Review
Version 1
Preserved in Portico This version is not peer-reviewed
From Sequence to Information
Version 1
: Received: 20 November 2019 / Approved: 22 November 2019 / Online: 22 November 2019 (02:28:15 CET)
How to cite: Popa, O.; Oldenburg, E.; Ebenhöh, O. From Sequence to Information. Preprints 2019, 2019110252. https://doi.org/10.20944/preprints201911.0252.v1 Popa, O.; Oldenburg, E.; Ebenhöh, O. From Sequence to Information. Preprints 2019, 2019110252. https://doi.org/10.20944/preprints201911.0252.v1
Abstract
Today massive amounts of sequenced metagenomic and -transcriptomic data from different ecological niches and environmental locations are available. Scientific progress depends critically on methods that allow extracting useful information from the various types of sequence data. Here, we will first discuss types of information contained in the various flavours of biological sequence data, and how this information can be interpreted to increase our scientific knowledge and understanding. We argue that a mechanistic understanding is required to consistently interpret experimental observations, and that this understanding is greatly facilitated by the generation and analysis of dynamic mathematical models. We conclude that, in order to construct mathematical models and to test mechanistic hypotheses, time-series data is of critical importance. We review diverse techniques to analyse time-series data and discuss various approaches by which time-series of biological sequence data was successfully used to derive and test mechanistic hypotheses. Analysing the bottlenecks of current strategies in the extraction of knowledge and understanding from data, we conclude that combined experimental and theoretical efforts should be implemented as early as possible during the planning phase of individual experiments and scientific research projects.
Keywords
data; sequence; information; entropy; genome; gene; proteins; time-series; modeling; meta-genomics; transcriptomics; proteomics; bioinformatics; DNA
Subject
Biology and Life Sciences, Ecology, Evolution, Behavior and Systematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
