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.

Keywords

PaceData scienceFocus (optics)Computer scienceGenomicsBiologyComputational biologyGenomeGeneticsGene

MeSH Terms

AnimalsData ScienceGenomicsHumansNanopore SequencingWhole Genome Sequencing

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Publication Info

Year
2020
Type
review
Volume
21
Issue
1
Pages
30-30
Citations
2449
Access
Closed

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Cite This

Shanika L. Amarasinghe, Shian Su, Xueyi Dong et al. (2020). Opportunities and challenges in long-read sequencing data analysis. Genome biology , 21 (1) , 30-30. https://doi.org/10.1186/s13059-020-1935-5

Identifiers

DOI
10.1186/s13059-020-1935-5
PMID
32033565
PMCID
PMC7006217

Data Quality

Data completeness: 86%