Accessing and applying molecular history
1
Machine Learning Research, Silver Spring, Maryland, United States
2
School of Biology, Georgia Institute of Technology, Atlanta, Georgia, United States
3
School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, United States
4
General Genomics, Atlanta, Georgia, United States
- Published
- Accepted
- Subject Areas
- Biochemistry, Bioinformatics, Computational Biology, Evolutionary Studies, Computational Science
- Keywords
- molecular evolution, machine learning, synthetic biology, evolutionary biochemistry, orthology inference, dynamic programming, BLAST, Markov processes, phyletic patterns
- Copyright
- © 2015 Stern et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. Accessing and applying molecular history. PeerJ PrePrints 3:e1293v1 https://doi.org/10.7287/peerj.preprints.1293v1
Abstract
Studying the evolutionary history of life’s molecules - DNA, RNA, and protein - reveals nature-based solutions to real-world problems. We discuss an approach to applied molecular evolution that is well-known within the field but may be unfamiliar to a wider audience. Using a case study at the intersection of molecular evolution and medicine, we introduce the fundamental concepts of orthology and paralogy. We also explain a practical entry point to molecular evolution named STORI: Selectable Taxon Ortholog Retrieval Iteratively. STORI is a machine learning algorithm designed to clear a bottleneck that researchers encounter when studying evolution.
Author Comment
This is the first version of a preprint. This is a preprint submission to PeerJ.