Xiaowo Wang & Xuegong Zhang on DNA Sequencing Technologies
Fast Breaking Papers Commentary, February 2011
|
Article: DEGseq: an R package for identifying differentially expressed genes from RNA-seq data
Authors: Wang, LK;Feng, ZX;Wang, X;Wang, XW;Zhang,
XG |
Xiaowo Wang & Xuegong Zhang talk with ScienceWatch.com and answer a few questions about this month's Fast Breaking Paper paper in the field of Computer Science.
Why do you think your paper is highly
cited?
High-throughput DNA sequencing technologies revolutionarily accelerate molecular biological findings, but there are still big gaps between data and biological knowledge. We proposed a novel method and implemented a software package trying to fill one of the gaps, i.e., identifying differentially expressed genes from RNA-sequencing data. It is one of the key questions biologists want to ask from their data and is fundamental to many genomic investigations. We were among the earliest to address this question.
Does it describe a new discovery, methodology, or
synthesis of knowledge?
We introduced a novel visualization-based method to identify differentially expressed genes from high-throughput sequencing data, and integrated other existing methods for such analyses in our software package, making it much easier for scientists without a computer science background to conduct such analyses.
Would you summarize the significance of your paper
in layman's terms?
In a multi-cellular organism, the genomic DNA, which stores the hereditable information, is almost identical in cells from different tissues. How can different kinds of cells have their special characteristics and play different functions? This is mainly due to the differential expression of genes. RNA sequencing technology provides a revolutionary approach for measuring gene expression at the RNA level.
Coauthor Xuegong Zhang
A basic question to be asked from RNA sequencing data is which genes are expressed differentially between two samples. The answer to the question is not trivial considering the fact that each measurement is the result of a random sampling from the truth, and the measurement is subject to noises. We were among the earliest to address this question, and developed a software package for detecting differentially expressed genes from the sequencing data.
How did you become involved in this research, and
how would you describe the particular challenges, setbacks, and
successes that you've encountered along the way?
Exploring biological mechanisms based on large amounts of high-throughput data is becoming a major protocol in current biological study. Statistics, computer science, mathematics, and engineering science are all involved in experiment design, model construction, data processing and knowledge interpretation, forming interdisciplinary research projects. Our team has focused on gene expression analysis for years. We were attracted by high-throughput sequencing technology at its emergence, and believe such technology will revolutionize the way to study biology.
We realized that the development of methods for processing and analyzing the sequencing data were behind the development of sequencing technology. Detecting differentially expressed genes from RNA-sequencing data is one of the key questions we identified in the analysis of sequencing data.
Where do you see your research leading in the
future?
Gene transcription is the first step towards its functionality. Our effort on the identification of differentially expressed genes is just a primary step for studying gene regulation. Further systematic analysis by integration of different types of biological information like epigenomics, as well as protein-protein and protein-DNA interactions, will lead to a better understanding of the underling mechanisms of development and complex diseases.
Do you foresee any social or political
implications for your research?
It is observed gene expression at the RNA level is severally altered in
many cancer cells. The characterization of gene expression profile is
crucial for understanding the oncogenesis and will benefit investigations
on cancer and development in the long term.
Dr. Xiaowo Wang
Assistant Professor of Bioinformatics
Bioinformatics Division, TNLIST
MOE Key Laboratory of Bioinformatics
Department of Automation
Tsinghua University
Beijing, China
Dr. Xuegong Zhang
Professor of Pattern Recognition and Bioinformatics
Bioinformatics Division, TNLIST
MOE Key Laboratory of Bioinformatics
Department of Automation
Tsinghua University
Beijing, China
KEYWORDS: MICROARRAYS; SINGLE.