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  1. Article ; Online: Publisher Correction: Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects.

    Akond, Zobaer / Ahsan, Md Asif / Alam, Munirul / Mollah, Md Nurul Haque

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 22306

    Language English
    Publishing date 2021-11-10
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-021-00399-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects.

    Akond, Zobaer / Ahsan, Md Asif / Alam, Munirul / Mollah, Md Nurul Haque

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 13060

    Abstract: Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is ...

    Abstract Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is popular for addressing the challenges of hidden population stratification and polygenic effects. However, most of these methods including LMM are sensitive to phenotypic outliers that may lead the misleading results. To overcome this problem, in this paper, we proposed a way to robustify the LMM approach for reducing the influence of outlying observations using the β-divergence method. The performance of the proposed method was investigated using both synthetic and real data analysis. Simulation results showed that the proposed method performs better than both linear regression model (LRM) and LMM approaches in terms of powers and false discovery rates in presence of phenotypic outliers. On the other hand, the proposed method performed almost similar to LMM approach but much better than LRM approach in absence of outliers. In the case of real data analysis, our proposed method identified 11 SNPs that are significantly associated with the rice flowering time. Among the identified candidate SNPs, some were involved in seed development and flowering time pathways, and some were connected with flower and other developmental processes. These identified candidate SNPs could assist rice breeding programs effectively. Thus, our findings highlighted the importance of robust GWAS in identifying candidate genes.
    MeSH term(s) Computer Simulation ; Flowers/genetics ; Flowers/physiology ; Gene Expression Profiling ; Gene Expression Regulation, Plant ; Gene Ontology ; Genes, Plant ; Genome-Wide Association Study ; Genotype ; Humans ; Linear Models ; Multifactorial Inheritance/genetics ; Oryza/genetics ; Oryza/physiology ; Phenotype ; Polymorphism, Single Nucleotide/genetics
    Language English
    Publishing date 2021-06-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-021-90774-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Comprehensive

    Akond, Zobaer / Rahman, Hafizur / Ahsan, Md Asif / Mosharaf, Md Parvez / Alam, Munirul / Mollah, Md Nurul Haque

    BioMed research international

    2022  Volume 2022, Page(s) 4955209

    Abstract: Dicer-like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) are known as the three major gene families that act as the critical components of RNA interference or silencing mechanisms through the noncoding small RNA molecules (miRNA and ... ...

    Abstract Dicer-like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) are known as the three major gene families that act as the critical components of RNA interference or silencing mechanisms through the noncoding small RNA molecules (miRNA and siRNA) to regulate the expressions of protein-coding genes in eukaryotic organisms. However, most of their characteristics including structures, chromosomal location, subcellular locations, regulatory elements, and gene networking were not rigorously studied. Our analysis identified 7
    MeSH term(s) Case-Control Studies ; Gene Expression Regulation, Plant/genetics ; Genes, Plant/genetics ; Hormones ; MicroRNAs ; Phylogeny ; Plant Proteins/genetics ; Plant Proteins/metabolism ; RNA Interference ; RNA, Small Interfering ; RNA-Dependent RNA Polymerase/genetics ; Stress, Physiological ; Triticum/genetics ; Triticum/metabolism
    Chemical Substances Hormones ; MicroRNAs ; Plant Proteins ; RNA, Small Interfering ; RNA-Dependent RNA Polymerase (EC 2.7.7.48)
    Language English
    Publishing date 2022-09-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2698540-8
    ISSN 2314-6141 ; 2314-6133
    ISSN (online) 2314-6141
    ISSN 2314-6133
    DOI 10.1155/2022/4955209
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections.

    Sarker, Bandhan / Rahaman, Md Matiur / Islam, Md Ariful / Alamin, Muhammad Habibulla / Husain, Md Maidul / Ferdousi, Farzana / Ahsan, Md Asif / Mollah, Md Nurul Haque

    PloS one

    2023  Volume 18, Issue 3, Page(s) e0281981

    Abstract: The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of ...

