Sequencing artifacts in the type A influenza databases and attempts to correct them
Article first published online: 7 FEB 2014
© 2014 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.
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Influenza and Other Respiratory Viruses
Volume 8, Issue 4, pages 499–505, July 2014
How to Cite
2014) Sequencing artifacts in the type A influenza databases and attempts to correct them. Influenza and Other Respiratory Viruses 8(4), 499–505.et al. (
- Issue published online: 13 JUN 2014
- Article first published online: 7 FEB 2014
- Manuscript Accepted: 27 DEC 2013
- United States Department of Agriculture
- Agricultural Research Service CRIS. Grant Number: 6612-32000-063
There are over 276 000 influenza gene sequences in public databases, with the quality of the sequences determined by the contributor.
As part of a high school class project, influenza sequences with possible errors were identified in the public databases based on the size of the gene being longer than expected, with the hypothesis that these sequences would have an error. Students contacted sequence submitters alerting them of the possible sequence issue(s) and requested they the suspect sequence(s) be correct as appropriate.
Type A influenza viruses were screened, and gene segments longer than the accepted size were identified for further analysis. Attention was placed on sequences with additional nucleotides upstream or downstream of the highly conserved non-coding ends of the viral segments.
Results and Conclusions
A total of 1081 sequences were identified that met this criterion. Three types of errors were commonly observed: non-influenza primer sequence wasn't removed from the sequence; PCR product was cloned and plasmid sequence was included in the sequence; and Taq polymerase added an adenine at the end of the PCR product. Internal insertions of nucleotide sequence were also commonly observed, but in many cases it was unclear if the sequence was correct or actually contained an error. A total of 215 sequences, or 22.8% of the suspect sequences, were corrected in the public databases in the first year of the student project. Unfortunately 138 additional sequences with possible errors were added to the databases in the second year. Additional awareness of the need for data integrity of sequences submitted to public databases is needed to fully reap the benefits of these large data sets.