Pinpointing differentially expressed domains in complex protein mixtures with the cloud service of PatternLab for Proteomics

Mass-spectrometry-based shotgun proteomics has become a widespread technology for analyzing complex protein mixtures. Here we describe a new module integrated into PatternLab for Proteomics that allows the pinpointing of differentially expressed domains. This is accomplished by inferring functional...

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Bibliographic Details
Published inJournal of proteomics Vol. 89; pp. 179 - 182
Main Authors Leprevost, F.V., Lima, D.B., Crestani, J., Perez-Riverol, Y., Zanchin, N., Barbosa, V.C., Carvalho, P.C.
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier B.V 26.08.2013
Subjects
Bioinformatics
Computational proteomics
Cryptococcus gattii - chemistry
Cryptococcus gattii - metabolism
data collection
Databases, Protein
Functional analysis
Fungal Proteins - biosynthesis
Fungal Proteins - chemistry
gene expression regulation
Protein domains
Protein Structure, Tertiary
Proteome - biosynthesis
Proteome - chemistry
Proteomics
Proteomics - methods
Computational proteomics
Functional analysis
Protein domains
Bioinformatics
Proteomics
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Summary:Mass-spectrometry-based shotgun proteomics has become a widespread technology for analyzing complex protein mixtures. Here we describe a new module integrated into PatternLab for Proteomics that allows the pinpointing of differentially expressed domains. This is accomplished by inferring functional domains through our cloud service, using HMMER3 and Pfam remotely, and then mapping the quantitation values into domains for downstream analysis. In all, spotting which functional domains are changing when comparing biological states serves as a complementary approach to facilitate the understanding of a system's biology. We exemplify the new module's use by reanalyzing a previously published MudPIT dataset of Cryptococcus gattii cultivated under iron-depleted and replete conditions. We show how the differential analysis of functional domains can facilitate the interpretation of proteomic data by providing further valuable insight. [Display omitted] •Able to pinpoint differentially expressed domains•Greatly simplifies functional analysis in proteomics•Uses cloud computing to not overburden local resources•Simplifies analysis of organisms with poorly annotated genomes
Bibliography:http://dx.doi.org/10.1016/j.jprot.2013.06.013
ISSN:1874-3919
1876-7737
DOI:10.1016/j.jprot.2013.06.013