    Abstract The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.
    MeSH term(s) Humans ; COVID-19/diagnosis ; COVID-19/genetics ; Transcriptome ; SARS-CoV-2/genetics ; SARS-CoV-2/metabolism ; Molecular Docking Simulation ; Aurora Kinase A/genetics ; Proscillaridin ; MicroRNAs/genetics ; Gene Regulatory Networks ; Biomarkers ; Genomics ; COVID-19 Testing
    Chemical Substances Aurora Kinase A (EC 2.7.11.1) ; Proscillaridin (KC6BL281EN) ; MicroRNAs ; Biomarkers
    Language English
    Publishing date 2023-03-13
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0281981
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification.

    Rahaman, Md Matiur / Ahsan, Md Asif / Chen, Ming

    Scientific reports

    2019  Volume 9, Issue 1, Page(s) 19526

    Abstract: Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of ... ...

    Abstract Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or a new analytical approach is needed to deal with these phenomics data. We proposed a statistical framework for the analysis of phenomics data by integrating DM and ML methods. The most popular supervised ML methods; Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine with linear (SVM-l) and radial basis (SVM-r) kernel are used for classification/prediction plant status (stress/non-stress) to validate our proposed approach. Several simulated and real plant phenotype datasets were analyzed. The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy.
    MeSH term(s) Algorithms ; Data Mining ; Discriminant Analysis ; Machine Learning ; Support Vector Machine
    Language English
    Publishing date 2019-12-20
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-019-55609-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: OverCOVID: an integrative web portal for SARS-CoV-2 bioinformatics resources.

    Ahsan, Md Asif / Liu, Yongjing / Feng, Cong / Hofestädt, Ralf / Chen, Ming

    Journal of integrative bioinformatics

    2021  Volume 18, Issue 1, Page(s) 9–17

    Abstract: Outbreaks of COVID-19 caused by the novel coronavirus SARS-CoV-2 is still a threat to global human health. In order to understand the biology of SARS-CoV-2 and developing drug against COVID-19, a vast amount of genomic, proteomic, interatomic, and ... ...

    Abstract Outbreaks of COVID-19 caused by the novel coronavirus SARS-CoV-2 is still a threat to global human health. In order to understand the biology of SARS-CoV-2 and developing drug against COVID-19, a vast amount of genomic, proteomic, interatomic, and clinical data is being generated, and the bioinformatics researchers produced databases, webservers and tools to gather those publicly available data and provide an opportunity of analyzing such data. However, these bioinformatics resources are scattered and researchers need to find them from different resources discretely. To facilitate researchers in finding the resources in one frame, we have developed an integrated web portal called OverCOVID (http://bis.zju.edu.cn/overcovid/). The publicly available webservers, databases and tools associated with SARS-CoV-2 have been incorporated in the resource page. In addition, a network view of the resources is provided to display the scope of the research. Other information like SARS-CoV-2 strains is visualized and various layers of interaction resources is listed in distinct pages of the web portal. As an integrative web portal, the OverCOVID will help the scientist to search the resources and accelerate the clinical research of SARS-CoV-2.
    MeSH term(s) COVID-19 ; Computational Biology/methods ; Databases, Factual ; Humans ; Internet ; Proteomics ; SARS-CoV-2
    Language English
    Publishing date 2021-03-19
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2147212-9
    ISSN 1613-4516 ; 1432-4385
    ISSN (online) 1613-4516
    ISSN 1432-4385
    DOI 10.1515/jib-2020-0046
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: A Generalized Approach for Measuring Relationships Among Genes.

    Wang, Lijun / Ahsan, Md Asif / Chen, Ming

    Journal of integrative bioinformatics

    2017  Volume 14, Issue 3

    Abstract: Several methods for identifying relationships among pairs of genes have been developed. In this article, we present a generalized approach for measuring relationships between any pairs of genes, which is based on statistical prediction. We derive two ... ...

