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Activity Number: 652 - Genomics, Metabolomics, Microbiome and NextGen Sequencing
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #307260
Title: A New Statistical Method to Investigate Translational Regulation Using Ribo-Profiling Data
Author(s): Keren Li* and Matthew Hope and Frank Fineis and Xiaozhong Wang and Ji-Ping Wang
Companies: Northwestern University and Northwestern University and Northwestern University and Northwestern University and Northwestern University
Keywords: Pattern comparison; Singular value decomposition; Eigenvalues; Negative binomial; Non-central chi-squared; Ribosome sequence

Emerging data suggest that the pluripotency of stem cells is regulated at the level of translation. We employ the recently developed Ribo-profiling technique to investigate translation dynamics in embryonic stem cells. High-density RNase footprints of ribosome-protected fragments provide quantitative and dynamic information of protein translation along mRNA. However, most of current Ribo data analyses focus on testing the occupancy of ribosomes using the total read counts per open-reading form, while the detailed footprint patterns are largely ignored. We propose a new statistical method for testing the difference of ribosome footprint patterns for each gene in different biological conditions. We assume that read counts at each given position of a transcript follow negative binomial distributions. If the read counts across samples follow the same pattern in the entire transcript region, the largest eigenvalue of data matrix is shown to represent the signal strength while the rest for noise.  We derive a test based non-central chi-squared distribution and apply it to real data to show its effectiveness in identifying genes with differential ribosome dynamic patterns genome-wide.

Authors who are presenting talks have a * after their name.

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