Welcome to the Service Section of our Bioinformatics Core Facility. We specialize in Next Generation Sequencing (NGS) data analysis and offer a comprehensive suite of services that encompass the full analytical workflow: from raw data from the sequencer, through primary and secondary analyses, to the statistical interpretation of data, data modeling, and visualization.
Our team employs state-of-the-art bioinformatics workflows for standardized analysis, which we continuously test, improve, and validate. This rigorous approach ensures that our workflows are reliable and precise enough to be utilized even in clinical settings. Details about these standardized workflows can be found below in the individual analysis categories.
Beyond maintaining and refining our standardized pipelines, we have the expertise and capacity to provide project-focused, custom bioinformatics support. This includes advanced statistical analyses, data modeling, machine learning training, and data visualization tailored to your specific project needs. You can browse through our past work in the "Custom Bioinformatics" section to see how we've helped researchers like you generate novel insights from complex biological data.
With our core facility at your service, you can unlock the potential of your genomic data. No matter the scale or complexity of your project, we're here to help you turn your data into meaningful, actionable results.
You can download the PDF containing a full pricelist here.
RNA sequencing (RNAseq) is a method used to study gene expression and transcriptomics. The bioinformatics analysis of RNAseq data involves several steps, including quality control, data preprocessing, alignment, quantification, differential gene expression analysis, and functional enrichment analysis.
If you are interested in RNAseq analysis, here at the Core Facility Bioinformatics, we are routinely analyzing data from:
- Classic RNA libraries (covering whole transcript body) as well as 3‘ end RNA libraries (covering sequences close to 3‘ end of polyA RNA).
- Model organisms such as human, mouse and A. thaliana, but we have also analyzed occasionally the common carp (C. carpio) and the bloodfluke planorb (B. glabrata).
- Non-model organisms, for which we can generate a de novo transcriptome assembly (ex: Cuban subterranean termite – P. simplex, see Reference assembly for de novo transcriptome assembly).
We provide quality report of the sequencing reads and alignment. We perform differential expression analysis and provide results in form of tables and different graphics (volcano plot, PCA, Venn diagram, heatmap, …). We can also provide functional enrichment analysis (Gene Onthology, KEGG pathway,…)
| RNA analysis of 1 control and 1 treatment Experiment description: - Two conditions - Five samples / conditions - 3’ RNAseq - Gene set enrichment analysis |
RNA analysis of 1 control and 2 treatments Experiment description: - Three conditions - Three samples / conditions - Classic RNAseq |
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| Service | Quantity | CZK | EUR | Service | Quantity | CZK | EUR |
| Demultiplexing + Fastq QC | 1 | 206 | 8 | Demultiplexing + Fastq QC | 1 | 206 | 8 |
| RNA primary analysis setup | 1 | 2 061 | 82 | RNA primary analysis setup | 1 | 2 061 | 82 |
| 3’ RNA primary analysis | 10 | 5 150 | 210 | RNA primary analysis | 9 | 9 279 | 369 |
| Differential expression | 1 | 5 153 | 206 | Differential expression | 1 | 5 153 | 206 |
| Gene set enrichment analysis | 1 | 4 122 | 165 | ||||
| Total | 16 692 | 671 | Total | 16 699 | 665 | ||
DNA sequencing offers a comprehensive view of the genetic landscape, and DNA variant analysis is crucial for understanding genetic diversity and disease mechanisms. We can process Next-Generation Sequencing (NGS) libraries of various sizes, including Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), and specific gene panels.
Our DNA-sequenced data analysis process consists of primary quality control (QC), mapping, and post-mapping QC to confirm adequate coverage, followed by variant calling, variant annotation, variant filtering, and visualization.
Our services include:
- Small variant calling, encompassing Single Nucleotide Polymorphisms (SNPs) and small insertions and deletions (indels). This can be performed on either germline or somatic samples. For germline small variant calling, we use a combination of three reliable variant callers: Strelka, HaplotypeCaller, and VarDict. Furthermore, we offer tumor-normal paired or tumor-only variant calling for germline samples, employing the SomaticSeq metacaller in these processes, which amalgamates data from Strelka2, VarDict, MuTect2, SomaticSniper, LoFreq, MuSE, and VarScan.
We utilize optimized methods for variant filtering, ensuring a reliable and high-quality output. All identified variants are thoroughly annotated using the Ensembl Variant Effect Predictor (VEP), inclusive of multiple plugins for pathogenicity scores and clinically relevant information. - Structural variant calling, which includes Copy Number Variants (CNVs) and discrepancy read variant calling. Our structural variant analysis can detect CNVs even from samples with a very low variant cell load, such as from circulating cell-free DNA (cfDNA). We identify CNVs from enriched libraries such as gene panels and WES, combining several tools for CNV detection, including CNVkit, Control-FREEC, GATK-CNV, and our in-house tool, jabCoNtool. We offer comprehensive structural variant analysis for WGS using Manta, GRIDSS, and Delly.
