Computational Errors and Biases in Short Read Next Generation SequencingIrina Abnizova1*, Rene te Boekhorst2 and Yuriy L Orlov3,4
- *Corresponding Author:
- Irina Abnizova
Wellcome Trust Sanger Institute
E-mail: [email protected]
Received Date: December 26, 2016; Accepted Date: January 16, 2017; Published Date: January 26, 2017
Citation: Abnizova I, te Boekhorst R, Orlov Y (2017) Computational Errors and Biases in Short Read Next Generation Sequencing. J Proteomics Bioinform 10:1-17. doi: 10.4172/jpb.1000420
Copyright: © 2017 Abnizova I, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Next generation sequencing technologies produce an astronomical amount of useful data, but also artefacts and errors. Some of these errors may mimic true biological signals, such as mutations, and therefore may invalidate conclusions. In next generation sequencing, two types of errors may occur: experimental and computational. Computational errors are those that stem from the digital post-processing of sequenced samples, and are the main subject of this paper. Post-processing involves procedures such as quality-scoring, aligning, assembling, variant calling, genotyping and error-correction of the data. This paper is about post-processing errors and computational methods to detect and correct them.