Browsing by Author "Iquebal, Mir Asif"

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    DeepAProt: Deep learning based abiotic stress protein sequence classification and identification tool in cereals
    (Frontiers in Plant Science, 2023) Ahmed, Bulbul; Haque, Md Ashraful; Iquebal, Mir Asif; Jaiswal, Sarika; Angadi, U.B; Kumar, Dinesh; Rai, Anil
    The impact of climate change has been alarming for the crop growth. The extreme weather conditions can stress the crops and reduce the yield of major crops belonging to Poaceae family too, that sustains 50% of the world’s food calorie and 20% of protein intake. Computational approaches, such as artificial intelligence-based techniques have become the forefront of prediction-based data interpretation and plant stress responses. In this study, we proposed a novel activation function, namely, Gaussian Error Linear Unit with Sigmoid (SIELU) which was implemented in the development of a Deep Learning (DL) model along with other hyper parameters for classification of unknown abiotic stress protein sequences from crops of Poaceae family. To develop this models, data pertaining to four different abiotic stress (namely, cold, drought, heat and salinity) responsive proteins of the crops belonging to poaceae family were retrieved from public domain. It was observed that efficiency of the DL models with our proposed novel SIELU activation function outperformed the models as compared to GeLU activation function, SVM and RF with 95.11%, 80.78%, 94.97%, and 81.69% accuracy for cold, drought, heat and salinity, respectively. Also, a web-based tool, named DeepAProt (http://login1.cabgrid. res.in:5500/) was developed using flask API, along with its mobile app. This server/App will provide researchers a convenient tool, which is rapid and economical in identification of proteins for abiotic stress management in crops Poaceae family, in endeavour of higher production for food security and combating hunger, ensuring UN SDG goal 2.0.
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    Genome-wide identification of long non-coding RNAs in pearl millet (Pennisetum glaucum (L.)) genotype subjected to drought stress
    (Agronomy, 2022) Kumar, Baibhav; Kumar, Animesh; Jaiswal, Sarika; Iquebal, Mir Asif; Angadi, Ulavappa B.; Tomar, Rukam s.; Rai, Anil; Kumar, Dinesh
    Pearl millet (Pennisetum glaucum L.) is affected by drought stress, affecting crop productivity and survival. Long non-coding RNAs (lncRNAs) are reported to play a vital role in the response to drought stress. LncRNAs represent a major part of non-protein coding RNAs and are present prevalently. These are involved in various biological processes, which may functionally act as RNA rather than getting transcribed as protein. We targeted genome-wide identification of lncRNAs in pearl millet from root and leaf tissues subjected to drought stress. A total of 879 lncRNAs were identified, out of which 209 (leaf control, root control), 198 (leaf treated, root treated), 115 (leaf control, leaf treated) and 194 (root control, root treated) were differentially expressed. Two lncRNAs were found as potential target mimics of three miRNAs from the miRBase database. Gene ontology study revealed that drought-responsive lncRNAs are involved in biological processes like ‘metabolic process’ and ‘cellular process’, molecular functions like ‘binding’ and ‘catalytic activities’ and cellular components like ‘cell’, ‘cell part’ and ‘membrane part’. LncRNA-miRNA-mRNA network shows that it plays a vital role in the stress-responsive mechanism through their activities in hormone signal transduction, response to stress, response to auxin and transcription factor activity. Only four lncRNAs were found to get a match with the lncRNAs present in the plant lncRNA database CANTATAdb, which shows its poorly conserved nature among species. This information has been cataloged in the pearl millet drought-responsive long non-coding RNA database (PMDlncRDB). The discovered lncRNAs can be used in the improvement of important traits, as well as CISPR-Cas technology, in the editing of ncRNAs in plants for trait improvement. Such a study will increase our understanding of the expression behavior of lncRNAs, as well as its underlying mechanisms under drought stress in pearl millet.
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    Potential use of microbial community genomes in various dimensions of agriculture productivity and its management: A review
    (Frontiers in Microbiology, 2022) Iquebal, Mir Asif; Jagannadham, Jaisri; Jaiswal, Sarika; Prabha, Ratna; Rai, Anil; Kumar, Dinesh
    Agricultural productivity is highly influenced by its associated microbial community. With advancements in omics technology, metagenomics is known to play a vital role in microbial world studies by unlocking the uncultured microbial populations present in the environment. Metagenomics is a diagnostic tool to target unique signature loci of plant and animal pathogens as well as beneficial microorganisms from samples. Here, we reviewed various aspects of metagenomics from experimental methods to techniques used for sequencing, as well as diversified computational resources, including databases and software tools. Exhaustive focus and study are conducted on the application of metagenomics in agriculture, deciphering various areas, including pathogen and plant disease identification, disease resistance breeding, plant pest control, weed management, abiotic stress management, post-harvest management, discoveries in agriculture, source of novel molecules/compounds, biosurfactants and natural product, identification of biosynthetic molecules, use in genetically modified crops, and antibiotic-resistant genes. Metagenomics-wide association studies study in agriculture on crop productivity rates, intercropping analysis, and agronomic field is analyzed. This article is the first of its comprehensive study and prospects from an agriculture perspective, focusing on a wider range of applications of metagenomics and its association studies.