Probabilistic models of biological enzymatic polymerization

Marshall Hampton, Miranda Galey, Clara Smoniewski, Sara L. Zimmer

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, hierarchies of probabilistic models are evaluated for their ability to characterize the untemplated addition of adenine and uracil to the 3’ ends of mitochondrial mRNAs of the human pathogen Trypanosoma brucei, and for their generative abilities to reproduce populations of these untemplated adenine/uridine “tails”. We determined the most ideal Hidden Markov Models (HMMs) for this biological system. While our HMMs were not able to generatively reproduce the length distribution of the tails, they fared better in reproducing nucleotide composition aspects of the tail populations. The HMMs robustly identified distinct states of nucleotide addition that correlate to experimentally verified tail nucleotide composition differences. However they also identified a surprising subclass of tails among the ND1 gene transcript populations that is unexpected given the current idea of sequential enzymatic action of untemplated tail addition in this system. Therefore, these models can not only be utilized to reflect biological states that we already know about, they can also identify hypotheses to be experimentally tested. Finally, our HMMs supplied a way to correct a portion of the sequencing errors present in our data. Importantly, these models constitute rare simple pedagogical examples of applied bioinformatic HMMs, due to their binary emissions.

Original languageEnglish (US)
Article numbere0244858
JournalPloS one
Volume16
Issue number1 January
DOIs
StatePublished - Jan 2021

Bibliographical note

Publisher Copyright:
Copyright: © 2021 Hampton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

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