Dataset Information

Complementing tissue characterization by integrating transcriptome profiling from the Human Protein Atlas and from the FANTOM5 consortium.

ABSTRACT: Understanding the normal state of human tissue transcriptome profiles is essential for recognizing tissue disease states and identifying disease markers. Recently, the Human Protein Atlas and the FANTOM5 consortium have each published extensive transcriptome data for human samples using Illumina-sequenced RNA-Seq and Heliscope-sequenced CAGE. Here, we report on the first large-scale complex tissue transcriptome comparison between full-length versus 5'-capped mRNA sequencing data. Overall gene expression correlation was high between the 22 corresponding tissues analyzed (R > 0.8). For genes ubiquitously expressed across all tissues, the two data sets showed high genome-wide correlation (91% agreement), with differences observed for a small number of individual genes indicating the need to update their gene models. Among the identified single-tissue enriched genes, up to 75% showed consensus of 7-fold enrichment in the same tissue in both methods, while another 17% exhibited multiple tissue enrichment and/or high expression variety in the other data set, likely dependent on the cell type proportions included in each tissue sample. Our results show that RNA-Seq and CAGE tissue transcriptome data sets are highly complementary for improving gene model annotations and highlight biological complexities within tissue transcriptomes. Furthermore, integration with image-based protein expression data is highly advantageous for understanding expression specificities for many genes.

SUBMITTER: Yu NY

PROVIDER: S-EPMC4538815 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Complementing tissue characterization by integrating transcriptome profiling from the Human Protein Atlas and from the FANTOM5 consortium.

Yu Nancy Yiu-Lin NY Hallström Björn M BM Fagerberg Linn L Ponten Fredrik F Kawaji Hideya H Carninci Piero P Forrest Alistair R R AR Hayashizaki Yoshihide Y Uhlén Mathias M Daub Carsten O CO

Nucleic acids research 20150627 14

Understanding the normal state of human tissue transcriptome profiles is essential for recognizing tissue disease states and identifying disease markers. Recently, the Human Protein Atlas and the FANTOM5 consortium have each published extensive transcriptome data for human samples using Illumina-sequenced RNA-Seq and Heliscope-sequenced CAGE. Here, we report on the first large-scale complex tissue transcriptome comparison between full-length versus 5'-capped mRNA sequencing data. Overall gene ex ...[more]

PMID: 26117540

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Project description:BackgroundTissue biopsy is an integral part of the diagnostic approach to lung cancer. It is however invasive and limited by heterogeneity. Liquid biopsies may complement tissue testing by providing additional molecular information and may be particularly helpful in patients from whom obtaining sufficient tissue for genomic profiling is challenging.MethodsPatients with suspected lung cancer (n = 71) were prospectively recruited. Blood and diagnostic tissue samples were collected within 48 h of each other. Plasma cell-free DNA (cfDNA) testing was done using an ultrasensitive amplicon-based next-generation sequencing (NGS) panel (plasma NGS testing). For cases diagnosed as non-small cell lung carcinoma (NSCLC) via histology or cytology, targeted testing for epidermal growth factor receptor (EGFR) mutations was performed using tissue biopsy samples (tissue EGFR testing), where available. Concordance of clinically actionable mutations between methods and sample types was assessed.ResultsFor confirmed NSCLC cases (n = 54), tissue EGFR test results were available only for 70.3% (38/54) due to sample inadequacies, compared to blood samples for 98.1% (53/54) cases. Tissue EGFR testing identified sensitizing EGFR (L858R or exon 19 deletion) mutation in 31.6% (12/38) of cases. Plasma NGS identified clinically actionable mutations in 37.7% (20/53) of cases, including EGFR mutations in two cases with no tissue EGFR results, and mutations in KRAS, BRAF, and MET. The overall sensitivity of sensitizing EGFR mutation detection by plasma NGS was 75% (9/12), and specificity was 100% (25/25) in patients tested in both tissue EGFR and plasma NGS (n = 37). In this cohort of patients, tissue EGFR testing alone informed clinical decisions in 22.2% (12/54) of cases. Adding plasma NGS to tissue EGFR testing increased the detection rate of actionable mutations to 42.6% (23/54), representing a 1.9-fold increase in clinically relevant findings. The average turnaround time of plasma NGS was shorter than standard tissue testing (10 vs. 29.9 days, p < 0.05).ConclusionsIn the first-line setting, plasma NGS was highly concordant with tissue EGFR testing. Plasma NGS increases the detection of actionable findings with a shorter time to results. This study outlines the clinical utility of complementary plasma mutation profiling in the routine management of lung cancer patients.

