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Assessing the consistency of public human tissue RNA-seq data sets
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. (Involved Human Prot Atlas Project)ORCID iD: 0000-0002-7692-1100
2015 (English)In: Briefings in Bioinformatics, ISSN 1467-5463, E-ISSN 1477-4054, Vol. 16, no 6, 941-949 p.Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

Sequencing-based gene expression methods like RNA-sequencing (RNA-seq) have become increasingly common, but it is often claimed that results obtained in different studies are not comparable owing to the influence of laboratory batch effects, differences in RNA extraction and sequencing library preparation methods and bioinformatics processing pipelines. It would be unfortunate if different experiments were in fact incomparable, as there is great promise in data fusion and meta-analysis applied to sequencing data sets. We therefore compared reported gene expression measurements for ostensibly similar samples (specifically, human brain, heart and kidney samples) in several different RNA-seq studies to assess their overall consistency and to examine the factors contributing most to systematic differences. The same comparisons were also performed after preprocessing all data in a consistent way, eliminating potential bias from bioinformatics pipelines. We conclude that published human tissue RNA-seq expression measurements appear relatively consistent in the sense that samples cluster by tissue rather than laboratory of origin given simple preprocessing transformations. The article is supplemented by a detailed walkthrough with embedded R code and figures.

Place, publisher, year, edition, pages
Oxford University Press, 2015. Vol. 16, no 6, 941-949 p.
Keyword [en]
RNA-seq, public data, meta-analysis, gene expression, clustering
National Category
Biochemistry and Molecular Biology Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-180145DOI: 10.1093/bib/bbv017ISI: 000365708700005PubMedID: 25829468OAI: oai:DiVA.org:kth-180145DiVA: diva2:893752
Note

QC 20160113

Available from: 2016-01-13 Created: 2016-01-07 Last updated: 2016-11-20Bibliographically approved
In thesis
1. Integration of RNA and protein expression profiles to study human cells
Open this publication in new window or tab >>Integration of RNA and protein expression profiles to study human cells
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cellular life is highly complex. In order to expand our understanding of the workings of human cells, in particular in the context of health and disease, detailed knowledge about the underlying molecular systems is needed. The unifying theme of this thesis concerns the use of data derived from sequencing of RNA, both within the field of transcriptomics itself and as a guide for further studies at the level of protein expression. In paper I, we showed that publicly available RNA-seq datasets are consistent across different studies, requiring only light processing for the data to cluster according to biological, rather than technical characteristics. This suggests that RNA-seq has developed into a reliable and highly reproducible technology, and that the increasing amount of publicly available RNA-seq data constitutes a valuable resource for meta-analyses. In paper II, we explored the ability to extrapolate protein concentrations by the use of RNA expression levels. We showed that mRNA and corresponding steady-state protein concentrations correlate well by introducing a gene-specific RNA-to-protein conversion factor that is stable across various cell types and tissues. The results from this study indicate the utility of RNA-seq also within the field of proteomics.

The second part of the thesis starts with a paper in which we used transcriptomics to guide subsequent protein studies of the molecular mechanisms underlying malignant transformation. In paper III, we applied a transcriptomics approach to a cell model for defined steps of malignant transformation, and identified several genes with interesting expression patterns whose corresponding proteins were further analyzed with subcellular spatial resolution. Several of these proteins were further studied in clinical tumor samples, confirming that this cell model provides a relevant system for studying cancer mechanisms. In paper IV, we continued to explore the transcriptional landscape in the same cell model under moderate hypoxic conditions.

To conclude, this thesis demonstrates the usefulness of RNA-seq data, from a transcriptomics perspective and beyond; to guide in analyses of protein expression, with the ultimate goal to unravel the complexity of the human cell, from a holistic point of view.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 54 p.
Series
TRITA-BIO-Report, ISSN 1654-2312
Keyword
RNA-seq, Transcriptomics, Proteomics, Malignant transformation, Cancer, Functional enrichment
National Category
Biological Sciences
Research subject
Biotechnology; Medical Technology
Identifiers
urn:nbn:se:kth:diva-196700 (URN)978-91-7729-209-8 (ISBN)
Public defence
2016-12-16, Rockefeller, Nobels väg 11, Solna, 13:00 (English)
Opponent
Supervisors
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20161121

Available from: 2016-11-21 Created: 2016-11-18 Last updated: 2016-11-21Bibliographically approved

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