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  • Tophat: uniquely mapped or multi-hit?

    hi all,
    I wonder how many hits you guys allow when using Tophat to map RNA-Seq reads. For me, I always use uniquely mapped reads(-g is set to be 1). However, I am not sure if this seems to be too conservative and thus results in potential true signals. The dilema is, if multi-hits are allowed, what is a good cutoff for the maximum hits? And for the downstream analysis(gene expression, splicing), not all the software tools can well handle multi-hit correction.

    I would like to listen to your comments and advice.

  • #2
    If you have a reference trasncriptome to use while mapping with Tophat, then I suggest to leave -g to its default value and use --prefilter-multihits. This takes care of many of the multiple mappings. Further at the cost of speed the --read-realign-edit-dist can be set to zero to ensure mapping of each read at each alignment step. This should ensure a more accurate mapping.

    For further downstream filtering Cufflinks can be run with --max-multiread-fraction 0.5 instead of 0.75 to ensure no transcript with more than 50% multiread mappings is built. But I think that is not a necessary step.

    Actually a -g of 10 can be good to maximum number of genes especially lowly expressed ones as reported by this publication
    Background Cellular function is regulated by the balance of stringently regulated amounts of mRNA. Previous reports revealed that RNA polymerase II (RNAPII), which transcribes mRNA, can be classified into the pausing state and the active transcription state according to the phosphorylation state of RPB1, the catalytic subunit of RNAPII. However, genome-wide association between mRNA expression level and the phosphorylation state of RNAPII is unclear. While the functional importance of pausing genes is clear, such as in mouse Embryonic Stem cells for differentiation, understanding this association is critical for distinguishing pausing genes from active transcribing genes in expression profiling data, such as microarrays and RNAseq. Therefore, we examined the correlation between the phosphorylation of RNAPII and mRNA expression levels using a combined analysis by ChIPseq and RNAseq. Results We first performed a precise quantitative measurement of mRNA by performing an optimized calculation in RNAseq. We then visualized the recruitment of various phosphorylated RNAPIIs, such as Ser2P and Ser5P. A combined analysis using optimized RNAseq and ChIPseq for phosphorylated RNAPII revealed that mRNA levels correlate with the various phosphorylation states of RNAPII. Conclusions We demonstrated that the amount of mRNA is precisely reflected by the phased phosphorylation of Ser2 and Ser5. In particular, even the most "pausing" genes, for which only Ser5 is phosphorylated, were detectable at a certain level of mRNA. Our analysis indicated that the complexity of quantitative regulation of mRNA levels could be classified into three categories according to the phosphorylation state of RNAPII.
    Last edited by swaraj; 06-27-2013, 07:17 AM.

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