ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.

Prokaryotic DNA binding proteins (DBPs) play pivotal roles in governing gene regulation, DNA replication, and various cellular functions. Accurate computational models for predicting prokaryotic DBPs hold immense promise in accelerating the discovery of novel proteins, fostering a deeper understanding of prokaryotic biology, and facilitating the development of therapeutics targeting for potential disease interventions. However, existing generic prediction models often exhibit lower accuracy in predicting prokaryotic DBPs. To address this gap, we introduce ProkDBP, a novel machine learning-driven computational model for prediction of prokaryotic DBPs. For prediction, a total of nine shallow learning algorithms and five deep learning models were utilized, with the shallow learning models demonstrating higher performance metrics compared to their deep learning counterparts. The light gradient boosting machine (LGBM), coupled with evolutionarily significant features selected via random forest variable importance measure (RF-VIM) yielded the highest five-fold cross-validation accuracy. The model achieved the highest auROC (0.9534) and auPRC (0.9575) among the 14 machine learning models evaluated. Additionally, ProkDBP demonstrated substantial performance with an independent dataset, exhibiting higher values of auROC (0.9332) and auPRC (0.9371). Notably, when benchmarked against several cutting-edge existing models, ProkDBP showcased superior predictive accuracy. Furthermore, to promote accessibility and usability, ProkDBP (https://iasri-sg.icar.gov.in/prokdbp/) is available as an online prediction tool, enabling free access to interested users. This tool stands as a significant contribution, enhancing the repertoire of resources for accurate and efficient prediction of prokaryotic DBPs.

RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes.

RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

Multivariate analysis and genetic dissection of staygreen and stem reserve mobilisation under combined drought and heat stress in wheat (Triticum aestivum L.).

Introduction: Abiotic stresses significantly reduce crop yield by adversely affecting many physio-biochemical processes. Several physiological traits have been targeted and improved for yield enhancement in limiting environmental conditions. Amongst them, staygreen and stem reserve mobilisation are two important mutually exclusive traits contributing to grain filling under drought and heat stress in wheat. Henceforth, the present study was carried out to identify the QTLs governing these traits and to identify the superiors' lines through multi-trait genotype-ideotype distance index (MGIDI) Methods: A mapping population consisting of 166 recombinant inbred lines (RILs) developed from a cross between HD3086 and HI1500 was utilized in this study. The experiment was laid down in alpha lattice design in four environmental conditions viz. Control, drought, heat and combined stress (heat and drought). Genotyping of parents and RILs was carried out with 35 K Axiom® array (Wheat breeder array). Results and Discussion: Medium to high heritability with a moderate to high correlation between traits was observed. Principal component analysis (PCA) was performed to derive latent variables in the original set of traits and the relationship of these traits with latent variables.From this study, 14 QTLs were identified, out of which 11, 2, and 1 for soil plant analysis development (SPAD) value, leaf senescence rate (LSR), and stem reserve mobilisation efficiency (SRE) respectively. Quantitative trait loci (QTLs) for SPAD value harbored various genes like Dirigent protein 6-like, Protein FATTY ACID EXPORT 3, glucan synthase-3 and Ubiquitin carboxyl-terminal hydrolase, whereas QTLs for LSR were found to contain various genes like aspartyl protease family protein, potassium transporter, inositol-tetrakisphosphate 1-kinase, and DNA polymerase epsilon subunit D-like. Furthermore, the chromosomal region for SRE was found to be associated with serine-threonine protein kinase. Serine-threonine protein kinases are involved in many signaling networks such as ABA mediated ROS signaling and acclimation to environmental stimuli. After the validation of QTLs in multilocation trials, these QTLs can be used for marker-assisted selection (MAS) in breeding programs.

CNN_FunBar: Advanced Learning Technique for Fungi ITS Region Classification.

