Title |
iMir: An integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq
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Published in |
BMC Bioinformatics, December 2013
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DOI | 10.1186/1471-2105-14-362 |
Pubmed ID | |
Authors |
Giorgio Giurato, Maria Rosaria De Filippo, Antonio Rinaldi, Adnan Hashim, Giovanni Nassa, Maria Ravo, Francesca Rizzo, Roberta Tarallo, Alessandro Weisz |
Abstract |
Qualitative and quantitative analysis of small non-coding RNAs by next generation sequencing (smallRNA-Seq) represents a novel technology increasingly used to investigate with high sensitivity and specificity RNA population comprising microRNAs and other regulatory small transcripts. Analysis of smallRNA-Seq data to gather biologically relevant information, i.e. detection and differential expression analysis of known and novel non-coding RNAs, target prediction, etc., requires implementation of multiple statistical and bioinformatics tools from different sources, each focusing on a specific step of the analysis pipeline. As a consequence, the analytical workflow is slowed down by the need for continuous interventions by the operator, a critical factor when large numbers of datasets need to be analyzed at once. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Montenegro | 1 | 25% |
United States | 1 | 25% |
Mexico | 1 | 25% |
France | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 3 | 75% |
Members of the public | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 1% |
Germany | 2 | <1% |
Italy | 2 | <1% |
Brazil | 2 | <1% |
United Kingdom | 2 | <1% |
New Zealand | 1 | <1% |
France | 1 | <1% |
Spain | 1 | <1% |
Belgium | 1 | <1% |
Other | 2 | <1% |
Unknown | 185 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 63 | 31% |
Student > Ph. D. Student | 47 | 23% |
Student > Master | 27 | 13% |
Student > Bachelor | 14 | 7% |
Student > Doctoral Student | 12 | 6% |
Other | 30 | 15% |
Unknown | 9 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 88 | 44% |
Biochemistry, Genetics and Molecular Biology | 56 | 28% |
Computer Science | 15 | 7% |
Engineering | 7 | 3% |
Medicine and Dentistry | 7 | 3% |
Other | 14 | 7% |
Unknown | 15 | 7% |