Title |
Overview of available methods for diverse RNA-Seq data analyses
|
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Published in |
Science China Life Sciences, January 2012
|
DOI | 10.1007/s11427-011-4255-x |
Pubmed ID | |
Authors |
Geng Chen, Charles Wang, TieLiu Shi |
Abstract |
RNA-Seq technology is becoming widely used in various transcriptomics studies; however, analyzing and interpreting the RNA-Seq data face serious challenges. With the development of high-throughput sequencing technologies, the sequencing cost is dropping dramatically with the sequencing output increasing sharply. However, the sequencing reads are still short in length and contain various sequencing errors. Moreover, the intricate transcriptome is always more complicated than we expect. These challenges proffer the urgent need of efficient bioinformatics algorithms to effectively handle the large amount of transcriptome sequencing data and carry out diverse related studies. This review summarizes a number of frequently-used applications of transcriptome sequencing and their related analyzing strategies, including short read mapping, exon-exon splice junction detection, gene or isoform expression quantification, differential expression analysis and transcriptome reconstruction. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 2% |
United Kingdom | 4 | <1% |
Canada | 4 | <1% |
Brazil | 4 | <1% |
Mexico | 3 | <1% |
Germany | 3 | <1% |
Argentina | 2 | <1% |
China | 2 | <1% |
Chile | 2 | <1% |
Other | 16 | 4% |
Unknown | 369 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 115 | 28% |
Researcher | 95 | 23% |
Student > Master | 54 | 13% |
Student > Bachelor | 30 | 7% |
Student > Doctoral Student | 21 | 5% |
Other | 64 | 15% |
Unknown | 37 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 255 | 61% |
Biochemistry, Genetics and Molecular Biology | 59 | 14% |
Computer Science | 20 | 5% |
Medicine and Dentistry | 14 | 3% |
Mathematics | 6 | 1% |
Other | 17 | 4% |
Unknown | 45 | 11% |