Eines bioinformàtiques i estadístiques per a la investigació biomèdica Anàlisi de dades amb Ingenuity Pathways Alex Sánchez Unitat d’Estadística i Bioinformàtica

07/07/2010 We are drowning in information and starved for knowledge John Naisbitt Who on efficient work is bent, Must choose the fittest instrument. Goehthe (Fausto)

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica3 Esquema de la presentación Más allá de los microarrays… Ingenuity Pathways Analysis –Visión general –Componentes –Tipos de estudios Ejemplos de uso –Exploración y búsqueda de información –Análisis de datos

Más allá de los microarrays …

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica5 Un experimento con microarrays... Listas de identificadores (genes, miRNAs, …) seleccionados

07/07/2010 So Where do we go from here? Or, How To Drive A Biologist Crazy? Ted Slater Proteomics Center of Emphasis Proteomics Center of Emphasis Pfizer Gobal R&D Michigan gi|  gi|  gi|  gi|  gi|  gi|  gi|  gi|  gi|  gi|  gi|  gi|  gi|  gi|  gi|  …

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica7 De las listas a la Biologia Enfoque tradicional para el análisis de las listas de genes: de uno en uno –Literatura, bases de datos,... Problema: –Tarea lenta, tediosa y, lo que es peor... –Ignora posibles interacciones Enfoque alternativo: Análisis Funcional o de “Significación Biológica”.

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica8 Los métodos de Análisis Funcional Son métodos automáticos para –Identificar procesos biológicos asociados con los resultados experimentales. –Determinar los temas funcionales comunes a grupos de genes seleccionados. –Analizar las conexiones entre genes, moléculas y enfermedades mediante la exploración automática de la literatura para descubrir asociaciones relevantes con los resultados experimentales. Facilitan el uso de información auxiliar. Ayudan a entender los fenómenos biológicos subyacentes.

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica9 Herramientas de Análisis Funcional Docenas de programas en los últimos 10 años Estudio directo de las listas de genes –Basadas en GO u otras BD (KEGG,...)  fatiGO, DAVID, GSEA, Babelomics... [SerbGO] Ingenuity Pathways Analysis Exploracion de relaciones en la literatura –PubMed, Scopus, HighWire, GOPubMed, … –Ingenuity Pathways Analysis Estudio de pathways asociados con las listas –Pathway Explorer, GenMapp, –Ingenuity Pathway Analysis

07/07/2010 Cursos y materiales CNIO –4th Course on Functional Analysis of Gene Expression4th Course on Functional Analysis of Gene Expression Canadian Bioinformatics Workshop –Interpreting gene lists from omics sets Interpreting gene lists from omics sets EADGENE and SABRE –Post-analyses WorkshopPost-analyses Workshop

Ejemplos de Análisis Funcional

07/07/2010 Ejemplo 1 The Polycomb group protein EZH2 is involved in progression of prostate cancer (Nature, 419 (10) ) –Varambally et al. (2002) estudian las diferencias entre cancer de prostata localizado (PCA) y metastático (MET) EZH2 sobreexpresado en MET Los casos de PCA con EZH2 alto  peor prognosis –Sugieren que EZH2 puede Estar implicado en la progresión PCA  MET Distinguir el PCA benigno del de mal pronóstico.

07/07/2010 Ejemplo 1 Análisis de microarrays  –Listas de genes up (55) y down (438) reg. Un análisis funcional permitirá estudiar –Que procesos biológicos (pathways) estan relacionados con los genes de las listas Bases de datos de anotaciones –Que funciones se presentan en las listas con una frecuencia distinta de la de todos los genes estudiados Análisis de enriquecimiento –Las herramientas disponibles en Babelomics son una buena opción para este análisis.Babelomics

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica14 Ejemplo 2 – De genes a Pathways

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica15 Los genes se agrupan por funciones

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica16 Las funciones se asocian a pathways

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica17 Los cambios de expresión se proyectan en el pathway

Introducción “The Ingenuity View”

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica19 Ingenuity Pathways Analysis Ingenuity Pathways Analysis (IPA) is an all-in-one software application that enables researchers to model, analyze, and understand the complex biological and chemical systems at the core of life science research

