Understanding protein lists from comparative proteomics studies
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1 Understanding protein lists from comparative proteomics studies Bing Zhang, Ph.D. Department of Biomedical Informatics Vanderbilt University School of Medicine
2 A typical comparative shotgun proteomics study IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI Li et.al. JPR, CSHL Proteomics Course, 07/30/2010
3 Understanding a protein list Level I What are the proteins/genes behind the IDs and what do we know about the functions of the proteins/genes? Level II Which biological processes and pathways are the most interesting in terms of the experimental question? Level III How do the proteins work together and are we missing some important proteins? 3 CSHL Proteomics Course, 07/30/2010
4 A typical result table from comparative shotgun proteomics studies All identified proteins Sequence coverage Count data Group totals Normalized totals Log ratio p False Discovery Rate (FDR) Li et.al. JPR, 2010 Sample data for this lecture can be downloaded from 4 CSHL Proteomics Course, 07/30/2010
5 Level one: information retrieval Query interface ( Output One-protein-at-a-time Time consuming Information is local and isolated Hard to automate the information retrieval process 5 CSHL Proteomics Course, 07/30/2010
6 Biomart: a batch information retrieval system Biomart is a query-oriented data management system. Particularly suited for providing 'data mining' like searches of complex descriptive data such as those related to genes and proteins Open source and can be customized Projects using Biomart for information retrieval include Ensembl, UniProt, InterPro, Reactome, and many others (see a complete list and get access to the tools from ) 6 CSHL Proteomics Course, 07/30/2010
7 Ensembl Biomart analysis Choose dataset Choose database: Ensembl Genes 58 Choose dataset: Homo sapiens genes (GRch37) Set filters Gene: a list of genes/proteins identified by various database IDs (e.g. IPI IDs) Gene Ontology: filter for proteins with specific GO terms (e.g. cell cycle) Protein domains: filter for proteins with specific protein domains (e.g. SH2 domain) Region: filter for genes in a specific chromosome region (e.g. chr1 1: or 11q13) Others Select output features Gene annotation information in the Ensembl database, e.g. gene description, chromosome name, gene start, gene end, strand, band, gene name, etc. External data: Gene Ontology, IDs in other databases Expression: anatomical system, development stage, cell type, pathology Protein domains: SMART, PFAM, Interpro, etc. 7 CSHL Proteomics Course, 07/30/2010
8 Ensembl Biomart: getting information for all proteins in a list Export all results to a file 8 CSHL Proteomics Course, 07/30/2010
9 Ensembl Biomart: filtering for a specific group of proteins Use the filter to select for a specific group or groups of proteins, e.g. cell cycle proteins, transcription factors, proteins with transmembrane domains, etc. 9 CSHL Proteomics Course, 07/30/2010
10 Level II: understanding a gene list at the functional group level Random Enrichment analysis: is a functional group (e.g. cell cycle) significantly associated with the experimental question? All identified proteins (1733) Observed 1590 annotated Filter for FDR<0.05 IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI IPI Compare MMP9 SERPINF1 A2ML1 F2 FN1 LYZ TNXB FGG MPO FBLN1 THBS1 HDLBP GSN FBN1 CA2 P11 CCL21 FGB Differentially expressed protein list (260 proteins) Extracellular space (670 proteins) 10 CSHL Proteomics Course, 07/30/2010
11 Enrichment analysis: hypergeometric test Significant proteins Non-significant proteins Proteins in the group k j-k j Total Other proteins n-k m-n-j+k m-j Total n m-n m Hypergeometric test: given a total of m proteins where j proteins are in the functional group, if we pick n proteins randomly, what is the probability of having k or more proteins from the group? p = min(n, j ) i= k m j n i j i m n Observed m n k j Zhang et.al. Nucleic Acids Res. 33:W741, CSHL Proteomics Course, 07/30/2010
12 Commonly used functional groups Gene Ontology ( Structured, precisely defined, controlled vocabulary for describing the roles of genes and gene products Three organizing principles: molecular function, biological process, and cellular component Pathways KEGG ( Pathway commons ( WikiPathways ( Cytogenetic bands 12 CSHL Proteomics Course, 07/30/2010
13 WebGestalt: Web-based Gene Set Analysis Toolkit 8 organisms 132 ID types webgestalt 73,986 functional groups Zhang et.al. Nucleic Acids Res. 33:W741, 2005 Duncan et al. BMC Bioinformatics. 11(Suppl 4) :P10, CSHL Proteomics Course, 07/30/2010
14 WebGestalt analysis Select the organism of interest. Upload a gene/protein list in the txt format, one ID per row. Optionally, a value can be provided for each ID. In this case, put the ID and value in the same row and separate them by a tab. Then pick the ID type that corresponds to the list of IDs. Categorize the uploaded ID list based upon GO Slim (a simplified version of Gene Ontology that focuses on high level classifications). Analyze the uploaded ID list for for enrichment in various biological contexts. You will need to select an appropriate predefined reference set or upload a reference set. If a customized reference set is uploaded, ID type also needs to be selected. After this, select the analysis parameters (e.g., significance level, multiple test adjustment method, etc.). Retrieve enrichment results by opening the respective results files. You may also open and/or download a TSV file, or download the zipped results to a directory on your desktop. 14 CSHL Proteomics Course, 07/30/2010
