Opportunities and challenges in long-read sequencing data analysis

Abstract

Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain.

Citation

BibTeX citation:

@article{l amarasinghe2020,
  author = {L Amarasinghe, Shanika and Su, Shian and Dong, Xueyi and
    Zappia, Luke and E Ritchie, Matthew and Gouil, Quentin},
  title = {Opportunities and Challenges in Long-Read Sequencing Data
    Analysis},
  journal = {Genome biology},
  volume = {21},
  number = {1},
  pages = {30},
  date = {2020-02-01},
  url = {https://lazappi.id.au/publications/2020-amarasinghe-long-read-tools},
  doi = {10.1186/s13059-020-1935-5},
  issn = {1465-6906},
  langid = {en},
  abstract = {Long-read technologies are overcoming early limitations in
    accuracy and throughput, broadening their application domains in
    genomics. Dedicated analysis tools that take into account the
    characteristics of long-read data are thus required, but the fast
    pace of development of such tools can be overwhelming. To assist in
    the design and analysis of long-read sequencing projects, we review
    the current landscape of available tools and present an online
    interactive database, long-read-tools.org, to facilitate their
    browsing. We further focus on the principles of error correction,
    base modification detection, and long-read transcriptomics analysis
    and highlight the challenges that remain.}
}

For attribution, please cite this work as:

L Amarasinghe, Shanika, Shian Su, Xueyi Dong, Luke Zappia, Matthew E Ritchie, and Quentin Gouil. 2020. “Opportunities and Challenges in Long-Read Sequencing Data Analysis.” Genome Biology 21 (February): 30. https://doi.org/10.1186/s13059-020-1935-5.