Computational Genomics Francisco García García BIER [email protected] Máster en Biotecnología Biomédica. UPV Why are we interested in Computational Genomics? The overall goal: Apply computational methods to biomedical and biotechnological problems Research interests: The development and application of novel bioinformatics methods aimed at discovering new drugs Identification of genes or proteins may be considered therapeutic targets Personalized medicine: tools for discovering and diagnostic Introduction Why Computational Genomics? Computational Genomics Genomics Transcriptomics Metabolomics Lipidomics Proteomics Epigenomics Introduction Omics sciences Computational Genomics How do these technologies work ? Introduction High throughput technologies: microarrays Computational Genomics How do these technologies work ? Reference genome Introduction High throughput technologies: Next Generation Sequencing Computational Genomics KEGG Gene Regulatory Biological pathways Ontology elements MiRNA, CisRed knowledge InterPro Transcription Factor Biocarta Motifs Binding Sites pathways Gene Expression Bioentities from in tissues literature Clinical ClinVar HUMSAVAR knowledge HGMD COSMIC Introduction Clinical and biological databases Computational Genomics Introduction Personalized Medicine Computational Genomics + Introduction Personalized Medicine Descripción de las sesiones 3 sesiones (7 horas) sobre el uso de herramientas web para el análisis e interpretación de datos de secuenciación. Toda la documentación (presentaciones + ejercicios) que necesitaremos durante estos días, estarán disponibles en este enlace http://bioinfo.cipf.es/mbb/. También en Poliformat. Docentes: Marta Hidalgo y Paco García. El enfoque de las sesiones será práctico y sólo introduciremos aquellos conceptos que precisemos para los ejercicios. Introduction Máster en Biotecnología Biomédica. UPV. Programa Sesión 1 • Introducción a las tecnologías NGS. • Estudios de detección de variación genómica. Pipeline de análisis de datos genómicos. • ¿Cómo detectar mutaciones de interés en estudios de exomas completos? Ejercicios con la herramienta web BiERapp. Sesión 2 Estudios de variación genómica: secuenciación genómica dirigida. ¿Cómo diseñar un panel de genes? ¿Cómo analizar e interpretar datos de paneles de genes?. Ejercicios con TEAM. Variabilidad genética española. Base de datos CSVS. Estudios transcriptómicos con datos de NGS. Pipeline de análisis de datos de expresión. ¿Cómo analizar datos de RNA-Seq desde la suite Babelomics? Sesión 3 Análisis de datos transcriptómicos en el contexto de las rutas de señalización. Ejercicios con las herramientas web hipathia y PathAct. Introduction Máster en Biotecnología Biomédica. UPV. Web tools to analyze omic data BIER [email protected] Máster en Biotecnología Biomédica. UPV NGS Data Analysis Pipeline Fastq Sequence preprocessing Fastq Alignment BAM Resequencing Visualization RNA-Seq BAM Data Analysis Data Analysis Variant calling RNA-Seq processing VCF Count matrix Variant annotation RNA-Seq data analysis Prioritization Functional analysis Introduction NGS data analysis: pipelines Fastq format We could say “it is a fasta with qualities”: 1. Header (like the fasta but starting with “@”) 2. Sequence (string of nt) 3. “+” and sequence ID (optional) 4. Encoded quality of the sequence @SEQ_ID GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT + !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65 Introduction NGS data analysis: files format BAM/SAM format @PG ID:HPG-Aligner VN:1.0 @SQ SN:20 LN:63025520 HWI-ST700660_138:2:2105:7292:79900#2@0/1 16 20 76703 254 76= * 0 0 GTTTAGATACTGAAAGGTACATACTTCTTTGTAGGAACAAGCTATCATGCTGCATTTCTATAATATCACATGAATA GIJGJLGGFLILGGIEIFEKEDELIGLJIHJFIKKFELFIKLFFGLGHKKGJLFIIGKFFEFFEFGKCKFHHCCCF AS:i:254 NH:i:1 NM:i:0 HWI-ST700660_138:2:2208:6911:12246#2@0/1 16 20 76703 254 76= * 0 0 GTTTAGATACTGAAAGGTACATACTTCTTTGTAGGAACAAGCTATCATGCTGCATTTCTATAATATCACATGAATA HHJFHLGFFLILEGIKIEEMGEDLIGLHIHJFIKKFELFIKLEFGKGHEKHJLFHIGKFFDFFEFGKDKFHHCCCF AS:i:254 NH:i:1 NM:i:0 HWI-ST700660_138:2:1201:2973:62218#2@0/1 0 20 76655 254 76M * 0 0 AACCCCAAAAATGTTGGAAGAATAATGTAGGACATTGCAGAAGACGATGTTTAGATACTGAAAGGGACATACTTCT FEFFGHHHGGHFKCCJKFHIGIFFIFLDEJKGJGGFKIHLFIJGIEGFLDEDFLFGEIIMHHIKL$BBGFFJIEHE AS:i:254 NH:i:1 NM:i:1 HWI-ST700660_138:2:1203:21395:164917#2@0/1 256 20 68253 254 4M1D72M * 0 0 NCACCCATGATAGACCAGTAAAGGTGACCACTTAAATTCCTTGCTGTGCAGTGTTCTGTATTCCTCAGGACACAGA #4@ADEHFJFFEJDHJGKEFIHGHBGFHHFIICEIIFFKKIFHEGJEHHGLELEGKJMFGGGLEIKHLFGKIKHDG AS:i:254 NH:i:3 NM:i:1 HWI-ST700660_138:2:1105:16101:50526#6@0/1 16 20 126103 246 53M4D23M * 0 0 AAGAAGTGCAAACCTGAAGAGATGCATGTAAAGAATGGTTGGGCAATGTGCGGCAAAGGGACTGCTGTGTTCCAGC FEHIGGHIGIGJI6FCFHJIFFLJJCJGJHGFKKKKGIJKHFFKIFFFKHFLKHGKJLJGKILLEFFLIHJIEIIB AS:i:368 NH:i:1 NM:i:4 SAM Specification: http://samtools.sourceforge.net/SAM1.pdf Introduction NGS data analysis: files format VCF format http://www.1000genomes.org/ Introduction NGS data analysis: files format Counts Sample Gene Introduction NGS data analysis: files format Transcriptomic Studies BIER [email protected] Máster en Biotecnología Biomédica. UPV 2. Mapping 4. Normalization 4. 