Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

DENTIST-using long reads for closing assembly gaps at high accuracy


Hiller,  Michael
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Ludwig, A., Pippel, M., Myers, G., & Hiller, M. (2022). DENTIST-using long reads for closing assembly gaps at high accuracy. GigaScience, 11. doi:10.1093/gigascience/giab100.

Cite as: https://hdl.handle.net/21.11116/0000-000A-D36D-E
Background: Long sequencing reads allow increasing contiguity and completeness of fragmented, short-read-based genome assemblies by closing assembly gaps, ideally at high accuracy. While several gap-closing methods have been developed, these methods often close an assembly gap with sequence that does not accurately represent the true sequence.
Findings: Here, we present DENTIST, a sensitive, highly accurate, and automated pipeline method to close gaps in short-read assemblies with long error-prone reads. DENTIST comprehensively determines repetitive assembly regions to identify reliable and unambiguous alignments of long reads to the correct loci, integrates a consensus sequence computation step to obtain a high base accuracy for the inserted sequence, and validates the accuracy of closed gaps. Unlike previous benchmarks, we generated test assemblies that have gaps at the exact positions where real short-read assemblies have gaps. Generating such realistic benchmarks for Drosophila (134 Mb genome), Arabidopsis (119 Mb), hummingbird (1 Gb), and human (3 Gb) and using simulated or real PacBio continuous long reads, we show that DENTIST consistently achieves a substantially higher accuracy compared to previous methods, while having a similar sensitivity.
Conclusion: DENTIST provides an accurate approach to improve the contiguity and completeness of fragmented assemblies with long reads. DENTIST's source code including a Snakemake workflow, conda package, and Docker container is available at https://github.com/a-ludi/dentist. All test assemblies as a resource for future benchmarking are at https://bds.mpi-cbg.de/hillerlab/DENTIST/.