15 WebGestalt: ID mapping Input list 260 significant proteins identified in the HNSCC study (CSHL2010_hnscc_sig_withLogRatio.txt) Mapping result Total number of User IDs: 260. Unambiguously mapped User IDs to Entrez IDs: 229. Unique User Entrez IDs: 224. The Enrichment Analysis will be based upon the unique IDs. 15 CSHL Proteomics Course, 07/30/2010
16 WebGestalt: GOSlim classification Biological process Molecular function Cellular component 16 CSHL Proteomics Course, 07/30/2010
17 WebGestalt: top 10 enriched GO biological processes Reference list: CSHL2010_hnscc_all_proteins.txt 17 CSHL Proteomics Course, 07/30/2010
18 WebGestalt: top 10 enriched cytogenetic bands In head and neck squamous cell carcinoma (HNSCC), 11q13 amplification occurs in 30 40% of tumors and correlates with an increase in tumor grade, lymph node metastases, recurrence and decreased survival. (Clark et al. Oncogene, 2008) 18 CSHL Proteomics Course, 07/30/2010
19 WebGestalt: top 10 enriched KEGG pathways 19 CSHL Proteomics Course, 07/30/2010
20 WebGestalt: top 10 enriched WikiPathways 20 CSHL Proteomics Course, 07/30/2010
21 Level III: understanding gene lists at the network level What is a protein-protein interaction network? Most proteins mediate their function through interacting with other proteins to form molecular machines or to participate in various regulatory processes. A protein-protein interaction network is a graph model of this complex system in which nodes (proteins) are connected by edges (interactions). How to get protein interaction information? Experiments: yeast two-hybrid; tandem affinity purification Computational prediction Databases: DIP ( MINT ( ); BIND ( ); BioGRID ( ); HPRD ( ); MIPS ( etc Why network-based analysis? Not limited by existing knowledge on protein functions and pathway annotations Allowing better understanding of the mechanisms by which the identified proteins work together to lead to a specific phenotype change Revealing proteins that are missed in the original experiment Identifying drug targets based on network topology Function prediction for unannotated proteins 21 CSHL Proteomics Course, 07/30/2010
22 Genes2Networks Genes2Networks takes a list of genes/proteins as seeds and identify all interacting proteins that fall on paths through the background network between them. Berger et.al. BMC Bioinformatics, 8:372, CSHL Proteomics Course, 07/30/2010
23 Genes2Networks: identifying significant intermediates Genes2Networks uses a Z-score value to evaluate the significance of intermediate nodes in the output subnetwork. The Z-score is computed for each intermediate node using a binomial proportions test. Links from the node Total links Subnetwork a c Whole network b d z = a c b d b d 1 b d c 23 CSHL Proteomics Course, 07/30/2010
24 Genes2Networks analysis Specify input gene/protein list (CSHL2010_hnscc_sig_entrezSymbol.txt). Select Max Path Length (a value of 1 will only return connections between seed nodes). Choose a z score cutoff for labeling graph nodes. Choose colors for graph nodes based on whether or not it is in the seed list or above the significance cutoff. Select databases to use for generating the background network. Select filtering options for the background dataset. Remove low quality interactions from the background dataset by removing interactions from high throughput experiments or interactions without enough evidence. 24 CSHL Proteomics Course, 07/30/2010
25 Genes2networks: HNSCC network Number of nodes: 139 Number of interactions: 225 Hubs (#interactions) FN1 (27) THBS1(16) FGA(11) FLNA(10) Significant intermediates (z score) F13A1 (11.6) NID1 (8.0) MFAP5(7.5) LCN1(7.5) BMP1 (7.2) Fibronectin (FN1) plays a major role in cell adhesion, growth, migration and differentiation. It is important for processes such as wound healing and embryonic development. Altered fibronectin expression, degradation, and organization has been associated with a number of pathologies, including cancer and fibrosis. 25 CSHL Proteomics Course, 07/30/2010
26 WebGestalt: pathway enrichment of the network Network analysis enriches proteins in the same pathways. Nodes in the subnetwork: CSHL2010_genes2networks_subnet_nodes.txt Reference list: Human genome 26 CSHL Proteomics Course, 07/30/2010
27 Understanding a protein list: summary Level I What are the proteins/genes behind the IDs and what do we know about the functions of the proteins/genes? Biomart ( Level II Which biological processes and pathways are the most interesting in terms of the experimental question? WebGestalt ( Related tools: DAVID ( GenMAPP ( Level III How do the proteins work together and are we missing some important proteins? Genes2Networks ( Related tools: Cytoscape ( STRING ( GeneMANIA ( Ingenuity ( Pathway Studio ( 27 CSHL Proteomics Course, 07/30/2010
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