    Abstract Several methods for identifying relationships among pairs of genes have been developed. In this article, we present a generalized approach for measuring relationships between any pairs of genes, which is based on statistical prediction. We derive two particular versions of the generalized approach, least squares estimation (LSE) and nearest neighbors prediction (NNP). According to mathematical proof, LSE is equivalent to the methods based on correlation; and NNP is approximate to one popular method called the maximal information coefficient (MIC) according to the performances in simulations and real dataset. Moreover, the approach based on statistical prediction can be extended from two-genes relationships to multi-genes relationships. This application would help to identify relationships among multi-genes.
    MeSH term(s) Datasets as Topic ; Genes ; Least-Squares Analysis ; Models, Genetic ; Models, Statistical
    Language English
    Publishing date 2017-07-21
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2147212-9
    ISSN 1613-4516 ; 1432-4385
    ISSN (online) 1613-4516
    ISSN 1432-4385
    DOI 10.1515/jib-2017-0026
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2.

    Ahsan, Md Asif / Liu, Yongjing / Feng, Cong / Zhou, Yincong / Ma, Guangyuan / Bai, Youhuang / Chen, Ming

    Briefings in bioinformatics

    2021  Volume 22, Issue 2, Page(s) 714–725

    Abstract: The coronavirus disease 2019 (COVID-19) pandemic, caused by the coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created an unprecedented threat to public health. The pandemic has been sweeping the globe, impacting more than ... ...

    Abstract The coronavirus disease 2019 (COVID-19) pandemic, caused by the coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created an unprecedented threat to public health. The pandemic has been sweeping the globe, impacting more than 200 countries, with more outbreaks still lurking on the horizon. At the time of the writing, no approved drugs or vaccines are available to treat COVID-19 patients, prompting an urgent need to decipher mechanisms underlying the pathogenesis and develop curative treatments. To fight COVID-19, researchers around the world have provided specific tools and molecular information for SARS-CoV-2. These pieces of information can be integrated to aid computational investigations and facilitate clinical research. This paper reviews current knowledge, the current status of drug development and various resources for key steps toward effective treatment of COVID-19, including the phylogenetic characteristics, genomic conservation and interaction data. The final goal of this paper is to provide information that may be utilized in bioinformatics approaches and aid target prioritization and drug repurposing. Several SARS-CoV-2-related tools/databases were reviewed, and a web-portal named OverCOVID (http://bis.zju.edu.cn/overcovid/) is constructed to provide a detailed interpretation of SARS-CoV-2 basics and share a collection of resources that may contribute to therapeutic advances. These information could improve researchers' understanding of SARS-CoV-2 and help to accelerate the development of new antiviral treatments.
    MeSH term(s) Antiviral Agents/therapeutic use ; Biomedical Research ; COVID-19/virology ; Computational Biology ; Drug Repositioning ; Humans ; SARS-CoV-2/isolation & purification ; SARS-CoV-2/physiology ; COVID-19 Drug Treatment
    Chemical Substances Antiviral Agents
    Language English
    Publishing date 2021-04-26
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2068142-2
    ISSN 1477-4054 ; 1467-5463
    ISSN (online) 1477-4054
    ISSN 1467-5463
    DOI 10.1093/bib/bbaa416
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Digital Biomass Accumulation Using High-Throughput Plant Phenotype Data Analysis.

    Rahaman, Md Matiur / Ahsan, Md Asif / Gillani, Zeeshan / Chen, Ming

    Journal of integrative bioinformatics

    2017  Volume 14, Issue 3

    Abstract: Biomass is an important phenotypic trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive, and they require numerous individuals to be cultivated for repeated measurements. With the advent of image- ... ...

    Abstract Biomass is an important phenotypic trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive, and they require numerous individuals to be cultivated for repeated measurements. With the advent of image-based high-throughput plant phenotyping facilities, non-destructive biomass measuring methods have attempted to overcome this problem. Thus, the estimation of plant biomass of individual plants from their digital images is becoming more important. In this paper, we propose an approach to biomass estimation based on image derived phenotypic traits. Several image-based biomass studies state that the estimation of plant biomass is only a linear function of the projected plant area in images. However, we modeled the plant volume as a function of plant area, plant compactness, and plant age to generalize the linear biomass model. The obtained results confirm the proposed model and can explain most of the observed variance during image-derived biomass estimation. Moreover, a small difference was observed between actual and estimated digital biomass, which indicates that our proposed approach can be used to estimate digital biomass accurately.
    MeSH term(s) Biomass ; Droughts ; Image Processing, Computer-Assisted ; Phenotype ; Plants/metabolism ; Stress, Physiological
    Language English
    Publishing date 2017-09-01
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2147212-9
    ISSN 1613-4516 ; 1613-4516
    ISSN (online) 1613-4516
    ISSN 1613-4516
    DOI 10.1515/jib-2017-0028
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: In silico identification and characterization of AGO, DCL and RDR gene families and their associated regulatory elements in sweet orange (Citrus sinensis L.).