Fungal species identification from metagenomic data is a highly challenging task. Internal Transcribed Spacer (ITS) region is a potential DNA marker for fungi taxonomy prediction. Computational approaches, especially deep learning algorithms, are highly efficient for better pattern recognition and classification of large datasets compared to in silico techniques such as BLAST and machine learning methods. Here in this study, we present CNN_FunBar, a convolutional neural network-based approach for the classification of fungi ITS sequences from UNITE+INSDC reference datasets. Effects of convolution kernel size, filter numbers, k-mer size, degree of diversity and category-wise frequency of ITS sequences on classification performances of CNN models have been assessed at all taxonomic levels (species, genus, family, order, class and phylum). It is observed that CNN models can produce >93% average accuracy for classifying ITS sequences from balanced datasets with 500 sequences per category and 6-mer frequency features at all levels. The comparative study has revealed that CNN_FunBar can outperform machine learning-based algorithms (SVM, KNN, Naïve-Bayes and Random Forest) as well as existing fungal taxonomy prediction software (funbarRF, Mothur, RDP Classifier and SINTAX). The present study will be helpful for fungal taxonomy classification using large metagenomic datasets.

PolyMorphPredict: A Universal Web-Tool for Rapid Polymorphic Microsatellite Marker Discovery From Whole Genome and Transcriptome Data.

Microsatellites are ubiquitously distributed, polymorphic repeat sequence valuable for association, selection, population structure and identification. They can be mined by genomic library, probe hybridization and sequencing of selected clones. Such approach has many limitations like biased hybridization and selection of larger repeats. In silico mining of polymorphic markers using data of various genotypes can be rapid and economical. Available tools lack in some or other aspects like: targeted user defined primer generation, polymorphism discovery using multiple sequence, size and number limits of input sequence, no option for primer generation and e-PCR evaluation, transferability, lack of complete automation and user-friendliness. They also lack the provision to evaluate published primers in e-PCR mode to generate additional allelic data using re-sequenced data of various genotypes for judicious utilization of previously generated data. We developed the tool (PolyMorphPredict) using Perl, R, Java and launched at Apache which is available at http://webtom.cabgrid.res.in/polypred/. It mines microsatellite loci and computes primers from genome/transcriptome data of any species. It can perform e-PCR using published primers for polymorphism discovery and across species transferability of microsatellite loci. Present tool has been evaluated using five species of different genome size having 21 genotypes. Though server is equipped with genomic data of three species for test run with gel simulation, but can be used for any species. Further, polymorphism predictability has been validated using in silico and in vitro PCR of four rice genotypes. This tool can accelerate the in silico microsatellite polymorphism discovery in re-sequencing projects of any species of plant and animal for their diversity estimation along with variety/breed identification, population structure, MAS, QTL and gene discovery, traceability, parentage testing, fungal diagnostics and genome finishing.

Effect of Pre-emergence Herbicides on Microbial Biomass and Biochemical Processes in a Typic Fluvaquent Soil Amended with Farm Yard Manure.

Application of thiobencarb, pendimethalin and pretilachlor at rates of 7.5, 10.0 and 2.5 kg a.i. ha(-1), respectively, under laboratory conditions, significantly increased microbial biomass C, N and P, resulting in greater availability of C, N and P in soil amended with farm yard manure. Application of thiobencarb highly induced microbial biomass C (46.3 %) and N (40.6 %), while pretilachlor and thiobencarb augmented microbial biomass P to the extent of 14.9 % and 14.1 %, respectively. Application of pendimethalin retained the highest amount of total N (19.9 %), soluble NO3 (-) (56 %) and available P (69.5 %) in soil. A similar trend was recorded with thiobencarb for oxidizable organic C (18.1 %) and with pretilachlor for exchangeable NH4 (+) (65.8 %). At the end of the experiment, the highest stimulation of bacteria was recorded with thiobencarb (29.6 %), while pretilachlor harboured the maximum number of actinomycetes (37.2 %) and fungi (40 %) in soil compared to the untreated control.

Non-symbiotic N2-fixation and phosphate-solubility in Gangetic alluvial soil as influenced by pre-emergence herbicide residues.

