Statistical analysis of genomic protein family and domain controlled annotations for functional investigation of classified gene lists.
BACKGROUND
The increasing protein family and domain based annotations constitute important information to understand protein functions and gain insight into relations among their codifying genes. To allow analyzing of gene proteomic annotations, we implemented novel modules within GFINDer, a Web system we previously developed that dynamically aggregates functional and phenotypic annotations of user-uploaded gene lists and allows performing their statistical analysis and mining.


RESULTS
Exploiting protein information in Pfam and InterPro databanks, we developed and added in GFINDer original modules specifically devoted to the exploration and analysis of functional signatures of gene protein products. They allow annotating numerous user-classified nucleotide sequence identifiers with controlled information on related protein families, domains and functional sites, classifying them according to such protein annotation categories, and statistically analyzing the obtained classifications. In particular, when uploaded nucleotide sequence identifiers are subdivided in classes, the Statistics Protein Families&Domains module allows estimating relevance of Pfam or InterPro controlled annotations for the uploaded genes by highlighting protein signatures significantly more represented within user-defined classes of genes. In addition, the Logistic Regression module allows identifying protein functional signatures that better explain the considered gene classification.


CONCLUSIONS
Novel GFINDer modules provide genomic protein family and domain analyses supporting better functional interpretation of gene classes, for instance defined through statistical and clustering analyses of gene expression results from microarray experiments. They can hence help understanding fundamental biological processes and complex cellular mechanisms influenced by protein domain composition, and contribute to unveil new biomedical knowledge about the codifying genes.
Masseroli
Marco
M
Dipartimento di Elettronica e Informazione, Politecnico di Milano, piazza Leonardo da Vinci 32, 20133 Milano, Italy. masseroli@elet.polimi.it
Bellistri
Elisa
E
Franceschini
Andrea
A
Pinciroli
Francesco
F
D002874:Chromosome Mapping / Q000379:methods*; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods; D005810:Multigene Family / Q000502:physiology*; D017434:Protein Structure, Tertiary; D020543:Proteome / Q000235:genetics* / Q000378:metabolism*
D020543:Proteome