I 529 Lab 5 02202009 AI Kwangmin Choi

I 529: Lab 5 02/20/2009 AI : Kwangmin Choi

Today’s topics • Gene Ontology prediction/mapping – Ami. Go • http: //amigo. geneontology. org/cgi-bin/amigo/go. cgi – PFP • http: //dragon. bio. purdue. edu/pfp/ – GOtcha • http: //www. compbio. dundee. ac. uk/gotcha/ • Pathway prediction/mapping – KAAS • http: //www. genome. jp/kegg/kaas

Gene Ontology • In a species-independent manner. , the GO project has developed three structured controlled vocabularies (ontologies) that describe gene products in terms of their associated

GO: biological process • A biological process is series of events accomplished by one or more ordered assemblies of molecular functions. – E. g. cellular physiological process or signal transduction. – E. g. pyrimidine metabolic process or alpha-glucoside transport. • It can be difficult to distinguish between a biological process and a molecular function, but the general rule is that a process must have more than one distinct steps. • A biological process is not equivalent to a pathway; at present, GO does not try to represent the dynamics or dependencies that would be required to fully describe a pathway.

GO: molecular functions • Molecular function describes activities, such as catalytic or binding activities, that occur at the molecular level. • GO molecular function terms represent activities rather than the entities (molecules or complexes) that perform the actions, • GO milecular function terms do not specify where or when, or in what context, the action takes place. – E. . g. (general) catalytic activity, transporter activity, or binding etc. – E. g. (specific) adenylate cyclase activity, Toll receptor binding etc.

GO: cellular components • A cellular component is just that, a component of a cell, but with the proviso that it is part of some larger object; • Less informative • This may be an anatomical structure – e. g. rough endoplasmic reticulum or nucleus • or a gene product group – e. g. ribosome, proteasome or a protein dimer

Ami. GO • URL http: //amigo. geneontology. org/cgi-bin/amigo/go. cgi • Ami. GO is the official tool for searching and browsing the Gene Ontology database • Simple blast search is provided (not useful) • Ami. GO consists of a controlled vocabulary of terms covering biological concepts, and a large number of genes or gene products whose attributes have been annotated using GO terms.

PFP (Automated Protein Function Prediction Server) • Hawkins, T. , Luban, S. and Kihara, D. 2006. Enhanced Automated Function Prediction Using Distantly Related Sequences and Contextual Association by PFP. Protein Science 15: 1550 -6. • The PFP algorithm has been shown to increase coverage of sequence-based function annotation more than fivefold by extending a PSI-BLAST search to extract and score GO terms individually • It applies the Function Association Matrix (FAM), to score significantly associating pairs of annotations.

PFP method • PFP uses a scoring scheme to rank GO annotations assigned to all of the most similar sequences according to – (1) their frequency of occurrence in those sequences – (2) the degree of similarity of the originating sequence to the query. • This is similar to the scoring basis for the R-value used by the GOtcha method to score annotations from pairwise alignment matches (Martin et al. 2004)

PFP method • • • A GO term, fa s(fa) is the final score assigned to the GO term, fa N is the number of the similar sequences retrieved by PSI-BLAST E_value(i) is the E-value given to the sequence I b = 2 (or log 10[100]) to allow the use of sequence matches to an E-value of 100. Function Association Matrix (FAM), – – – fj is a GO term assigned to the sequence i. P(fa | fj) is the conditional probability that fa is associated with fj, c(fa, fj) is number of times fa and fj are assigned simultaneously to each sequence in Uni. Prot c(fj) is the total number of times fj appeared in Uni. Prot, μ is the size of one dimension of the FAM (i. e. , the total number of unique GO terms) ɛ is the pseudo-count.

PFP • Web server http: //dragon. bio. purdue. edu/pfp/queue/116 8_kw. f. result. html • Local installation – http: //dragon. bio. purdue. edu/pfp/dist – Installed in /home/kwchoi/public_html/PFP – You need to specify the path of blastpgp – And also need BLOSUM 62

PFP (Automated Protein Function Prediction Server) • PFP output – /home/kwchoi/public_html/I 529 -09 -lab/Lab 5/Data/pfp_data • Columns – – – 1: predicted GO term 2: GO category (f/p/c) 3: raw term score 4: term p-value 5: rank (by p-value) 6: confidence to be exact match 7: rank (by column 7) 8: confidence within 2 edges on the GO DAG 9: rank (by column 8) 10: confidence within 4 edges on the GO DAG 11: rank (by column 10) 12: GO term short definition

GOtcha • The GOtcha method – Martin et al. BMC Bioinformatics (2004) 5: 178. • GOtcha assigns functional terms transitively based upon sequence similarity. • These terms are ranked by probability and displayed graphically on a subtree of Gene Ontology.

Gotcha method • GOtcha performs a BLAST search of the query sequence against individual well annotated genomes. • Annotations are transitively assigned from all hits, with a score corresponding to the Evalue, individual GO-terms receiving cumulative scores from multiple sequence similarity matches. • Cumulative scores are normalized and, for each term, two scores are obtained – – • the I-score which is normalized to the root node, the C-score which is the cumulative score at the root node. For each GO-term a precomputed scoring table is used to establish the assignment likelihood for that term given that I-score and that C-score. This is represented as a probability

Pathway mapping • E. g E. coli K-12 pathway (00300)

KAAS • KAAS (KEGG Automatic Annotation Server) provides functional annotation of genes in a genome by BLAST comparisons against a manually curated set of ortholog groups in KEGG GENES. • The result contains KO (KEGG Orthology) assignments and automatically generated KEGG pathways. • Moriya, Y. , Itoh, M. , Okuda, S. , Yoshizawa, A. , and Kanehisa, M. ; KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W 182 -W 185 (2007). [NAR]

KAAS • Web server: http: //www. genome. jp/kegg/kaas/ • KAAS works best when a complete set of genes in a genome is known. Prepare query amino acid sequences and use the BBH (bidirectional best hit) method to assign orthologs. • KAAS can also be used for a limited number of genes. Prepare query amino acid sequences and use the SBH (single-directional best hit) method to assign orthologs. • When ESTs are comprehensive enough, a set of consensus contigs can be generated by the EGassembler server and used as a gene set for KAAS with the BBH method. Otherwise, use ESTs as they are with the SBH method.

KAAS workflow

Pathway mapping • KAAS returns – KO list – KEGG Atlas Metabolism map [Create atlas] – Pathway maps [Create all maps] – Hierarchy files • You can highlight KEGG maps using KEGG API – http: //www. genome. jp/kegg/soap/doc/keggapi_man ual. html – See: color_pathway_by_objects