Home > QIAGEN CLC Microbial Genomics Module (commercial plugin)
The QIAGEN CLC Microbial Genomics Module offers tools and workflows for a broad range of bioinformatics needs for microbiome analysis, isolate characterization, functional metagenomics and resistance identification. The module extends the capabilities of QIAGEN CLC Genomics Workbench to support analysis of bacterial, viral and eukaryotic (fungal) genomes and metagenomes. It is also compatible with both the QIAGEN CLC Genomics Cloud Engine and QIAGEN CLC Genomics Server.
QIAGEN CLC Microbial Genomics Module offers unparalleled options for analyzing both amplicon and whole metagenome sequencing data. You can easily analyze your NGS data using Operational Taxonomic Unit (OTU) clustering, Amplicon Sequence Variants (ASV) detection or taxonomic profiling tools. You can then aggregate it for large-scale comparative genomics studies using QIAGEN CLC’s metadata tools. Immediately begin your metagenome analysis with preconfigured workflows, and apply easy-to-use statistical tools to compare microbial communities across many samples.
Whether you’re focused on public health epidemiology, clinical microbiology research or basic microbial genomics research, QIAGEN CLC Microbial Genomics Module provides state-of-the-art tools for strain typing of bacterial, fungal and viral genomes. For bacterial isolates, users benefit from the Identify MLST tool, which enables rapid typing and comparative genomics using globally accepted schemas from multiple public MLST databases, along with tools to download and modify those schemas based on each lab’s specific needs. QIAGEN CLC Microbial Genomics Module also provides assembly- and reference-free tools including the Find Best Matches using K-mer Spectra and Create K-mer Tree tools, which are especially useful for organisms for which MLST typing is not appropriate, such as viruses or fungi. In addition, users have the option of using our Create SNP Tree tool to compare novel isolates to gold-standard reference genomes using a read-mapping and SNP variant calling approach. 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.
To support you in the fight against the worldwide threat of emerging antimicrobial resistant (AMR) pathogens, a comprehensive set of tools and databases designed to work together are readily available for download and immediate use. The tools specifically developed for AMR include those for analysis of both assembled isolate genomes or metagenomes, as well as tools for the assembly-free detection of AMR markers directly from your FASTQ data.
These tools are further enhanced by integrated access to popular, publicly available databases for AMR and virulence factor characterization, including the Comprehensive Antimicrobial Resistance Database (CARD) (1), ResFinder (2), ARG-ANNOT (3), NCBI Bacterial Antimicrobial Resistance Reference Gene Database (4), PointFinder (5) and VirDB (6) databases. Furthermore, as part of QIAGEN’s commitment to develop novel resources and tools for AMR research, the QIAGEN CLC Microbial Genomics Module includes two unique databases for AMR bioinformatics research: QIAGEN Microbial Insights AR (QMI-AR) database and ARES-Genetics ARESdb.
If you are interested in understanding the underlying functional classes of genes and organisms in your sample, then QIAGEN CLC Microbial Genomics Module can help you. Users can carry out assembly of both individual genomes or metagenomes with QIAGEN CLC’s best-in-class de novo assembler. Our core assembly tool, De Novo Assemble, can be used for a huge range of genomes from all branches of the of the phylogenetic tree. For example, it could be used for assembling novel RNA viruses for the FDA ARGOS Genome Standards Project (7), or for succeeding where other assemblers failed in assembling the largest eukaryotic genome ever attempted (8). In addition, within QIAGEN CLC Microbial Genomics Module, we include an optimized assembler for metagenomics samples, De Novo Assemble Metagenome, that has been shown to perform exceptionally well with large and complex metagenomics samples. For an example of a large-scale metagenomics assembly using QIAGEN CLC’s de novo assembler, see the Parks et. al. 2017 study in Nature Microbiology (9)where they recovered over 8000 novel genomes from SRA.
Following de novo assembly, there are numerous additional tools within QIAGEN CLC Microbial Genomics Module to assist researchers in deeply characterizing their samples without the need to use command line tools. This includes tools for binning contigs into distinct groups using our Bin Pangenomes by Sequence or Bin Pangenomes by Taxonomy tools, which can be used to identify plasmids and “metagenomic assembled genomes” (MAGs) from within metagenomics data. For assemblies of bacterial microbiome samples, users can leverage the Find Prokaryotic Genes tools to identify coding sequences (CDSs). These CDSs can then be annotated with the Annotate CDS with BLAST or DIAMOND tools, and characterized using Gene Ontology classifications or by identifying PFAM domains contained within them. Collectively, these tools can be used for functional metagenomics or metatranscriptomic analysis of microbiome samples. To translate identifications of EC numbers into metabolically relevant and understandable entities the Identify Pathways tool can be used to translate EC abundance tables and differential abundance tables into MetaCyc pathways.
Using the right reference data is crucial for accurate microbial genomics research, whether you’re working with complex microbiome samples or isolates of specific strains. While it’s possible to download over 200,000 genomes from NCBI directly within QIAGEN CLC Microbial Genomics Module, the larger your database, the greater the need to curate and maintain the contents of it. Database size and content also affect compute resource requirements and specificity of your downstream tools. The new Create Microbial Reference Database tool is designed to overcome these common challenges in metagenome analysis. For exploration of microbial diversity in novel sample types, use a comprehensive database. For highest precision and faster turnaround time in routine use, simply remove irrelevant taxa from the reference database. In addition, you can download specific references from the NCBI Pathogen Project.
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