gut microbial metabolism. Here, we perform a large-scale computational genomic analysis to show that this enzyme superfamily has undergone a remarkable expansion in Clostridiales, and occurs throughout a diverse array of >1000 different families of putative metabolic gene clusters. Analysis of the enzyme content of these gene clusters suggests that they encode pathways with a wide range of predicted substrate classes, including saccharides, amino acids/peptides and lipids. Altogether, these results indicate a potentially important role of this protein superfamily in the human gut, and our dataset provides significant opportunities for the discovery of novel pathways that may have significant effects on human health.

Possible traces of resonance signaling in the genome

Although theories regarding the role of sequence-specific DNA resonance in biology have abounded for over 40 years, the published evidence for it is lacking. Here, the authors reasoned that for sustained resonance signaling, the number of oscillating DNA sequences per genome should be exceptionally high and that, therefore, genomic repeats of various sizes are good candidates for serving as resonators. Moreover, it was suggested that for the two DNA sequences to resonate, they do not necessarily have to be identical. Therefore, the existence of sequences differing in the primary sequence but having similar resonating sub-structures was proposed. It was hypothesized that such sequences, named HIDERs, would be enriched in the genomes of multicellular species. Specifically, it was hypothesized that delocalized electron clouds of purine-pyrimidine sequences could serve as the basis of HIDERs. The consequent computational genomic analysis confirmed the enrichment of purine-pyrimidine HIDERs in a few selected genomes of mammals, an insect, and a plant, compared to randomized sequence controls. Similarly, it was suggested that hypothetical delocalized proton clouds of the hydrogen bonds of multiple stacked bases could serve as sequence-dependent hydrogen-bond-based HIDERs. Similarly, the enrichment of such HIDERs was observed. It is suggested that these enrichments are the first evidence in support of sequence-specific resonance signaling in the genome.

The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook.

Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.

Comparative genomics of Mycoplasma: Insights on genome reduction and identification of potential antibacterial targets

Background: Mycoplasmas are cell wall-deficient bacteria which cause respiratory and urogenital infections in human. Mycoplasmas are resistant to many of the available antibiotics because of their high mutation rates and lack of cell wall. Various studies have identified the emergence of treatment-resistant Mycoplasma isolates. Novel drug target identification has become significant for the development of successful antibacterial treatments. Computational genomic analysis plays a significant role in facilitating the identification of potential drug targets in many bacterial pathogens. Methods: In the present study, 12 Mycoplasma genomes were subjected to comparative genomic analysis to reinforce the understanding of their genomic organization and to identify potential drug targets. The distributions of genes under the Clusters of Orthologous Groups of proteins (COG) functional categories were analyzed for all the 12 Mycoplasma genomes. Genes from each functional category that are conserved across all the Mycoplasma genomes were extracted to identify the backbone genome of Mycoplasma species. The genes in the backbone genome were subjected to similarity search against a database of essential genes to validate their essentiality. Essentiality of these genes was further analyzed based on their function and subcellular localization. Results: The 12 Mycoplasma genomes under study were found to exhibit marked similarities in COG functional category distributions. An overall loss of genes in various functional categories has been observed in all the 12 Mycoplasma genomes. In all the 12 Mycoplasma genomes under study, a maximum reduction in the genes involved in the secondary metabolites biosynthesis, transport, and catabolism (Q) is observed. Conclusion: Comparative genomic studies have identified a set of 170 genes which are commonly present all the 12 Mycoplasma genomes. Further analysis of these genes has identified a set of 158 core essential genes which serve as a promising cluster of novel antibacterial targets.

Development of an AmpliSeqTM Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting Pain

Background: Many gene variants modulate the individual perception of pain and possibly also its persistence. The limited selection of single functional variants is increasingly being replaced by analyses of the full coding and regulatory sequences of pain-relevant genes accessible by means of next generation sequencing (NGS).Methods: An NGS panel was created for a set of 77 human genes selected following different lines of evidence supporting their role in persisting pain. To address the role of these candidate genes, we established a sequencing assay based on a custom AmpliSeqTM panel to assess the exomic sequences in 72 subjects of Caucasian ethnicity. To identify the systems biology of the genes, the biological functions associated with these genes were assessed by means of a computational over-representation analysis.Results: Sequencing generated a median of 2.85 ⋅ 106 reads per run with a mean depth close to 200 reads, mean read length of 205 called bases and an average chip loading of 71%. A total of 3,185 genetic variants were called. A computational functional genomics analysis indicated that the proposed NGS gene panel covers biological processes identified previously as characterizing the functional genomics of persisting pain.Conclusion: Results of the NGS assay suggested that the produced nucleotide sequences are comparable to those earned with the classical Sanger sequencing technique. The assay is applicable for small to large-scale experimental setups to target the accessing of information about any nucleotide within the addressed genes in a study cohort.

