raffaele.calogero@unito.it

Practical data analysis courses for life science

Torino

DUKE-NUS

(2016-)

ATC EMBL

(2010-)

MBC

(1998-)

Infrastructure since 2020

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• Italy: students (15) use their computer as terminals and run their analyses on miniPC (32 GB RAM 1Tb SSD, 6 cores, linux)

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• Germany: computer room with 30 workstations (32 Gb RAM, 512Gb SSD, 12 cores, linux)

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• Singapore: students (15-20) use their computer as terminals and run their analyses on a local server (512 Gb RAM 128 cores, linux)

Infrastructure from 2022

• Italy: students will use their computer as terminals and run their analyses on a cloud computing infrastructure (linux).

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• Germany: students will use their computer as terminals and run their analyses on a cloud computing infrastructure (linux).

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• Singapore: students will use their computer as terminals and run their analyses on a local server (linux).

Course characteristics

• Devoted to life scientists without any previous knowledge in scripting/programming and data analysis.

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• Five days theory & hands-on covering RNAseq and scRNAseq.
• Each day starts with “R pils” and ends with “R exercises”
• Course starts using only a GUI and ends using only R scripts

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• Topics:
• Lecture on RNAseq and scRNAseq (experimental design and technical critical points) (T)
• Lecture on reproducibility in bioinformatics (T)
• Data QC (RNAseq/scRNAseq) (T&P)
• Data reduction (RNAseq/scRNAseq) (T&P)
• Differential expression (RNAseq) (T&P)
• Clustering (scRNAseq) (T&P)
• Cluster-specific markers detection (scRNAseq) (T&P)
• Biological features characterization (RNAseq/scRNAseq) (T&P)
• Revision exercises

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rCASC

Tangaro et al. BMC Bioinformatics. 2021 Nov 8;22(Suppl 15):544

Alessandri et al. Int. J. Mol. Sci. 2021, 22(23), 12755

Alessandri et al. NPJ Syst Biol Appl. 2021 Jan 5;7(1):1

Alessandri et al. Gigascience. 2019 Sep 1;8(9):giz105

Kulkarni et al. BMC Bioinformatics. 2018 Oct 15;19(Suppl 10):349

Beccuti et al. Bioinformatics. 2018 Mar 1;34(5):871-872

Sanges et al. Bioinformatics. 2007 Dec 15;23(24):3406-8

Alessandri et al. GigaScience 2019

Course exercises as a real experiment

• The course exercises are build following the working path of a real experiment.
• Each section of the course is organized in the following way:

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Students follow the instruction to run an exercise

Instructors supervise students’ activities

Results are discussed

Theory intro

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Exercise description

Course as a cooking show

raffaele.calogero@unito.it