Biology subjects

Mollah, M. N. H.

Publications and source records attributed to Mollah, M. N. H..

3 recordsLinked to original sources

bioRxiv2020

Bioinformatic analysis based genome-wide identification, characterization, diversification and regulatory transcription components of RNA silencing machinery genes in wheat (Triticum aestivum L.)

Dicer-Like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) gene families are known as RNA silencing machinery genes or RNAi genes. They have important activities at post-transcriptional and chromatin modification levels. They regulate gene expression relating to different stresses, growth, and development in eukaryotes. A complete cycle of gene silencing is occurred by the collaboration of these three families. However, these gene families are not yet rigorously studied in the economically important wheat genome. Our bioinformatic analysis based genome-wide identification, characterization, diversification and regulatory components of these gene families identified 7 TaDCL, 39 TaAGO and 16 TaRDR genes from wheat genome against RNAi genes of Arabidopsis thaliana. Phylogenetic analysis of wheat genome with Arabidopsis and rice RNAi genes showed that TaDCL, TaAGO and TaRDR proteins are clustered into four, eight and four subgroups respectively. Domain, motif and exon-intron structure analyses showed that the TaDCL, TaAGO and TaRDR proteins conserve identical characteristics within groups while retain diverse differences between groups. GO annotations implied that a number of biological and molecular pathways are linked to RNAi mechanism in wheat. Gene networking between transcription factors and RNAi proteins indicates that ERF is the leading family linked to maximum RNAi genes followed by MIKC-MADS, C2H2, BBR-BPC, MYB, and Dof. Cis-regulatory elements associated to RNAi genes are predicted to act as regulatory components against various environmental conditions. Expressed sequence tag analysis showed that larger numbers of RNAi genes are expressed in different tissues and organs predicted to play roles for healthy plants and grains. Expression analysis of 7 TaDCL genes using qRT-PCR showed that only TaDCL3a and TaDCL3b had root specific significant expression (p-value<0.05) with no expression in leaf validated EST results. Besides, TaDCL3b and TaDCL4 significantly prompted in drought condition indicating their potential role in drought stress tolerance. Overall results would however help researchers for in-depth biological investigation of these RNAi genes in wheat crop improvement.

genomics↗

bioRxiv2020

Robust Hierarchical Co-clustering to Explore Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds

Toxicogenomics combines high throughput molecular technologies with statistical and machine learning approaches to discover a similar group of doses of chemical compounds (DCCs) and genes to explore toxicogenomic biomarkers and their regulatory DCCs. This is also very important in the toxicity study of environmental stressors, synthetic chemicals and drug discovery and development process. Different clustering algorithms are concerned with the discovering of interesting clusters/groups of row or column entities of a dataset. Among those hierarchical clustering (HC) and logistic probabilistic hidden variable model (LPHVM) can identify toxicogenomic biomarkers and their regulatory DCCs forming co-cluster. However, the HC method is very sensitive to outlying observations. On the other hand, though LPHVM is a robust approach, it consumes more time for calculation since it is Expectation-Maximization (EM) based iterative approach. Additionally, the LPHVM creates artificiality problem taking absolute value of the data matrix. Therefore, to overcome these problems in this paper, we proposed a robust hierarchical co-clustering (RHCOC) algorithm to co-cluster genes and DCCs simultaneously with a view to explore toxicogenomic biomarkers and their regulatory DCCs. The performance of the proposed RHCOC algorithm over the conventional HC for clustering genes and DCCs of toxicogenomic data has been investigated based on the simulation study. The results of the simulation study have shown that the RHCOC approaches produce far lower clustering error rate (ER) than the conventional HC approaches in presence of outlying observations in the dataset. Otherwise they perform equally in absence of outlier in the dataset. To explore biomarker co-clusters consisting of toxicogenomic biomarker genes and their regulatory DCCs we used control chart for individual measurement (CCIM). We have also investigated the performance of the proposed approach in the case of the pathway level real life fold change gene expression (FCGE) toxicogenomic data analysis. The biomarker co-clusters consisting of toxicogenomic biomarker genes and their regulatory DCCs and biomarker genes explored by the proposed approaches have been validated by the literature and functional annotation. Our method is implemented in R package "rhcoclust" available on github (https://github.com/mdbahadur/rhcoclust).

bioinformatics↗

bioRxiv2020

In Silico Identification, Characterization and Diversity Analysis of RNAi Genes and their Associated Regulatory Elements in Sweet Orange (Citrus sinensis L)

RNA interference (RNAi) plays key roles in post-transcriptional and chromatin modification levels as well as regulates various eukaryotic gene expressions which 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. However, the genes of these three RNAi families are largely unknown yet in sweet orange (Citrus sinensis), though it is an economically important fruit plant all over the world. Therefore, a comprehensive investigation for genome-wide identification, characterization and diversity analysis of RNA silencing genes in C. sinensis was conducted and identified 4 CsDCL, 8 CsAGO and 4 CsRDR as RNAi genes. To characterize and validate the predicted genes of RNAi families, various bioinformatics analysis was conducted. Phylogenetic analysis clustered the predicted CsDCLs, CsAGOs and CsRDRs genes into four, six and four subgroups with the relevant genes of Arabidopsis respectively. The domain and motif composition analysis, the gene structure for all three-gene families exhibited almost homogeneity within the same group members while showed significant differences in between groups. The GO enrichment analysis results clearly indicated that the predicted genes have direct involvement into the RNAi process as expected in C. sinensis. Moreover, Cis-regulatory elements and regulatory transcription factor analysis of the reported RNAi genes demonstrated the diverse connection to the huge biological functions and regulatory pathways. The expressed sequence tag (EST) analysis showed that these genes are highly expressed in fruit and leaves which indicate that these reported genes have great involvement in C. sinensis food, flowering and fruit production. The expression analysis of the reported RNAi genes might be more useful to explore the most effective RNAi genes in C. sinensis for further biotechnological application.

genomics↗