Bioinformatics comparison of sulfate-reducing metabolism nucleotide sequences

G. Tremberger Jr.; Sunil Dehipawala; A. Nguyen; E. Cheung; R. Sullivan; T. Holden; D. Lieberman; T. Cheung

doi:10.1117/12.2188390

11 September 2015 Bioinformatics comparison of sulfate-reducing metabolism nucleotide sequences

G. Tremberger Jr., Sunil Dehipawala, A. Nguyen, E. Cheung, R. Sullivan, T. Holden, D. Lieberman, T. Cheung

Proceedings Volume 9606, Instruments, Methods, and Missions for Astrobiology XVII; 960610 (2015) https://doi.org/10.1117/12.2188390
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

The sulfate-reducing bacteria can be traced back to 3.5 billion years ago. The thermodynamics details of the sulfur cycle have been well documented. A recent sulfate-reducing bacteria report (Robator, Jungbluth, et al , 2015 Jan, Front. Microbiol) with Genbank nucleotide data has been analyzed in terms of the sulfite reductase (dsrAB) via fractal dimension and entropy values. Comparison to oil field sulfate-reducing sequences was included. The AUCG translational mass fractal dimension versus ATCG transcriptional mass fractal dimension for the low temperature dsrB and dsrA sequences reported in Reference Thirteen shows correlation R-sq ~ 0.79 , with a probably of about 3% in simulation. A recent report of using Cystathionine gamma-lyase sequence to produce CdS quantum dot in a biological method, where the sulfur is reduced just like in the H2S production process, was included for comparison. The AUCG mass fractal dimension versus ATCG mass fractal dimension for the Cystathionine gamma-lyase sequences was found to have R-sq of 0.72, similar to the low temperature dissimilatory sulfite reductase dsr group with 3% probability, in contrary to the oil field group having R-sq ~ 0.94, a high probable outcome in the simulation. The other two simulation histograms, namely, fractal dimension versus entropy R-sq outcome values, and di-nucleotide entropy versus mono-nucleotide entropy R-sq outcome values are also discussed in the data analysis focusing on low probability outcomes.

Citation Download Citation

G. Tremberger Jr., Sunil Dehipawala, A. Nguyen, E. Cheung, R. Sullivan, T. Holden, D. Lieberman, and T. Cheung "Bioinformatics comparison of sulfate-reducing metabolism nucleotide sequences", Proc. SPIE 9606, Instruments, Methods, and Missions for Astrobiology XVII, 960610 (11 September 2015); https://doi.org/10.1117/12.2188390

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available