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Full-Text Articles in Computational Biology

Mutations Of Adjacent Amino Acid Pairs Are Not Always Independent, Jyotsna Ramanan, Peter Revesz Oct 2015

Mutations Of Adjacent Amino Acid Pairs Are Not Always Independent, Jyotsna Ramanan, Peter Revesz

CSE Conference and Workshop Papers

Evolutionary studies usually assume that the genetic mutations are independent of each other. This paper tests the independence hypothesis for genetic mutations with regard to protein coding regions. According to the new experimental results the independence assumption generally holds, but there are certain exceptions. In particular, the coding regions that represent two adjacent amino acids seem to change in ways that sometimes deviate significantly from the expected theoretical probability under the independence assumption.


A Computational Model Of The Spread Of Ancient Human Populations Based On Mitochondrial Dna Samples, Peter Revesz Oct 2015

A Computational Model Of The Spread Of Ancient Human Populations Based On Mitochondrial Dna Samples, Peter Revesz

CSE Conference and Workshop Papers

The extraction of mitochondrial DNA (mtDNA) from ancient human population samples provides important data for the reconstruction of population influences, spread and evolution from the Neolithic to the present. This paper presents a mtDNA-based similarity measure between pairs of human populations and a computational model for the evolution of human populations. In a computational experiment, the paper studies the mtDNA information from five Neolithic and Bronze Age populations, namely the Andronovo, the Bell Beaker, the Minoan, the Rössen and the Únětice populations. In the past these populations were identified as separate cultural groups based on geographic location, age and the …


Classification Of Genomic Sequences By Latent Semantic Analysis, Samuel F. Way Aug 2012

Classification Of Genomic Sequences By Latent Semantic Analysis, Samuel F. Way

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Evolutionary distance measures provide a means of identifying and organizing related organisms by comparing their genomic sequences. As such, techniques that quantify the level of similarity between DNA sequences are essential in our efforts to decipher the genetic code in which they are written.

Traditional methods for estimating the evolutionary distance separating two genomic sequences often require that the sequences first be aligned before they are compared. Unfortunately, this preliminary step imposes great computational burden, making this class of techniques impractical for applications involving a large number of sequences. Instead, we desire new methods for differentiating genomic sequences that eliminate …