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A deep proteome and transcriptome abundance atlas of 29 healthy human tissues

Genome‐, transcriptome‐ and proteome‐wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative pr...

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Detalles Bibliográficos
Autores principales: Wang, Dongxue, Eraslan, Basak, Wieland, Thomas, Hallström, Björn, Hopf, Thomas, Zolg, Daniel Paul, Zecha, Jana, Asplund, Anna, Li, Li‐hua, Meng, Chen, Frejno, Martin, Schmidt, Tobias, Schnatbaum, Karsten, Wilhelm, Mathias, Ponten, Frederik, Uhlen, Mathias, Gagneur, Julien, Hahne, Hannes, Kuster, Bernhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6379049/
https://www.ncbi.nlm.nih.gov/pubmed/30777892
http://dx.doi.org/10.15252/msb.20188503
Descripción
Sumario:Genome‐, transcriptome‐ and proteome‐wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein‐level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs, that few proteins show tissue‐specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation.