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Detecting life signatures with RNA sequence similarity measures
Szymon Wasik1, Natalia Szostak1, Mateusz Kudla2
1Institute of Computing Science, Poznan University of Technology, Poznan, Poland; Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland; European Centre for Bioinformatics and Genomics, Poznan, Poland.
Journal of Theoretical Biology
|December 19, 2018
Summary
This study introduces computational "life probes" using normalized compression distance and Levenshtein distance to identify biological genetic sequences. These probes effectively detect life signatures in short RNA fragments, supporting the RNA World hypothesis.
Area of Science:
- Origin of Life studies
- Computational Biology
- Astrobiology
Background:
- The RNA World hypothesis is a leading theory for life's origins.
- Verifying hypothetical ancient genetic sequences as biological is challenging.
- Computer simulations are increasingly used to study early life.
Purpose of the Study:
- To develop computational 'life probes' for identifying biological genetic sequences.
- To assess the sensitivity of these probes to life's signatures.
- To validate methods within the context of the RNA World hypothesis.
Main Methods:
- Decision algorithms based on Normalized Compression Distance (NCD).
- Decision algorithms based on Levenshtein Distance (LD).
- Validation using short genetic sequences (<100 nucleotides).
Main Results:
- Both NCD and LD successfully constructed effective 'life probes'.
- Probes significantly outperformed random decision procedures.
- Replication-related RNA fragments showed stronger discriminatory power.
Conclusions:
- Computational 'life probes' can effectively detect signatures of life in short RNA samples.
- Simple computational methods can identify biological characteristics in genetic sequences.
- Findings support the plausibility of the RNA World hypothesis.
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