We are committed to offering the most comprehensive and accurate DNA variant analysis. Our services can be adapted to a wide range of research needs. Whether you are studying common model organisms or non-model species, our skilled bioinformatics team is equipped to manage your DNA sequencing data with precision and expertise.
Example 1
DNA libraries from 96 patients were analyzed using the Vascular Malformations custom gene panel. Germline small variant calling was performed to identify genetic factors related to vascular malformations.
| Service | Quantity | CZK | EUR |
| Demultiplexing + Fastq QC | 1 | 206 | 8 |
| DNA primary analysis setup | 1 | 2 061 | 82 |
| Gene-panel primary analysis | 96 | 9 888 | 384 |
| Small variant calling setup | 1 | 2 061 | 82 |
| Gene-panel small variant calling | 96 | 9 888 | 384 |
| Total | 24 104 | 940 |
Example 2
Tumor and normal samples from 16 patients were analyzed using Whole Exome Sequencing (WES). Somatic variant calling and Copy Number Variant (CNV) calling were conducted to detail the genomic landscape of the tumors.
| Service | Quantity | CZK | EUR |
| Demultiplexing + Fastq QC | 1 | 206 | 8 |
| DNA primary analysis setup | 1 | 2 061 | 82 |
| WES primary analysis | 32 | 16 480 | 672 |
| Small variant calling setup | 1 | 2 061 | 82 |
| WES small variant calling | 16 | 8 240 | 336 |
| Structural variant calling setup | 1 | 2 061 | 82 |
| WES structural variant calling | 16 | 8 240 | 336 |
| Discount for small and structural variant calling | 30 % | -6 181 | -251 |
| Total | 33 168 | 1 347 |
16S metagenomics analysis is widely used to study microorganisms in the gut microbiome and understand their evolutionary relationships. The bioinformatics analysis of NGS data involves crucial steps like demultiplexing, quality control, trimming, filtering, error rate estimation, sequence clustering, taxonomy assignment, phylogenetic tree construction, and visualization. These steps collectively enable an extensive exploration of the microbial community and its dynamics.
Here at the Core Facility Bioinformatics we provide customers with comprehensive quality reports of the sequencing reads. We also recognize the importance of sequence clustering in 16S metagenomics analysis. This process, often based on sequence similarity thresholds, allows the identification of operational taxonomic units (OTUs) or amplicon sequence variants (ASVs). OTUs or ASVs represent distinct microbial taxa or species present in the gut microbiome. Clustering facilitates the reduction of data complexity while retaining important taxonomic information, enabling downstream analyses such as taxonomic assignment and phylogenetic tree construction. For this purpose we include widely used tools such as DADA2 (using ASVs) and Mothur (using OTUs) into our analysis pipeline.
Metagenomics results are visualized using various techniques such as composition barplots, heatmaps, and phylogenetic trees. These visualizations should help researchers understand the structure and diversity of complex microbial communities. We are also able to perform differential abundance analysis.
| 16S analysis of 1 control and 1 treatment Experiment description: - Two conditions - 20 samples per condition |
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| Service | Quantity | CZK | EUR |
| 16S metagenomics analysis setup | 1 | 4 122 | 165 |
| 16S metagenomics analysis | 40 | 2 880 | 120 |
| Differential abundance analysis | 1 | 5 153 | 206 |
| Total | 12 155 | 491 | |
Peak calling is a bioinformatics technique locating enriched regions of a genome usually conducted as secondary analysis after the alignment. This enrichment typically comes from experiments using chromatin profiling (i.e. CUT&RUN) or immunoprecipitation protocol to seek an interaction of proteins with DNA/RNA (i.e., ChIP-seq - protein/DNA interaction; CLIP-seq - protein/RNA interaction). The common analysis involves steps like peaks annotation, motif discovery, data visualization (e.g., FRiP, peak profiles), correction based on Irreproducible Discovery Rate (IDR), and Differential Peak Calling (DPC), but more steps can be provided tailored to the individual needs in forms of a Custom analysis.
Example
ChIP-seq experiment including primary analysis and differential peak calling for 10 samples (1 control and 4 treatment samples, 2 replicates each).
| Service | Quantity | CZK | EUR |
| Demultiplexing + Fastq QC | 1 | 206 | 8 |
| DNA primary analysis setup | 1 | 2 061 | 82 |
| WES primary analysis | 10 | 5 150 | 210 |
| Peak calling setup | 1 | 2 061 | 82 |
| Peak calling | 8 | 773 | 31 |
| Differential expression | 1 | 5 153 | 206 |
| Additional comparison for differential expression | 3 | 1 545 | 63 |
| Total | 16 949 | 682 |
CRISPR, standing for Clustered Regularly Interspaced Short Palindromic Repeats, is a groundbreaking tool that has transformed the field of genetic engineering with its high-precision genome editing capabilities.
