It causes diverse infections including those common in chronic lung infections of people with cystic fibrosis (CF) where it is difficult to eradicate ( 6). aeruginosa is found in soil ( 1) and freshwater samples ( 2, 3), and it is cultured for biotechnology applications ( 4, 5). The opportunistic pathogen Pseudomonas aeruginosa is found in a wide array of environments. The re-analysis of these data in aggregate is a powerful approach for hypothesis generation and testing, and this approach can be applied to transcriptome datasets in other species. Our processing and quality control methods provide a scalable framework for taking full advantage of the troves of biological information hibernating in the depths of microbial gene expression data. Finally, annotations are programmatically collected for those samples with sufficient meta-data and expression-based metrics are used to further enhance strain assignment for each sample.
The filtered data are then normalized to enable cross experiment comparisons. aeruginosa RNA-seq data are filtered using technically and biologically driven criteria with characteristics tailored to bacterial gene expression and that account for the effects of alignment to different reference genomes. aeruginosa reference genomes (strains PAO1 and PA14), that transformed raw sequence data into a uniformly processed data in the form of sample-wise normalized counts. We first developed a pipeline, using the Salmon pseudo-aligner and two different P. aeruginosa transcriptomes from hundreds of studies performed by over seventy-five different research groups. In the work we present here, we draw on over 2,300 P.
CLC GENOMICS WORKBENCH VERSUS DESEQ2 ARCHIVE
Over the past two decades, thousands of RNA sequencing (RNA-seq) gene expression profiles of Pseudomonas aeruginosa have been made publicly available via the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA).
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