Work

Prediction Interval Ranking Score: Identification of Invariant Expression from Time Series

Year: 2018

Type: preprint

Abstract: Motivation Identification of constitutive reference genes is critical for analysis of gene expression. Large numbers of high throughput time series expression data are available, but current methods for identifying invariant expression are not tailored for time series. Identification of reference genes from these data sets can benefit from methods which incorporate the additional information they provide. Results Here we show that we can improve identification of invariant expression from time series by modelling the time component of the data. We implement the Prediction Interval Ranking Score (PIRS) software, which screens high throughput time series data and provides a ranked list of reference candidates. We expect that PIRS will improve the quality of gene expression analysis by allowing researchers to identify the best reference genes for their system from publicly available time series. Availability PIRS can be downloaded and installed with dependencies using ‘pip install pirs’ and Python code and documentation is available for download at https://github.com/aleccrowell/PIRS . Contact [email protected]

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.1101/482794

Authors: Alexander M Crowell, Jennifer J. Loros, Jay C Dunlap

Institution: Dartmouth College

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of General Medical Sciences, National Institute of Biomedical Imaging and Bioengineering

Awards: Identification and Analysis of Circadian Clock-Controlled Genes, Identification and Analysis of Circadian Clock-Controlled Genes, Multiscale Modeling of Circadian Rhythms, Genetic and Molecular Dissection of the Neurospora Clock