Accurate base calls generated from sequencing data are required for downstream biological interpretation, particularly in the case of rare variants. CallSim is a software application that provides evidence for the validity of base calls believed to be sequencing errors and it is applicable to Ion Torrent and 454 data. The algorithm processes a single read using a Monte Carlo approach to sequencing simulation, not dependent upon information from any other read in the data set. Three examples from general read correction, as well as from error-or-variant classification, demonstrate its effectiveness for a robust low-volume read processing base corrector. Specifically, correction of errors in Ion Torrent reads from a study involving mutations in multidrug resistant Staphylococcus aureus illustrates an ability to classify an erroneous homopolymer call. In addition, support for a rare variant in 454 data for a mixed viral population demonstrates "base rescue" capabilities. CallSim provides evidence regarding the validity of base calls in sequences produced by 454 or Ion Torrent systems and is intended for hands-on downstream processing analysis. These downstream efforts, although time consuming, are necessary steps for accurate identification of rare variants.
Keyphrases
- electronic health record
- single cell
- multidrug resistant
- staphylococcus aureus
- single molecule
- big data
- machine learning
- high resolution
- randomized controlled trial
- deep learning
- copy number
- systematic review
- emergency department
- sars cov
- patient safety
- adverse drug
- biofilm formation
- klebsiella pneumoniae
- methicillin resistant staphylococcus aureus
- candida albicans
- social media