The QIAGEN CLC Microbial Genomics Module is an extension to the QIAGEN CLC Genomics Workbench and provides tools and workflows for a broad range of bioinformatics needs for microbiome analysis, isolate characterization, functional metagenomics and antimicrobial resistance characterization. The module supports analysis of bacterial, viral and eukaryotic (fungal) genomes and metagenomes, and is compatible with both the QIAGEN CLC Genomics Cloud Engine and QIAGEN CLC Genomics Server.
QIAGEN CLC Microbial Genomics Module is also part of the QIAGEN Microbial Genomics Pro Suite
The QIAGEN CLC Microbial Genomics Module provides extensive tools to support advanced bioinformatics and genomics analysis of antimicrobial resistance (AMR) genes and markers. 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.
Extensive integrated access to multiple AMR databases include 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. In addition, as part of QIAGEN’s commitment to the US Center for Disease Control’s Global AMR Challenge, we provide two additional novel resources for AMR research: the QIAGEN Microbial Insights AR (QMI-AR) database and ARES-Genetics ARESdb.
Whether you’re focused in the areas of public health epidemiology, clinical microbiology research or basic microbial genomics research, QIAGEN CLC Microbial Genomics Module provides you with state of the art tools for strain typing bacterial, fungal and viral genomes. For bacterial isolates, users benefit from tools for traditional MLST-, cgMLST-, wgMLST- or SNP-based analysis and advanced assembly-free k-mer-based strain typing. 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.
The QIAGEN CLC Microbial Genomics Module also includes an interactive ‘Minimum Spanning Tree’ (MST) visualization for instant and intuitive overview for outbreak analysis.
QIAGEN CLC Microbial Genomics Module offers unparalleled options for analysis of both amplicon/OTU and whole metagenome sequencing data. The tools provide a fully integrated solution for everything from 16S/ITS microbiome profiling, shotgun metagenomics profiling, metagenomics assembly, automated gene finding and annotation with BLAST or DIAMOND. The software also provides innovative tools for binning of contigs from de novo assemblies – an important step in identifying plasmids and other mobile elements. All of these tools can also connect to a variety of established public databases, available for direct download within QIAGEN CLC, or use databases built and curated by the users themselves.
For the general requirements, please refer to the relevant
QIAGEN CLC Workbench system requirements.
Special requirements for the Taxonomic Profiler
Running the Taxonomic Profiler with a 14 GB database requires a minimum of 16 GB RAM, and running with a 56 GB database requires a minimum of 64 GB RAM.
When creating your reference database with the Create Microbial Reference Database tool, you will get a warning about the memory requirements needed for running the Taxonomic Profiler with this database.
Special requirements for De Novo Assemble Metagenome
It is recommended to have at least 16 GB RAM when running the De Novo Assemble Metagenome.
Special requirements for the PCoA 3D Viewer
Special requirements for third party tools
Tools using DIAMOND and ShortBRED require CPU with an AVX instruction set.
We frequently release updates and improvements such as new functionalities, bug fixes or plugins. A full list of recent changes is included on the latest improvements page.
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.