Common technologies and data analysis methods

In functional genomics studies, it is crucial to assess the diverse technologies available and recognise their appropriate applications. When designing a personalised functional genomics experiment using these techniques, it is essential to consider several factors. These comprise experimental parameters, sample preparation approaches, and the desired scope of analysis. Moreover, comprehending the steps entailed in the analysis of microarray and RNA-seq experiments is pivotal. These include pre-processing of raw data, quality assessment, normalisation, differential expression analysis, and the interpretation of results, ultimately contributing to informed scientific insights.

The next tutorial in this collection presents common technologies applied in functional genomics investigations, comprising microarrays and next-generation sequencing (NGS), with particular attention to RNA-sequencing (RNA-seq). You will identify significant factors to consider during experiment design, explore techniques for data analysis, and plan the optimal technology strategy based on specific contexts.

Go to ‘Functional genomics II: Common technologies and data analysis methods‘

In genomics investigations, generating your own data is not always a prerequisite to initiate your research. Abundant publicly accessible experimental data can assist you in formulating original questions and devising experiments tailored to your own research context. On the next page, you will explore an EMBL-EBI resource that facilitates both the access and submission of experimental data.