With a constant evolution of technologies, laboratory biologists are faced with an increasing need of bioinformatics skills to deal with high-throughput data storage, retrieval and analysis.

Although several resources developped for such tasks have a web interface (most of the time, the first choice of biologitsts), many operations can be more efficiently handled with command lines (CLI).

Python is an open-source and general-purpose scripting language which runs on all major operating systems. It was designed to be easily read and written with comparatively simple syntax, and is thus a good choice for beginners in programming. Python is applied in many disciplines and is one of the most common languages for bioinformatics. The Python community enthusiastically maintains a rich collection of libraries/modules for everything from web development to machine learning. Other programming languages such as R have comparable functionality to Python, however some tasks are more natural (and easier!) in Python.

With a constant evolution of technologies, laboratory biologists are faced with an increasing need of bioinformatics skills to deal with high-throughput data storage, retrieval and analysis.

Although several resources developped for such tasks have a web interface (most of the time, the first choice of biologitsts), many operations can be more efficiently handled with command lines (CLI).

Usage of NGS is increasing in several biological fields due to a very rapid decrease in cost. However, it often results in hundreds of Gbs of data making the downstream analysis very challenging and requires bioinformatics skills.

In this module, we will introduce the most used sequencing technologies and explain their decryption concepts.

We will also introduce the repositories e.g. the European Nucleotide Archive (ENA), Sequence Read Archive (SRA) from which you could retrieve raw data based on specific experiments. We will practice the usage of command line tools to search and fetch NGS raw data in a powerful way.

Finally, using different datasets, we will practice screening for quality control, filtering reads for better downstream analysis, mapping reads to reference genome and visualize the output.

CAS-PMO 2018-2019