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QIAGEN CLC Microbial Genomics Module

Analysis of microbial genomics and metagenomics made easy

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 the 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.

Microbiome taxonomic profiling

QIAGEN CLC Microbial Genomics Module offers unparalleled options for analyzing both amplicon and whole metagenome sequencing data. Conduct Operation Taxonomic Unit (OTU) clustering or Amplicon Sequence Variants (ASV) detection of short read amplicon data, classify long read amplicons, and run taxonomic profiling on your whole metagenome samples with dedicated tools.
Aggregate results for large-scale comparative genomics studies using QIAGEN CLC’s metadata tools. Conduct metagenome analysis with preconfigured workflows and compare microbial communities across many samples.

Advanced tools for typing of microbial genomes

Whether you are 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 a comprehensive set of tools for the analysis of isolates.

Antimicrobial resistance characterization

To support you in the fight against the worldwide threat of emerging antimicrobial-resistant (AMR) pathogens, an extensive set of tools and databases designed to work together are readily available for download and immediate use. The tools specifically developed for the detection of 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 VFDB (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.

De novo assembly of isolates and metagenomes

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. Carry out assembly of both individual genomes and metagenomes with QIAGEN CLC’s De Novo Assembly tool, which can be used for a 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). QIAGEN CLC Microbial Genomics Module, adds assembler optimized for metagenomics samples, De Novo Assemble Metagenome, shown to perform 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.

Functional metagenomics – Go beyond the assembly

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.

Quick and easy reference database customization

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 the specificity of your downstream tools. The Create Microbial Reference Database tool was designed to overcome these common challenges in metagenome analysis.

QIAseq xHyb workflows

  • QIAseq xHYB AMR Markers – a workflow for finding antimicrobial resistance markers from QIAseq xHYB AMR Panel data.
  • QIAseq xHYB Viral Panel Data – a workflow for performing taxonomic profiling and mapping viral reads for variant calling for QIAseq xHYB Respiratory, Viral STI, Adventitious Agent and MPXV Panel data.

 Supported features

  • Antimicrobial resistance detection
  • De novo assembly of metagenomes
  • Microbial gene finding
  • Identifying Viral Integration site into host genome
  • Identifying resistances, acquired genes and mutations, in metagenomic or isolate NGS samples
  • Access to leading Antimicrobial Resistance Gene Databases QMI-AR, CARD, ResFinder and PointFinder
  • Access to Marker Databases derived from the QMI-AR, CARD and ARG-ANNOT resources
  • Find genes on finished genomes or contigs and annotate with PFAM, Gene Ontology and BLAST or DIAMOND
  • Microbiome profiling
  • Taxonomic profiling of amplicon and full metagenomic data
  • De novo or reference-based OTU-clustering of 16S, 18S and ITS amplicon data
  • Amplicon Sequence Variants (ASV) detection
  • Classification of long read amplicon data
  • Easy access to common reference databases such as Greengenes, Silva and UNITE
  • Custom-built genome reference database by direct import of microbial genomes from NCBI for highly precise taxonomic assignment
  • Metagenome de novo assembly, binning of contigs, gene finding and functional profiling of microbiomes using PFAM, Gene Ontology and BLAST
  • Stacked bar charts, area charts, sunburst charts and heat maps to explore and compare the taxonomic composition of samples, or sample groups
  • User-friendly statistical tools deliver leading-class performance for alpha and beta diversity estimation, PERMANOVA analysis and differential abundance analysis
  • Pathogen typing and outbreak analysis
  • Automated identification and characterization of microbes using whole genome data – a single, easy to use workflow reports NGS-MLST, taxonomy, closest known reference genome and antimicrobial resistance genes for microbial isolates
  • K-mer trees and whole genome SNP trees for accurate outbreak investigation and source tracking of pathogens
  • Biochemical pathway identification using MetaCyc

Read the latest improvements here.

