Tolerance Data 2012 Torrent !FULL!

Tolerance Data 2012 Torrent !FULL!

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Tolerance Data 2012 Torrent

here, we describe the transcriptomic analysis of a specific and well-characterized subset of fibroblasts, human dermal-derived fibroblasts (hdf). hdfs display a characteristic gene expression profile with activated migration- and wound-healing processes in response to fgf2 treatment. the ability to detect differential expressed genes (degs) by rna-seq is very dependent on the choice of the reference transcriptome. to evaluate which reference transcriptome is the most suitable for the present analyses, we prepared six different sets of rna-seq data, representing a range of gene expression levels, with the human genome hg38 as the reference and three different transcriptomes: hg38, ensembl human (grch38), and ensembl human (grch37). for each combination, we analyzed the sensitivity of the dge analysis to the choice of the reference transcriptome. we found that, when the hg38 transcriptome was used as the reference, there was a small but significant number of dge genes with false discovery rates (fdr) of less than 0.05. when the grch38 transcriptome was used as the reference, we found that a large number of dge genes (35% in the case of fgf2 treated cells) had significantly different gene expression levels (p-values < 0.05). this difference could be due to the fact that the ensembl human (grch38) transcriptome includes the transcript sequences of several genes (e.g. pseudogenes) that are not present in the ensembl human (grch37) transcriptome. in fact, we found that when the dge analysis was carried out using the ensembl human (grch38) transcriptome, there was a smaller number of degs with fdr of less than 0. however, the significant number of degs detected using the ensembl human (grch38) transcriptome and the fact that we identified a large number of degs using a custom transcriptome of grch37 provides evidence that the grch38 transcriptome is not a suitable reference for the analysis of the hdfs. finally, we found that the transcriptome produced from the human genome hg38 seems to be a good choice for reference-based dge analysis, since we found a low number of degs when we compared hg38 and grch38 transcriptomes (5-10% of the degs found when comparing the hg38 and grch38 transcriptomes). the data generated in this study has been submitted to the geo database and the geo accession number is gse75018.

to verify that these differences were not caused by batch effects, we repeated the experiment described above with only the two experimental groups (i.e. il-1 treated and untreated) pooled together for sequencing (fig. 3b). as expected, the bowtie2 alignment algorithm performed better on the pooled data, as did the other mappers. this shows that the observed differences are in fact not due to batch effects, but are real biological differences. interestingly, both platforms showed the same behavior with bowtie2. however, the percentage of reads aligned to the mouse genome was much lower for the pooled data compared to the data from individual mice, and the differences between the platforms were no longer significant. we hypothesized that this might be due to the higher sequencing depth (2.4 million reads on average) achieved on the pooled data, which resulted in saturation of the reads in the mouse genome. although this hypothesis cannot be rigorously tested with the data we have, it would explain the low alignment rate in the pooled data. to explore how the use of the different alignment algorithms affects the detection of differential gene expression, we analyzed our dataset with rabema. rabema first classifies reads into expressed genes, transcripts and exons; and then, it computes the f-measure for each class independently (see section 4.3 in additional file 1 for a detailed explanation of rabema). we only considered the gene expression f-measure because we are interested in the accuracy of the gene-wise quantification, and since genes are the smallest units of the genome, they tend to have the highest number of reads per gene. the f-measure for gene expression and transcript expression are shown in fig. 4a, and the f-measure for exon expression is shown in fig. 4b. on the left side of each figure is the data produced using the default aligner parameters (bowtie2), and on the right is the same data produced using the adapted aligner parameters (bowtie2). 5ec8ef588b


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