
@ARTICLE{Kaminow2022-dz,
  title    = "Pan-human consensus genome significantly improves the accuracy of
              {RNA-seq} analyses",
  author   = "Kaminow, Benjamin and Ballouz, Sara and Gillis, Jesse and Dobin,
              Alexander",
  abstract = "The Human Reference Genome serves as the foundation for modern
              genomic analyses. However, in its present form, it does not
              adequately represent the vast genetic diversity of the human
              population. In this study, we explored the consensus genome as a
              potential successor of the current reference genome and assessed
              its effect on the accuracy of RNA-seq read alignment. To find the
              best haploid genome representation, we constructed consensus
              genomes at the pan-human, superpopulation, and population levels,
              using variant information from The 1000 Genomes Project
              Consortium. Using personal haploid genomes as the ground truth,
              we compared mapping errors for real RNA-seq reads aligned to the
              consensus genomes versus the reference genome. For reads
              overlapping homozygous variants, we found that the mapping error
              decreased by a factor of approximately two to three when the
              reference was replaced with the pan-human consensus genome. We
              also found that using more population-specific consensuses
              resulted in little to no increase over using the pan-human
              consensus, suggesting a limit in the utility of incorporating a
              more specific genomic variation. Replacing the reference with
              consensus genomes impacts functional analyses, such as
              differential expressions of isoforms, genes, and splice
              junctions.",
  journal  = "Genome Res.",
  volume   =  32,
  number   =  4,
  pages    = "738--749",
  month    =  apr,
  year     =  2022,
  language = "en"
}


@ARTICLE{Srivastava2020-tm,
  title    = "Alignment and mapping methodology influence transcript abundance
              estimation",
  author   = "Srivastava, Avi and Malik, Laraib and Sarkar, Hirak and Zakeri,
              Mohsen and Almodaresi, Fatemeh and Soneson, Charlotte and Love,
              Michael I and Kingsford, Carl and Patro, Rob",
  abstract = "BACKGROUND: The accuracy of transcript quantification using
              RNA-seq data depends on many factors, such as the choice of
              alignment or mapping method and the quantification model being
              adopted. While the choice of quantification model has been shown
              to be important, considerably less attention has been given to
              comparing the effect of various read alignment approaches on
              quantification accuracy. RESULTS: We investigate the influence of
              mapping and alignment on the accuracy of transcript
              quantification in both simulated and experimental data, as well
              as the effect on subsequent differential expression analysis. We
              observe that, even when the quantification model itself is held
              fixed, the effect of choosing a different alignment methodology,
              or aligning reads using different parameters, on quantification
              estimates can sometimes be large and can affect downstream
              differential expression analyses as well. These effects can go
              unnoticed when assessment is focused too heavily on simulated
              data, where the alignment task is often simpler than in
              experimentally acquired samples. We also introduce a new
              alignment methodology, called selective alignment, to overcome
              the shortcomings of lightweight approaches without incurring the
              computational cost of traditional alignment. CONCLUSION: We
              observe that, on experimental datasets, the performance of
              lightweight mapping and alignment-based approaches varies
              significantly, and highlight some of the underlying factors. We
              show this variation both in terms of quantification and
              downstream differential expression analysis. In all comparisons,
              we also show the improved performance of our proposed selective
              alignment method and suggest best practices for performing
              RNA-seq quantification.",
  journal  = "Genome Biol.",
  volume   =  21,
  number   =  1,
  pages    = "239",
  month    =  sep,
  year     =  2020,
  keywords = "Quantification; RNA-seq; Read-alignment",
  language = "en"
}

@Article{Lawrence2013,
    title = {Software for Computing and Annotating Genomic Ranges},
    author = {Michael Lawrence and Wolfgang Huber and Herv\'e Pag\`es and Patrick Aboyoun and Marc Carlson and Robert Gentleman and Martin Morgan and Vincent Carey},
    year = {2013},
    journal = {{PLoS} Computational Biology},
    volume = {9},
    issue = {8},
    doi = {10.1371/journal.pcbi.1003118},
    url = {http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118},
  }
