The DOI matches its Crossref record; 105 of the 105 references it cites were found in a registry or an index.
Denoising DNA deep sequencing data—high-throughput sequencing errors and their correction
Open accessAbstract
Characterizing the errors generated by common high-throughput sequencing platforms and telling true genetic variation from technical artefacts are two interdependent steps, essential to many analyses such as single nucleotide variant calling, haplotype inference, sequence assembly and evolutionary studies. Both random and systematic errors can show a specific occurrence profile for each of the six prominent sequencing platforms surveyed here: 454 pyrosequencing, Complete Genomics DNA nanoball sequencing, Illumina sequencing by synthesis, Ion Torrent semiconductor sequencing, Pacific Biosciences single-molecule real-time sequencing and Oxford Nanopore sequencing. There is a large variety of programs available for error removal in sequencing read data, which differ in the error models and statistical techniques they use, the features of the data they analyse, the parameters they determine from them and the data structures and algorithms they use. We highlight the assumptions they make and for which data types these hold, providing guidance which tools to consider for benchmarking with regard to the data properties. While no benchmarking results are included here, such specific benchmarks would greatly inform tool choices and future software development. The development of stand-alone error correctors, as well as single nucleotide variant and haplotype callers, could also benefit from using more of the knowledge about error profiles and from (re)combining ideas from the existing approaches presented here.
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Cites
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Cited by
Trost, B., Walker, S., Wang, Z., Thiruvahindrapuram, B., MacDonald, J. R., Sung, W. W. L., Pereira, S. L., Whitney, J., Chan, A. J. S., Pellecchia, G., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.12.007 · Cited by 191
A comprehensive evaluation of long read error correction methods
Zhang, H., Jain, C., & Aluru, S. (2020) · BMC Genomics · doi:10.1186/s12864-020-07227-0 · Cited by 150
Biology and medicine in the landscape of quantum advantages
Cordier, B. A., Sawaya, N. P. D., Guerreschi, G. G., & McWeeney, S. K. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0541 · Cited by 87
Pan-cancer analysis of systematic batch effects on somatic sequence variations
Choi, J.-H., Hong, S.-E., & Woo, H. G. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1627-7 · Cited by 14
Eboreime, J., Choi, S.-K., Yoon, S.-R., Arnheim, N., & Calabrese, P. (2016) · PLOS ONE · doi:10.1371/journal.pone.0158340 · Cited by 11
Metadata
- Authors
- Laehnemann, D., Borkhardt, A., & McHardy, A. C.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-05-29
- Volume
- 17
- Issue
- 1
- Pages
- 154-179
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W1903088142
- Cited by
- 296
- References
- 105
- FWCI
- 12.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
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