Computational mapping of the differentially expressed gene-lncRNA pairs present at the root nodule developmental stages of Arachis hypogaea
Rizvi, A. Z.; Dhusia, K.
Show abstract
RNA-sequencing (RNA-seq) data analysis of the different stages of root nodules formation in peanut Arachis hypogaea investigate the genetic features. Genes related to the root nodules formations in this plant are extensively studied [1] [2] [3] [4] [5], but less information is present for their relations with long noncoding RNAs (lncRNAs). Bioinformatics techniques are utilised here to identify the novel lncRNAs present in the publically available RNA-seq data reported [6] for the different stages of root nodules formation in this plant. Highly correlated, significant, and Differentially Expressed (DE) gene-lncRNA pairs are also detected to understand the epigenetic control of lncRNA. These pairs are further differentiated between cis and trans antisense lncRNAs and lincRNAs based on their functions and positions from the genes. Obtained results are the catalogue for the highly correlated and significant DE gene-lncRNA pairs related to root nodules formation in A. hypogaea.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- In Silico Identification, Characterization and Diversity Analysis of RNAi Genes and their Associated Regulatory Elements in Sweet Orange (Citrus sinensis L) 98%
- Identification, characterization of Apyrase (APY) gene family in rice (Oryza sativa) and analysis of the expression pattern under various stress conditions 97%
- AlnC: An extensive database of long non-coding RNAs in Angiosperms 97%
Similar papers in this journal
Similar papers in this journal
- Comparative Analysis, Diversification and Functional Validation of Plant Nucleotide-Binding Site Domain Genes 95%
- Application of Pedimap -- a pedigree visualization tool -- to facilitate the decisioning of rice breeding in Sri Lanka 95%
- Mitochondrial Genome of Garcinia mangostana L. variety Mesta 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.