Abstract 926: Multi-Center Mutation Calling in Multiple Cancers: The MC3 Project

Abstract The Multi-Center Mutation Calling in Multiple Cancers (MC3) data set provides consistent variant calling and filtering across the 10K patients in The Cancer Genome Atlas (TCGA). The MC3 was a collaborative science effort, driven by a consortium of researchers across multiple institutions, to form a TCGA capstone project focused on cross-tumor type automated analysis. This dataset covers 33 different cancer types using an ensemble of 7 advanced mutation-calling algorithms with scoring and artifact filtering, implemented for sharing in a reproducible, portable, standardized workflow. The resulting dataset represents several million core-hours of computational time on over 400 TB of short read data using the current state-of-the-art variant calling and filtering methods. In the past decade, the precipitous drop in sequencing cost from $10M to $1000 has allowed for larger cohorts of data to be analyzed. In 2016 there were an estimated 1.6M new cancer diagnoses in the United States. Scaling computational systems and genomic analysis to work at this scale requires the coordination of many institutions, many experiments and many computational techniques. Aside from problems of scale, there are several issues that prevent large analyses: 1) deployment of reproducible computing methods in new computing environments, 2) the ability to deploy methods without manual intervention, 3) the biases of single methods and the need for consensus, and 4) the large amount of noise and false positives that come from data including both germline sequencing, heterogeneous tumor sequencing, technical artifacts, and low variant allele fraction of observed reads. The MC3 project dealt with these issues by 1) modeling the pipeline using Common Workflow Language (CWL) format with the required software packages deployed using Docker software containerization technology, ensuring the ability to deploy analysis on new computer systems; 2) crafting the entire pipeline using best practice parameters and applying them consistently, across the entire cohort; we have also analyzed validation data to determine particular cancer type where these parameters do not fit; 3) deploying an ensemble of 7 variant calling methods including MuTect, MuSE, Radia, Somatic Sniper, Pindel, Indelocator and Varscan2 (both indel and SNP calling); and 4) applying a robust set of additional filters, as well as applying meta-calling based on results of multiple callers, to reduce false-positive rates. Over 20 million variants were produced and the data generated by this work have formed the basis of the somatic exome variant analysis presented in the other papers from the TCGA PanCanAtlas project. A set of over 3 million high-quality variants from a public release of this data has been made available so that researchers may easily use this open resource. Citation Format: Kyle Ellrott, Mathew Bailey, Gordon Saksena, Kyle Covington, Cyriac Kandoth, Chip Stewart, Michael McLellan, Heidi Sofia, Carolyn Hutter, Gad Getz, David Wheeler, Li Ding. Multi-Center Mutation Calling in Multiple Cancers: The MC3 Project [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 926.

The Role of Gut Microbiome in the Pathogenesis of Prostate Cancer: A Prospective, Pilot Study

To elucidate potential biomarkers or mechanistic principles involved with the gut microbiota and its impact on prostate cancer pathogenesis. We performed a prospective case-control pilot study evaluating the gut microbiome of 20 men with either benign prostatic conditions (n = 8) or intermediate or high risk clinically localized prostate cancer (Gleason ≥4 + 3 cN0M0) (n = 12) undergoing care at tertiary referral center from September 1, 2015 to March 1, 2016. Key exclusion criteria included recent antibiotic use, significant gastrointestinal disorders, hormonal or systemic therapy for prostate cancer. Computational genomics analysis was performed on collected stool samples using MetaPhlAn2 and HUMAnN2 platforms. Linear discriminant analysis effect size method was used to support high-dimensional class comparisons to find biologically relevant features. Kruskal-Wallis sum-rank test was used to detect features with significant differential abundance with respect to class, with biological consistency investigated using a set of pairwise tests among subclasses using the Wilcoxon rank-sum test, both to an α ≤0.05. Higher relative abundance of Bacteriodes massiliensis was seen in prostate cancer cases compared to controls. Faecalibacterium prausnitzii and Eubacterium rectalie had higher relative abundance among controls. Biologically significant differences were also found in relative gene, pathway, and enzyme abundance. Biologically significant differences exist in the gut microbial composition of men with prostate cancer compared to benign controls. These differences may play a role in the pathobiology of prostate cancer, and warrant further exploration.

