Bottini Silvia, Del Tordello Elena, Fagnocchi Luca, Donati Claudio, Muzzi Alessandro
GSK Vaccines Srl Siena, Italy.
Computational Biology Unit, Research and Innovation Centre, Fondazione Edmund Mach San Michele all'Adige, Italy.
Front Mol Biosci. 2016 Dec 20;3:82. doi: 10.3389/fmolb.2016.00082. eCollection 2016.
is a pipeline for bacterial transcriptome studies based on high-density microarray experiments. The main algorithm , 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, , 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 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 implementation is written in Python programming language and is freely available at https://github.com/silviamicroarray/chipSAD.
是一个基于高密度微阵列实验的细菌转录组研究流程。主要算法将杂交信号分析与探针的基因组位置相结合，识别基因组中转录为mRNA的部分。该流程包括一个程序，用于在多个实验中构建源自同一基因座的转录本的多重比对，并提供一种比较不同条件下mRNA表达的方法。最后，该流程包括一种与基因组注释相比对检测到的转录本进行注释的方法。总体而言，我们的流程允许在单个框架中对染色体的编码和非编码部分进行转录谱分析。重要的是，由于其通用特性，它不仅可广泛应用于分析微阵列信号，还可用于分析来自其他高通量技术（如RNA测序）的数据。当前实现是用Python编程语言编写的，可在https://github.com/silviamicroarray/chipSAD上免费获取。