At our Bioinformatics Core Facility, we provide exhaustive analysis services for Genome-wide CRISPR-Cas9 knockout screen experiments. The procedure for CRISPR bioinformatics analysis is a multistep process that commences with quality control and data preprocessing, and progresses to the crucial stage of alignment and counting of sgRNAs, which essentially quantifies the proliferation of knockout gene cells.
To optimize the precision of the alignment and counting of reads from various single guide RNAs (sgRNAs), we integrate the use of the MAGeCK tool with our own custom-developed in-house algorithm. This unique pairing of techniques augments the sensitivity of the alignment process, thereby assuring superior quality in our data analysis.
As an additional part of our service, we conduct an analysis of differential cell proliferation per knockout gene between different conditions. The outcome of this analysis is presented in the form of comprehensive tables and a variety of graphical visualizations such as volcano plots, box plots, and heatmaps, among others. If required, we can also perform gene set enrichment analysis on the differentially proliferated knockout gene cells, thereby providing a more holistic view of the results.
Example: Genome-wide CRISPR-Cas9 knockout screen experiments with human cells infected with GeCKO library treated by viral infection. Samples were extracted 24 and 48 hours after the treatment with one untreated control. Three biological replicates were used per condition.
| Service | Quantity | CZK | EUR |
| Demultiplexing + Fastq QC | 1 | 206 | 8 |
| CRISPR analysis setup | 1 | 5 153 | 206 |
| CRISPR analysis | 9 | 4 635 | 189 |
| Differential expression | 1 | 5 153 | 206 |
| Additional comparison for differential expression | 1 | 515 | 21 |
| Total | 15 662 | 630 |
Reference assembly is a bioinformatics technique merging the relatively short sequencing reads into the original sequence. Typically, the short reads come from genomic DNA or gene transcripts (RNA). The length of reads can vary between tens and hundred thousands of bases. We provide De-novo as well as Reference-guided assembly.
The common analysis involves steps like the raw reads quality control, quality and/or adapter trimming, correction of sequencing errors, alignment to the reference (only for reference-guided assembly), sequence reconstruction performed by various assemblers with different settings, joining of assemblies based on consensus sequence or gene prediction, various quality control steps, and automatic annotation using several tools and databases (e.g., UniProt/SwissProt, NCBI’s nucleotide, RefSeq).
Example 1
De-novo transcriptome assembly from 26 paired-end samples.
| Service | Quantity | CZK | EUR |
| Demultiplexing + Fastq QC | 1 | 206 | 8 |
| Transcriptome assembly - price in hours of work | 16 | 16 496 | 656 |
| Total | 16 702 | 664 |
Example 2
Genome assembly of bacteria from 4 paired-end samples.
| Service | Quantity | CZK | EUR |
| Demultiplexing + Fastq QC | 1 | 206 | 8 |
| small Genome assembly | 1 | 8 245 | 330 |
| Total | 8 451 | 338 |
Our expertise in bioinformatics spans across various Next Generation Sequencing (NGS) library types and species. We have experience analyzing data from humans, model organisms, plants, and non-model organisms, such as Nothobrachius furzeri where we analyzed mutational profiles of naturally occurring tumors.
Recently, we've focused our development efforts on three critical areas of bioinformatics:
- Radiogenomics: We combine functional imaging and genomics for advanced diagnostics. Our recent work in this field has been published and can be found here.
- Spatial Transcriptomics: We've developed software for spatial transcriptomics analysis, which can provide valuable insights into the spatial organization of gene expression within a tissue. You can access and learn more about our software here.
- Multiomics: Our team is adept at combining and analyzing multiple data sources, including RNA, DNA, protein, and metabolomic data, to provide a comprehensive molecular description of tumors. This multi-faceted approach enhances our understanding of disease mechanisms and may inform more targeted treatment strategies.
In addition to these areas, we offer an extensive range of custom analysis services, including but not limited to:
- Single Cell RNA Analysis: We handle projects involving single cell RNA sequencing, enabling a high-resolution understanding of cell populations and their functions.
- Nanopore Sequencing Analysis: We perform long-read analysis from nanopore sequencing, providing detailed insights into genome structure and function.
- Transcription Factor Enrichment Analysis: We analyze the presence and prevalence of transcription factors, key regulators of gene expression.
- MicroRNA Expression Analysis: We study the role of microRNAs in tumor progression, revealing potential therapeutic targets.
- Phylogenetic and Amino-Acid Conservation Analysis: We conduct investigations into the evolutionary relationships between species and the conservation of amino acids, aiding in the understanding of functional biology.
- Polyuridylation Quantification: We measure the level of polyuridylation, a key post-transcriptional modification, which can provide insights into gene regulation.
With our custom bioinformatics services, our team of experts will help you through the complexities of bioinformatics to unlock the full potential of your genomic data.
