The Cosmos-Hub is a powerful tool for functional profiling of mixed transcriptomic datasets, particularly from microbial consortia (metatranscriptomics). It bypasses traditional alignment and gene-by-gene counting, instead focusing on k-mer–based matching to generate global functional profiles.
  • Yes, you can input RNA-seq data into the HUB—but this only makes sense for applications where function-level (not gene-level) interpretation is acceptable.
  • For traditional host-focused applications (e.g., looking at a handful of specific cytokine genes in human cells), the HUB will not provide the individual gene counts that many researchers require. However, we can assist through our custom bioinformatics offerings by contacting info@cosmos-hub.com.
By keeping these differences in mind, you can choose the appropriate pipeline for your biological questions, ensuring a smooth and informed RNA-seq or metatranscriptomic analysis workflow.

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Overview

Transcriptomic (RNA-seq) analyses have traditionally focused on a single organism (e.g., human, mouse, or a particular bacterium). They typically aim to quantify the expression levels of each gene in the organism’s genome, then compare these levels across various conditions (e.g., treated vs. untreated). Metatranscriptomic analyses, on the other hand, attempt to capture the transcriptome of mixed microbial communities—multiple bacterial (and sometimes fungal, viral, or other) species within a given sample. This approach focuses on identifying the functional (i.e., gene-category–level) expression patterns of entire microbial consortia. Cosmos-Hub offers a k-mer–based functional pipeline that can be applied to both metatranscriptomic data and “traditional” transcriptomic (RNA-seq) data. However, there are some major differences and caveats for traditional host RNA-seq.

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The Traditional RNA-seq Pipeline (Host-Focused)

Typical Research Goals
  • Identify individual genes that are upregulated or downregulated in response to treatment.
  • Look for specific host pathways (e.g., inflammatory pathways, adhesion molecules, growth factors) to see which are differentially expressed.
  • Example: “CADM1 was higher in Treatment vs. Control,” and we interpret its specific biological role.
A standard host RNA-seq pipeline typically includes:
  1. Raw Reads Quality Control
    1. Trimming low-quality bases/adapters
    2. Checking read quality via tools like FastQC
  2. Alignment to a Reference Genome
    1. Mapping reads to the known genome/transcriptome (e.g., human, mouse, a single bacterial species)
    2. Tools commonly used: STAR, HISAT2, Bowtie2
  3. Count Summarization
    1. Generating counts for each annotated gene (or transcript)
    2. Tools commonly used: featureCounts, HTSeq
  4. Differential Expression Analysis
    1. Statistical comparison of gene expression across conditions (e.g., treated vs. control)
    2. Tools commonly used: MaAsLin2, LEfSe, DESeq2, EdgeR, Limma
  5. Functional/Pathway Analysis
    1. Performing gene set enrichment analysis (GSEA) or pathway analyses
    2. Tools such as HUMAnN2, GSEA, DAVID, or KEGG-based analyses
Because Cosmos-Hub’s functional pipeline does not perform steps 2-4 (alignment, count summarization, classical differential expression), it is not ideal for researchers who need detailed, gene-by-gene expression patterns in a host organism.

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Cosmos-Hub’s Host-Agnostic Functional Profiling

