Using expected sequence features to improve basecalling accuracy of amplicon pyrosequencing data.
BACKGROUND
Amplicon pyrosequencing targets a known genetic region and thus inherently produces reads highly anticipated to have certain features, such as conserved nucleotide sequence, and in the case of protein coding DNA, an open reading frame. Pyrosequencing errors, consisting mainly of nucleotide insertions and deletions, are on the other hand likely to disrupt open reading frames. Such an inverse relationship between errors and expectation based on prior knowledge can be used advantageously to guide the process known as basecalling, i.e. the inference of nucleotide sequence from raw sequencing data.


RESULTS
The new basecalling method described here, named Multipass, implements a probabilistic framework for working with the raw flowgrams obtained by pyrosequencing. For each sequence variant Multipass calculates the likelihood and nucleotide sequence of several most likely sequences given the flowgram data. This probabilistic approach enables integration of basecalling into a larger model where other parameters can be incorporated, such as the likelihood for observing a full-length open reading frame at the targeted region. We apply the method to 454 amplicon pyrosequencing data obtained from a malaria virulence gene family, where Multipass generates 20 % more error-free sequences than current state of the art methods, and provides sequence characteristics that allow generation of a set of high confidence error-free sequences.


CONCLUSIONS
This novel method can be used to increase accuracy of existing and future amplicon sequencing data, particularly where extensive prior knowledge is available about the obtained sequences, for example in analysis of the immunoglobulin VDJ region where Multipass can be combined with a model for the known recombining germline genes. Multipass is available for Roche 454 data at http://www.cbs.dtu.dk/services/MultiPass-1.0 , and the concept can potentially be implemented for other sequencing technologies as well.
Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, Kongens Lyngby, DK-2800, Denmark. rask@cbs.dtu.dk.|Division of Medical Parasitology, Department of Microbiology, New York University Langone Medical Center, 341 East 25th Street, New York, NY, 10010, USA. rask@cbs.dtu.dk.|School of Biosciences, The University of Melbourne, Parkville, Victoria, 3010, Australia. rask@cbs.dtu.dk.
Thomas S
TS
Rask
Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, Kongens Lyngby, DK-2800, Denmark.
Bent
B
Petersen
Division of Medical Parasitology, Department of Microbiology, New York University Langone Medical Center, 341 East 25th Street, New York, NY, 10010, USA.
Donald S
DS
Chen
Division of Medical Parasitology, Department of Microbiology, New York University Langone Medical Center, 341 East 25th Street, New York, NY, 10010, USA.|School of Biosciences, The University of Melbourne, Parkville, Victoria, 3010, Australia.
Karen P
KP
Day
Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, Kongens Lyngby, DK-2800, Denmark.
Anders Gorm
AG
Pedersen
D000465:Algorithms; D016054:DNA, Protozoan / Q000235:genetics / Q000302:isolation & purification*; D008958:Models, Molecular; D016366:Open Reading Frames; D010963:Plasmodium falciparum / Q000235:genetics; D015800:Protozoan Proteins / Q000235:genetics; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA
D016054:DNA, Protozoan; D015800:Protozoan Proteins; C085731:erythrocyte membrane protein 1, Plasmodium falciparum
Amplicon sequencing; Basecalling; Bayesian methods