Consensus Error Rate
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in percentage. Percentage of errors per base within single reads of the maximum length. As explained in Glenn (2011), error rates among platforms are not exactly comparable. The reported Ion Torrent rates range from 0.46% to oxford nanopore error rate 2.4%. Final Error rates SOLiD rates are from reads with bases consistent on double or pacbio cost per run triple sequencing only. Final Error rate for PacBio applies only to the consensus sequencing for three independent reads of the same template. For pacbio vs illumina Illumina, errors at ≤0.1% is achieved for ≥ 85% of reads (not all reads). See Glenn (2011) for additional details. Instrument Primary Errors Single-pass Error Rate (%) Final Error Rate (%) 3730xl (capillary) Substitution 0.1-1 0.1-1 454, all http://www.pacb.com/uncategorized/a-closer-look-at-accuracy-in-pacbio/ models Indel 1 1 Illumina, all models Substitution ~0.1 ~0.1 Ion Torrent - all chips Indel ~1 ~1 SOLiD - 5500xl A-T bias ~5 ≤ 0.1 Oxford Nanopore deletions ≥ 4* 4* PacBio RS indel ~13 ≤ 1 * Information based on company sources alone (independent data not yet available); it is not clear if the 4% error rate reported by Oxford Nanopore refers to a single-pass rate or is what is achieved after reading both strands http://www.molecularecologist.com/next-gen-table-3c-2013/ and producing a consensus sequence. David Ya, hard to compare. The final error rate for PacBio runs using their Quiver assembler is better than Q50 (99.999%) and you'll find publications recently at Q60 (99.9999%). That's without circular consensus reads, just consensus from raw reads. Related to the lack of bias perhaps: http://genomebiology.com/2013/14/5/R51 Search for: Stay up to date Subscribe via email Enter your email address to subscribe to this blog and receive notifications of new posts by email. Join 234 other subscribers Email Address Latest comments Archives by month Archives by month Select Month October 2016 (1) September 2016 (10) August 2016 (17) July 2016 (12) June 2016 (17) May 2016 (17) April 2016 (17) March 2016 (28) February 2016 (20) January 2016 (15) December 2015 (14) November 2015 (12) October 2015 (12) September 2015 (14) August 2015 (12) July 2015 (19) June 2015 (9) May 2015 (20) April 2015 (23) March 2015 (22) February 2015 (24) January 2015 (21) December 2014 (21) November 2014 (27) October 2014 (10) September 2014 (5) August 2014 (8) July 2014 (6) June 2014 (6) May 2014 (8) April 2014 (10) March 2014 (10) February 2014 (7) January 2014 (7) December 2013 (8) November 2013 (9) October 2013 (10) September 2013 (8) August 2013 (10) July 2013 (7) June 2013 (8) May 2013 (8) April 2013 (12) March 2013 (14) February
Health Search databasePMCAll DatabasesAssemblyBioProjectBioSampleBioSystemsBooksClinVarCloneConserved DomainsdbGaPdbVarESTGeneGenomeGEO DataSetsGEO ProfilesGSSGTRHomoloGeneMedGenMeSHNCBI Web SiteNLM CatalogNucleotideOMIMPMCPopSetProbeProteinProtein ClustersPubChem BioAssayPubChem CompoundPubChem SubstancePubMedPubMed http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3856802/ HealthSNPSRAStructureTaxonomyToolKitToolKitAllToolKitBookToolKitBookghUniGeneSearch termSearch Advanced Journal list Help Journal ListProc Natl Acad Sci U S Av.110(49); 2013 Dec 3PMC3856802 Proc Natl Acad Sci U S A. 2013 Dec 3; 110(49): 19872–19877. Published online 2013 Nov 15. doi: 10.1073/pnas.1319590110PMCID: PMC3856802GeneticsHigh-throughput DNA sequencing errors are reduced by orders of magnitude error rate using circle sequencingDianne I. Lou,a,1 Jeffrey A. Hussmann,b,1 Ross M. McBee,a Ashley Acevedo,c Raul Andino,c,2 William H. Press,b,d,2 and Sara L. Sawyera,2aDepartment of Molecular Biosciences,bInstitute for Computational Engineering and Sciences, anddDepartment of Integrative Biology, University of Texas at Austin, Austin, TX, 78712; andcDepartment of Microbiology and Immunology, consensus error rate University of California, San Francisco, CA, 941222To whom correspondence may be addressed. E-mail: ude.saxetu.sc@sserpw, Email: ude.fscu@onidna.luar, or ; Email: ude.saxetu.nitsua@saras.Contributed by William H. Press, October 17, 2013 (sent for review August 31, 2013)Author contributions: D.I.L., J.A.H., R.M.M., A.A., R.A., W.H.P., and S.L.S. designed research; D.I.L., J.A.H., and R.M.M. performed research; D.I.L., J.A.H., and R.M.M. analyzed data; D.I.L., J.A.H., and S.L.S. wrote the paper.1D.I.L. and J.A.H. contributed equally to this work.Author information ► Copyright and License information ►Copyright notice Freely available online through the PNAS open access option.See "In This Issue" in volume 110 on page 19653.See "Reply to Schmitt et al.: Data-filtering schemes for avoiding double-counting in circle sequencing" in volume 111 on page E1561.See letter "Risks of double-counting in deep sequencing" in volume 111 on page E1560.This article has been cited by other articles in PMC.SignificanceThis paper presents a library preparation m