| 1. |
- Nookaew, Intawat, 1977, et al.
(författare)
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A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays : a case study in Saccharomyces cerevisiae
- 2012
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 40:20, s. 10084-10097
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Tidskriftsartikel (refereegranskat)abstract
- RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach. High reproducibility among biological replicates (correlation >= 0.99) and high consistency between the two platforms for analysis of gene expression levels (correlation >= 0.91) are reported. The results from differential gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays) for gene expression analysis and addresses the contribution of the different steps involved in the analysis of RNA-seq data.
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| 2. |
- Bergman, Julia, et al.
(författare)
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The human adrenal gland proteome defined by transcriptomics and antibody-based profiling.
- 2017
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Ingår i: Endocrinology. - : Endocrine Society. - 0013-7227 .- 1945-7170. ; 158:2, s. 239-251
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Tidskriftsartikel (refereegranskat)abstract
- The adrenal gland is a composite endocrine organ with vital functions that include the synthesis and release of glucocorticoids and catecholamines. To define the molecular landscape that underlies the specific functions of the adrenal gland, we combined a genome-wide transcriptomics approach using messenger RNA sequencing of human tissues with immunohistochemistry-based protein profiling on tissue microarrays. Approximately two-thirds of all putative protein coding genes were expressed in the adrenal gland, and the analysis identified 253 genes with an elevated pattern of expression in the adrenal gland, with only 37 genes showing a markedly greater expression level (more than fivefold) in the adrenal gland compared with 31 other normal human tissue types analyzed. The analyses allowed for an assessment of the relative expression levels for well-known proteins involved in adrenal gland function but also identified previously poorly characterized proteins in the adrenal cortex, such as the FERM (4.1 protein, ezrin, radixin, moesin) domain containing 5 and the nephroblastoma overexpressed (NOV) protein homolog. We have provided a global analysis of the adrenal gland transcriptome and proteome, with a comprehensive list of genes with elevated expression in the adrenal gland and spatial information with examples of protein expression patterns for corresponding proteins. These genes and proteins constitute important starting points for an improved understanding of the normal function and pathophysiology of the adrenal glands.
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| 3. |
- Fagerberg, Linn, 1981-
(författare)
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Mapping the human proteome using bioinformatic methods
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The fundamental goal of proteomics is to gain an understanding of the expression and function of the proteome on the level of individual proteins, on the level of defined cell types and on the level of the entire organism. In this thesis, the human proteome is explored using membrane protein topology prediction methods to define the human membrane proteome and by global protein expression profiling, which relies on a complex study of the location and expression levels of proteins in tissues and cells. A whole-proteome analysis was performed based on the predicted protein-coding genes of humans using a selection of membrane protein topology prediction methods. The study used a majority decision-based method, which estimated that approximately 26% of the human genes encode for a membrane protein. The prediction results are displayed in a visualization tool to facilitate the selection of antigens to be used for antibody generation. Global protein expression profiles in a large number of cells and tissues in the human body were analyzed for more than 4000 protein targets, based on data from the antibody-based immunohistochemistry and immunofluorescence methods within the framework of the Human Protein Atlas project. The results revealed few cell-type specific proteins and a high fraction of human proteins expressed in most cells, suggesting that cell and tissue specificity is attained by a fine-tuned regulation of protein levels. The expression profiles were also used to analyze the relationship between 45 cell lines by hierarchical clustering and principal component analysis. The global protein expression patterns overall reflected the tumor origin of the cells, and also allowed for identification of proteins of importance for distinguishing different categories of cell lines, as defined by phenotype of progenitor cell. In addition, the protein distribution in 16 subcellular compartments in three of the human cell lines was mapped. A large fraction of proteins were localized in two or more compartments and, in line with previous results, a majority of proteins were detected in all three cell lines. Finally, mass spectrometry-based protein expression levels were compared to RNA-seq-based transcript expression levels in three cell lines. Highly ubiquitous mRNA expression was found and the changes of expression levels between the cell lines showed high correlations between proteins and transcripts. Large general differences in abundance of proteins from various functional classes were observed. A comparison between categories based on expression levels revealed that, in general, genes with varying expression levels between the cell lines or only expressed in one cell line were highly enriched for cell-surface proteins. These studies show a path for a systematic analysis to characterize the proteome in human cells, tissues and organs.
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| 4. |
- Klevebring, Daniel, 1981-, et al.
(författare)
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Analysis of transcript and protein overlap in a human osteosarcoma cell line
- 2010
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 11:1, s. 684-
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Tidskriftsartikel (refereegranskat)abstract
- Background: An interesting field of research in genomics and proteomics is to compare the overlap between the transcriptome and the proteome. Recently, the tools to analyse gene and protein expression on a whole-genome scale have been improved, including the availability of the new generation sequencing instruments and high-throughput antibody-based methods to analyze the presence and localization of proteins. In this study, we used massive transcriptome sequencing (RNA-seq) to investigate the transcriptome of a human osteosarcoma cell line and compared the expression levels with in situ protein data obtained in-situ from antibody-based immunohistochemistry (IHC) and immunofluorescence microscopy (IF). Results: A large-scale analysis based on 2749 genes was performed, corresponding to approximately 13% of the protein coding genes in the human genome. We found the presence of both RNA and proteins to a large fraction of the analyzed genes with 60% of the analyzed human genes detected by all three methods. Only 34 genes (1.2%) were not detected on the transcriptional or protein level with any method. Our data suggest that the majority of the human genes are expressed at detectable transcript or protein levels in this cell line. Since the reliability of antibodies depends on possible cross-reactivity, we compared the RNA and protein data using antibodies with different reliability scores based on various criteria, including Western blot analysis. Gene products detected in all three platforms generally have good antibody validation scores, while those detected only by antibodies, but not by RNA sequencing, generally consist of more low-scoring antibodies. Conclusion: This suggests that some antibodies are staining the cells in an unspecific manner, and that assessment of transcript presence by RNA-seq can provide guidance for validation of the corresponding antibodies.
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| 5. |
- Thul, Peter J., et al.
(författare)
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A subcellular map of the human proteome
- 2017
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Ingår i: Science. - : American Association for the Advancement of Science. - 0036-8075 .- 1095-9203. ; 356:6340
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Tidskriftsartikel (refereegranskat)abstract
- Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.
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