Herramientas para el diseño y el análisis de datos de paneles de genes

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1 Herramientas para el diseño y el análisis de datos de paneles de genes Hospital Sant Pau Barcelona, 16 Jun 2016 BIER Francisco García fgarcia@cipf.es Departamento de Genómica Computacional. CIPF

2 Príncipe Felipe Research Center Goal: biomedical research Basic research in genes, targets, molecular and cellular processes, Nanomedicine and Computational Medicine Translation into clinical practice: personalized medicine, cancer, rare diseases, metabolic and functional impairment Introduction Príncipe Felipe Research Center (CIPF)

3 Who are we? The Computational Genomics Department, in Research Center Prince Felipe Team: multidisciplinary group of 14 researchers and technicians led by Joaquín Dopazo Introduction Genomic Computational Department

4 Who are we? Introduction Genomic Computational Department

5 Why are we interested in Computational Genomics? The overall goal of the department: Apply computational methods biotechnological problems to biomedical and 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 Genomic Computational Department

6 Why are we interested in Computational Genomics? Introduction Personalized Medicine

7 Why are we interested in Computational Genomics? + Introduction Personalized Medicine and Mendelian Diseases

8 Big Data Genomics Transcriptomics Metabolomics Lipidomics Proteomics Introduction Omics sciences Epigenomics

9 Big Data Biological knowledge Clinical knowledge Gene Ontology KEGG pathways MiRNA, CisRed Biocarta pathways InterPro Motifs HUMSAVAR Gene Expression in tissues Transcription Factor Binding Sites Bioentities from literature ClinVar HGMD COSMIC Introduction Regulatory elements Clinical and biological databases

10 How do we work? Our department collaborates in different research projects and converts researcher needs into bioinformatics solutions Free software for several reasons: Any customer can try our tools The scientific community can test our software This is the current trend in Computational Genomics Introduction Genomic Computational Department

11 How do we work? Introduction Genomic Computational Department

12 How do we work? El CIBER en su Área Temática de Enfermedades Raras (CIBERER) es el centro de referencia en España en investigación sobre enfermedades raras: Objetivo: coordinar y favorecer la investigación básica, clínica y epidemiológica, así como potenciar que la investigación que se desarrolla en los laboratorios llegue al paciente, y dé respuestas científicas a las preguntas nacidas de la interacción entre médicos y enfermos. El CIBERER se compone de un equipo humano de más de 700 profesionales e integra a 62 grupos de investigación. Introduction Genomic Computational Department BIER

13 How do we work? Curso CIBERER de análisis de datos genómicos, 2830 Sep 2016 en Valencia: International course of Genomic Data Analysis, Mar 2017, Valencia: Introduction Genomic Data Analysis courses

14 Web tools to analyze gene panel data BIER CIPF Genomic Computational Department

15 Outline 1) Introduction to NGS Data Analysis 2) TEAM 3) PanelMaps Web Tools Introduction to NGS data analysis

16 NGS technologies How do these technologies work? Reference genome Introduction NGS data analysis

17 Genomics Data Analysis Pipeline Primary Analysis Secondary Introduction 1. Sequence preprocessing Fastq 2. Mapping BAM 3. Variant calling VCF 4. Variant annotation VCF 5. Variant prioritization Studies of genomic variation

18 Fastq format We could say it is a fasta with qualities : Header (like the fasta but starting ) Sequence (string of nt) + and sequence ID (optional) Encoded quality of the GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT +!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65 Introduction NGS data analysis: files format

