On identifying statistical redundancy at the level of amino acid subsequences

Sandun Rajapaksa; DInithi Sumanaweera; Maria Garcia De La Banda; Peter Stuckey; David Abramson; Lloyd Allison; Arthur Lesk; Arun Konagurthu

doi:10.1109/BIBM52615.2021.9669282

On identifying statistical redundancy at the level of amino acid subsequences

Sandun Rajapaksa, DInithi Sumanaweera, Maria Garcia De La Banda, Peter Stuckey, David Abramson, Lloyd Allison, Arthur Lesk, Arun Konagurthu

Department of Data Science & AI

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

This paper presents a framework to characterize and identify local sequences of proteins that are statistically redundant under the measure of Shannon information content while accounting for variations in their occurrences over evolutionary insertions, deletions, and substitutions of amino acids. The identification of such local sequences provides insights for downstream studies on proteins. Here, we have applied our methods to amino acid sequence data sets derived from a database corresponding to 935,552 substructural regions of varying sizes, covering 113,724 proteins from the protein data bank. The results identify, among others, a surjective mapping between 110,598 local sequences (with an average length of 82 amino acids per sequence) and 1,493 topological shapes. The C++ source code and supporting material are available from https://lcb.infotech.monash.edu.au/bibm2021.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine
Editors	Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	64-71
Number of pages	8
ISBN (Electronic)	9781665401265
ISBN (Print)	9781665429825
DOIs	https://doi.org/10.1109/BIBM52615.2021.9669282
Publication status	Published - 2021
Event	IEEE International Conference on Bioinformatics and Biomedicine 2021 - Virtual, Online, United States of America Duration: 9 Dec 2021 → 12 Dec 2021 https://ieeexplore.ieee.org/xpl/conhome/9669261/proceeding (Proceedings)

Publication series

Name	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference	IEEE International Conference on Bioinformatics and Biomedicine 2021
Abbreviated title	BIBM 2021
Country/Territory	United States of America
City	Virtual, Online
Period	9/12/21 → 12/12/21
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9669261/proceeding (Proceedings)

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10.1109/BIBM52615.2021.9669282

Cite this

Rajapaksa, S., Sumanaweera, DI., De La Banda, M. G., Stuckey, P., Abramson, D., Allison, L., Lesk, A., & Konagurthu, A. (2021). On identifying statistical redundancy at the level of amino acid subsequences. In Y. Huang, L. Kurgan, F. Luo, X. T. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine (pp. 64-71). (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BIBM52615.2021.9669282

Rajapaksa, Sandun ; Sumanaweera, DInithi ; De La Banda, Maria Garcia et al. / On identifying statistical redundancy at the level of amino acid subsequences. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine. editor / Yufei Huang ; Lukasz Kurgan ; Feng Luo ; Xiaohua Tony Hu ; Yidong Chen ; Edward Dougherty ; Andrzej Kloczkowski ; Yaohang Li. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2021. pp. 64-71 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

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title = "On identifying statistical redundancy at the level of amino acid subsequences",

abstract = "This paper presents a framework to characterize and identify local sequences of proteins that are statistically redundant under the measure of Shannon information content while accounting for variations in their occurrences over evolutionary insertions, deletions, and substitutions of amino acids. The identification of such local sequences provides insights for downstream studies on proteins. Here, we have applied our methods to amino acid sequence data sets derived from a database corresponding to 935,552 substructural regions of varying sizes, covering 113,724 proteins from the protein data bank. The results identify, among others, a surjective mapping between 110,598 local sequences (with an average length of 82 amino acids per sequence) and 1,493 topological shapes. The C++ source code and supporting material are available from https://lcb.infotech.monash.edu.au/bibm2021. ",

author = "Sandun Rajapaksa and DInithi Sumanaweera and {De La Banda}, {Maria Garcia} and Peter Stuckey and David Abramson and Lloyd Allison and Arthur Lesk and Arun Konagurthu",

note = "Funding Information: We acknowledge the Univ. of Queensland Research Computing Centre (RCC) and Monash e-Research Centre (MeRC) for their support for this research. The authors thank David Green, Chao Jin, Mark Endrei and Marlies Hankel from RCC, and Wojtek Goscinski, Damien Leong, and Philip Chan from MeRC for their technical support. SR{\textquoteright}s PhD research is supported by Monash Graduate Scholarship. Publisher Copyright: {\textcopyright} 2021 IEEE.; IEEE International Conference on Bioinformatics and Biomedicine 2021, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1109/BIBM52615.2021.9669282",

