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  1. Article: A network-based systems biology approach for identification of shared Gene signatures between male and female in COVID-19 datasets.

    Shahjaman, Md / Rezanur Rahman, Md / Rabiul Auwul, Md

    Informatics in medicine unlocked

    2021  Volume 25, Page(s) 100702

    Abstract: The novel coronavirus (SARS-CoV-2) has expanded rapidly worldwide. Now it has covered more than 150 countries worldwide. It is referred to as COVID-19. SARS-CoV-2 mainly affects the respiratory systems of humans that can lead up to serious illness or ... ...

    Abstract The novel coronavirus (SARS-CoV-2) has expanded rapidly worldwide. Now it has covered more than 150 countries worldwide. It is referred to as COVID-19. SARS-CoV-2 mainly affects the respiratory systems of humans that can lead up to serious illness or even death in the presence of different comorbidities. However, most COVID-19 infected people show mild to moderate symptoms, and no medication is suggested. Still, drugs of other diseases have been used to treat COVID-19. Nevertheless, the absence of vaccines and proper drugs against the COVID-19 virus has increased the mortality rate. Albeit sex is a risk factor for COVID-19, none of the studies considered this risk factor for identifying biomarkers from the RNASeq count dataset. Men are more likely to undertake severe symptoms with different comorbidities and show greater mortality compared with women. From this standpoint, we aim to identify shared gene signatures between males and females from the human COVID-19 RNAseq count dataset of peripheral blood cells using a robust voom approach. We identified 1341 overlapping DEGs between male and female datasets. The gene ontology (GO) annotation and pathway enrichment analysis revealed that DEGs are involved in various BP categories such as nucleosome assembly, DNA conformation change, DNA packaging, and different KEGG pathways such as cell cycle, ECM-receptor interaction, progesterone-mediated oocyte maturation, etc. Ten hub-proteins (UBC, KIAA0101, APP, CDK1, SUMO2, SP1, FN1, CDK2, E2F1, and TP53) were unveiled using PPI network analysis. The top three miRNAs (mir-17-5p, mir-20a-5p, mir-93-5p) and TFs (PPARG, E2F1 and KLF5) were uncovered. In conclusion, the top ten significant drugs (roscovitine, curcumin, simvastatin, fulvestrant, troglitazone, alvocidib, L-alanine, tamoxifen, serine, and doxorubicin) were retrieved using drug repurposing analysis of overlapping DEGs, which might be therapeutic agents of COVID-19.
    Language English
    Publishing date 2021-08-18
    Publishing country England
    Document type Journal Article
    ISSN 2352-9148
    ISSN 2352-9148
    DOI 10.1016/j.imu.2021.100702
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Robust identification of differentially expressed genes from RNA-seq data.

    Shahjaman, Md / Manir Hossain Mollah, Md / Rezanur Rahman, Md / Islam, S M Shahinul / Nurul Haque Mollah, Md

    Genomics

    2019  Volume 112, Issue 2, Page(s) 2000–2010

    Abstract: Background: Identification of differentially expressed genes (DEGs) under two or more experimental conditions is an important task for elucidating the molecular basis of phenotypic variation. In the recent years, next generation sequencing (RNA-seq) has ...

    Abstract Background: Identification of differentially expressed genes (DEGs) under two or more experimental conditions is an important task for elucidating the molecular basis of phenotypic variation. In the recent years, next generation sequencing (RNA-seq) has become very attractive and competitive alternative to the microarrays because of reducing the cost of sequencing and limitations of microarrays. A number of methods have been developed for detecting the DEGs from RNA-seq data. Most of these methods are based on either Poisson distribution or negative binomial (NB) distribution. However, identification of DEGs based on read count data using skewed distribution is inflexible and complicated of in presence of outliers or extreme values.
    Results: Most of the existing DEGs selection methods produce lower accuracies and higher false discoveries in presence of outliers. There are some robust approaches such as edgeR_robust and DEseq2 perform well in presence of outliers for large sample case. But they show weak performance for small-sample case, in presence of outliers. To address this issues an alternative approach has emerged by transforming the RNA-seq data into microarray like data. Among various transformation methods voom using limma pipeline is proven better for RNA-seq data. However, limma by voom transformation is sensitive to outliers for small-sample case. Therefore, in this paper, we robustify the voom approach using the minimum β-divergence method. We demonstrate the performance of the proposed method in a comparison of seven popular biomarkers selection methods: DEseq, DEseq2, SAMseq, Bayseq, limma (voom), edgeR and edgeR_robust using both simulated and real dataset. Both types of experimental results show that the performance of the proposed method improve over the competing methods, in presence of outliers and in absence of outliers it keeps almost equal performance with these methods.
    Conclusion: We observe the improved performance of the proposed method from simulation and real RNA-seq count data analysis for both small-and large-sample cases, in presence of outliers. Therefore, our proposal is to use the proposed method instead of existing methods to obtain the better performance for selecting the DEGs.
    MeSH term(s) Algorithms ; Animals ; Gene Expression Profiling/methods ; Gene Expression Profiling/standards ; Humans ; Mice ; MicroRNAs/genetics ; RNA-Seq/methods ; RNA-Seq/standards ; Transcriptome
    Chemical Substances MicroRNAs
    Language English
    Publishing date 2019-11-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 356334-0
    ISSN 1089-8646 ; 0888-7543
    ISSN (online) 1089-8646
    ISSN 0888-7543
    DOI 10.1016/j.ygeno.2019.11.012
    Database MEDical Literature Analysis and Retrieval System OnLINE

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