BIOINFORMATICS IN AQUACULTURE. Aleksei Krasnov AKVAFORSK (Ås, Norway) Bergen, September 21, 2007

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1 BIOINFORMATICS IN AQUACULTURE Aleksei Krasnov AKVAFORSK (Ås, Norway) Bergen, September 21, 2007

2 Research area Functional genomics of salmonids Major in diseases, stress and toxicity Experience is in - Sequence analyses and annotation - Construction and use of microarrays - Management of gene expression data - Microarray statistics

3 Genome-wide level: sequence databases and servers, large microarray platforms and gene expression warehouses Automatic computer-forced analyses have (almost) reached the limit Collaboration between bioinformatic experts and biologists is poor Domain-specific databases linked to tools (modules) for data analyses and annotations Single-gene studies in fish health, welfare, nutrition, reproduction etc Limited use of genomic information Accurace, resolution Number of genes

4 SEQUENCES (mrna) 1. Processing and storage of primary data (GB) Salmo Oncorhynchus (September 7, 2007) 2. Clustering (Unigene, TIGR, GRASP etc) Unique sequences (Unigene) 3. Construction of contigs (TIGR, GRASP) Contigs are problematic (error prone) due to Duplicated genes Multi-gene families with conserved paralogs Splicing variants Unigene deliberately declines from built of contigs

5 IDENTIFICATION AND ANNOTATION Blastn / blastx search across nucleotide / protein databases Identification fully depends on the reference set Search across large databases (e.g. Swissprot, Uniprot) gives best hits to fish proteins with obscure names and poor annotations Sequence banks change continuously Many genes have multiple names, nomenclature is available only for human No rules how to deal with low similarities and uncertain homologies Curated inspection is required Functional annotation (Gene Ontology - GO) Transfer from putative homologs Problems in GO Phylogenetic conservation of function is not granted Structural annotation, assignment to multi-gene families, search for domains (Interpro, Ensembl) Has not been accomplished for salmonids at the genome level (?) Blast is insufficient, more sophisitcated approaches are required (e.g. Hidden Markov Models HMM)

6 GENE INDICES (TIGR, GRASP)

7 +The Gene Index provides - Blastn across salmon or rainbow trout sequences - Sequences and reading frames - Links to Genbank - Positions of EST in contigs - GO annotations - Links to pathways - However - Limited possibilities for search and retrieve of data, especially in a multi-gene format - Many important annotation are missing - Not adapted for comparative genomic studies etc... Shall we wait until developers will introduce everything we need? No database / server is able to meet all requirements What to do with new sequences (EST)?

8 Our first experience in development of software (written by Petri Pehkonen, student of computer science department as a practical exercise) - Stand-alone blast operates with user-specificed sequence databases - Parser, forms for selection of matches, export of data and iterative searches - Database (MS Access) is used for Gene Index

9 Small and simple effort in development of software helped to resolve many problems Analysis, structural and functional annotations of EST Search for members multi-gene families and functional classes Design of microarrays Linking to web databases, e.g. Harvester knowledge base

10

11 Small and simple effort in development of software helped to resolve many problems Analysis, structural and functional annotations of EST Search for members multi-gene families and functional classes Design of microarrays Linking to web databases, e.g. Harvester knowledge base Much more can be done if several groups will join efforts! What do we need?

12 What do we need? Personal opinion Diverse databases / pipe-lines (adapted for research areas, projects, labs personal preferences) MUST be designed by biologists Only USERS know what data and analyses they need Design of database is a time-consuming task The rest is done easily by computer people Toolkit for common use Basic sequences analyses tools adapted for database (blast, sequence alignment, translation, synonymous / non synonymous substitutions etc) Parsers Interfaces and forms to launch application, to import, query, format and export data Task requires joint effort

13 AREA FOR COLLABORATION: IMMUNOGENOMICS A simple question: what immune genes have been (not) identified in salmonid fish? - Interferon-dependent genes? - Immunoglobulins? - Homologs to surface antigens (CD)? A simple solution: - Retrieve all immune proteins by linking GO to Swissprot / Uniprot (use SRS at EBI)

14 SRS is database of databases, an extremely useful and powerful source

15 IMMUNOGENOMICS A task: - Retrieve all immune proteins by linking GO to Swissprot / Uniprot (use SRS at EBI) - Run blast False positives: - Uncertain homology - Errors in annotations False negatives: - Many important genes with immune functions are not annotated in GO - Many fish homologs are not recognized with blast due to low sequence conservation, more powerful methods are required (e.g. HMM) To identify fish immune we must compile a set of reference genes and use advanced methods of sequence analyses To use results we need more precise and extensive annotations (Immune Ontology), description of each gene

