Bioinformatics analyses aimed at answering your research questions.
We apply best-practice analysis methodologies for testing hypotheses or exploring large data sets, and even develop new methods if necessary. Next-generation sequencing data is the focus of most of our projects, but we do also continuously encounter other biological data types.
Due to our broad experience in working with a range of research groups and companies, it is more likely than not that we are familiar with your research topic and the best approaches to get the most out of your data.
Identify and understand genomic variation and mutations.
Whole-genome, whole-exome and targeted sequencing allows mapping and studying of genetic variants or mutations. Our genome variation analysis identifies SNPs, indels, gene copy numbers, and genomic rearrangements from the various types of DNA-sequencing and microarray data.
Annotating the variants with allele frequencies in public domain databases, pathogenicity predictions and known clinical associations allows us to focus on the variants that matter. Tailored downstream bioinformatics analysis of variants and mutations coupled with phenotypic data enables the discovery of novel associations.
For non-model organisms, we produce annotated genome assemblies with computational post-processing steps in order to ensure the best possible starting point for future studies.
Uncover differences in gene expression and pathways.
Transcriptomics refers to the study of gene expression on the level of single genes and pathways. Bioinformatics analysis of RNA-sequencing or expression microarray data allows pinpointing of molecular mechanisms between the genotype and phenotype. Special questions in the field involve — among others — fusion genes, lncRNAs, microRNAs, and alternative splice patterns.
For non-model organisms, RNA-seq datasets offer significant benefits in assembling genomes as well as naturally assembling and annotating entire transcriptomes. High-quality gene models then enable expression studies just like in a model organism.
Combine your data with public resources and bioinformatics analyses for a deeper understanding of gene regulation.
Epigenomics aims at mapping the dynamic state of the DNA. This may mean segments of open chromatin, histone locations, methylated CpG islands or binding sites of transcription factors in promoters and enhancers, for example. Our bioinformatics analyses of epigenomic NGS data allow association of the identified genomic sites to phenotypic attributes. Furthermore, the sites can be annotated with public domain database information in order to help interpret the biological meaning of these events.
Do you want to hear more?
Leave your email address here with a brief description of your needs, and we will contact you to get things moving forward!