07/07/2010 IPA Challenge Integrate, Interpret, Gain Therapeutic Insight from Experimental Data Expression Arrays Proteomics Traditional Assays Experimental Platforms FAS VEGFA bevacizumab Molecules Apoptosis Angiogenesis Cellular Processes Cancer Disease Processes Disease/physiologi cal response Overlapping cellular processes/pathway s Molecular Interactions Molecular Perturbation

07/07/2010 IPA Challenge Gain Rapid Understanding of Experimental Systems Expression Arrays Proteomics Traditional Assays Experimental Platforms FAS VEGFA bevacizumab Molecules Angiogenesis Apoptosis Cellular Processes Cancer Disease Processes Guide in vivo/in vitro assays Search for genes implicated in disease Identify related cellular processes/pathway s Generate hypothesis

07/07/2010 Ingenuity Platform Findings manually extracted from full text Extensive libraries of metabolic and signaling pathways Chemical and drug information Scalable best-in-class content acquisition processes Designed to enable computation Consists of biological objects and processes in organized into major branches Robust, up-to-date synonym library Knowledge infrastructure tools and processes for structuring biological and chemical knowledge Ingenuity Knowledge Base Content Ontology

07/07/2010 Ingenuity Knowledge Base: Content Expert Extraction: Full text from top journals Coverage of peer-reviewed journals, plus review articles and textbooks Manually extracted by Ph.D. scientists Import Annotations, Findings: OMIM, GO, Entrez Gene Tissue and Fluid Expression Location Molecular Interactions (e.g. BIND, DIP, TarBase) Internally curated knowledge: Signaling & Metabolic Pathways Drug/Target/Disease relationships Toxicity Lists All findings are structured for computation and updated regularly

07/07/ /06/2009Alex Sánchez. Unitat d'Estadística i Bioinformatica24

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica25 Como trabajan juntos

07/07/2010 Tipos de análisis

07/07/2010 Preguntas y respuestas

Instalación, acceso y uso

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica29 Instalación y puesta en marcha IPA funciona en línea. –No se instala. Tan sólo se accede a él Para utilizarlo se necesita una cuenta –Prueba (15 días). –Acceso (IRHUVH y HVH) mediante reserva previa a la UEB y en horario de mañana o tarde. Funciona en Windows o Mac, pero no en Linux

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica30 Requisitos del sistema

07/07/2010 Acceso

07/07/2010 Formas de arrancar IPA

07/07/2010 El entorno de Ingenuity Pantallas, menús, ayudas

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica34 Pantalla de inicio rápido

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica35 Gestor de proyectos

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica36 Barra de búsqueda

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica37 Ayuda (1) Sistemas de ayuda

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica38 Ayuda (2) Tutoriales

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica39 Ayuda (3). Workflows

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica40 Programa de formación

Capacidades básicas del programa Búsqueda, Análisis, Comunicación

07/07/ /06/2009Alex Sánchez. Unitat d'Estadística i Bioinformatica42

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica43 Search & Explore

07/07/2010 Search and Explore Biological & Chemical Knowledge

07/07/2010 Gene View / Chem View

07/07/2010 Dynamic Signalling & Metabolic Pathways

07/07/2010 My Pathway & Lists Build custom libraries of pathways representing mechanism of action and mechanism of toxicity. Create custom, literature- supported signaling pathways with proteins of interest. Store collections of custom pathways and lists for subsequent core, IPA-Tox™, IPA-Biomarker™, or IPA-Metabolomics™ analyses. Use the Grow and Connect tools to edit and expand networks based on the molecular relationships most relevant to the project: –Transcriptional networks –Phosphorylation cascades –Protein-Protein or Protein-DNA interaction networks –microRNA-mRNA target networks –Chemical effects on proteins Use Search results as building blocks for custom pathways –Identify cross-talk between biological processes and pathways –Understand whether gene lists and signatures are tightly connected at the molecular level

07/07/2010 Path Explorer Path Designer

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica49 Analyze & Interpret data IPA Core Analysis IPA Tox Analysis IPA Biomarker Analysis IPA Metabolomic Analysis

07/07/2010

IPA Core Analysis

07/07/2010 IPA-Biomarker™ Analysis IPA-Biomarker identifies the most promising and relevant biomarker candidates within experimental data. –Prioritize molecular biomarker candidates based on key biological characteristics. –Elucidate mechanism linking potential markers to a disease or biological process of interest. –Perform analysis across biomarker lists to find biomarker candidates unique to a disease stage or common across all stages. –Understand the molecular differences between patient populations.

