Gene expression analysis gives us a snapshot of the transcriptome of a sample, giving us information about the activity of the cells in that sample. The majority of analyses of gene expression data aim to measure changes in expression between sample groups that vary in terms of a treatment or experimental condition.

Our RNA-seq analysis pipeline includes:

• Quality control evaluation of raw sequencing data
• Alignment of sequencing reads onto the genome of interest with STAR
• Gene quantifications
• Differential gene expression analysis (e.g. edgeR, DESeq2)
• Functional enrichment analysis (e.g. clusterProfiler, Cytoscape)

 A project with 10 libraries may require at least 30 hours.

Gene expression at the cellular level can be studied by the analysis of Single-cell RNA Sequencing (scRNAseq) data. By doing this, information and variation that would typically be hidden in a bulk RNAseq, are revealed.

Our single cell data analysis pipeline includes:

• Quality assessment of the data and pre-processing
• Alignment and/or quantification (e.g. STAR, CellRanger, salmon)
• Dimensional reduction analyses with Seurat
• Cell type annotation of identified clusters
• Differential expression analysis
• Functional enrichment analysis (e.g. clusterProfiler, Camera, Cytoscape)
• Inference of cell-cell interactions between clusters

A project with 10 libraries may require at least 30 hours.

Research on epigenetic analysis may examine changes in DNA methylation, DNA-protein interactions, chromatin accessibility, histone modifications, and other processes.

We offer a variety of analysis services and products designed exclusively for epigenetic study.

DNA methylation

DNA methylation is an epigenetic modification which affects DNA without changing its sequence in any way. Although the functional impact of DNA methylation depends on the context, it is associated with the control of gene expression in response to environmental or developmental factors.

We have extensive experience in analyzing methylation data generated by bisulphite sequencing methods (e.g. WGBS, RRBS, Amplicon sequencing).

Our methylation data analysis pipeline includes:

• QC of sequencing results
• Read alignment to the reference genome and methylation extraction with Bismark
• Differential methylation analysis (e.g. edgeR, GeneDMRs, methylKit, DSS )
• Ontology enrichment analysis using different databases (e.g. GO, KEGG, REACTOME)
• Identifying hypo, hyper and partially methylated regions with MethPipe

A project with 10 libraries may require at least 30 hours.

ChIP-seq Analysis

Chromatin immunoprecipitation sequencing (ChIP-seq) analysis can be used to analyse DNA-protein interactions. It can also be applied to analyze histone modifications and identify transcription factors.

Our ChIP-seq analysis pipeline includes:

• Quality control evaluation of raw sequencing data
• Alignment of sequencing reads onto the genome of interest
• Peak calling to identify regions of ChIP-seq signal enrichment with MACS2
• Visualisation of peaks/binding sites
• Differential binding analysis (e.g. csaw, diffReps)
• Motif analysis with MEME-ChIP

A project with 10 libraries may require at least 20 hours.

Evolutionary biology studies the origin of life as well as the diversification and adaption of various life forms across time. Phylogenetic trees are frequently used in the study of biological data to illustrate the findings. The evolution of related species or genes can be shown through the phylogenetic tree.

We offer bioinformatics services in phylogenetics, population genetics, macroevolution, and other analyses related to evolutionary biology.

Our evolutionary analysis pipeline includes:

• Read preprocessing
• Multiple sequence alignment
• constructing a phylogenetic tree (e.g. RAxML (Maximum likelihood method), MrBayes (Bayesian Inference), MEGA X (neighbor joining method)
• Divergence time estimation with BEAST and BEAST2
• Inferring speciation and extinction rates

A project with 10 libraries may require at least 20 hours.

We also have experience in less frequently used NGS applications, such as eCLIP-seq and Ribo-seq.

 eCLIP-seq analysis

eCLIP-seq (Enhanced cross-linking immunoprecipitation sequencing) can be used to analyse protein-RNA interaction and binding sites or locate RNA modification sites on a genome-wide scale.

Our eCLIP-seq analysis pipeline includes:

• Read preprocessing and trimming adapters and adapter-dimers with cutadapt
• Mapping to repeat elements and filtering with STAR
• Mapping filtered reads to genome with STAR
• De-Duplication with UMI-tools deduplicate
• Peak calling to identify regions of eCLIP-seq signal enrichment (e.g. PEAKachu, CLIPPER)
• Motif detection with MEME-ChIP
• Crosslink site extraction and counting with htseq-clip
• Detection of differentially binding regions with DEWSeq

A project with 10 libraries may require at least 40 hours.

Ribo-seq analysis

Ribo-Seq (ribosome profiling), gives a "snapshot" of all the ribosomes that are active in a cell at a certain time. Researchers can use this data to identify the proteins that cells are actively translating.

Our Ribo-Seq pipeline includes:

Quality control evaluation of raw sequencing data

• Alignments HISAT2 (e.g. HISAT2, STAR)
• Read counting
• Sample-wise correlation analysis
• Analysis of differentially expressed genes with edgeR
• GO term enrichment analysis
• Differential ribosome loading analysis (translational efficiency)

A project with 10 libraries may require at least 20 hours.