Transcriptome

The transcriptome is the complete set of transcripts in a cell, and their quantity, for a specific developmental stage or physiological condition. Understanding the transcriptome is essential for interpreting the functional elements of the genome and revealing the molecular constituents of cells and tissues, and also for understanding development and so on.

Our group is interested in the transcriptome analysis using the RNA-Seq of high throughput sequencing technology to catalogue all species of transcript, including mRNAs,  non-coding RNAs, to determine the transcriptional structure of genes, in terms of their start sites, 5′and 3′ ends, splicing patterns and otherpost-transcriptional modifications, and to quantify the changing expression levels of each transcript during development and under different conditions.

Projects:

1. 9311 transcriptome analysis across different developmental stages

We start to get a comprehensive transcriptome analysis about it, not only the coding genes, but also all the non-coding RNA including small RNAs. This data will be very valuable in improving the annotation and our understanding of genes.

2. allelic expression analysis based on LYP9 and its inbreeding parents

We will begin another project about the allelic expression in a hybrid rice whose genome is sequenced. It is a good opportunity to identify all the cis-acting elements in the rice genome.

3. De novo assembly of locust   transcriptome

we perform the  de novo assembly of locus transcriptome using the read from deep sequencing to obtain the transcripts of locus.

Expression-Tag profiling

Genes differentially expressed in different tissues, during development, or during specific pathologies are of foremost interest to both basic and pharmaceutical research. We are interested in investigating various regulations in different organisms using tag-based expression profiling technology.

Tag-based mRNA profiling is a revolutionary approach to expression analysis that generates expression profiles for any transcript from any organism. Driven by high throughput sequencing technology, tag-based mRNA profiling creates whole genome wide expression profiles by sequencing over 1 million cDNA tags per sample rather than employing hybridization techniques associated with gene expression microarrays.

 Finished Projects:

We have finished more than one hundred projects and have accumulated abundant experiences on digital gene expression data analysis.

Small RNA analysis

We study small RNAs which play critical roles in the regulation of many biological processes in most eukaryotes. MicroRNAs, endogenous siRNAs and Piwi-interacting RNAs are three major classes of small RNAs. We mainly focus on microRNAs, which interact with target mRNAs by base paring to induce mRNA cleavage or translation inhibition. By deep sequencing and computational analysis, we detect and profile both known and novel microRNAs at unprecedented sensitivity. Most microRNAs are highly conserved across closely related species, and some even show conservation between invertebrates and vertebrates. The origin of new microRNA genes and their subsequent evolution is rarely unknown. We also try to explore the origin of these newly emerged microRNA genes in the human lineage.

Whole genome scale methylation profiling

DNA methylation is an important modification of DNA, which plays a vital role in regulating gene expression. Coupled with the next generation sequencing technology, DNA methylation analysis in whole genome scale rapidly becomes a powerful technique in the research of gene regulation and disease progression in recent years.

The team focuses on the bioinformatics analysis of DNA methylation profiling in whole genome scale. DNA is sequenced by Illumina Genome Analyser following the bisulfite treatment in which all the unmethylated C bases are converted to T bases. The bioinformatics analysis will gather detailed information of whether each C base is methylated and thus provide DNA methylation profiles of different gene regions in whole genome scale.