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Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome

BACKGROUND: Transcriptogram profiling is a method to present and analyze transcription data in a genome-wide scale that reduces noise and facilitates biological interpretation. An ordered gene list is produced, such that the probability that the genes are functionally associated exponentially decays...

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Autores principales: da Silva, Samoel RM, Perrone, Gabriel C, Dinis, João M, de Almeida, Rita MC
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4326474/
https://www.ncbi.nlm.nih.gov/pubmed/25539829
http://dx.doi.org/10.1186/1471-2164-15-1181
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author da Silva, Samoel RM
Perrone, Gabriel C
Dinis, João M
de Almeida, Rita MC
author_facet da Silva, Samoel RM
Perrone, Gabriel C
Dinis, João M
de Almeida, Rita MC
author_sort da Silva, Samoel RM
collection PubMed
description BACKGROUND: Transcriptogram profiling is a method to present and analyze transcription data in a genome-wide scale that reduces noise and facilitates biological interpretation. An ordered gene list is produced, such that the probability that the genes are functionally associated exponentially decays with their distance on the list. This list presents a biological logic, evinced by the selective enrichment of successive intervals with Gene Ontology terms or KEGG pathways. Transcriptograms are expression profiles obtained by taking the average of gene expression over neighboring genes on this list. Transcriptograms enhance reproducibility and precision for expression measurements of functionally correlated gene sets. RESULTS: Here we present an ordering list for Homo sapiens and apply the transcriptogram profiling method to different datasets. We show that this method enhances experiment reproducibility and enhances signal. We applied the method to a diabetes study by Hwang and collaborators, which focused on expression differences between cybrids produced by the hybridization of mitochondria of diabetes mellitus donors with osteosarcoma cell lines, depleted of mitochondria. We found that the transcriptogram method revealed significant differential expression in gene sets linked to blood coagulation and wound healing pathways, and also to gene sets that do not represent any metabolic pathway or Gene Ontology term. These gene sets are connected to ECM-receptor interaction and secreted proteins. CONCLUSION: The transcriptogram profiling method provided an automatic way to define sets of genes with correlated expression, reduce noise in genome-wide transcription profiles, and enhance measure reproducibility and sensitivity. These advantages enabled biologic interpretation and pointed to differentially expressed gene sets in diabetes mellitus which were not previously defined. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-1181) contains supplementary material, which is available to authorized users.
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spelling pubmed-43264742015-02-14 Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome da Silva, Samoel RM Perrone, Gabriel C Dinis, João M de Almeida, Rita MC BMC Genomics Research Article BACKGROUND: Transcriptogram profiling is a method to present and analyze transcription data in a genome-wide scale that reduces noise and facilitates biological interpretation. An ordered gene list is produced, such that the probability that the genes are functionally associated exponentially decays with their distance on the list. This list presents a biological logic, evinced by the selective enrichment of successive intervals with Gene Ontology terms or KEGG pathways. Transcriptograms are expression profiles obtained by taking the average of gene expression over neighboring genes on this list. Transcriptograms enhance reproducibility and precision for expression measurements of functionally correlated gene sets. RESULTS: Here we present an ordering list for Homo sapiens and apply the transcriptogram profiling method to different datasets. We show that this method enhances experiment reproducibility and enhances signal. We applied the method to a diabetes study by Hwang and collaborators, which focused on expression differences between cybrids produced by the hybridization of mitochondria of diabetes mellitus donors with osteosarcoma cell lines, depleted of mitochondria. We found that the transcriptogram method revealed significant differential expression in gene sets linked to blood coagulation and wound healing pathways, and also to gene sets that do not represent any metabolic pathway or Gene Ontology term. These gene sets are connected to ECM-receptor interaction and secreted proteins. CONCLUSION: The transcriptogram profiling method provided an automatic way to define sets of genes with correlated expression, reduce noise in genome-wide transcription profiles, and enhance measure reproducibility and sensitivity. These advantages enabled biologic interpretation and pointed to differentially expressed gene sets in diabetes mellitus which were not previously defined. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-1181) contains supplementary material, which is available to authorized users. BioMed Central 2014-12-24 /pmc/articles/PMC4326474/ /pubmed/25539829 http://dx.doi.org/10.1186/1471-2164-15-1181 Text en © da Silva et al.; licensee BioMed Central. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
da Silva, Samoel RM
Perrone, Gabriel C
Dinis, João M
de Almeida, Rita MC
Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome
title Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome
title_full Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome
title_fullStr Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome
title_full_unstemmed Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome
title_short Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome
title_sort reproducibility enhancement and differential expression of non predefined functional gene sets in human genome
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4326474/
https://www.ncbi.nlm.nih.gov/pubmed/25539829
http://dx.doi.org/10.1186/1471-2164-15-1181
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