Probabilistic Approach to Understand Errors in Sequencing and its based Applications

Abstract

Next Generation Sequencing has been applied in many areas of biology, including quantification of gene expression, Genome-Wide Association Study (GWAS), gene finding, Motif discovery and much more. Due to the vast area of application and importance in key findings in the field, massive genomic data is being generated using high throughput sequencing. Therefore, sequencing quality also needs to be evaluated in light of different applications. In order to develop effective diagnostic and therapeutic approaches, we need to accurately characterize and identify sequencing errors and distinguish these errors from their true genetic variant in sequencing, i.e. misreads follow a binomial distribution and it further can be approximated to the Poisson process for longer sequences. However, the insertion and deletion rates are 1000 times lower than substitution error rates and, therefore, less significant. The model assumes that error arrival at a position is not dependent on an error at other positions. Furthermore, errors in sequences can cause an error in studies based on multiple sequences and they also follow Binomial -Poisson Distribution (for example -Alignment is a merging of two Binomial processes for short sequences and it further can be approximated to Poisson for long sequences (for example -genomic sequence). It provides a systematic way to evaluate the accuracy in sequencing-based applications. Many error suppressing algorithms or techniques are there, and our Binomial Poisson model can provide a further systematic understanding of error behavior in short sequences so that more techniques for error removal can be developed with much efficient suppression rates.