19661376

From SEQwiki
Jump to: navigation, search

This reference describes BayesCall.

PMID PMID 19661376
Title BayesCall: A model-based basecalling algorithm for high-throughput short-read sequencing.
Year 2009
Journal Genome Research
Author Kao WC, Stevens K, Song YS.
Volume 19
Start page 1884


Error: No contents found at URL http://www.ebi.ac.uk/europepmc/webservices/rest/MED/19661376/citations/4000.

According to Europe PubMed Central, this reference has Error: no local variable "citations" was set. " Error: no local variable "citations" was set. " is not a number. citations.

For reference, you can check Google Scholar, which lacks an API because Google ...


Error: Invalid JSON. According to Almetric, this reference has an Altmetric score of Error: no local variable "altscore" was set. " Error: no local variable "altscore" was set. " is not a number..

Full text description

Extracting sequence information from raw images of fluorescence is the foundation underlying several high-throughput sequencing platforms. Some of the main challenges associated with this technology include reducing the error rate, assigning accurate base-specific quality scores, and reducing the cost of sequencing by increasing the throughput per run. To demonstrate how computational advancement can help to meet these challenges, a novel model-based basecalling algorithm BayesCall is introduced for the Illumina sequencing platform. Being founded on the tools of statistical learning, BayesCall is flexible enough to incorporate various features of the sequencing process. In particular, it can easily incorporate time-dependent parameters and model residual effects. This new approach significantly improves the accuracy over Illumina's basecaller Bustard, particularly in the later cycles of a sequencing run. For 76-cycle data on a standard viral sample PhiX174, BayesCall improves Bustard's average per-base error rate by about 52%. The probability of observing each base can be readily computed in BayesCall, and this probability can be transformed into a useful base-specific quality score with a high discrimination ability. A detailed study of BayesCall's performance is presented here.