TY - GEN
T1 - Use of average mutual information signatures to construct phylogenetic trees for fungi
AU - Sayood, Khalid
AU - Atkin, Audrey L.
AU - Newcomb, Garin
N1 - Funding Information:
ACKNOWLEDGMENTS This work was supported in part by the National Science Foundation research grant #1244247 to A.L.A. Any opinions, findings, conclusions or recommendations expressed in this report are ours and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/27
Y1 - 2017/9/27
N2 - Average mutual information (AMI) has been applied to many fields, including various aspects of bioinformatics. In this paper, we evaluate its performance as a measure of evolutionary distance between sequences. We use the internal transcribed spacer (ITS) regions for 16 fungal sequences as representative sequences used for species comparison. We generate profiles based on the AMI for each species' ITS sequence. We then populate a distance matrix for the set of species using either a Euclidean or correlation distance between AMI profiles. We generate phylogenetic trees using the distance matrices as input. While these trees do not exactly match the accepted fungal phylogeny, there are sufficient commonalities to merit further investigation of AMI as a distance metric and tool for inferring relationships. We also simulate the evolution of an ITS sequence in order to observe how point mutations affect the distance between AMI profiles, concluding that a correlation distance performs slightly better than a Euclidean distance.
AB - Average mutual information (AMI) has been applied to many fields, including various aspects of bioinformatics. In this paper, we evaluate its performance as a measure of evolutionary distance between sequences. We use the internal transcribed spacer (ITS) regions for 16 fungal sequences as representative sequences used for species comparison. We generate profiles based on the AMI for each species' ITS sequence. We then populate a distance matrix for the set of species using either a Euclidean or correlation distance between AMI profiles. We generate phylogenetic trees using the distance matrices as input. While these trees do not exactly match the accepted fungal phylogeny, there are sufficient commonalities to merit further investigation of AMI as a distance metric and tool for inferring relationships. We also simulate the evolution of an ITS sequence in order to observe how point mutations affect the distance between AMI profiles, concluding that a correlation distance performs slightly better than a Euclidean distance.
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U2 - 10.1109/EIT.2017.8053394
DO - 10.1109/EIT.2017.8053394
M3 - Conference contribution
AN - SCOPUS:85033663057
T3 - IEEE International Conference on Electro Information Technology
SP - 398
EP - 403
BT - 2017 IEEE International Conference on Electro Information Technology, EIT 2017
PB - IEEE Computer Society
T2 - 2017 IEEE International Conference on Electro Information Technology, EIT 2017
Y2 - 14 May 2017 through 17 May 2017
ER -