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References

  1. Jia, B. et al. (2017) CARD 2017: Expansion and Model-Centric Curation of the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research 45: D566–73.
  2. Zankari, E. et al. (2012) Identification of Acquired Antimicrobial Resistance Genes. The Journal of Antimicrobial Chemotherapy 67: 2640–2644.
  3. Gupta, S. et al. (2014) ARG-ANNOT, a New Bioinformatic Tool to Discover Antibiotic Resistance Genes in Bacterial Genomes. Antimicrobial Agents and Chemotherapy 58: 212–220.
  4. Feldgarden, M. et al. (2019) Validating the NCBI AMRFinder Tool and Resistance Gene Database Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of NARMS Isolates. Antimicrobial Agents and Chemotherapy A63: e00483-19.
  5. Zankari, E. et al. (2017) PointFinder: A Novel Web Tool for WGS-Based Detection of Antimicrobial Resistance Associated with Chromosomal Point Mutations in Bacterial Pathogens. The Journal of Antimicrobial Chemotherapy 72: 2764–2768.
  6. Liu, B. et al. (2019) VFDB 2019: A Comparative Pathogenomic Platform with an Interactive Web Interface. Nucleic Acids Research 47: D687–D692.
  7. Sichtig, H. et al. (2019) FDA-ARGOS Is a Database with Public Quality-Controlled Reference Genomes for Diagnostic Use and Regulatory Science. Nature Communications 10 (1): 3313.
  8. Kuzmin, D. et al. (2019) Stepwise Large Genome Assembly Approach: A Case of Siberian Larch (Larix Sibirica Ledeb). BMC Bioinformatics 20 (S1): 37–52.
  9. Parks, D. et al. (2017) Recovery of Nearly 8,000 Metagenome-Assembled Genomes Substantially Expands the Tree of Life. Nature Microbiology 2 (11): 1533–1542.

 

Downloads

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Download CLC Microbial Genomics Module

Version

Platform support

Download

23.0.3

QIAGEN CLC Genomics Workbench


 [23.9.9, 23.0.3, 23.0.1]

23.0.0

QIAGEN CLC Genomics Workbench


 [23.0]

22.1.2

QIAGEN CLC Genomics Workbench


 [22.0.3, 22.0.2, 22.0.1, 22.0]

21.1.1

QIAGEN CLC Genomics Workbench


 [21.0.6, 21.0.5, 21.0.4, 21.0.3, 21.0.2, 21.0.1, 21.0]

20.1.1

QIAGEN CLC Genomics Workbench


 [20.0.5, 20.0.4, 20.0.3, 20.0.2, 20.0.1, 20.0]

4.8.0

QIAGEN CLC Genomics Workbench


 [12.0.4, 12.0.3]

4.5.0

QIAGEN CLC Genomics Workbench


 [12.0.2, 12.0.1]

4.1.0

QIAGEN CLC Genomics Workbench


 [12.0]

3.6.1

Biomedical Genomics Workbench


 [5.0.2, 5.0.1]

QIAGEN CLC Genomics Workbench


 [11.0.2, 11.0.1]

3.0.0

Biomedical Genomics Workbench


 [5.0.0]

QIAGEN CLC Genomics Workbench


 [11.0.0]

2.5.5

Biomedical Genomics Workbench


 [4.1.3, 4.1.2]

QIAGEN CLC Genomics Workbench


 [10.1.3, 10.1.2]

2.5.1

Biomedical Genomics Workbench


 [4.1.1, 4.1.0]

QIAGEN CLC Genomics Workbench


 [10.1.1, 10.1.0]

2.0.0

Biomedical Genomics Workbench


 [4.0]

QIAGEN CLC Genomics Workbench


 [10.0.1, 10.0]

1.6.2

Biomedical Genomics Workbench


 [3.5.4]

QIAGEN CLC Genomics Workbench


 [9.5.4]

1.6.1

Biomedical Genomics Workbench


 [3.5.3, 3.5.2, 3.5.1, 3.5]

