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Abak Masud, B.

Publications and source records attributed to Abak Masud, B..

2 recordsLinked to original sources

CompCorona: A Web Portal for Comparative Analysis of the Host Transcriptome of PBMC and Lung SARS-CoV-2, SARS-CoV, and MERS-CoV

MotivationUnderstanding the host response to SARS-CoV-2 infection is crucial for deciding on the correct treatment of this epidemic disease. Although several recent studies reported the comparative transcriptome analyses of the three coronaviridae (CoV) members; namely SARS-CoV, MERS-CoV, and SARS-CoV-2, there is yet to exist a web-tool to compare increasing number of host transcriptome response datasets against the pre-processed CoV member datasets. Therefore, we developed a web application called CompCorona, which allows users to compare their own transcriptome data of infected host cells with our pre-built datasets of the three epidemic CoVs, as well as perform functional enrichment and principal component analyses (PCA). ResultsComparative analyses of the transcriptome profiles of the three CoVs revealed that numerous differentially regulated genes directly or indirectly related to several diseases (e.g., hypertension, male fertility, ALS, and epithelial dysfunction) are altered in response to CoV infections. Transcriptome similarities and differences between the host PBMC and lung tissue infected by SARS-CoV-2 are presented. Most of our findings are congruent with the clinical cases recorded in the literature. Hence, we anticipate that our results will significantly contribute to ongoing studies investigating the pre-and/or post-implications of SARS-CoV-2 infection. In addition, we implemented a user-friendly public website, CompCorona for biomedical researchers to compare users own CoV-infected host transcriptome data against the built-in CoV datasets and visualize their results via interactive PCA, UpSet and Pathway plots. AvailabilityCompCorona is freely available on the web at http://compcorona.mu.edu.tr Contacttugbasuzek@mu.edu.tr

bioinformatics↗

TCGAnalyzeR: a web application for integrative visualization of molecular and clinical data of cancer patients for cohort discovery

MotivationThe vast size and complexity of The Cancer Genome Atlas (TCGA) database with multidimensional molecular and clinical data of ~11,000 cancer patients of 33 cancer types challenge the effective utilization of this valuable resource. Therefore, we built a web application named TCGAnalyzeR with the main idea of presenting an integrative visualization of mutations, transcriptome profile, copy number variation and clinical data allowing researchers to facilitate the identification of customized patient cohorts and gene sets for better decision-making for oncologists and cancer researchers. ResultsWe present TCGAnalyzeR for integrative visualization of pre-analyzed TCGA data with the several novel modules: (i) Simple nucleotide variations with driver prediction; (ii) Recurrent copy number alterations; (iii) Differential expression in tumor versus normal, with pathway enrichment and the survival analysis; (iii) TCGA clinical data and survival analysis; (iv) External subcohorts from literature, curatedTCGAData and BiocOncoTK R packages; (v) Internal patient clusters determined using iClusterPlus R package or signature-based expression analysis. TCGAnalyzeR provides clinical oncologists and cancer researchers interactive and integrative representations of these multi-omic, pan-cancer TCGA data with availability of subcohort analysis and visualization. TCGAnalyzeR can be used to create their own custom gene sets for pan-cancer comparisons, to create custom patient subcohorts comparing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, etc) along with our internal patient clusters, to visualize cohort-centric or gene-centric results along with pathway enrichment and survival analysis graphically on an interactive web tool. AvailabilityTCGAnalyzeR is freely available on the web at http://tcganalyzer.mu.edu.tr. Contacttugbasuzek@mu.edu.tr Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