Assessing computational genomics skills: Our experience in the H3ABioNet African bioinformatics network.

The H3ABioNet pan-African bioinformatics network, which is funded to support the Human Heredity and Health in Africa (H3Africa) program, has developed node-assessment exercises to gauge the ability of its participating research and service groups to analyze typical genome-wide datasets being generated by H3Africa research groups. We describe a framework for the assessment of computational genomics analysis skills, which includes standard operating procedures, training and test datasets, and a process for administering the exercise. We present the experiences of 3 research groups that have taken the exercise and the impact on their ability to manage complex projects. Finally, we discuss the reasons why many H3ABioNet nodes have declined so far to participate and potential strategies to encourage them to do so.

Computational genomic analysis of PARK7 interactome reveals high BBS1 gene expression as a prognostic factor favoring survival in malignant pleural mesothelioma.

The aim of our study was to assess the differential gene expression of Parkinson protein 7 (PARK7) interactome in malignant pleural mesothelioma (MPM) using data mining techniques to identify novel candidate genes that may play a role in the pathogenicity of MPM. We constructed the PARK7 interactome using the ConsensusPathDB database. We then interrogated the Oncomine Cancer Microarray database using the Gordon Mesothelioma Study, for differential gene expression of the PARK7 interactome. In ConsensusPathDB, 38 protein interactors of PARK7 were identified. In the Gordon Mesothelioma Study, 34 of them were assessed out of which SUMO1, UBC3, KIAA0101, HDAC2, DAXX, RBBP4, BBS1, NONO, RBBP7, HTRA2, and STUB1 were significantly overexpressed whereas TRAF6 and MTA2 were significantly underexpressed in MPM patients (network 2). Furthermore, Kaplan-Meier analysis revealed that MPM patients with high BBS1 expression had a median overall survival of 16.5 vs. 8.7 mo of those that had low expression. For validation purposes, we performed a meta-analysis in Oncomine database in five sarcoma datasets. Eight network 2 genes (KIAA0101, HDAC2, SUMO1, RBBP4, NONO, RBBP7, HTRA2, and MTA2) were significantly differentially expressed in an array of 18 different sarcoma types. Finally, Gene Ontology annotation enrichment analysis revealed significant roles of the PARK7 interactome in NuRD, CHD, and SWI/SNF protein complexes. In conclusion, we identified 13 novel genes differentially expressed in MPM, never reported before. Among them, BBS1 emerged as a novel predictor of overall survival in MPM. Finally, we identified that PARK7 interactome is involved in novel pathways pertinent in MPM disease.

Validation and Application of a Real-time PCR Protocol for the Specific Detection and Quantification of Clavibacter michiganensis subsp. sepedonicus in Potato.

Clavibacter michiganensis subsp. sepedonicus (Cms) multiplies very rapidly, passing through the vascular strands and into the stems and petioles of a diseased potato. Therefore, the rapid and specific detection of this pathogen is highly important for the effective control of the pathogen. Although several PCR assays have been developed for detection, they cannot afford specific detection of Cms. Therefore, in this study, a computational genome analysis was performed to compare the sequenced genomes of the C. michiganensis subspecies and to identify an appropriate gene for the development of a subspecies-specific PCR primer set (Cms89F/R). The specificity of the primer set based on the putative phage-related protein was evaluated using genomic DNA from seven isolates of Cms and 27 other reference strains. The Cms89F/R primer set was more specific and sensitive than the existing assays in detecting Cms in in vitro using Cms cells and its genomic DNA. This assay was also able to detect at least 1.47×102 copies/μl of cloned-amplified target DNA, 5 fg of DNA using genomic DNA or 10−6 dilution point of 0.12 at OD600 units of cells per reaction using a calibrated cell suspension.