Abstract
The existence of complete genome sequences makes it important to develop different approaches for classification of large-scale data sets and to make extraction of biological insights easier. Here, we propose an approach for classification of complete proteomes/protein sets based on protein distributions on some basic attributes. We demonstrate the usefulness of this approach by determining protein distributions in terms of two attributes: protein lengths and protein intrinsic disorder contents (ID). The protein distributions based on L and ID are surveyed for representative proteome organisms and protein sets from the three domains of life. The two-dimensional maps (designated as fingerprints here) from the protein distribution densities in the LD space defined by ln(L) and ID are then constructed. The fingerprints for different organisms and protein sets are found to be distinct with each other, and they can therefore be used for comparative studies. As a test case, phylogenetic trees have been constructed based on the protein distribution densities in the fingerprints of proteomes of organisms without performing any protein sequence comparison and alignments. The phylogenetic trees generated are biologically meaningful, demonstrating that the protein distributions in the LD space may serve as unique phylogenetic signals of the organisms at the proteome level.
Original language | English |
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Article number | 9784161 |
Journal | International Journal of Genomics |
Volume | 2018 |
DOIs | |
State | Published - 2018 |
Funding
This work is supported by the U.S. Department of Energy (DOE), Office of Science, Genomic Science Program, under Award no. DE-SC0008834. The College of Engineering & Computer Science, SimCenter, University of Tennessee Chattanooga, 701 East M. L. King Blvd., Chattanooga, TN 37403, USA, is the current address of Hao-Bo Guo. Oak Ridge National Laboratory is managed by UT-Battelle LLC for the US DOE under Contract no. DE-AC05-00OR22725. A presentation for a part of this work has been given at the Quantitative Biology 2017 Meeting in Beijing.