Project description:UnlabelledProtein interaction networks are important for the understanding of regulatory mechanisms, for the explanation of experimental data and for the prediction of protein functions. Unfortunately, most interaction data is available only for model organisms. As a possible remedy, the transfer of interactions to organisms of interest is common practice, but it is not clear when interactions can be transferred from one organism to another and, thus, the confidence in the derived interactions is low. Here, we propose to use a rich set of features to train Random Forests in order to score transferred interactions. We evaluated the transfer from a range of eukaryotic organisms to S. cerevisiae using orthologs. Directly transferred interactions to S. cerevisiae are on average only 24% consistent with the current S. cerevisiae interaction network. By using commonly applied filter approaches the transfer precision can be improved, but at the cost of a large decrease in the number of transferred interactions. Our Random Forest approach uses various features derived from both the target and the source network as well as the ortholog annotations to assign confidence values to transferred interactions. Thereby, we could increase the average transfer consistency to 85%, while still transferring almost 70% of all correctly transferable interactions. We tested our approach for the transfer of interactions to other species and showed that our approach outperforms competing methods for the transfer of interactions to species where no experimental knowledge is available. Finally, we applied our predictor to score transferred interactions to 83 targets species and we were able to extend the available interactome of B. taurus, M. musculus and G. gallus with over 40,000 interactions each. Our transferred interaction networks are publicly available via our web interface, which allows to inspect and download transferred interaction sets of different sizes, for various species, and at specified expected precision levels.Availabilityhttp://services.bio.ifi.lmu.de/coin-db/.

Project description:Efforts to obtain a comprehensive genome sequence for rainbow trout are ongoing and will be complemented by transcriptome information that will enhance genome assembly and annotation. Previously, transcriptome reference sequences were reported using data from different sources. Although the previous work added a great wealth of sequences, a complete and well-annotated transcriptome is still needed. In addition, gene expression in different tissues was not completely addressed in the previous studies. In this study, non-normalized cDNA libraries were sequenced from 13 different tissues of a single doubled haploid rainbow trout from the same source used for the rainbow trout genome sequence. A total of ~1.167 billion paired-end reads were de novo assembled using the Trinity RNA-Seq assembler yielding 474,524 contigs > 500 base-pairs. Of them, 287,593 had homologies to the NCBI non-redundant protein database. The longest contig of each cluster was selected as a reference, yielding 44,990 representative contigs. A total of 4,146 contigs (9.2%), including 710 full-length sequences, did not match any mRNA sequences in the current rainbow trout genome reference. Mapping reads to the reference genome identified an additional 11,843 transcripts not annotated in the genome. A digital gene expression atlas revealed 7,678 housekeeping and 4,021 tissue-specific genes. Expression of about 16,000-32,000 genes (35-71% of the identified genes) accounted for basic and specialized functions of each tissue. White muscle and stomach had the least complex transcriptomes, with high percentages of their total mRNA contributed by a small number of genes. Brain, testis and intestine, in contrast, had complex transcriptomes, with a large numbers of genes involved in their expression patterns. This study provides comprehensive de novo transcriptome information that is suitable for functional and comparative genomics studies in rainbow trout, including annotation of the genome.

Dataset Information

Complementing tissue characterization by integrating transcriptome profiling from the Human Protein Atlas and from the FANTOM5 consortium.

Publications

Complementing tissue characterization by integrating transcriptome profiling from the Human Protein Atlas and from the FANTOM5 consortium.

Similar Datasets

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