Chipster training courses

We run several bioinformatics courses on different topics every year in Finland and abroad. The courses at CSC are open for everybody, but you can also contact us to discuss options for hosting a course on your site.

We make our course materials publicly available so that anyone can download them for their own use. The materials include slides and exercises, and many courses have also lecture videos. The exercise data are available as example sessions on the Chipster server, and we also provide ready-made analysis sessions which you can use as a reference when doing exercises on your own.

We also provide training accounts to our Chipster server in Finland. Note, that users from Finnish universities and research institutes can use their HAKA and VIRTU logins to access Chipster, so no training account is needed. However, we kindly ask you to inform us about upcoming Chipster courses (number of participants, type of analysis jobs) so that we can add computing resources to the server if needed.

We run currently the following Chipster courses (follow the links for more info). CSC runs also other bioinformatics courses, you can find the upcoming courses in our course calendar.

1. RNA-seq data analysis
2. Single cell RNA-seq data analysis
3. Virus detection using small RNA-seq
4. Microbial community analysis of amplicon sequencing data (16S)
5. Detection and annotation of genomic variants
6. ChIP-seq data analysis
7. Microarray data analysis

RNA-seq data analysis

• check the quality of reads with FastQC and MultiQC
• remove bad quality data with Trimmomatic
• infer strandedness with RseQC
• align reads to the reference genome with HISAT2 and STAR
• perform alignment level quality control using RseQC and SAMtools
• quantify expression by counting reads per genes using HTSeq
• check the experiment level quality with PCA plots and heatmaps
• analyze differential expression with DESeq2 and edgeR
• take multiple factors (including batch effects) into account in differential expression analysis

• slides
• lecture videos (2019)
• exercises (data is available on Chipster server in the example sessions listed in the exercise sheets)
1. ENCODE data with two samples. These exercises cover the whole workflow.
2. drosophila pasilla dataset. These exercises focus on differential expression analysis and how to take confounding factors into account.
3. parathyroid dataset. As before, but even harder.
4. lung and lymphnode comparison. Test your new skills with minimal instructions!
• additional material: We have collected instructions for RNA-seq and written a book which might be useful for you when getting started.

Single cell RNA-seq data analysis

• create Seurat v3 object
• perform QC and filter out low quality cells
• normalize expression values
• detect highly variable genes using VST
• scale data and regress out unwanted variability
• perform principle component analysis (PCA) and select PCs to be used for clustering
• cluster cells and find marker genes for a cluster
• visualize clusters with UMAP and tSNE
• run canonical correlation analysis (CCA) to identify common sources of variation between two datasets
• integrate samples using the mutual nearest neighbor approach (anchors)
• find conserved cluster marker genes for two samples
• find differentially expressed genes in a cluster between two samples
• visualize genes with cell type specific responses in two samples

• slides (2020)
• lecture videos (2020)
• exercises: Clustering 10X Genomics data and Integrated analysis of two samples (2020). The data are available on Chipster server in the example sessions listed in the exercise sheet.
• Note: excellent lecture videos covering scRNA-seq theory from another course (May 2019)

• check the quality of reads with FastQC
• tag reads with cell and molecular barcodes
• trim and filter reads
• align reads to the reference genome with HISAT2 and STAR
• tag reads with gene names
• visualize aligned reads in genomic context using the Chipster genome browser
• estimate the number of usable cells by checking the inflection point
• detect bead synthesis errors
• create and filter DGE
• create Seurat v2 object
• regress out unwanted variability
• detect variable genes and perform principle component analysis
• cluster cells and find marker genes for a cluster
• run canonical correlation analysis (CCA) to identify common sources of variation between two datasets
• align two samples for integrated analysis
• find conserved cluster markers for two samples
• find differentially expressed genes in a cluster between two samples
• visualize genes with cell type specific responses in two samples

• slides (March 2019)
• exercises 1: Preprocessing DropSeq data. The data is available on Chipster server in the example session listed in the exercise sheet.
• exercises 2: Clustering 10X Genomics data. The data is available on Chipster server in the example session listed in the exercise sheet.
• exercises 3: Integrated analysis of two samples using 10X Genomics data. The data is available on Chipster server in the example session listed in the exercise sheet.

Virus detection using small RNA-seq

• run VirusDetect and interpret the result files
• subtract reads originating from the host genome
• set parameters for filtering the BLAST matches

• slides
• lecture video from a webinar
• exercises (data is available on Chipster server in the example sessions listed in the exercise sheets)
1. Potato dataset with different amounts of reads.
2. Rasberry dataset demonstrates how to provide the host genome.

Microbial community analysis of 16S amplicon sequencing data

• check the quality of reads with FastQC and MultiQC
• remove bad quality data with Trimmomatic and Mothur
• remove redudancy, perform alignment and preclustering with Mothur
• do taxonomic classification with Mothur
• perform statistical analysis (based on R packages vegan, rich, biodiversityR, pegas, labdsv and DESeq2)

• lecture videos
• slides
• Community analysis with Chipster
• Introduction to community analysis (2017) by Anu Mikkonen
• exercises. The data is available on Chipster server in the example session listed in the exercise sheet.

Detection and annotation of genomic variants

• check the quality control with FastQC and PRINSEQ
• remove bad quality data with Trimmomatic
• align reads to the reference genome with BWA
• perform alignment level quality control with SAMtools
• mark duplicates with Picard
• call and filter variants with Samtools, BCFtools and VCFtools. Note that the GATK variant calling platform will be integrated in Chipster in 2019.
• annotate variants with Variant Effect Predictor (VEP)
• visualize variants in genomic context with the Chipster genome browser

• slides
1. Variant analysis with Chipster
2. Ensembl variation data resources (Variant Effect Predictor VEP described from slide 52 onwards) by Ben Moore
3. Effects of variants on protein structure by Hanka Venselaar
• lecture videos
• exercises. The data is available on Chipster server in the example session listed in the exercise sheet.

ChIP-seq data analysis

• check the quality control with FastQC
• align reads to the reference genome with Bowtie
• call peaks with MACS2
• filter peaks
• visualize reads and peaks in genomic context with the Chipster genome browser
• retrieve nearby genes
• pathway analysis
• motif detection

• slides
• exercises. The data is available on Chipster server in the example session listed in the exercise sheet.

Microarray data analysis

• perform quality control
• normalize data
• find differentially expressed genes and take batch effects into acount
• perform clustering
• visualize results in different ways
• pathway analysis

• slides (including exercises). The data is available on Chipster server in the example sessions listed in the exercises.