An integrated transcriptome mapping the regulatory network of coding and long non-coding RNAs provides a genomics resource in chickpea

Abstract

Large-scale transcriptome analysis can provide a systems-level understanding of biological processes. To accelerate functional genomic studies in chickpea, we perform a comprehensive transcriptome analysis to generate full-length transcriptome and expression atlas of protein-coding genes (PCGs) and long non-coding RNAs (lncRNAs) from 32 different tissues/organs via deep sequencing. The high-depth RNA-seq dataset reveal expression dynamics and tissue-specificity along with associated biological functions of PCGs and lncRNAs during development. The coexpression network analysis reveal modules associated with a particular tissue or a set of related tissues. The components of transcriptional regulatory networks (TRNs), including transcription factors, their cognate cis-regulatory motifs, and target PCGs/lncRNAs that determine developmental programs of different tissues/organs, are identified. Several candidate tissue-specific and abiotic stress-responsive transcripts associated with quantitative trait loci that determine important agronomic traits are also identified. These results provide an important resource to advance functional/translational genomic and genetic studies during chickpea development and environmental conditions.

Phenotypic and Genetic Characterization of a Near-Isogenic Line Pair: Insights into Flowering Time in Chickpea.

“…thaliana. The TPM values, calculated from the chickpea expression atlas [52], categorized LOC101515142 (CaMED16b), LOC101489432 (CaELF3a), LOC101499101 (BBX24-like) and LOC101507442 (VRN1-like) into three different clusters (Fig. 7 and Additional le 11).…”

“…The CDS of the candidate genes were utilized to identify their corresponding matches through reciprocal BLASTn in the chickpea expression atlas during development, available in the NCBI GEO database under the accession GSE147831 [52]. The expression atlas data were then imported, classi ed and analyzed using a custom R script to convert the FPKM data into TPM, facilitating comparison between different tissues and genes (GitHub/AGR114molecularBreeding/chickpea/GEO).…”

“…One notable achievement is the comprehensive mapping of variation acquired through the sequencing of 3,171 cultivated and 195 wild accessions alongside phenotypic data [51], now publicly accessible via the CicerSeq repository. Additional resources like Atlas GEO chickpea complement these datasets, providing a robust foundation for comprehensive investigations into gene function and transcriptional pattern expression in various tissues through chickpea development [52]. This extensive dataset serves as a vital resource for genomic and diversity research, facilitating a deeper molecular-level understanding of traits essential for enhancing chickpea cultivation.…”

Unravelling Differential DNA Methylation Patterns in Genotype Dependent Manner under Salinity Stress Response in Chickpea

“…Recent studies have shown evidence in favor of epigenetic regulation influencing the stress response in plants through the regulation of gene expression [33]. Due to the advancement in sequencing technologies, various genomic resources of chickpeas, such as reference genome, transcriptome, non-coding RNAs, and genetic variations, are available [4,[34][35][36][37]. However, the dynamics and relationship between DNA methylation and gene expression for stress adaptation have been rarely studied in this crop plant.…”

Finding Consensus on the Reference Genomes : A Chickpea Case Study

“…More than 25 K out of 28 K non-redundant predicted gene models could be functionally annotated. Since then, the NCBI assembly GCF_000331145.1 has become the de facto reference genome for the kabuli genotype (Jain et al 2022)…”