    Mosharaf, Md Parvez / Rahman, Hafizur / Ahsan, Md Asif / Akond, Zobaer / Ahmed, Fee Faysal / Islam, Md Mazharul / Moni, Mohammad Ali / Mollah, Md Nurul Haque

    PloS one

    2020  Volume 15, Issue 12, Page(s) e0228233

    Abstract: RNA interference (RNAi) plays key roles in post-transcriptional and chromatin modification levels as well as regulates various eukaryotic gene expressions which are involved in stress responses, development and maintenance of genome integrity during ... ...

    Abstract RNA interference (RNAi) plays key roles in post-transcriptional and chromatin modification levels as well as regulates various eukaryotic gene expressions which are involved in stress responses, development and maintenance of genome integrity during developmental stages. The whole mechanism of RNAi pathway is directly involved with the gene-silencing process by the interaction of Dicer-Like (DCL), Argonaute (AGO) and RNA-dependent RNA polymerase (RDR) gene families and their regulatory elements. However, these RNAi gene families and their sub-cellular locations, functional pathways and regulatory components were not extensively investigated in the case of economically and nutritionally important fruit plant sweet orange (Citrus sinensis L.). Therefore, in silico characterization, gene diversity and regulatory factor analysis of RNA silencing genes in C. sinensis were conducted by using the integrated bioinformatics approaches. Genome-wide comparison analysis based on phylogenetic tree approach detected 4 CsDCL, 8 CsAGO and 4 CsRDR as RNAi candidate genes in C. sinensis corresponding to the RNAi genes of model plant Arabidopsis thaliana. The domain and motif composition and gene structure analyses for all three gene families exhibited almost homogeneity within the same group members. The Gene Ontology enrichment analysis clearly indicated that the predicted genes have direct involvement into the gene-silencing and other important pathways. The key regulatory transcription factors (TFs) MYB, Dof, ERF, NAC, MIKC_MADS, WRKY and bZIP were identified by their interaction network analysis with the predicted genes. The cis-acting regulatory elements associated with the predicted genes were detected as responsive to light, stress and hormone functions. Furthermore, the expressed sequence tag (EST) analysis showed that these RNAi candidate genes were highly expressed in fruit and leaves indicating their organ specific functions. Our genome-wide comparison and integrated bioinformatics analyses provided some necessary information about sweet orange RNA silencing components that would pave a ground for further investigation of functional mechanism of the predicted genes and their regulatory factors.
    MeSH term(s) Argonaute Proteins/genetics ; Citrus sinensis/genetics ; Computer Simulation ; Expressed Sequence Tags ; Fruit/metabolism ; Gene Expression Profiling/methods ; Gene Expression Regulation, Plant/genetics ; Genes, Plant/genetics ; Genome, Plant/genetics ; Multigene Family/genetics ; Phylogeny ; Plant Proteins/genetics ; RNA Interference/physiology ; RNA-Dependent RNA Polymerase/genetics ; Regulatory Sequences, Nucleic Acid/genetics ; Ribonuclease III/genetics ; Transcription Factors/metabolism
    Chemical Substances Argonaute Proteins ; Plant Proteins ; Transcription Factors ; RNA-Dependent RNA Polymerase (EC 2.7.7.48) ; Ribonuclease III (EC 3.1.26.3)
    Language English
    Publishing date 2020-12-21
    Publishing country United States
    Document type Journal Article
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0228233
    Database MEDical Literature Analysis and Retrieval System OnLINE

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