An experiment has been conducted under laboratory conditions to investigate the effect of two pre-emergence herbicides viz., thiobencarb (at 1.5 and 4.5 kg a.i. ha(-1)) and pretilachlor (at 0.5 and 1.5 kg a.i. ha(-1)), on the changes of growth and activities of aerobic non-symbiotic N2-fixing bacteria and phosphate-solubilizing microorganisms in relation to availability of mineral nitrogen and soluble phosphorus in the Gangetic alluvial soil (Typic Haplustept) of West Bengal, India. Application of herbicides, in general, significantly increased growth and activities of microorganisms, resulting in greater release of available nitrogen and soluble phosphorus in soil; and the stimulation was more pronounced when the herbicides were applied at their lower concentrations (recommended field application rates), more so with thiobencarb, as compared to pretilachlor. As compared to untreated control, application of thiobencarb at lower concentration increased the proliferation of aerobic non-symbiotic N2-fixing bacteria, phosphate-solubilizing microorganisms and non-symbiotic N2-fixing capacity of soil to the extent of 54.0, 44.6 and 31.7%, respectively; and accumulated the highest amount of available nitrogen (37.8%) and phosphorus (54.5%) in soil, while pretilachlor at field application rate highly induced (37.2%) phosphate-solubilizing capacity of soil. At higher concentration, pretilachlor was superior to thiobencarb in augmenting the growth and activities of phosphate-solubilizers. The results of the present study also indicated that gradual increase in concentration of the herbicides over their recommended field application rates was not much conducive for growth and activities of microorganisms, and subsequent release of nutrients in soil.

Effect of thiobencarb and pretilachlor on microorganisms in relation to mineralization of C and N in the Gangetic alluvial soil of West Bengal.

An experiment was conducted under laboratory conditions to investigate the effect of two pre-emergence herbicides, viz., thiobencarb (at 1.5 and 4.5 kg active ingredient (a.i.) ha(-1)) and pretilachlor (at 0.5 and 1.5 kg a.i. ha(-1)), on the growth and multiplication of some microorganisms (bacteria, actinomycetes and fungi) in relation to transformations and availability of C and N in the Gangetic alluvial soil (Typic Haplustept) of West Bengal, India. Application of both the herbicides, in general, significantly increased microbial biomass, resulting in greater retention, mineralization and availability of oxidizable organic C and N in soil, and the stimulations were more pronounced when the herbicides were applied at their lower concentrations (recommended field application rates), more so with thiobencarb, as compared to pretilachlor. Compared to untreated control soil, the application of thiobencarb at lower concentration increased the proliferation of total bacteria, actinomycetes and fungi by 57.3, 36.6 and 55.2%, respectively, and released the highest amount (40.2%) of soluble NO3(-) in soil, while pretilachlor at field application rate induced the growth and multiplication of bacteria and fungi by 58.3 and 17.6%, respectively. Irrespective of the concentrations, the stimulations were at par for both the herbicides towards the retention of oxidizable organic C, total N and exchangeable NH4(+) in soil.

A comparative study on the decomposition of edible and non-edible oil cakes in the Gangetic alluvial soil of West Bengal.

An experiment has been conducted under laboratory conditions to investigate the effect of decomposition of two edible oil cakes, viz. mustard cake (Brassica juncea L) and groundnut cake (Arachis hypogaea L), and two non-edible oil cakes, viz. mahua cake (Madhuca indica Gmel) and neem cake (Azadirachta indica Juss), at the rate of 5.0 t ha(-1) on the changes of microbial growth and activities in relation to transformations and availability of some plant nutrients in the Gangetic alluvial (Typic Haplustept) soil of West Bengal, India. Incorporation of oil cakes, in general, highly induced the proliferation of total bacteria, actinomycetes, and fungi, resulting in greater retention and availability of oxidizable C, N, and P in soil. As compared to untreated control, the highest stimulation of total bacteria and actinomycetes was recorded with mustard cake (111.9 and 84.3 %, respectively) followed by groundnut cake (50.5 and 52.4 %, respectively), while the fungal colonies were highly accentuated due to the incorporation of neem cake (102.8 %) in soil. The retention of oxidizable organic C was highly increased due to decomposition of non-edible oil cakes, more so under mahua cake (14.5 %), whereas edible oil cakes and groundnut cake in particular exerted maximum stimulation (16.7 %) towards the retention of total N in soil. A similar trend was recorded towards the accumulation of available mineral N in soil and this was more pronounced with mustard cake (45.6 %) for exchangeable NH4 (+) and with groundnut cake (63.9 %) for soluble NO3 (-). The highest retention of total P (46.9 %) was manifested by the soil when it was incorporated with neem cake followed by the edible oil cakes; while the available P was highly induced due to the addition of edible oil cakes, the highest being under groundnut cake (23.5 %) followed by mustard cake (19.6 %).