Martin Zwahlen

Bio: Martin Zwahlen is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Gene & Human Protein Atlas. The author has an hindex of 9, co-authored 13 publications receiving 12194 citations.

Tissue-based map of the human proteome

Abstract: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

Towards a knowledge-based Human Protein Atlas

Abstract: 1. Anonymous. Nat. Biotechnol. 28, 987 (2010). 2. Plosila, W.H. Econ. Dev. Q. 18, 113–126 (2004). 3. Stayn, S. BNA Med. Law Pol. Rep. 5, 718–725 (2006). 4. Lomax, G. & Stayn, S. BNA Med. Law Pol. Rep. 7, 695–698 (2008). 5. Levine, A.D. Public Adm. Rev. 68, 681–694 (2008). 6. Levine, A.D. Nat. Biotechnol. 24, 865–866 (2006). 7. McCormick, J.B., Owen-Smith, J. & Scott, C.T. Cell Stem Cell 4, 107–110 (2009). 8. Fossett, J.W., Ouellette, A.R., Philpott, S., Magnus, D. & Mcgee, G. Hastings Cent. Rep. 37, 24–35 (2007). 9. Mintrom, M. Publius 39, 606–631 (2009). 10. Scott, C.T., McCormick, J.B. & Owen-Smith, J. Nat. Biotechnol. 27, 696–697 (2009). 11. Takahashi, K. & Yamanaka, S. Cell 126, 663–676 (2006). Foundation and the Georgia Research Alliance, and Georgia Tech. They thank J. Walsh at Georgia Tech for helpful comments on an earlier version of this manuscript. They also appreciate the assistance they received with data collection from officials in various state stem cell agencies. A.D.L. would also like to thank A. Jakimo, whose comment at a meeting of the Interstate Alliance on Stem Cell Research inspired collection of these data. stem cell programs, as well as similar state programs supporting other areas of science, is uncertain. The analysis here suggests that state stem cell funding programs are sufficiently large and established that simply ending the programs, at least in the absence of substantial investment in the field by other funding sources, could have deleterious effects. Such action would fail to capitalize on the initial efforts of scientists who have been drawn to the field of stem cell research by state programs and leave many stem cell scientists suddenly searching for funding to continue their research. Large-scale state funding for basic research is a relatively new phenomenon, and many questions remain about the impact of these programs on the development of scientific fields and the careers of scientists. The influence of state funding programs on the distribution of research publications, the acquisition of future external funding, the creation of new companies and the translation of basic research into medical practice, for instance, are important unanswered questions. Similarly, comparing state funding programs with federal funding programs as well as foundations could offer new insight into the relative priorities of different funding bodies and the extent to which their funding portfolios overlap or are distinct. We hope the analysis presented here and the public release of the underlying database will inspire additional analysis of state science funding programs generally and state-funded stem cell science in particular.

A subcellular map of the human proteome

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 ...

A single-cell type transcriptomics map of human tissues.

Abstract: Advances in molecular profiling have opened up the possibility to map the expression of genes in cells, tissues, and organs in the human body. Here, we combined single-cell transcriptomics analysis with spatial antibody-based protein profiling to create a high-resolution single–cell type map of human tissues. An open access atlas has been launched to allow researchers to explore the expression of human protein-coding genes in 192 individual cell type clusters. An expression specificity classification was performed to determine the number of genes elevated in each cell type, allowing comparisons with bulk transcriptomics data. The analysis highlights distinct expression clusters corresponding to cell types sharing similar functions, both within the same organs and between organs.

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

Abstract: Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

Tissue-based map of the human proteome

Abstract: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses.

Abstract: Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.

Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation

Abstract: The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55,000 organisms (>4800 viruses, >40,000 prokaryotes and >10,000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.