PDA18 - Proteomics Data Analysis

Mass spectrometry based proteomic experiments generate ever larger datasets and, as a consequence, complex data interpretation challenges. In this course, the concepts and methods required to tackle these challenges will be introduced, covering peptide and protein identification, quantification, and differential analysis. Moreover, more advanced experimental designs and blocking will also be introduced. The core focus will be on shotgun proteomics data, and quantification using label-free precursor peptide (MS1) ion intensities. The course will rely exclusively on free and user-friendly software, all of which can be directly applied in your lab upon returning from the course. You will also learn how to submit data to PRIDE/ProteomeXchange, which is a common requirement for publication in the field, and how to browse and reprocess publicly available data from online repositories. The course will thus provide a solid basis for beginners, but will also bring new perspectives to those already familiar with standard data interpretation procedures in proteomics.

PDA19 - Proteomics Data Analysis

Mass spectrometry based proteomic experiments generate ever larger datasets and, as a consequence, complex data interpretation challenges. In this course, the concepts and methods required to tackle these challenges will be introduced, covering peptide and protein identification, quantification, and differential analysis. Moreover, more advanced experimental designs and blocking will also be introduced. The core focus will be on shotgun proteomics data, and quantification using label-free precursor peptide (MS1) ion intensities. The course will rely exclusively on free and user-friendly software, all of which can be directly applied in your lab upon returning from the course. You will also learn how to submit data to PRIDE/ProteomeXchange, which is a common requirement for publication in the field, and how to browse and reprocess publicly available data from online repositories. The course will thus provide a solid basis for beginners, but will also bring new perspectives to those already familiar with standard data interpretation procedures in proteomics.

Biome-Shiny: Automatic, interactive plots to visualize environmental diversity | 28/Nov @ IGC Open Day Universities

Metagenomics is a branch that processes large quantities of biological data, extracted directly from an environment. In order to derive useful information from this great volume of data, it is necessary to make use of informatics support, which, in turn, implies to a need to know how to use various tools and programming languages, and the need to develop tools to simplify the data analysis. Biome-Shiny is a tool created to bridge the gap between traditional Biology and Bioinformatics, providing a user-friendly interface to visualize microbiome data, allowing any user to generate and export interactive plots and tables, without needing to understand the background code.

PSLS20 - Practical Statistics for the Life Sciences

This intermediate level course is one of our Foundations courses. It covers essential statistical concepts and methods for extracting insights from empirical data in the life sciences. The course positions applied statistics, starting from important aspects of experimental design and data exploration. We then move into statistical modeling and data analysis. We will focus on the link between linear regression and analysis of variance. Together, these methods contribute to the study of General Linear Models. The course also introduces the basics of non-parametric testing, and addresses categorical data analysis and logistic regression.