19 ID:HPG-Aligner VN:1.0 SN:20 LN: = * 0 0 GTTTAGATACTGAAAGGTACATACTTCTTTGTAGGAACAAGCTATCATGCTGCATTTCTATAATATCACATGAATA GIJGJLGGFLILGGIEIFEKEDELIGLJIHJFIKKFELFIKLFFGLGHKKGJLFIIGKFFEFFEFGKCKFHHCCCF AS:i:254 NH:i:1 NM:i:0 HWI-ST700660_138:2:2208:6911:12246#2@0/ = * 0 0 GTTTAGATACTGAAAGGTACATACTTCTTTGTAGGAACAAGCTATCATGCTGCATTTCTATAATATCACATGAATA HHJFHLGFFLILEGIKIEEMGEDLIGLHIHJFIKKFELFIKLEFGKGHEKHJLFHIGKFFDFFEFGKDKFHHCCCF AS:i:254 HWI-ST700660_138:2:1201:2973:62218#2@0/ M * 0 0 AACCCCAAAAATGTTGGAAGAATAATGTAGGACATTGCAGAAGACGATGTTTAGATACTGAAAGGGACATACTTCT FEFFGHHHGGHFKCCJKFHIGIFFIFLDEJKGJGGFKIHLFIJGIEGFLDEDFLFGEIIMHHIKL$BBGFFJIEHE AS:i:254 NH:i:1 NM:i:0 NH:i:1 NM:i:1 HWI-ST700660_138:2:1203:21395:164917#2@0/ M1D72M * 0 0 NCACCCATGATAGACCAGTAAAGGTGACCACTTAAATTCCTTGCTGTGCAGTGTTCTGTATTCCTCAGGACACAGA #4@ADEHFJFFEJDHJGKEFIHGHBGFHHFIICEIIFFKKIFHEGJEHHGLELEGKJMFGGGLEIKHLFGKIKHDG AS:i:254 NH:i:3 NM:i:1 HWI-ST700660_138:2:1105:16101:50526#6@0/ M4D23M * 0 0 AAGAAGTGCAAACCTGAAGAGATGCATGTAAAGAATGGTTGGGCAATGTGCGGCAAAGGGACTGCTGTGTTCCAGC FEHIGGHIGIGJI6FCFHJIFFLJJCJGJHGFKKKKGIJKHFFKIFFFKHFLKHGKJLJGKILLEFFLIHJIEIIB AS:i:368 NH:i:1 NM:i:4 SAM Specification: Introduction NGS data analysis: files format

20 VCF format Introduction NGS data analysis: files format

21 BED format Introduction NGS data analysis: files format

22 Genomics Data Analysis Pipeline Primary Analysis Secondary Introduction 1. Sequence preprocessing Fastq 2. Mapping BAM 3. Variant calling VCF 4. Variant annotation VCF PanelMaps BiERapp 5. Variant prioritization TEAM Studies of genomic variation CSVS

23 Outline 1) Introduction to NGS Data Analysis 2) TEAM 3) PanelMaps Web Tools Targeted Enrichment Analysis and Management

24 Can I interpret sequencing data for diagnostic? BIER TEAM Targeted Enrichment Analysis and Management

25 Introduction Sequencing data Biological knowledge TEAM ClinVar HGMD HUMSAVAR COSMIC Diagnostic TEAM Targeted Enrichment Analysis and Management

26 How does TEAM work? 1. VCF files 2. Gene panel TEAM ClinVar HUMSAVAR TEAM HGMD COSMIC Targeted Enrichment Analysis and Management

27 Getting information SIFT SIFT predicts whether an amino acid substitution affects protein function Interpretation: 1 (tolerated) to 0 (not tolerated) PolyPhen Polymorphism Phenotyping is a tool which predicts possible impact of an amino acid substitution on the structure and function of a human protein Interpretation: 1 (probably damage) to 0 (benign) TEAM Targeted Enrichment Analysis and Management

28 Getting information Consequence type or effect TEAM Targeted Enrichment Analysis and Management

29 How does TEAM work? 1. Defining panel 2. Uploading input data 3. Getting results TEAM Targeted Enrichment Analysis and Management

30 How to define a panel? 1. Name of panel 3. Adding: 2. Diseases - more genes - mutations 4. Save panel TEAM Targeted Enrichment Analysis and Management

31 How to define a panel? Adding new mutations Checking mutations from Genome Viewer TEAM Targeted Enrichment Analysis and Management

32 Web results 1. OVERVIEW TEAM 2. DIAGNOSTIC Targeted Enrichment Analysis and Management

33 Web results 3. SECONDARY FINDINGS TEAM Targeted Enrichment Analysis and Management

34 Reporting results 4. REPORT PDF TEAM Targeted Enrichment Analysis and Management

35 Remarks TEAM is a free web tool Easy-to-use and powerful TEAM helps you for diagnostic TEAM Targeted Enrichment Analysis and Management

36 More information TEAM Tutorial: BIER TEAM Targeted Enrichment Analysis and Management

37 Outline 1) Introduction to NGS Data Analysis 2) TEAM 3) PanelMaps Web Tools Introduction to NGS data analysis

38 Can I visualize and detect deletions for gene panel? BIER PanelMaps A web tool to analyze gene panel data

39 How does PanelMaps work? 1. BAM files 2. BED file PanelMaps PanelMaps Detecting altered regions for targeted sequencing

40 How does PanelMaps work? PanelMaps Coverage description

41 How does PanelMaps work? PanelMaps Detection of altered regions

42 How does PanelMaps work? PanelMaps Detecting altered regions for targeted sequencing

43 Any comment or question? Web tools for gene panel data