Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals.

Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.

Negative Impacts of Arsenic on Plants and Mitigation Strategies.

Arsenic (As) is a metalloid prevalent mainly in soil and water. The presence of As above permissible levels becomes toxic and detrimental to living organisms, therefore, making it a significant global concern. Humans can absorb As through drinking polluted water and consuming As-contaminated food material grown in soil having As problems. Since human beings are mobile organisms, they can use clean uncontaminated water and food found through various channels or switch from an As-contaminated area to a clean area; but plants are sessile and obtain As along with essential minerals and water through roots that make them more susceptible to arsenic poisoning and consequent stress. Arsenic and phosphorus have many similarities in terms of their physical and chemical characteristics, and they commonly compete to cause physiological anomalies in biological systems that contribute to further stress. Initial indicators of arsenic's propensity to induce toxicity in plants are a decrease in yield and a loss in plant biomass. This is accompanied by considerable physiological alterations; including instant oxidative surge; followed by essential biomolecule oxidation. These variables ultimately result in cell permeability and an electrolyte imbalance. In addition, arsenic disturbs the nucleic acids, the transcription process, and the essential enzymes engaged with the plant system's primary metabolic pathways. To lessen As absorption by plants, a variety of mitigation strategies have been proposed which include agronomic practices, plant breeding, genetic manipulation, computer-aided modeling, biochemical techniques, and the altering of human approaches regarding consumption and pollution, and in these ways, increased awareness may be generated. These mitigation strategies will further help in ensuring good health, food security, and environmental sustainability. This article summarises the nature of the impact of arsenic on plants, the physio-biochemical mechanisms evolved to cope with As stress, and the mitigation measures that can be employed to eliminate the negative effects of As.

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features.

DNA-binding proteins play a very important role in the structural composition of the DNA. In addition, they regulate and effect various cellular processes like transcription, DNA replication, DNA recombination, repair and modification. The experimental methods used to identify DNA-binding proteins are expensive and time consuming and thus attracted researchers from computational field to address the problem. In this paper, we present iDNAProt-ES, a DNA-binding protein prediction method that utilizes both sequence based evolutionary and structure based features of proteins to identify their DNA-binding functionality. We used recursive feature elimination to extract an optimal set of features and train them using Support Vector Machine (SVM) with linear kernel to select the final model. Our proposed method significantly outperforms the existing state-of-the-art predictors on standard benchmark dataset. The accuracy of the predictor is 90.18% using jack knife test and 88.87% using 10-fold cross validation on the benchmark dataset. The accuracy of the predictor on the independent dataset is 80.64% which is also significantly better than the state-of-the-art methods. iDNAProt-ES is a novel prediction method that uses evolutionary and structural based features. We believe the superior performance of iDNAProt-ES will motivate the researchers to use this method to identify DNA-binding proteins. iDNAProt-ES is publicly available as a web server at: http://brl.uiu.ac.bd/iDNAProt-ES/ .