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Genome Center

BIOINFORMATICS UNIT

The Bioinformatics Unit is a team of senior bioinformaticians dedicated to supporting sequencing projects with advanced data analysis.

We have built a solid and vast expertise over the years following the technological developments and needs of our network of collaborators. The expertise is reflected in the diversity of workflows that we currently offer and support [see below], covering the analysis of various types of sequencing data.

Communication, collaboration and training are the key aspects of a successful project. We strive for having in-depth meetings at all stages of the project, where research questions and data are discussed thoroughly. We can offer multiple levels of support, depending on the experience and knowledge of the user. One one hand, we dedicate significant effort in thoroughly documenting our pipelines, so that users can follow the analysis. On the other hand, we export results to meaningful data formats, allowing users to follow-up with comprehensive exploration of their data. Lastly, we actively engage in teaching initiatives, such as the Data Analysis Café and regular lectures on campus, with the aim of empowering users to independently analyse and interprete their data.

The work of our team is supported by significant state-of-the-art IT infrastructure. From raw data processing and data sharing to secure data archiving and safe handling of sensitive human data, our work ensures seamless and secure management of your research data.

Data analysis packages

For data generated at the DcGC, we offer the following analysis packages:

  • Single-cell RNA-seq
  • Spatial Transcriptomics
  • Bulk Differential Gene Expression
  • Mapping and Counting
  • Peak Calling
  • Validation of CRISPR/Cas experiments
  • Variation analysis

Each package includes basic quality control, a specific set of analyses and output files, and a round of discussion and assistance with interpreting the results.

For detailed information on requirements, analysis, output, and next steps, please refer to our guidelines.

We additionally offer the following packages (read more):

  • Setup of Custom Species
  • Sequence Data Upload
  • Data Retrieval from Archive
1.scRNA-seq
Single-cell RNA-seq analysis
Applicable to single-cell RNA-seq data from:
Smart-seq2, 10x Genomics and Parse Biosciences

The analysis includes mapping and generating a gene-by-cell counts table, followed by an in-house pipeline that includes quality control and filtering, normalization, dimensionality reduction, clustering and UMAP, and cluster annotation, all documented in a detailed report.

Learn More
2.spatial
Spatial transcriptomics analysis
Applicable to spatial data from 10x Genomics’ Visium and Xenium platforms

The analysis includes mapping and generating a gene-by-spot counts table, followed by an in-house pipeline that includes typical single-cell RNA-seq analysis steps plus visualizations of the results in their spatial context.

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3.bulk DGE
Differential gene expression analysis
Applicable to bulk RNA-seq data

The analysis includes mapping and generating a gene-by-sample counts table, followed by an exploratory analysis with PCA, batch correction, sample correlation, and finally the identification of genes with differential expression between conditions of interest.

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4.mapping & counting
Mapping and Counting
Applicable to:
bulk RNA-seq data, single-cell RNA-seq data, spatial RNA-seq and RNA in-situ data

If you plan to conduct an in-depth analysis independently, we offer a basic analysis package that includes mapping raw transcriptome data to the reference genome and counting mapped reads per gene.

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5.peak calling
Peak calling analysis
Applicable to bulk ATAC-seq, CUT&RUN, and CUT&TAG data

We use and contribute to nfcore pipelines, which process raw ATAC-seq, CUT&RUN and CUT&TAG data by mapping the reads to the reference genome, filtering and calling peaks.

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6.CRISPR/Cas
Validation of CRISPR/Cas experiments
Applicable to bulk DNA-seq data

To facilitate rapid and intuitive interpretation of CRISPR/Cas editing experiments, we use CRISPResso2, which maps reads to the target region, quantifies substitutions, insertions and deletions (indels), and categorizes indels in coding regions as frameshift and in-frame events.

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7.variation analysis
Variation analysis
Applicable to bulk DNA-seq data

Using the nfcore/sarek pipeline, we map raw reads against the reference genome, identify germline and somatic small nucleotide variants (SNVs) and copy-number variants (CNV), and perform functional annotation to predict the impact of these variants on genes, transcripts, proteins, and regulatory regions.

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Teaching

Data Analysis Café

The bioinformatics team of the DcGC hosts a monthly Data Analysis Café, where you can engage with an experienced bioinformatician in a one-on-one meeting to discuss your research questions.

We send out a monthly email reminder with a registration link. If you are not yet receiving these emails and you would like to subscribe, please email us.

Consultation Experimental Design

The bioinformatics team is available to help shaping experiments that allow for good data analysis. Good data in is good data out.