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Characteristics of 454 pyrosequencing data—enabling realistic simulation with flowsim

Balzer, Susanne, Malde, Ketil, Lanzén, Anders, Sharma, Animesh, Jonassen, Inge·2010·Bioinformatics
DOI 10.1093/bioinformatics/btq365PMC PMC2935434
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Abstract

Motivation: The commercial launch of 454 pyrosequencing in 2005 was a milestone in genome sequencing in terms of performance and cost. Throughout the three available releases, average read lengths have increased to ∼500 base pairs and are thus approaching read lengths obtained from traditional Sanger sequencing. Study design of sequencing projects would benefit from being able to simulate experiments. Results: We explore 454 raw data to investigate its characteristics and derive empirical distributions for the flow values generated by pyrosequencing. Based on our findings, we implement Flowsim, a simulator that generates realistic pyrosequencing data files of arbitrary size from a given set of input DNA sequences. We finally use our simulator to examine the impact of sequence lengths on the results of concrete whole-genome assemblies, and we suggest its use in planning of sequencing projects, benchmarking of assembly methods and other fields. Availability: Flowsim is freely available under the General Public License from http://blog.malde.org/index.php/flowsim/ Contact: susanne.balzer@imr.no; ketil.malde@imr.no

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Fig. 1. (a) A 454 flowgram: cyclic flowing during one read. The light signal strengths (flow values) are directly translated into homopolymer runs. (b) Absolute frequencies of flow values (E.coli). Left: original data, no quality-trimming; right: quality-trimmed. The trimming algorithm enhances the separation of the homopolymer length distributions and levels out discrepancies between the nucleotides such that the curves for the four nucleotides are nearly identical.
Fig. 1. (a) A 454 flowgram: cyclic flowing during one read. The light signal strengths (flow values) are directly translated into homopolymer runs. (b) Absolute frequencies of flow values (E.coli). Left: original data, no quality-trimming; right: quality-trimmed. The trimming algorithm enhances the separation of the homopolymer length distributions and levels out discrepancies between the nucleotides such that the curves for the four nucleotides are nearly identical.CC BY-NC · PubMed Central
Fig. 2. (a) Absolute frequencies of flow values by flow cycle. A total of 200 flow cycles of a Titanium run correspond to 200×4 = 800 flows. The first two flow cycles contain the TCAG tag and are omitted here. Towards the end of a run, flow values tend to lie further away from their ideal values (integers), but are obviously less in number because many values from later flow cycles have been trimmed away. (b) Standard deviation of flow values (difference in relation to their closest integer), by flow cycle. Standard deviation increases almost linearly. Only flow values <5.5 were included.
Fig. 2. (a) Absolute frequencies of flow values by flow cycle. A total of 200 flow cycles of a Titanium run correspond to 200×4 = 800 flows. The first two flow cycles contain the TCAG tag and are omitted here. Towards the end of a run, flow values tend to lie further away from their ideal values (integers), but are obviously less in number because many values from later flow cycles have been trimmed away. (b) Standard deviation of flow values (difference in relation to their closest integer), by flow cycle. Standard deviation increases almost linearly. Only flow values <5.5 were included.CC BY-NC · PubMed Central
Fig. 3. Empirical distributions (smoothed average of E.coli and D.labrax) on logarithmic scale. In gray: fitted (log-) normal distributions.
Fig. 3. Empirical distributions (smoothed average of E.coli and D.labrax) on logarithmic scale. In gray: fitted (log-) normal distributions.CC BY-NC · PubMed Central
Fig. 4. De novo and reference-based N50 for E.coli. Both real and simulated 454 data were assembled using Newbler v2.3.
Fig. 4. De novo and reference-based N50 for E.coli. Both real and simulated 454 data were assembled using Newbler v2.3.CC BY-NC · PubMed Central

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Research works printed but not linked: 16

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Metadata

Authors
Balzer, S., Malde, K., Lanzén, A., Sharma, A., & Jonassen, I.
Affiliations
1 Institute of Marine Research, PO Box 1870, N−5817, 2Department of Informatics, University of Bergen, PO Box 7803, N-5020, 3Computational Biology Unit, Bergen Center for Computational Science, Thormøhlensgate 55, N-5008 and 4Department of Biology, University of Bergen, PO Box 7803, N-5020, Bergen
Venue
Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2010-09-04
Volume
26
Issue
18
Pages
i420-i425
ISSN
  • 1367-4811
  • 1367-4803
DOI
10.1093/bioinformatics/btq365
PMC
PMC2935434
Cited by
124
References
0
Language
en
Licence
creativecommons.org/licenses/by-nc/2.0/uk/ (vor, 2220 d)

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CoChat. (2026). Citation check: Characteristics of 454 pyrosequencing data—enabling realistic simulation with flowsim. https://app.cochat.ai/papers/10.1093/bioinformatics/btq365
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Balzer, S., Malde, K., Lanzén, A., Sharma, A., & Jonassen, I. (2010). Characteristics of 454 pyrosequencing data—enabling realistic simulation with flowsim. Bioinformatics. https://doi.org/10.1093/bioinformatics/btq365