SRSF shape analysis for sequencing data reveal new differentiating patterns. (October 2017)

• Record Type: Journal Article
• Authors: Wesolowski, Sergiusz; Vera, Daniel; Wu, Wei
• Is Part Of: Computational biology and chemistry. Volume 70(2017)
• Page Start: 56
• Page End: 64
• Publication Date: 2017-10
• Subjects: Next generation sequencing -- RNA-seq -- MNase-seq -- Genomics -- Functional data analysis -- Dynamic time warping -- Shape analysis -- Statistics -- Functional statistics -- Square root slope functions; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.07.004
• Languages: English
• ISSNs: 1476-9271

Abstract

Highlights: New framework (SRSFseq) based on square root slope functions shape analysis to analyse Illumina sequencing data is proposed. Performance of this new framework is evaluated in analyzing RNA-seq data at the exon level. New differential patterns in RNAseq data are detected. Abstract: Motivation: Sequencing-based methods to examine fundamental features of the genome, such as gene expression and chromatin structure, rely on inferences from the abundance and distribution of reads derived from Illumina sequencing. Drawing sound inferences from such experiments relies on appropriate mathematical methods to model the distribution of reads along the genome, which has been challenging due to the scale and nature of these data. Results: We propose a new framework (SRSFseq) based on square root slope functions shape analysis to analyse Illumina sequencing data. In the new approach the basic unit of information is the density of mapped reads over region of interest located on the known reference genome. The densities are interpreted as shapes and a new shape analysis model is proposed. An equivalent of a Fisher test is used to quantify the significance of shape differences in read distribution patterns between groups of density functions in different experimental conditions. We evaluated the performance of this new framework to analyze RNA-seq data at the exon level, which enabled the detection of variation in read distributions and abundances between experimental conditions not detected by other methods. Thus, the method is a suitable supplement to the state-of-the-art count based techniques. The variety of density representations and flexibility of mathematical design allow the model to be easily adapted to other data types or problems in which the distribution of reads is to be tested. The functional interpretation and SRSF phase-amplitude separation technique give an efficient noise reduction procedure improving the sensitivity and specificity of the method.