The rapidly evolving high-throughput sequencing (HTS) technologies generate voluminous genomic and metagenomic sequences, which can help classify the microbial communities with high accuracy in many ecosystems. Conventionally, the rule-based binning techniques are used to classify the contigs or scaffolds based on either sequence composition or sequence similarity. However, the accurate classification of the microbial communities remains a major challenge due to massive data volumes at hand as well as a requirement of efficient binning methods and classification algorithms. Therefore, we attempted here to implement iterative K-Means clustering for the initial binning of metagenomics sequences and applied various machine learning algorithms (MLAs) to classify the newly identified unknown microbes. The cluster annotation was achieved through the BLAST program of NCBI, which resulted in the grouping of assembled scaffolds into five classes, i.e., bacteria, archaea, eukaryota, viruses and others. The annotated cluster sequences were used to train machine learning algorithms (MLAs) to develop prediction models to classify unknown metagenomic sequences. In this study, we used metagenomic datasets of samples collected from the Ganga (Kanpur and Farakka) and the Yamuna (Delhi) rivers in India for clustering and training the MLA models. Further, the performance of MLAs was evaluated by 10-fold cross validation. The results revealed that the developed model based on the Random Forest had a superior performance compared to the other considered learning algorithms. The proposed method can be used for annotating the metagenomic scaffolds/contigs being complementary to existing methods of metagenomic data analysis. An offline predictor source code with the best prediction model is available at (https://github.com/Nalinikanta7/metagenomics).

Low light intensity is a major abiotic stress that severely affects rice yields, particularly in India and Southeast Asia, causing yield reductions of 35–40% during the wet season compared to the dry season. Tolerant rice genotypes exhibit adaptive changes at anatomical, physiological, biochemical, and molecular levels under low-light stress, enabling higher yields compared to susceptible varieties. Our study identified 20 novel QTLs associated with grain yields and nine related traits under low-light and control (normal)-light conditions, using a recombinant inbred line (RIL) population derived from the cross between the low-light-tolerant variety Swarnaprabha and the low-light-susceptible variety IR8. Across the Kharif seasons of 2019 and 2021, 33 stable QTLs were identified, with 11, 13, and 9 QTLs specific to low-light, normal-light, and both conditions, respectively. Of these, Swarnaprabha contributed 28 QTLs, while five were contributed by IR8. Notably, the study identified 11 and 9 novel QTLs under low-light and both conditions, respectively. Three hotspot regions on chromosomes 1, 4, and 8 were identified. These regions harbored 10 novel QTLs and revealed twenty candidate genes, out of which three key hub genes, OsAUX1, OsSBDCP1, and OsNPF5.16, were identified. These hub genes are involved in hormone signaling, starch metabolism, and nitrogen metabolism, respectively. A comprehensive expression analysis of these genes indicated that they are linked to low-light tolerance, offering deeper insights into the genetic and molecular mechanisms underlying low-light resilience. These findings provide valuable genomic resources and potential markers for breeding programs for improving rice productivity under low-light conditions.