QIAGEN CLC Genomics Workbench


 [9.5.3, 9.5.2, 9.5.1, 9.5]

1.5.1

Biomedical Genomics Workbench


 [3.0.1, 3.0]

QIAGEN CLC Genomics Workbench


 [9.0.1, 9.0]

1.4.1

Biomedical Genomics Workbench


 [2.5.4, 2.5.3]

QIAGEN CLC Genomics Workbench


 [8.5.4, 8.5.3]

1.2.2

Biomedical Genomics Workbench


 [2.5.2]

QIAGEN CLC Genomics Workbench


 [8.5.2]

1.2.1

Biomedical Genomics Workbench


 [2.5.1, 2.5]

QIAGEN CLC Genomics Workbench


 [8.5.1, 8.5]

1.0

Biomedical Genomics Workbench


 [2.1.2, 2.1.1, 2.1]

QIAGEN CLC Genomics Workbench


 [8.0.3, 8.0.2, 8.0.1, 8.0]
Server Plugin Download
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Download CLC Microbial Genomics Server Extension

Version

Platform support

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23.0.3

QIAGEN CLC Genomics Server


 [23.0.1]

23.0.0

QIAGEN CLC Genomics Server


 [23.0]

22.1.2

QIAGEN CLC Genomics Server


 [22.0.3, 22.0.2, 22.0.1, 22.0]

21.1.1

QIAGEN CLC Genomics Server


 [21.0.6, 21.0.5, 21.0.4, 21.0.3, 21.0.2, 21.0.1, 21.0]

20.1.1

QIAGEN CLC Genomics Server


 [20.0.5, 20.0.4, 20.0.3, 20.0.2, 20.0.1, 20.0]

4.8.0

QIAGEN CLC Genomics Server


 [11.0.4, 11.0.3]

4.5.0

QIAGEN CLC Genomics Server


 [11.0.2, 11.0.1]

4.1.0

QIAGEN CLC Genomics Server


 [11.0]

3.6.1

Biomedical Genomics Server Solution


 [10.0.2, 10.0.1]

QIAGEN CLC Genomics Server


 [10.0.2, 10.0.1]

3.0.0

Biomedical Genomics Server Solution


 [10.0]

QIAGEN CLC Genomics Server


 [10.0.0]

2.5.5

Biomedical Genomics Server Solution


 [9.1.3, 9.1.2]

QIAGEN CLC Genomics Server


 [9.1.3, 9.1.2]

2.5.1

Biomedical Genomics Server Solution


 [9.1.1, 9.1]

QIAGEN CLC Genomics Server


 [9.1.1, 9.1.0]

2.0.0

Biomedical Genomics Server Solution


 [9.0]

QIAGEN CLC Genomics Server


 [9.0]

1.6.2

Biomedical Genomics Server Solution


 [8.5.4]

QIAGEN CLC Genomics Server


 [8.5.4]

1.6.1

Biomedical Genomics Server Solution


 [8.5.3, 8.5.2, 8.5.1, 8.5]

QIAGEN CLC Genomics Server


 [8.5.3, 8.5.2, 8.5.1, 8.5]

1.5.1

Biomedical Genomics Server Solution


 [8.0.1, 8.0]

QIAGEN CLC Genomics Server


 [8.0.1, 8.0]

1.4.1

Biomedical Genomics Server Solution


 [7.5.4, 7.5.3]

QIAGEN CLC Genomics Server


 [7.5.4, 7.5.3]

1.2.2

Biomedical Genomics Server Solution


 [7.5.2]

QIAGEN CLC Genomics Server


 [7.5.2]

1.2.1

Biomedical Genomics Server Solution


 [7.5.1, 7.5]

QIAGEN CLC Genomics Server


 [7.5.1, 7.5]

1.0

Biomedical Genomics Server Solution


 [7.0.3, 7.0.2, 7.0.1, 7.0]

QIAGEN CLC Genomics Server


 [7.0.3, 7.0.2, 7.0.1, 7.0]
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