MyMedR

Displaying all 2 publications

Abstract:

Sort:

Fulltext CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates

Low JZB, Khang TF, Tammi MT

BMC Bioinformatics, 2017 12 28;18(Suppl 16):575.
PMID: 29297307 DOI: 10.1186/s12859-017-1974-4

BACKGROUND: In current statistical methods for calling differentially expressed genes in RNA-Seq experiments, the assumption is that an adjusted observed gene count represents an unknown true gene count. This adjustment usually consists of a normalization step to account for heterogeneous sample library sizes, and then the resulting normalized gene counts are used as input for parametric or non-parametric differential gene expression tests. A distribution of true gene counts, each with a different probability, can result in the same observed gene count. Importantly, sequencing coverage information is currently not explicitly incorporated into any of the statistical models used for RNA-Seq analysis.
RESULTS: We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data.
CONCLUSIONS: Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .
Fulltext Transcripts and MicroRNAs Responding to Salt Stress in Musa acuminata Colla (AAA Group) cv. Berangan Roots

Lee WS, Gudimella R, Wong GR, Tammi MT, Khalid N, Harikrishna JA

PLoS One, 2015;10(5):e0127526.
PMID: 25993649 DOI: 10.1371/journal.pone.0127526

Physiological responses to stress are controlled by expression of a large number of genes, many of which are regulated by microRNAs. Since most banana cultivars are salt-sensitive, improved understanding of genetic regulation of salt induced stress responses in banana can support future crop management and improvement in the face of increasing soil salinity related to irrigation and climate change. In this study we focused on determining miRNA and their targets that respond to NaCl exposure and used transcriptome sequencing of RNA and small RNA from control and NaCl-treated banana roots to assemble a cultivar-specific reference transcriptome and identify orthologous and Musa-specific miRNA responding to salinity. We observed that, banana roots responded to salinity stress with changes in expression for a large number of genes (9.5% of 31,390 expressed unigenes) and reduction in levels of many miRNA, including several novel miRNA and banana-specific miRNA-target pairs. Banana roots expressed a unique set of orthologous and Musa-specific miRNAs of which 59 respond to salt stress in a dose-dependent manner. Gene expression patterns of miRNA compared with those of their predicted mRNA targets indicated that a majority of the differentially expressed miRNAs were down-regulated in response to increased salinity, allowing increased expression of targets involved in diverse biological processes including stress signaling, stress defence, transport, cellular homeostasis, metabolism and other stress-related functions. This study may contribute to the understanding of gene regulation and abiotic stress response of roots and the high-throughput sequencing data sets generated may serve as important resources related to salt tolerance traits for functional genomic studies and genetic improvement in banana.

Filters

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links