Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome
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Publication Date: | 2014 |
Other Authors: | , , |
Format: | Article |
Language: | eng |
Source: | Repositório Institucional da UFRGS |
Download full: | http://hdl.handle.net/10183/111840 |
Summary: | 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. |
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Silva, Samoel Renan Mello daPerrone, Gabriel CuryDinis, João MedeirosAlmeida, Rita Maria Cunha de2015-03-07T01:57:15Z20141471-2164http://hdl.handle.net/10183/111840000953783Background: 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.application/pdfengBMC Genomics. London. Vol. 15 (Dec. 2014), 1181, 18 p.TranscriptomaExpressão gênicaBiofísicaTranscriptogramGene expression analysisTranscriptomeMicroarrayReproducibility enhancement and differential expression of non predefined functional gene sets in human genomeEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000953783.pdf000953783.pdfTexto completo (inglês)application/pdf2384939http://www.lume.ufrgs.br/bitstream/10183/111840/1/000953783.pdfe1987e677ff1b293bda5d87b7a82807bMD51TEXT000953783.pdf.txt000953783.pdf.txtExtracted Texttext/plain74123http://www.lume.ufrgs.br/bitstream/10183/111840/2/000953783.pdf.txt77ac548804928fed84d0b4f9c57fb99eMD52THUMBNAIL000953783.pdf.jpg000953783.pdf.jpgGenerated Thumbnailimage/jpeg1965http://www.lume.ufrgs.br/bitstream/10183/111840/3/000953783.pdf.jpg3d13897eeb11f02d8e42e0671b0d2c30MD5310183/1118402024-03-29 06:18:37.124312oai:www.lume.ufrgs.br:10183/111840Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2024-03-29T09:18:37Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
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 |
spellingShingle |
Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome Silva, Samoel Renan Mello da Transcriptoma Expressão gênica Biofísica Transcriptogram Gene expression analysis Transcriptome Microarray |
title_short |
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_sort |
Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome |
author |
Silva, Samoel Renan Mello da |
author_facet |
Silva, Samoel Renan Mello da Perrone, Gabriel Cury Dinis, João Medeiros Almeida, Rita Maria Cunha de |
author_role |
author |
author2 |
Perrone, Gabriel Cury Dinis, João Medeiros Almeida, Rita Maria Cunha de |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Silva, Samoel Renan Mello da Perrone, Gabriel Cury Dinis, João Medeiros Almeida, Rita Maria Cunha de |
dc.subject.por.fl_str_mv |
Transcriptoma Expressão gênica Biofísica |
topic |
Transcriptoma Expressão gênica Biofísica Transcriptogram Gene expression analysis Transcriptome Microarray |
dc.subject.eng.fl_str_mv |
Transcriptogram Gene expression analysis Transcriptome Microarray |
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. |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014 |
dc.date.accessioned.fl_str_mv |
2015-03-07T01:57:15Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
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publishedVersion |
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http://hdl.handle.net/10183/111840 |
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1471-2164 |
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000953783 |
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http://hdl.handle.net/10183/111840 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
BMC Genomics. London. Vol. 15 (Dec. 2014), 1181, 18 p. |
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info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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