07/07/2010 IPA.Tox Analysis IPA-Tox delivers a focused toxicity and safety assessment of candidate compounds. –Enables assessment of the toxicity and safety of compounds early in the development process. –Provides expert molecular toxicology data interpretation to non-expert users. –Reveals clinical pathology endpoints associated with a dataset. –Generates new hypotheses that may not have been revealed using traditional toxicology approaches. –Elucidates mechanism of toxicity and identify potential markers of toxicity.

07/07/2010 IPA-Metabolomics Analysis IPA-Metabolomics extracts rich pathway information from metabolomics data. –Overcomes the metabolomics data analysis challenge by integrating transcriptomics, proteomics, and metabolomics data to enable a complete systems biology approach. –Provides the critical context necessary to gain insights into cell physiology and metabolism from metabolite data.

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica55 Communicate & Collaborate Share Report Interactive Pathways Integrate with other software

Resumen y recapitulación

07/07/2010 Resumen El análisis funcional mejora la comprensión de los fenómenos biológicos mediante el estudio simultáneo de grupos de valores. Ingenuity Pathways permite –Explorar –Analizar –Comunicar y compartir

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica58 Ventajas e inconvenientes Intuitivo y fácil de usar Integración de todas las funciones Muy potente en humanos y cáncer  No tan potente en otras especies o enfermedades.  No es libre sino que hay que pagarlo  No incorpora algoritmos avanzados potentes como GSEA

Networks and Pathways in IPA

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica60 Networks A network is a set of terms (“nodes”) related by a set of relations (“edges”). IPA transforms a list of genes into a set of relevant networks based on information maintained in the Ingenuity Pathways Knowledge Base (IPKB) This knowledge base has been abstracted into a large network, called the Global Molecular Network, composed of thousands of genes and gene products that interact with each other.

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica61 A network in IPA

07/07/2010 Networks in IPA Purpose: –To show as many interactions between user-specified molecules in a given dataset and how they might work together at the molecular level Why are Ingenuity networks biologically interesting? –Highly-interconnected networks are likely to represent significant biological function

07/07/2010 Key Terminology Focus Molecule: –Molecules that are from uploaded list, pass filters are applied, and are available for generating networks Networks: –Generated de novo based upon input data –Do not have directionality –Contain molecules involved in a variety of Canonical Pathways Canonical Pathways (Signaling and Metabolic) –Are generated prior to data input, based on the literature –Do NOT change upon data input –Do have directionality (proceed “from A to Z”) My Pathways and Path Designer Pathways –Custom built pathways manually created based on user input

07/07/2010 Viewing networks

07/07/2010 How Networks Are Generated 1.Focus molecules are “seeds” 2.Focus molecules with the most interactions to other focus molecules are then connected together to form a network 3.Non-focus molecules from the dataset are then added 4.Molecules from the Ingenuity’s KB are added 5.Resulting Networks are scored and then sorted based on the score 35 molecules per network for visualization purposes

07/07/2010 Calculation of Score for Networks in IPA Based on the Right-tailed Fisher's Exact Test Used as a means to rank/sort networks so that those with the most focus molecules are at the top of the list Takes into account the number of focus molecules in the network and the size of the network Not an indication of the quality or biological significance of the network

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica67 Network notation (1) (Help  legend)

07/07/2010 Alex Sánchez. Unitat d'Estadística i Bioinformatica68 Network notation (2)

07/07/2010 Significance Calculations Measures the likelihood that a function is over-represented by the molecules in your dataset Expressed as a p-value calculated by using the right-tailed Fisher's Exact Test Range indicates most significant low level function to least significant low-level function

07/07/2010 Multiple Testing Correction Benjamini-Hochberg method of multiple testing correction Calculates False Discovery Rate –Threshold indicates the fraction of false positives among significant functions % (1/20) may be a false positive

07/07/2010 Which p-value calculation should I use? What is the significance of function X relative to the dataset? –Use right-tailed Fisher’s Exact test result What is the significance of function X relative to all the other functions in the dataset? –Use Benjamini-Hochberg multiple testing correction