miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression
1 Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan, Republic of China
2 Institute of Biotechnology, National Cheng Kung University, Tainan 701, Taiwan, Republic of China
3 Institute of Plant and Microbial Biology, Academia Sinica, Nankang, Taipei 11529, Taiwan, Republic of China
4 Department of Biological Science and Technology, National Chiao Tung University, Hsin-Chu 300, Taiwan, Republic of China
5 Institute of Tropical Plant Science, National Cheng Kung University, Tainan 701, Taiwan, Republic of China
BMC Bioinformatics 2009, 10:328 doi:10.1186/1471-2105-10-328Published: 12 October 2009
MicroRNAs (miRNAs), small non-coding RNAs of 19 to 25 nt, play important roles in gene regulation in both animals and plants. In the last few years, the oligonucleotide microarray is one high-throughput and robust method for detecting miRNA expression. However, the approach is restricted to detecting the expression of known miRNAs. Second-generation sequencing is an inexpensive and high-throughput sequencing method. This new method is a promising tool with high sensitivity and specificity and can be used to measure the abundance of small-RNA sequences in a sample. Hence, the expression profiling of miRNAs can involve use of sequencing rather than an oligonucleotide array. Additionally, this method can be adopted to discover novel miRNAs.
This work presents a systematic approach, miRExpress, for extracting miRNA expression profiles from sequencing reads obtained by second-generation sequencing technology. A stand-alone software package is implemented for generating miRNA expression profiles from high-throughput sequencing of RNA without the need for sequenced genomes. The software is also a database-supported, efficient and flexible tool for investigating miRNA regulation. Moreover, we demonstrate the utility of miRExpress in extracting miRNA expression profiles from two Illumina data sets constructed for the human and a plant species.
We develop miRExpress, which is a database-supported, efficient and flexible tool for detecting miRNA expression profile. The analysis of two Illumina data sets constructed from human and plant demonstrate the effectiveness of miRExpress to obtain miRNA expression profiles and show the usability in finding novel miRNAs.