Project description:The plant microbiota plays crucial roles in sustaining plant health and productivity. Advancing plant microbiome research and designing sustainable practices for agriculture requires in-depth assessments of microorganisms associated with different host plants; however, there is little information on functional aspects of many microorganisms of interest. Therefore, we enriched microorganisms from the phyllosphere of 110 rice genotypes and subjected them to shotgun metagenomic sequencing to reconstruct bacterial genomes from the obtained datasets. The approach yielded a total of 1.34 terabases of shotgun-sequenced metagenomic data. By separately recovering bacterial genomes from each of the 110 rice genotypes, we recovered 569 non-redundant metagenome-assembled genomes (MAGs) with a completeness higher than 50% and contaminations less than 10%. The MAGs were primarily assigned to Alphaproteobacteria, Gammaproteobacteria, and Bacteroidia. The presented data provides an extended basis for microbiome analyses of plant-associated microorganisms. It is complemented by detailed metadata to facilitate implementations in ecological studies, biotechnological mining approaches, and comparative assessments with genomes or MAGs from other studies.
Project description:In this study, we sequenced small RNA content from three different rice cultivars employing Illumina technology. More than 15 million reads were generated using Illumina high-throughput sequencing platform. After pre-processing, distinct small RNA sequences were identified for each rice cultivars.
Project description:In this study, we sequenced small RNA content from three different rice cultivars employing Illumina technology. More than 15 million reads were generated using Illumina high-throughput sequencing platform. After pre-processing, distinct small RNA sequences were identified for each rice cultivars. We collected seedlings of different rice cultivars and total RNA isolated was subjected to Illumina sequencing. The sequenced data was further filtered using NGS QC Toolkit to obtain high-quality reads. The filtered reads were pre-processed using modified perl script provided in the miRTools software. After quality control, the identical reads were collapsed into a unique read and read count for each sequence was recorded. All the filtered unique reads from each sample were mapped on the rice genome to find their location.
Project description:Using flow cytometry and metagenomics to improve the recovery of metegenome-assembled genomes in an enrichment culture from activated sludge of a wastewater treatment plant
Project description:In this study, we aim to present a global view of transcriptome dynamics in different rice cultivars (IR64, Nagina 22 and Pokkali) under control and stress conditions. More than 50 million high quality reads were obtained for each tissue sample using Illumina platform. Reference-based assembly was performed for each rice cultivar. The transcriptome dynamics was studied by differential gene expression analyses between stress treatment and control sample.
Project description:In this study, we aim to present a global view of transcriptome dynamics in different rice cultivars (IR64, Nagina 22 and Pokkali) under control and stress conditions. More than 50 million high quality reads were obtained for each tissue sample using Illumina platform. Reference-based assembly was performed for each rice cultivar. The transcriptome dynamics was studied by differential gene expression analyses between stress treatment and control sample. We collected seedlings of three rice cultivars subjected to control (kept in water), desiccation (transferred on folds of tissue paper) and salinity (transferred to beaker containing 200 mM NaCl solution) treatments. Total RNA isolated from these tissue samples was subjected to Illumina sequencing. The sequence data was further filtered using NGS QC Toolkit to obtain high-quality reads. The filtered reads were mapped to Japonica reference genome using Tophat software. Cufflinks was used for reference-based assembly and differential gene expression was studied using cuffdiff software. The differentially expressed genes during various abiotic stress conditions were identified.