Servizi per la didattica
PORTALE DELLA DIDATTICA

Bioinformatics

01QGTMV

A.A. 2019/20

Course Language

English

Course degree

Master of science-level of the Bologna process in Biomedical Engineering - Torino

Course structure
Teaching Hours
Lezioni 30
Esercitazioni in laboratorio 30
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Ficarra Elisa Professore Associato ING-INF/05 30 0 0 0 6
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/05 6 C - Affini o integrative Attività formative affini o integrative
2018/19
Software solutions will be studied for the analysis of genetic data provided by the latest generation biotechnologies (e.g. DNA/RNA next generation sequencers, Third generation sequencing technology, etc..). During the course, it will be described the state of the art of such technologies, and it will be deeply studied computational and algorithmic issues for the development of tool-flows for complex genetic analyses. It will be explained deep learning techniques (e.g. Convolutional Neural Networks - CNNs) and their application to biological and medical problems. Moreover, it will be presented also techniques for genetic data distributed computing. In particular, it will be introduced the Clustering programming. During the course, the basic concepts of the molecular biology will be introduced, and the programming language Python will be presented (even if every programming language will be allowed in the final project development). The purpose of the course is therefore to provide training in order to make students experts of the biomolecular/genetic issues, technologies and processing techniques the most advanced in the field of biotechnology and genetic analysis.
Software solutions will be studied for the analysis of genetic data provided by the latest generation biotechnologies (e.g. DNA/RNA next generation sequencers, Third generation sequencing technology, etc..). During the course, it will be described the state of the art of such technologies, and it will be deeply studied computational and algorithmic issues for the development of tool-flows for complex genetic analyses. It will be explained deep learning techniques (e.g. Convolutional Neural Networks - CNNs) and their application to biological and medical problems. Moreover, it will be presented also techniques for genetic data distributed computing. In particular, it will be introduced the Clustering programming. During the course, the basic concepts of the molecular biology will be introduced, and the programming language Python will be presented (even if every programming language will be allowed in the final project development). The purpose of the course is therefore to provide training in order to make students experts of the biomolecular/genetic issues, technologies and processing techniques the most advanced in the field of biotechnology and genetic analysis.
The student should acquire i) the knowledge of the latest generation biotechnologies for genetic and molecular screening, ii) the knowledge of some the most up-to-date genetic issues in the personalised medicine approach, iii) the knowledge of the main SW solutions for complex bioinformatics analyses, and of computer science techniques such as deep learning, text mining, graph optimization, iv) the ability to design and implement effective and computationally efficient algorithmic solutions for biological problems, vi) the experience on SW optimization techniques on cluster infrastructures.
The student should acquire i) the knowledge of the latest generation biotechnologies for genetic and molecular screening, ii) the knowledge of some the most up-to-date genetic issues in the personalised medicine approach, iii) the knowledge of the main SW solutions for complex bioinformatics analyses, and of computer science techniques such as deep learning, text mining, graph optimization, iv) the ability to design and implement effective and computationally efficient algorithmic solutions for biological problems, vi) the experience on SW optimization techniques on cluster infrastructures.
High level language computer programming (eg C, C + + or Java), and optionally scripting languages.
High level language computer programming (eg C, C + + or Java), and optionally scripting languages.
- Introduction to the Bioinformatics: Concepts of Molecular Biology, Computational, technological and efficacy requirements of the algorithms, Relevant problems in research, industry and businesses - DNA-, microRNA- and RNA-sequencing: Description of sequencing technologies, algorithmic and computational issues, main tools used for sequencing and data analysis, issues related to software development for advanced analyses (e.g. mutations and gene fusions detection, microRNA and long RNA non coding identification and expression, etc.), SW optimization on parallel and distributed infrastructures. - Bioinformatics techniques for the study and the prediction of regulatory processes: SW techniques for the prediction of molecular interactions, gene/non-coding RNA expression, data integration and correlation, derivation of regulatory networks in Complex Systems. - Deep Learning techniques: introduction to some well known and used methodologies (e.g. Neural Networks, Convolution Neural Network, LSTM, etc.), application of such techniques to genetic and biological studies. - Cluster programming: implementation of analysis pipelines on computer clusters.
- Introduction to the Bioinformatics: Concepts of Molecular Biology, Computational, technological and efficacy requirements of the algorithms, Relevant problems in research, industry and businesses - DNA-, microRNA- and RNA-sequencing: Description of sequencing technologies, algorithmic and computational issues, main tools used for sequencing and data analysis, issues related to software development for advanced analyses (e.g. mutations and gene fusions detection, microRNA and long RNA non coding identification and expression, etc.), SW optimization on parallel and distributed infrastructures. - Bioinformatics techniques for the study and the prediction of regulatory processes: SW techniques for the prediction of molecular interactions, gene/non-coding RNA expression, data integration and correlation, derivation of regulatory networks in Complex Systems. - Deep Learning techniques: introduction to some well known and used methodologies (e.g. Neural Networks, Convolution Neural Network, LSTM, etc.), application of such techniques to genetic and biological studies. - Cluster programming: implementation of analysis pipelines on computer clusters.
The course will include exercises and computer lab sessions on some of studied tools and on the development of new SW solutions.
The course will include exercises and computer lab sessions on some of studied tools and on the development of new SW solutions.
Course slides, scientific research papers, web documents and short educational movies.
Course slides, scientific research papers, web documents and short educational movies.
Modalità di esame: prova scritta; progetto individuale; progetto di gruppo;
Alternatively, final written test or research project. In both the cases, previously detailed learning outcomes should be evaluated (see in particular points i), ii), iii) and iv)) In details: Written test on all the course topics (max score 30L/30): - It consists of 2 questions about course topics, and 2 exercises on Python programming - Duration: 2h. - Use of notes, course slides, handbooks, Python examples or exercises is forbidden Project (max score 30L/30): - Project groups should be 3-4 members. - There are no midterm deadlines for the project completion - Oral presentation is about the project development, contest, issues, and results (slides). Questions concern the project and related topics covered in the course. A demo is also required. - User manual should be provided to the professor, as well as developed code, no later than two days before the project presentation
Exam: written test; individual project; group project;
Alternatively, final written test or research project. In both the cases, previously detailed learning outcomes should be evaluated (see in particular points i), ii), iii) and iv)) In details: Written test on all the course topics (max score 30L/30): - It consists of 2 questions about course topics, and 2 exercises on Python programming - Duration: 2h. - Use of notes, course slides, handbooks, Python examples or exercises is forbidden Project (max score 30L/30): - Project groups should be 3-4 members. - There are no midterm deadlines for the project completion - Oral presentation is about the project development, contest, issues, and results (slides). Questions concern the project and related topics covered in the course. A demo is also required. - User manual should be provided to the professor, as well as developed code, no later than two days before the project presentation


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