BaseClear offers dedicated metagenomics services to answer two fundamental questions in microbiome studies – “what microbial species are there?” and “which molecular functions are they performing?”

Metagenomics analysis allows the exploration of the diversity, composition and functional capacity of microbial communities. With the development of next-generation sequencing technologies and advances in bioinformatics and biostatistics, studying metagenomic communities delivers increasingly valuable data and insights. Our experts are happy to discuss the details and best technical approach that can be used to answer the research questions on your next metagenomics projects.

Functional Metagenomics Analysis

Our state-of-the-art functional metagenomics analysis pipeline starts with data quality control and optional host filtering, followed by de novo metagenome assembly, gene prediction and annotation. We then calculate the abundance of the annotated genes in the metagenome sample. Additionally, antimicrobial resistance genes (AMR) and virulence factors are identified from the predicted gene sequences.

  • High throughput functional analysis of both human and non-human metagenomic data
  • Supports analysis of small to large human or animal clinical trial projects including samples from a wide range of microbiomes
  • Dedicated analysis workflows supporting all domains of life, (DNA) virus and phage genes.
  • Rigorously validated analysis workflow supported by industry benchmarking
  • Statistical associations between abundance and diversity of functional genes and sample metadata

For every sample, we provide:

  • Predicted gene sequences and their protein translations
  • Annotation of the non-redundant gene catalogue with multiple state-of-the-art databases and functional units including orthologs, modules and pathways
  • Functional abundance profiling tables of non-redundant genes and KEGG orthologs, COGs and CAZy subfamilies in Reads Per Kilobase (RPK) and Copies Per Million (CPM)
  • Extended resistome and virulence genes analysis using multiple databases
  • Custom analysis report with high resolution publication-ready figures and tables

Commercial applications:

  • Clinical trials – human or animal
  • Discovery of novel industrial enzymes
  • Discovery of probiotics
  • Food and beverage production
  • Antimicrobial resistance screening (AMR)
  • Understanding the effects of the addition of antimicrobial ingredients in a product
  • Understanding the effects of supplementation with probiotic on gut microbiome
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Taxonomic Analysis

Our highly accurate, rapid and robust method to profile taxonomic abundance in metagenome samples employs a separate, dedicated assembly-free pipeline. Our curated Metagenome taxonomic classification databases for specific applications include: infant, pathogen, human skin, bovine rumen, poultry and swine microbiome. Databases can be customized on demand. Following the taxonomic metagenome classification, the relative abundances of microorganisms is calculated per sample and summarised in tables.  Our group of scientists can also support your researchers with various biostatistic analysis and data visualization for publications & marketing purposes, e.g.. visualisation of relative abundances, alpha and beta diversity and significant associations with trial metadata.

How are we unique?

  • Analysis performed under the supervision of the scientists and microbiome experts
  • Curated and fully validated databases covering all kingdoms – viruses, archaea, bacteria and eukaryotes
  • Rapid (in days) taxonomic classification to support large clinical trials
  • High classification sensitivity and accurate relative abundance estimation
  • Taxonomic classification reports on the full phylogenomic lineage from domain to species level
  • Strain-level classification and relative abundances
  • Inclusion of unculturable and understudied microorganisms in the form of metagenome-assembled genomes (MAGs) to the host-specific databases dramatically increases the percentage of classified reads
    • The infant microbiome database reduced the percentage of unclassified reads to under 10% in all validation runs
    • The bovine rumen microbiome database with over 4,000 rumen MAGs, reduced the percentage of unclassified reads to under 30% from over 72%.

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