QIAGEN CLC new release

Check out some of the many new features delivered in the QIAGEN CLC solutions

QIAGEN CLC Genomics Workbench 20.0:

A host of new features help you scale your research, and allow you to ramp up your productivity by taking your multi-sample analyses to the next level:

Start workflows directly from raw sequence data: No need to import FASTQ files first.
Advanced batching made simple: Your study may produce many samples, but don’t worry– we’ve got you covered on the analysis. Workflows make it easy to streamline the analysis of large numbers of samples, especially when many tools are involved. When running workflows in ‘Batch’ mode, it is now possible to match inputs up with each other (e.g., a number of case samples and matched controls) and analyze the entire dataset in the same run. Furthermore, workflows can now be built with ‘Iterate’ and ‘Collect and Distribute’ control elements (Figure 1, light green), which allow customized batching and aggregation across batch units within a single workflow, while reducing the need for manual interaction and optimizing resource consumption when executed on QIAGEN CLC Genomics Server (see below).

**Figure 1.** The ‘Iterate’ and ‘Collect and Distribute’ control elements allow batching over sections of the workflow. In this example, fastq files from a two-level factorial RNA-seq experiment performed in triplicate can be analyzed in a single workflow. The reads are trimmed, quality controlled (QC’ed) and the RNA-seq analysis reads are mapped, sample by sample. Then the RNA-seq expression levels are compared among groups, and comparisons are collected to create heat maps, Venn diagrams and PCA plots. Finally, trimming, QC and RNA-seq analysis read mapping reports are combined across samples. The workflow was used to analyze data from De Maio et al. (2016), comparing the transcriptional profile (RNA-seq) of Dengue virus 2 and mock infected human cells at 24 and 36 hours post-infection. The samples (accessions) are described in a CLC metadata table according to infection status and time point prior to workflow execution.

Work smarter with metadata: Use metadata as a versatile and convenient way to help you organize your samples and results within the workbench. Metadata can help you find objects, define the grouping of inputs in batching, direct samples to different paths in a workflow (e.g., in a tumor-normal or trio study) or be used in statistical analyses and visualization for RNA-seq. All you need to get started is an Excel spreadsheet. Quickly retrieve the results you want, even for large batches of samples. Metadata tables now organize the workflow results so you can quickly find the answers you need.
Automatically export reports from workflows in pdf or JSON format: Using JSON formatted results enables advanced users to programmatically parse the reports and create custom reports, and fully integrate their CLC workflows into existing systems. Export reports and combined reports directly from workflows in pdf or JSON formats (Figure 1, dark blue elements) from QIAGEN CLC Genomics Workbench 20.0 or QIAGEN CLC Genomics Server 20.0, with the option to include a history log for file provenance.
Combining reports: When executing multi-step workflows and batching over multiple samples, each step and each sample will create multiple reports, a situation that quickly generates information overload. Gain a quick overview of crucial QC parameters and main results by combining reports and results across tools and samples, using the new ‘Combine Reports’ tool, which is fully compatible with the advanced batching functionalities (Figure 1, light blue workflow elements, and example output report in Figure 2). Reports from over 20 NGS-related tools, including biomedical and microbial tools, are supported, as well as statistics over variant tracks.

**Figure 2.** With the ‘Combined Reports’ tool you can gain a quick overview of the main results in your analysis. In this case, the GC-content has been summarized from the QC reports of 12 RNA-seq samples from De Maio et al. (2016).

The QIAGEN CLC Genomics Workbench 20.0 and QIAGEN CLC Genomics Server 20.0 also feature updates to many tools, as well as significant performance improvements over previous versions. You can the full list of latest improvements here.
Additional content can be added to the comprehensive toolbox in QIAGEN CLC Genomics Workbench 20.0 and QIAGEN CLC Genomics Server 20.0 by installing feature-rich modules and plugins. Both the free Biomedical Genomics Analysis plugin and the QIAGEN CLC Microbial Genomics Module have been updated substantially for this release (see below).

QIAGEN CLC Main Workbench:

The new ‘Biomolecule Generator’ tool makes it possible to generate or extract biomolecules based on symmetry information in PDB files.
A homology model of a sequence can be created in just two steps, using the new ‘Find and Model Structure’ tool. The tool identifies suitable protein templates from the Protein Data Bank (PDB) and automatically builds a structure model for a given input sequence. From the resulting table, a structure model of the sequence can be created with one click.
Molecule structures in a Molecule Project can be exported to a PDB file.
These new tools apply to both QIAGEN CLC Main Workbench and QIAGEN CLC GenomicsWorkbench. You can the full list of latest improvements here.

QIAGEN CLC Genomics Server:

A new workflow-queuing option has been introduced, so that workflows utilizing advanced batching functionalities can be executed efficiently in a multi-node environment.
All new features now available with the other QIAGEN CLC products mentioned in this blog are also applicable to QIAGEN CLC Genomics Server.

QIAGEN CLC Microbial Genomics Module:

With whole genome sequencing revolutionizing clinical microbiology, MLST of microbial genomes is rapidly becoming the standard. QIAGEN CLC Microbial Genomics Module now includes cg/wgMLST in addition to the interactive minimum spanning tree visualization of MLSTs for outbreak analysis (Figure 3). The tools also provide direct access to pubMLST.org and other online public databases with internationally recognized schemas. Collectively, these tools provide researchers with total flexibility in one tool set for the analysis of isolates – regardless whether it’s a virus, bacteria or fungal genome. You can the full list of latest improvements here.

Biomedical Genomics Analysis Plugin:

QIAGEN CLC Genomics Workbench now supports even more QIAseq UMI-based library preparation kits and panels, via a series of new ready-to-use workflows accessible through the Biomedical Genomics Analysis plugin, including:

The QIAseq Multimodal Panels are supported in a single-workflow solution.
The QIAseq Fusion XP Panels are supported, including variant calling, fusion detection and expression quantification.
Easy analysis of the QIAseq MSI Booster Panel in hg19 and hg38 – a new MSI workflow is provided, making it possible to create a shared baseline for multiple samples.
The QIAseq Methylation Panel and QIAseq Methyl Library Kit are now supported, including differential methylation-level calling.
Additional improvements include: Improved reporting and de-multiplexing for the QIAseq 3′ UPX solutions, better detection of gene fusions with new visualizations, and integration of fusion and CNV calling with QCI Interpret through export of CNV and fusion call results in VCF format. You can the full list of latest improvements here.

View all supported QIAseq panels here.

Don’t miss our on-demand webinar where we review these latest features of the QIAGEN CLC Genomics Workbench 20, and discuss:

One-click solutions and expert tools for NGS data analysis
Working with reads from various platforms (Illumina, IonTorrent, Oxford Nanopore, Pacific Biosciences, BGI/MGI)
Tailored solutions for RNA-seq, DNA-seq and methylation
Efficient algorithms for read trimming, mapping, de novo assembly and variant calling
Effective management of reference data
Scalable processing of many samples, with advanced workflow and reporting capabilities
Easy installation on Windows, Mac and Linux

References:

De Maio F.A. et al. (2016). The Dengue virus NS5 protein intrudes in the cellular spliceosome and modulates splicing. PLoS
Pathog. 12(8):e1005841.