Project description:In this study, we aim to present a global view of transcriptome dynamics during flower development in chickpea. We generated around 234 million high-quality reads for eight flower development stages (ranging from 16 to 40 million reads for each stage) and 91 million high-quality reads from three vegetative tissues using Illumina high-throughput sequencing GAII platform. Because of non-availability of reference genome sequence, we mapped the reads to chickpea transcriptome comprised of 34,760 transcripts for estimation of their transcriptional activity in different tissue samples. The transcriptome dynamics was studied by comparison of gene expression during flower development stages with vegetative tissues. We collected different tissue samples used in this study 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 mapped to 34760 chickpea transcripts and reads per kilobase per million (RPKM) was calculated for each gene in all the sample to measure their gene expression. Differential expression analysis was performed using DESeq software. The genes preferentially expression during various stages of flower development as compared to vegetative stages and those with speciifc expression were identified.
Project description:In this study, we aim to present a global view of transcriptome dynamics in different tissues/organs/developmental stage in chickpea. We generated about ~31-95 million reads from each of 94 libraries representing 32 different tissues/organs using Illumina platform. We generated a hybrid assembly of these data along with PacBio data to produce full-length transcriptome assembly. We mapped the reads to the transcriptome assembly for estimation of the abundance of coding and long non-coding transcripts in different tissue samples. The transcriptome dynamics was studied by differential and tissue-specific expression analyses, and co-expression network and transcriptional regulatory network analyses.
Project description:In this study, we sequenced small RNA content from seven major tissues/organs employing Illumina technology. More than 154 million reads were generated using Illumina high-throughput sequencing GAII platform, which represented more than 20 million distinct small RNA sequences. After pre-processing, several conserved and novel miRNAs were identified in chickpea. Further, the putative targets of chickpea miRNAs were identified and their functional categorization was analyzed. In addition, we identified miRNAs exhibitng differential and specific expression in various tissues/organs. We collected different tissue samples used in this study 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 screened stepwise against annotated non-coding RNA sequences, including plant snoRNA, tRNA and rRNA. The remaining reads were screened against repeat sequences from RepBase and chickpea chloroplast sequence. Conserved miRNAs were identified based on similarity with miRBase database and novel miRNAs were identified using miRDeep-P pipeline. For differential expression analysis, the read count for each miRNA was normalized using DESeq software. The genes preferentially and specifically expressed in various tissues/organs were identified.
Project description:In this study, we aim to present a global view of transcriptome dynamics during flower development in chickpea. We generated around 234 million high-quality reads for eight flower development stages (ranging from 16 to 40 million reads for each stage) and 91 million high-quality reads from three vegetative tissues using Illumina high-throughput sequencing GAII platform. Because of non-availability of reference genome sequence, we mapped the reads to chickpea transcriptome comprised of 34,760 transcripts for estimation of their transcriptional activity in different tissue samples. The transcriptome dynamics was studied by comparison of gene expression during flower development stages with vegetative tissues.
Project description:In this study, we sequenced small RNA content from seven major tissues/organs employing Illumina technology. More than 154 million reads were generated using Illumina high-throughput sequencing GAII platform, which represented more than 20 million distinct small RNA sequences. After pre-processing, several conserved and novel miRNAs were identified in chickpea. Further, the putative targets of chickpea miRNAs were identified and their functional categorization was analyzed. In addition, we identified miRNAs exhibitng differential and specific expression in various tissues/organs.
Project description:In this study, we aim to present a global view of transcriptome dynamics during seed development in a large-seeded chickpea (genotype JGK3). We generated about 1.5 billion high-quality reads from 24 libraries (leaf and seven seed developmental stages in three biological replicates) using Illumina high-throughput sequencing platform. We mapped the reads to the kabuli chickpea genome for estimation of their transcript abundance in different tissue samples. The transcriptome dynamics was studied by differential gene expression analyses between different samples/stages.
Project description:In this study, we aim to present a global view of transcriptome dynamics during seed development in a small-seeded chickpea (genotype Himchana 1). We generated about 1.5 billion high-quality reads from 24 libraries (leaf and seven seed developmental stages in three biological replicates) using Illumina high-throughput sequencing platform. We mapped the reads to the kabuli chickpea genome for estimation of their transcript abundance in different tissue samples. The transcriptome dynamics was studied by differential gene expression analyses between different samples/stages.
Project description:In this study, we aim to present a global view of transcriptome dynamics during various abiotic stresses in chickpea. We generated about 252 million high-quality reads from eight libraries (control, desiccation, salinity and cold stress samples for roots and shoots) using Illumina high-throughput sequencing GAII platform. We mapped the reads to the desi chickpea genome for estimation of their transcript abundance in different tissue samples. The transcriptome dynamics was studied by differential gene expression analyses between stress treatment and control sample. We collected different tissue samples (root and shoot tissues of 10-day-old seedlings subjected to control (kept in water), desiccation (transferred on folds of tissue paper), salinity (transferred to beaker containing 150 mM NaCl solution) and cold (kept in water at 4 M-BM-1 1M-BM-0C) stress for 5 h. Total RNA isolated from these tissue samples was subjected to Illumina sequencing. The sequenced data was further filtered using NGS QC Toolkit to obtain high-quality reads. The filtered reads were mapped to annotated chickpea genome using TopHat and fragments per exon kilobase per million (FPKM) was calculated using Cufflinks software for each gene in all the sample to measure their gene expression. Differential expression analysis was performed using Cuffdiff software. The differentially expressed genes during various abiotic stress conditions were identified.
Project description:In this study, we aim to present a global view of transcriptome dynamics during various abiotic stresses in chickpea. We generated about 252 million high-quality reads from eight libraries (control, desiccation, salinity and cold stress samples for roots and shoots) using Illumina high-throughput sequencing GAII platform. We mapped the reads to the desi chickpea genome for estimation of their transcript abundance in different tissue samples. The transcriptome dynamics was studied by differential gene expression analyses between stress treatment and control sample.