Title |
PIPE-chipSAD: A Pipeline for the Analysis of High Density Arrays of Bacterial Transcriptomes
|
---|---|
Published in |
Frontiers in Molecular Biosciences, December 2016
|
DOI | 10.3389/fmolb.2016.00082 |
Pubmed ID | |
Authors |
Silvia Bottini, Elena Del Tordello, Luca Fagnocchi, Claudio Donati, Alessandro Muzzi |
Abstract |
PIPE-chipSAD is a pipeline for bacterial transcriptome studies based on high-density microarray experiments. The main algorithm chipSAD, integrates the analysis of the hybridization signal with the genomic position of probes and identifies portions of the genome transcribing for mRNAs. The pipeline includes a procedure, align-chipSAD, to build a multiple alignment of transcripts originating in the same locus in multiple experiments and provides a method to compare mRNA expression across different conditions. Finally, the pipeline includes anno-chipSAD a method to annotate the detected transcripts in comparison to the genome annotation. Overall, our pipeline allows transcriptional profile analysis of both coding and non-coding portions of the chromosome in a single framework. Importantly, due to its versatile characteristics, it will be of wide applicability to analyse, not only microarray signals, but also data from other high throughput technologies such as RNA-sequencing. The current PIPE-chipSAD implementation is written in Python programming language and is freely available at https://github.com/silviamicroarray/chipSAD. |
X Demographics
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 50% |
Scientists | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 5 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 2 | 40% |
Student > Bachelor | 2 | 40% |
Student > Ph. D. Student | 1 | 20% |
Professor > Associate Professor | 1 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 2 | 40% |
Agricultural and Biological Sciences | 1 | 20% |
Biochemistry, Genetics and Molecular Biology | 1 | 20% |
Immunology and Microbiology | 1 | 20% |
Computer Science | 1 | 20% |
Other | 0 | 0% |