Effects of GC Bias in Next-Generation-Sequencing Data on De Novo Genome Assembly
Open accessAbstract
Next-generation-sequencing (NGS) has revolutionized the field of genome assembly because of its much higher data throughput and much lower cost compared with traditional Sanger sequencing. However, NGS poses new computational challenges to de novo genome assembly. Among the challenges, GC bias in NGS data is known to aggravate genome assembly. However, it is not clear to what extent GC bias affects genome assembly in general. In this work, we conduct a systematic analysis on the effects of GC bias on genome assembly. Our analyses reveal that GC bias only lowers assembly completeness when the degree of GC bias is above a threshold. At a strong GC bias, the assembly fragmentation due to GC bias can be explained by the low coverage of reads in the GC-poor or GC-rich regions of a genome. This effect is observed for all the assemblers under study. Increasing the total amount of NGS data thus rescues the assembly fragmentation because of GC bias. However, the amount of data needed for a full rescue depends on the distribution of GC contents. Both low and high coverage depths due to GC bias lower the accuracy of assembly. These pieces of information provide guidance toward a better de novo genome assembly in the presence of GC bias.
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High-throughput sequencing and sample dilution are modeled using random sampling because GC content in the files did not show practically significant bias in the PCR GC bias test.
“Illumina sequencing was previously observed to have bias on GC-extreme sequences15,27,28, but GC content in our files did not show practically significant bias in the PCR GC bias test. Therefore, high-throughput sequencing and sample dilution are modeled using random sampling.”
From Quantifying Molecular Bias in DNA Data Storage (2019) · cites this paper as [27]
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- Authors
- Chen, Y.-C., Liu, T., Yu, C.-H., Chiang, T.-Y., & Hwang, C.-C.
- Affiliations
- National Cheng Kung University (TW)
- Corresponding author
- Hwang, Chi-Chuan
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-04-29
- Volume
- 8
- Issue
- 4
- Pages
- e62856
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3639258
- OpenAlex
- W1998330439
- Cited by
- 285
- References
- 43
- Citations by year
- 2026: 7 · 2025: 22 · 2024: 25 · 2023: 20 · 2022: 26 · 2021: 48 · 2020: 30 · 2019: 13 · 2018: 27 · 2017: 23 · 2016: 18 · 2015: 11 · 2014: 12 · 2013: 2
- FWCI
- 6.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- GC content bias, de novo genome assembly, next-generation sequencing, assembly completeness, read coverage, assembly accuracy
- MeSH terms
- Reference Standards, Artifacts, Genome, Bacterial, GC Rich Sequence, Genomics, High-Throughput Nucleotide Sequencing
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