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We have to normalise read count data from RNA-Seq experiments in order to account for the fact that some genomic regions are mapped more than others. i.e. we get the tags per million reads (TPM). In other words, they have a higher sequencing depth/vertical coverage.

Why are some genomic regions mapped more than others; what is the reason for this non-uniform distribution of sequencing depth?

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For RNAseq, the differences in the read depth can simply arise because of expression (that's trivial). Highly expressed regions will have higher number of reads.

However the read depth non-uniformity also arises because of the nucleotide composition of the regions. This is true for all kinds of nucleic acid sequencing such as RNAseq, whole genome sequencing and ChIP seq. This is called sequence bias. This also depends on the sequencing methodology. A variety of factors play a role such as how well can the region be sheared, how well can it be amplified etc.

For details have a look at this article by Ross et al (2013).

Sequencing technologies are vulnerable to multiple sources of bias. Methods based on bacterial cloning and Sanger-chemistry sequencing [8] were subject to many coverage-reducing biases, notably at GC extremes, palindromes, inverted repeats, and sequences toxic to the bacterial host [9–17]. Illumina sequencing [18] has been shown to lose coverage in regions of high or low GC [19–22], a phenomenon also seen in other 'next-generation' technologies [3, 6]. PCR amplification during library construction is a known source of undercoverage of GC-extreme regions [20, 21] and similar biases may also be introduced during bridge PCR for cluster amplification on the Illumina flowcell [23]. Illumina strand-specific errors can lead to coverage biases by impairing aligner performance [24]. Ion Torrent [25], like 454 [26], utilizes a terminator-free chemistry that may limit its ability to accurately sequence long homopolymers [4, 27, 28], and may also be sensitive to coverage biases introduced by emulsion PCR in library construction. Complete Genomics [29] also uses amplification along with a complex library construction process. The Pacific Biosciences [30] process is amplification-free; therefore, one might expect it to exhibit lower levels of coverage bias than the other technologies.

In addition to sources in the wet lab, bias can be introduced by any of the computational steps in the sequencing pipeline. Signal-processing and base calling limitations could result in under-representation or increased error rates in some locations, as can inaccurate alignment. An inaccurate reference or sample-reference differences can cause coverage or accuracy variations that may be misdiagnosed as sequencing bias. Therefore, detecting bias is only the first step and must be followed by more detailed experiments to assign responsibility to the library preparation, sequencing, or computational stages.

Some analysis software try to correct for these biases while estimating the read abundance.


Ross, Michael G., et al. "Characterizing and measuring bias in sequence data." Genome Biol 14.5 (2013): R51.

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