Sign in
BioinformaticsBioinformatics
Peer-reviewed journal article

L1EM: A tool for accurate locus specific LINE-1 RNA quantification

Wilson McKerrowiD, David FenyöiD

Abstract

MotivationLINE-1 elements are retrotransposons that are capable of copying their sequence to new genomic loci. LINE-1 derepression is associated with a number of disease states, and has the potential to cause significant cellular damage. Because LINE-1 elements are repetitive, it is difficult to quantify RNA at specific LINE-1 loci and to separate transcripts with protein coding capability from other sources of LINE-1 RNA.\n\nResultsWe provide a tool, L1-EM that uses the expectation maximization algorithm to quantify LINE-1 RNA at each genomic locus, separating transcripts that are capable of generating retrotransposition from those that are not. We show the accuracy of L1-EM on simulated data and against long read sequencing from HEK cells.\n\nAvailabilityL1-EM is written in python. The source code along with the necessary annotations are available at https://github.com/FenyoLab/L1EM and distributed under GPLv3.\n\nContactwilson.mckerrow@nyulangone.org, david@fenyolab.org

PDFDOICode

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.