16 From EST to microarrays Genome-wide platforms (GRASP, TRAITS) + Good for screening and search for markers - Lack of spot replicates means low accuracy - High quality annotation of large numbers of genes is VERY problematic Medium-size, specialized platforms (e.g. 1.8 K immunochip) - Many important genes are missing, especially those with unknown functions + Spot replicates ensure high sensitivity and accuracy + Coverage of functions that are most important for a research area + High quality annotations are feasible

17 Most important and challenging task is to make sense from gene expression data JVI considers manuscripts that include microarrays and similar parallel profiling analyses of viral or cellular gene expression. However, such manuscripts will be published only if they provide novel insight into the biology of the virus or the infected cell or if they form the basis for additional experiments that provide such insights JVI, guide for authors, 2007

18 STANDARD MICROARRAY MANUSCRIPT INCLUDES - Lists of genes divided into clusters - GO analyses (enriched / depleted classes in the list and / or clusters) - Genes placed on the maps of pathway - Such results are produced easily and do not have any value per se - Papers without important biological findings should not be published - Statistics / data mining is a useful subsidary tool - Researchers should not be confined to any particular method or software - Results are always noisy and should be taken with great caution

19 To find pros and cons of data mining procedures it is important to have samples as a database with strong and flexible querying. Solution: relational database with utilities for data querying and analyses Fish_Chips.exe Fish_Chips includes: - Simple ontology of samples (~ 300) - Easy and flexible querying by experiments, genes, parameters (expression ratio, log-er, ranks etc)

20 Fish_Chips.exe Simple ontolog

21 Fish_Chips.exe Fish_Chips includes: - Simple ontology of samples - Easy and flexible querying by experiments, genes, parameters (expression ratio, log-er, ranks etc) - Sequences, annotations (GO, KEGG), links to web databases Built-in statistical analyses (comparison by GO classes, t-test) - Direct link to Statistica (ANOVA, exact Fisher s test, mean expression profiles) - Formatting of data for external applications (cluster analysis)

22 Cluster analysis why? Lack of choice In microarray analyses number of measurements is greater than number of samples Cluster analysis one of few methods that can work with such data Extremely simple, entirely formal no theory behind Many technical and biological problems Genes members of cluster are co-regulated. Is that true?! - Clusters are found in any data set - Different procedures produce different clusters, clusters must be checked for strength - Similar expression profiles are often observed only in small data sets, most clusters are destroyed by addition of samples - Even strong clusters do not necessarily have bilogical siginificance Example: Genes that were up- or down-regulated in only one outlier form a strong and highly significant cluster

23 Classification of samples. Clustering helps to see the structure of experiment and interaction of factors Separate and combined effects of estrogen and parasitic kidney disease on hepatic gene expression in rainbow trout (Helmut Segner, Univ. Bern) T LE ILE I IHE HE Infection and infection + estrogen Estrogen When two challenges are combined response to pathogen is greater Euclidian distance metric, Ward s method (available only in statistical packages)

24 Finding transcription modules enhances resolution of analyses - Differemtially expressed genes were clustered - Cluster members were checked for correlation to mean expression profile (r > 0.7) - Multiple regression evaluated effects of factors (p < 0.05) and their interaction C Log (Expression ratio) D Log (Expression ratio) Beta E2 = Beta PKD = T LE HE I Study groups T LE HE I Study groups ILE IHE Beta E2 = Beta PKD = ILE IHE Complement component C3 Complement component C5 Complement component C9 Complement factor Bf-1 Complement factor H Properdin C type lectin receptor B Toll-like receptor 20a Ceruloplasmin Endothelial leukocyte adhesion molecu Ig kappa chain V-IV region B17-2 Profilin CC chemokine SCYA110-2 Adenosine kinase 2 Acute phase protein G1/S-specific cyclin D2 Histone deacetylase 4 Fibroblast growth factor-20 Bone morphogenetic protein 8-like Metallothionein-IL Stress 70 protein chaperone Thioredoxin-like protein 4A Induced with parasite, no response to estrogen Bcl2-associated X protein C3a anaphylatoxin chemotactic recepto CC chemokine SCYA110-1 Cytokine inducible SH2-containing prote Egl nine homolog 2 Hemopexin Ig kappa chain V-I region WEA Interferon-related regulator 2-1 Liver-expressed antimicrobial peptide 2 Membrane-type mosaic serine protease Myelin basic protein-1 NAD-dependent deacetylase sirtuin 5 Peptidyl-prolyl cis-trans isomerase 2-2 Semaphorin 7A Induced with parasite, suppressed with estrogen

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