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Pipeline publication

[…] the Ensembl 68 reference transcriptome . Resulting mapped reads were stored in BAM files. Duplicate rates were relatively high at around 80% (A), which is common in RNA-seq data where highly expressed genes can affect duplication rates. Additionally, we used relatively short single-end reads and a SNP-tolerant read mapper. Wiggle tracks of these samples were generated for visualization in the UCSC genome browser using our in-house “” Perl script (Supplementary materials). To make the signal heights comparable in the browser view, these wiggle tracks were generated from normalized BAM files in which excess reads were randomly discarded from all but the smallest BAM file using the Picard Tools DownsampleSam program (, in order to use a similar number of reads from all samples. Transcript quantification was performed with cufflinks and cuffdiff v2.1.1 , using the RefSeq transcriptome which was downloaded from the UCSC genome browser website in June 2013. Expression levels were quantified as fragments per kilobase per million mapped reads (FPKM) and normalized across all samples by the cuffdiff program, which computes expression levels at both the individual transcript level and the gene level, the latter by combining the data from all gene-associated transcripts per gene. Quality control of the expression data was performed using the R package cummeRbund (, and no batch effect was observed despite the fact that HKC2 day2 had fewer lowly expressed genes (B). All wiggle tracks and the transcript- and gene-level FPKM tables are available under GEO accession number GSE59827., , This work is supported by NWO/ALW/MEERVOUD/836.12.010 (HZ), NGI/BioRange program of the NBIC (MO), Radboud University fellowship (HZ) and NCMLS PhD fellowship (ENK). We would like to thank Simon van Heeringen for allowing us to provide his script as a supplementary file., Supplementary data to this article can be found online at] […]

Pipeline specifications

Software tools Picard, Cufflinks, CummeRbund