language = "English",

isbn = "9781665429825",

series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "64--71",

editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, {Xiaohua Tony} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",

booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine",

address = "United States of America",

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}

Rajapaksa, S, Sumanaweera, DI, De La Banda, MG , Stuckey, P, Abramson, D, Allison, L, Lesk, A & Konagurthu, A 2021, On identifying statistical redundancy at the level of amino acid subsequences. in Y Huang, L Kurgan, F Luo, XT Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 64-71, IEEE International Conference on Bioinformatics and Biomedicine 2021, Virtual, Online, United States of America, 9/12/21. https://doi.org/10.1109/BIBM52615.2021.9669282

On identifying statistical redundancy at the level of amino acid subsequences. / Rajapaksa, Sandun; Sumanaweera, DInithi; De La Banda, Maria Garcia et al.
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine. ed. / Yufei Huang; Lukasz Kurgan; Feng Luo; Xiaohua Tony Hu; Yidong Chen; Edward Dougherty; Andrzej Kloczkowski; Yaohang Li. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2021. p. 64-71 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - On identifying statistical redundancy at the level of amino acid subsequences

AU - Rajapaksa, Sandun

AU - Sumanaweera, DInithi

AU - De La Banda, Maria Garcia

AU - Stuckey, Peter

AU - Abramson, David

AU - Allison, Lloyd

AU - Lesk, Arthur

AU - Konagurthu, Arun

N1 - Funding Information: We acknowledge the Univ. of Queensland Research Computing Centre (RCC) and Monash e-Research Centre (MeRC) for their support for this research. The authors thank David Green, Chao Jin, Mark Endrei and Marlies Hankel from RCC, and Wojtek Goscinski, Damien Leong, and Philip Chan from MeRC for their technical support. SR’s PhD research is supported by Monash Graduate Scholarship. Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - This paper presents a framework to characterize and identify local sequences of proteins that are statistically redundant under the measure of Shannon information content while accounting for variations in their occurrences over evolutionary insertions, deletions, and substitutions of amino acids. The identification of such local sequences provides insights for downstream studies on proteins. Here, we have applied our methods to amino acid sequence data sets derived from a database corresponding to 935,552 substructural regions of varying sizes, covering 113,724 proteins from the protein data bank. The results identify, among others, a surjective mapping between 110,598 local sequences (with an average length of 82 amino acids per sequence) and 1,493 topological shapes. The C++ source code and supporting material are available from https://lcb.infotech.monash.edu.au/bibm2021.

AB - This paper presents a framework to characterize and identify local sequences of proteins that are statistically redundant under the measure of Shannon information content while accounting for variations in their occurrences over evolutionary insertions, deletions, and substitutions of amino acids. The identification of such local sequences provides insights for downstream studies on proteins. Here, we have applied our methods to amino acid sequence data sets derived from a database corresponding to 935,552 substructural regions of varying sizes, covering 113,724 proteins from the protein data bank. The results identify, among others, a surjective mapping between 110,598 local sequences (with an average length of 82 amino acids per sequence) and 1,493 topological shapes. The C++ source code and supporting material are available from https://lcb.infotech.monash.edu.au/bibm2021.

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U2 - 10.1109/BIBM52615.2021.9669282

DO - 10.1109/BIBM52615.2021.9669282

M3 - Conference Paper

AN - SCOPUS:85125172127

SN - 9781665429825

T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

SP - 64

EP - 71

BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine

A2 - Huang, Yufei

A2 - Kurgan, Lukasz

A2 - Luo, Feng

A2 - Hu, Xiaohua Tony

A2 - Chen, Yidong

A2 - Dougherty, Edward

A2 - Kloczkowski, Andrzej

A2 - Li, Yaohang

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - IEEE International Conference on Bioinformatics and Biomedicine 2021

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ER -

Rajapaksa S, Sumanaweera DI, De La Banda MG , Stuckey P, Abramson D, Allison L et al. On identifying statistical redundancy at the level of amino acid subsequences. In Huang Y, Kurgan L, Luo F, Hu XT, Chen Y, Dougherty E, Kloczkowski A, Li Y, editors, Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2021. p. 64-71. (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). doi: 10.1109/BIBM52615.2021.9669282

On identifying statistical redundancy at the level of amino acid subsequences

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