3. Quantification 6. Functional Profiling Functional 6. 5. Differential expression 5. Differential 1. Sequence1. preprocessing 1. Sequence1. preprocessing RNA-Seq Data Analysis Data RNA-Seq Primary Secondary RNA-Seq Data Pipeline Data Analysis RNA-Seq Babelomics 5 Babelomics Babelomics 5 http://babelomics.bioinfo.cipf.es/ Babelomics 5 Analyzing omics data + functional profiling Differential Expression NORMALIZATION UPLOAD EDIT + FUNCTIONAL DATA DATA DIFFERENTIAL PROFILING EXPRESSION Babelomics 5 Analyzing omics data + functional profiling Supervised and Unsupervised Classification RPKM TMM CLUSTERING UPLOAD NORMALIZE EDIT DATA DATA DATA PREDICTORS Babelomics 5 Analyzing omics data + functional profiling Signaling Pathways Analysis http://hipathia.babelomics.org/ hiPhatia Signaling Pathways Analysis Genomic Variation Studies BIER [email protected] Máster en Biotecnología Biomédica. UPV 2. Mapping 3. Variant calling Variant 3. 4. Variant prioritization 4. Variant 1. 1. Sequence preprocessing 1. 1. Sequence preprocessing Resequencing Data Analysis Data Resequencing Analysis Secondary Primary Genomics Data Analysis Pipeline Analysis Pipeline Data Genomics Pipeline How do we prioritize variants in whole exome studies? http://courses.babelomics.org/bierapp/ BIER BiERapp Discovering variants Introduction Whole-exome sequencing has become a fundamental tool for the discovery of disease-related genes of familial diseases but there are difficulties to find the causal mutation among the enormous background There are different scenarios, so we need different and immediate strategies of prioritization Vast amount of biological knowledge available in many databases We need a tool to integrate this information and filter immediately to select candidate variants related to the disease BiERapp Discovering variants How does BiERapp work? Filterings VCF file BiERapp multisample VARIANT CellBase BiERapp Discovering variants BiERapp VCF files VCF Discovering variants Discovering 2. 2. Mapping 3. Variant calling 3. Variant Input: fileVCF 4. Variant prioritization Variant 4. 1. Sequence1. preprocessing 1. Sequence1. preprocessing Primary Analysis econdary S BiERapp Can I interpret sequencing data for diagnostic? http://courses.babelomics.org/team/ BIER TEAM Targeted Enrichment Analysis and Management Gene panel Sequencing Biological data knowledge ClinVar HUMSAVAR HGMD TEAM COSMIC Diagnostic TEAM Targeted Enrichment Analysis and Management Gene panel 1. VCF files 2. Gene panel TEAM ClinVar HGMD HUMSAVAR COSMIC TEAM Targeted Enrichment Analysis and Management CSVS: CIBERER Spanish Variant Server Repositorio de frecuencias de variantes en la población española http://csvs.babelomics.org/ CSVS CIBERER Spanish Variant Server CIBERER Spanish Variant Server CSVS Local genetic variability Tool interface http://csvs.babelomics.org/ CSVS CIBERER Spanish Variant Server Genome Maps Visualizador genómico que interactúa con bases de datos funcionales http://genomemaps.org/ Genome Maps A next-generation web-based genome browser Tool interface Genome Maps A next-generation web-based genome browser Cell Maps Herramienta de modelización y visualización de redes biológicas http://cellmaps.babelomics.org/ Cell Maps Visualizing and integrating biological networks Cell Maps 1)Es una herramienta que permite la integración, visualización y el análisis de redes biológicas. 2)El input es un fichero donde indicamos las relaciones entre los nodos de nuetra red. Opcionalmente podemosincluir un fichero con los atributos de cada nodo. 3)El output gráfico es una red en la que se muestran las relaciones de los distintos nodos que la integran. Tutorial: https://github.com/opencb/cell-maps/wiki Cell Maps Visualizing and integrating biological networks Tool interface Cell Maps Visualizing and integrating biological networks Cell Maps: inputs Cell Maps Visualizing and integrating biological networks Cell Maps: outputs Cell Maps Visualizing and integrating biological networks Omics Data Integration from a Systems Biology perspective BIER Francisco García Omics Data Integration [email protected] Omics Data Integration Patient Technologies Data Analysis Integration and interpretation Molecular and clinical model Introduction Omics Data Integration Multidimensional Gene Set Analysis MicroRNA-Seq & mRNA-Seq Patterns miRNA1 0.5 Case Control miRNA2 1.2 miRNA3 1.3 miRNA4 1.7 microRNA-