Genomics

The analysis of the genome (DNA) enables the identification of mutations and genetic variations associated to disease and is important for personalized treatments. In cancer research, this is crucial for identifying driver mutations - genetic alterations that promote tumor development - and for understanding tumor evolution through clonality analysis.

We analyze data from Whole Genome and Exome sequencing (WGS/WES), targeted sequencing, SNP panels and genotyping microarrays.

Our analyses:

• Variant analysis (SNP, indels, copy number variation, structural variations)

• Variant annotation (predict effect on gene expression or protein function, clinical relevance)

Transcriptomics

Studying the cell’s transcriptome (RNA), aka the ‘read out of the genome’, allows to identify genes and pathways that are involved in specific diseases, conditions or responses to treatments. At the single cell level this can provide insight into rare cell populations and cellular trajectories. At the transcript level it allows insight into RNA diversity and cellular processes/mechanisms that in turn may vary across cell types and individuals.

We analyze data from expression microarrays (miRNA, mRNA) and various types of RNA sequencing protocols (small RNA-seq, mRNA-seq and single cell RNA-seq).

Our analyses:

• Gene quantification and annotation (RNA types and abundance)

• Target prediction (miRNA)

• Cluster analysis and dimensional reduction (k-means, PCA, MDS, tSNE )

• Differential expression analysis

• Pathway analysis of differentially expressed genes (up/downregulated)(ORA, GSEA)

• Alternative splicing, transcriptomic diversity

• Detection of fusion genes

• Variant calling (RNA editing)

• Cell type deconvolution (bulk mRNA-seq)

Epigenomics

Epigenomic modifications are modifications that affect the genome without modifying the underlying DNA sequence. These can be chemical modifications such as DNA methylation (= addition of a methyl-group to the 5th C of the cytosine base) or proteins (and their modifications) binding to the DNA altering the DNA’s structural properties (packaging, 3D structure). Modifications of the epigenome are thought to affect the DNA’s transcriptional potential (‘chromatin state’), or in other words, regulate gene expression. They provide insight into biological mechanisms and processes and are used as biomarkers.

DNA methylation

We analyze DNA methylation mainly from the Illumina Infinium BeadChip microarray (human, mouse), but also from sequenced based technologies such as Whole Genome Bisulfite Sequencing (WGBS), Reduced Representation Bisulfite Sequencing (RRBS) and bisulfite-free technologies such as enzymatic Methyl-seq.

Our analyses:

• Exploratory data analysis (PCA)

• Differential methylation analysis of single CpGs (DMC) and regions (DMRs)

• Functional annotation of DMCs and DMRs (associated genes, overlapping regulatory elements, CGI context)

• Identification of large scale methylation patterns: unmethylated regions (UMRs), hypermethylated regions (UMRs), partially methylated domains (PMDs)

• Cell type deconvolution

• Age prediction

• Prediction of Copy Number Variation (CNV)

Chromatin

We process mainly sequencing data from Chromatin Immunoprecipitation (ChIP-seq) to study DNA protein binding (histones, transcription factors) and Assay for Transposase-Accessible Chromatin (ATAC-seq) to identify open chromatin regions.

Our analyses:

• Exploratory data analysis (PCA)

• Peak calling

• Functional annotation peaks (genes, regulatory)

• Binding motif analysis

• Differential analysis

Epitranscriptomics

Similarly to epigenomic modifications, epitranscriptomic modifications are modifications of the transcriptome that do not alter the underlying RNA sequence. Studying these modifications allows insight into RNA biology, cellular processes and mechanism that can be important in development and disease.

We mainly process data from infrared crosslinking Immunopreciptation(irCLIP) for the study of RNA-protein interactions.

Our analyses:

• Exploratory data analysis (PCA)

• Peak calling

• Functional annotation peaks (RNA biotype, genes, gene region)

• Binding motif analysis

• Differential analysis