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Authors: Benjamin A. Neely 1 and Paul E. Anderson 2

Affiliations: 1 National Institute of Standards and Technology, United States ; 2 College of Charleston, United States

ISBN: 978-989-758-214-1

ISSN: 2184-4305

Keyword(s): Genomics, Trancriptomics, Proteomics, Proteogenomics, Proteotranscriptomics.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Bioinformatics ; Biomedical Engineering ; Genomics and Proteomics ; Transcriptomics

Abstract: As the speed and quality of different analytical platforms increase, it is more common to collect data across multiple biological domains in parallel (\textit{i.e.}, genomics, transcriptomics, proteomics, and metabolomics). There is a growing interest in algorithms and tools that leverage heterogeneous data streams in a meaningful way. Since these domains are typically non-linearly related, we evaluated whether results from one domain could be used to prioritize another domain to increase the power of detection, maintain type 1 error, and highlight biologically relevant changes in the secondary domain. To perform this feature prioritization, we developed a methodology called Complementary Domain Prioritization that utilizes the underpinning biology to relate complementary domains. Herein, we evaluate how proteomic data can guide transcriptomic differential expression analysis by analyzing two published colorectal cancer proteotranscriptomic data sets. The proposed strategy im proved detection of cancer-related genes compared to standard permutation invariant filtering approaches and did not increase type I error. Moreover, this approach detected differentially expressed genes that would not have been detected using filtering alone while also highlighted pathways that might have otherwise been overlooked. These results demonstrate how this strategy can effectively prioritize transcriptomic data and drive new hypotheses, though subsequent validation studies are still required. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Neely, B. and Anderson, P. (2017). Complementary Domain Prioritization: A Method to Improve Biologically Relevant Detection in Multi-Omic Data Sets. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017) ISBN 978-989-758-214-1 ISSN 2184-4305, pages 68-80. DOI: 10.5220/0006151500680080

@conference{bioinformatics17,
author={Benjamin A. Neely. and Paul E. Anderson.},
title={Complementary Domain Prioritization: A Method to Improve Biologically Relevant Detection in Multi-Omic Data Sets},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={68-80},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006151500680080},
isbn={978-989-758-214-1},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)
TI - Complementary Domain Prioritization: A Method to Improve Biologically Relevant Detection in Multi-Omic Data Sets
SN - 978-989-758-214-1
IS - 2184-4305
AU - Neely, B.
AU - Anderson, P.
PY - 2017
SP - 68
EP - 80
DO - 10.5220/0006151500680080

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