Genomic big data management and computing
A.A. 2026/2027
Obiettivi formativi
Many projects in the genomics field rely on increasingly large data sets, analyzing, for example, genomes of thousands of individuals affected by a particular disease. It is paramount to understand how large data sets can be managed and processed in an efficient way and how next-generation sequencing processing pipelines and workflows can be used to benefit such large-scale projects.
The objective of the course is to illustrate and discuss key aspects regarding the management, processing and analysis of big data for genomics (mainly data obtained by Next-Generation Sequencing), as well as introduce some of the existing approaches, analysis systems and technologies used. Practical applications will be illustrated using both dedicated programming and query languages (PySpark, GMQL), and specific computational platforms and distributed systems (Galaxy, Apache Spark, Cloud Computing). Also "downstream" analysis examples to underscore the necessity of big data management and computing in genomics will be illustrated.
The objective of the course is to illustrate and discuss key aspects regarding the management, processing and analysis of big data for genomics (mainly data obtained by Next-Generation Sequencing), as well as introduce some of the existing approaches, analysis systems and technologies used. Practical applications will be illustrated using both dedicated programming and query languages (PySpark, GMQL), and specific computational platforms and distributed systems (Galaxy, Apache Spark, Cloud Computing). Also "downstream" analysis examples to underscore the necessity of big data management and computing in genomics will be illustrated.
Risultati apprendimento attesi
Given the vastness of the topics presented, the ultimate goal of the course is not an in-depth knowledge of specific data analysis approaches, but rather to provide a broad overview of different solutions paired with the understanding of strengths and weaknesses of different methodologies and computing environments for managing scientific workflows used for big data analysis in the field of genomics.
Periodo: Primo semestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Programma
Seminar lectures and practical in informatics room on the following topics:
- Course introduction: Motivation, course information, introduction.
- Main concepts and issues: challenges that need to be addressed; advantages of workflow management for big data applications; multi-source and heterogeneous data integration; reproducibility of results.
- Querying and extracting heterogeneous big genomics data (using the Genomic Data Model and GenoMetric Query Language as examples)
- Workflow management systems for biomedical applications: Galaxy; brief overview of KNIME and other systems
- Distributed programming with Apache Spark:
· Introduction to distributed processing on the cloud and motivation
· Main features of Apache Spark
· Spark APIs for Python and R
- Pattern mining, search and analysis of heterogeneous big genomic data:
· Hidden Markow Models for state pattern analysis
· Chromatin state discovery with ChromHMM and Segway
- Data processing with cloud computing services:
· Introduction to cloud computing for genomics
· Amazon Web Services (AWS) and similar services
- Use cases / example analyses of heterogeneous data:
· Non-negative matrix factorization as an example of multivariate analysis, applied to the identification of mutational signatures from large-scale datasets of somatic mutations in cancer
- Course introduction: Motivation, course information, introduction.
- Main concepts and issues: challenges that need to be addressed; advantages of workflow management for big data applications; multi-source and heterogeneous data integration; reproducibility of results.
- Querying and extracting heterogeneous big genomics data (using the Genomic Data Model and GenoMetric Query Language as examples)
- Workflow management systems for biomedical applications: Galaxy; brief overview of KNIME and other systems
- Distributed programming with Apache Spark:
· Introduction to distributed processing on the cloud and motivation
· Main features of Apache Spark
· Spark APIs for Python and R
- Pattern mining, search and analysis of heterogeneous big genomic data:
· Hidden Markow Models for state pattern analysis
· Chromatin state discovery with ChromHMM and Segway
- Data processing with cloud computing services:
· Introduction to cloud computing for genomics
· Amazon Web Services (AWS) and similar services
- Use cases / example analyses of heterogeneous data:
· Non-negative matrix factorization as an example of multivariate analysis, applied to the identification of mutational signatures from large-scale datasets of somatic mutations in cancer
Prerequisiti
Knowledge of programming in Python and R
Metodi didattici
Frontal lessons (theory) and exercise sessions with practical exercises and discussion of the solutions.
Materiale di riferimento
Recommended articles (not required to pass the exam):
· Batut et al. (2018) Community-driven data analysis training for biology. Cell Systems 6: 752-758.
· Fillbrunn et al. (2017) KNIME for reproducible cross-domain analysis of life science data. Journal of Biotechnology 261: 149-156.
· Grüning et al. (2018) Practical bomputational reproducibility in the life sciences. Cell Systems 6(6): P631-P635.
· Hoffman et al. (2012) Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods 9(5): 473-476.
· Kulkarni and Frommolt (2017) Challenges in the setup of large-scale Next-Generation Sequencing analysis workflows. Computational and Structural Biotechnology Journal 15: 471-477.
· Langmead et al. (2018) Cloud computing as a platform for genomic data analysis and collaboration. Nat Rev Genet 19(4): 208-219.
· Li et al. (2015) An NGS workflow blueprint for DNA sequencing data and its application in individualized molecular oncology. Cancer Informatics 14(S5): 87-107.
· Batut et al. (2018) Community-driven data analysis training for biology. Cell Systems 6: 752-758.
· Fillbrunn et al. (2017) KNIME for reproducible cross-domain analysis of life science data. Journal of Biotechnology 261: 149-156.
· Grüning et al. (2018) Practical bomputational reproducibility in the life sciences. Cell Systems 6(6): P631-P635.
· Hoffman et al. (2012) Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods 9(5): 473-476.
· Kulkarni and Frommolt (2017) Challenges in the setup of large-scale Next-Generation Sequencing analysis workflows. Computational and Structural Biotechnology Journal 15: 471-477.
· Langmead et al. (2018) Cloud computing as a platform for genomic data analysis and collaboration. Nat Rev Genet 19(4): 208-219.
· Li et al. (2015) An NGS workflow blueprint for DNA sequencing data and its application in individualized molecular oncology. Cancer Informatics 14(S5): 87-107.
Modalità di verifica dell’apprendimento e criteri di valutazione
The assessment will be based on a written exam to be taken in the exam sessions defined by the school and covering all aspects in the syllabus.
BIO/11 - BIOLOGIA MOLECOLARE - CFU: 1
ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI - CFU: 5
ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI - CFU: 5
Conferenze: 48 ore
Docente:
Piro Rosario Michael
Turni:
Turno
Docente:
Piro Rosario Michael