Framework to Assess Optimal Sequencing Depth Involving Long Noncoding RNAs

Bajram Metjahic

Document Type

Article

Publication Date

Summer 2018

JAX Location

In: Student Reports, Summer 2018, The Jackson Laboratory

Abstract

Long non-coding RNAs (lncRNAs) are a significant factor in transcriptional regulation, neural plasticity, and methylation, amongst other processes. Multiple cancers-as well as chronic brain diseases such as addiction have been shown to cause differential expression in these and other regulatory RNAs. This differential expression may be implicated in fortifying cancer and addiction patl1ways. The optimal sequencing depth for the detection of lncRNAs varies, ranging from JOM reads to 90M reads. We therefore set out to assess the optimal sequencing depth for such experiments. Thus, utilizing basic scripting language and the R programming language, we have designed a modifiable framework that will help assess the optimal sequencing depth of long non-coding RNAs. The framework utilizes various statistical metl1ods to assess the quality of sequencing data at dlifferent depths. The original goal of this framework was to define sequencing depth needs that are to be .generated from Collaborative Cross founder strain mice. Preliminary data from test datasets show that sequencing depths as low as 70 percent or even less are not extremely dissimilar to the full depth in certain cases.

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