期刊
BIOINFORMATICS
卷 30, 期 9, 页码 1322-1324出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btu013
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资金
- European Union 7th Framework Program (PRIME-XS project) [262067]
- BBSRC Tools and Resources Development Fund [BB/K00137X/1]
- Prospectom project (Mastodons CNRS challenge)
- Biotechnology and Biological Sciences Research Council [BB/K00137X/1, BB/H024247/1] Funding Source: researchfish
- BBSRC [BB/K00137X/1, BB/H024247/1] Funding Source: UKRI
Motivation: Experimental spatial proteomics, i.e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools. Results: Here we present , a complete infrastructure to support and guide the sound analysis of quantitative mass-spectrometry-based spatial proteomics data. It provides functionality for unsupervised and supervised machine learning for data exploration and protein classification and novelty detection to identify new putative sub-cellular clusters. The software builds upon existing infrastructure for data management and data processing.
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