Why are some genomic regions sequenced more than the others?
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.