Cosmos-Hub can analyze metatranscriptomic data using a k-mer–based approach to assign reads to functional categories (e.g., MetaCyc pathways, Pfam domains, Gene Ontology terms, CAZy families, Enzyme Commission numbers). Here’s how it broadly works:
  1. Raw Reads Input
    1. Reads are checked and processed through Cosmos-Hub’s “FUNCTIONAL Host-Agnostic Profiling”
  2. Functional Assignment via K-mers
    1. Reads are compared against large reference databases of known functional genes/sequences.
    2. The pipeline assigns reads to functional categories (e.g., “all adhesion-related genes”) rather than to individual genes based on the database entries.
  3. Relative Abundance/Copies-per-Million (CPM) Calculation
    1. For each functional category, pipeline calculates a relative abundance metric (CPM).
  4. Statistical Analysis
    1. Utilize heatmaps and barcharts to
    2. Tools such as LEfSe, MaAsLin2, or MaAsLin3 can be used on the functional categories to identify which categories are differentially abundant between groups.
    3. This process is analogous to differential expression analysis, but at the functional (gene-set) level rather than at the individual gene level.
Why This is Great for MetatranscriptomicsIn metatranscriptomics, multiple microbial genomes are present in a sample. Using a single reference genome for alignment can be impractical, and analyzing thousands of genes across dozens or hundreds of species individually becomes cumbersome.
  • A functional approach is often more biologically meaningful for mixed communities (e.g., gut microbiome, soil microbiome) since the focus is on which biochemical pathways are active.
  • This approach gives a high-level overview of the community’s functional capacity and changes in response to treatments or environmental shifts.
When It’s Not Ideal for Host TranscriptomicsFor host-focused RNA-seq (human, mouse, or single-bacterium), researchers often need to pinpoint specific genes (e.g., TNF-α, IL-6, CADM1) and see if they are up or down.The Cosmos-Hub pipeline groups reads into broad functional categories, so you won’t get a direct gene-by-gene differential expression result such as “CADM1 has a 2.5-fold increase in expression.” Instead, you’ll be able to see larger-scaled trends such as “Genes relating to cellular adhesion were more expressed in the treatment group.”

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Differential Expression: HUB vs. Traditional Pipelines

In traditional pipelines for differential abundance, tools like DESeq2 or EdgeR specifically handle gene-level count data to yield p-values, fold changes, and adjusted p-values for each gene. By contrast, in the Cosmos-Hub:
  • You receive functional category abundances (e.g., GO terms, MetaCyc pathways).
  • You can perform differential analysis using something like LEfSe, MaAsLin2, or MaAsLin3 on these functional categories.
  • The results might look like: “Adhesion genes have higher relative abundance in the Treatment group,” rather than “Gene X was upregulated in the Treatment group.”

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Example Use Cases & References

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Metatranscriptomic Study of the Human Gut Microbiome

  • Researchers might use the Cosmos-Hub to identify changes in carbohydrate metabolism pathways (CAZy families) among different treatment groups.
  • For an example of such functional metatranscriptomic approaches, see:
    • Franzosa et al. (2014). Functional metagenomic profiling of fecal samples from healthy humans. Genome Medicine.
    • Shakya et al. (2019). A multi-omics approach for understanding host–microbe interactions. mSystems.

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Soil Microbiome Activity Under Different Environmental Conditions

  • K-mer–based approaches can quickly identify shifts in nitrogen cycling or stress-response genes in a complex microbial community.
  • Example reference:
    • Tringe et al. (2005). Comparative metagenomics of microbial communities. Science.

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Traditional Host RNA-seq for Disease Biomarkers

  • Typically uses alignment and gene-count–based analyses.
  • Standard references might include:
    • Anders et al. (2013). Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nature Protocols.
    • Love et al. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology.

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Recommendations & Best Practices

  • Use Cosmos-Hub for Metatranscriptomics
    • When you have a mixed microbial sample and your question is: “Which functional categories are expressed, and how do these categories change across conditions?”
    • The HUB’s functional approach is faster, more straightforward, and more biologically oriented for diverse microbial communities.
  • Use Traditional RNA-seq Pipelines for Host or Single-Organism Transciptomes
    • If your research question is: “Which exact genes in a single genome (e.g., human, mouse, or E. coli) are up or down in response to treatment X?”
    • This requires alignment to a single genome and classic differential expression analysis (DESeq2, EdgeR, etc.).
  • Consider a Hybrid Approach
    • There are some cases where you can use both approaches: (e.g., metatranscriptome of bacteria and a host cell line in the same sample), you could process your data in two ways:
      • Host alignment using standard RNA-seq tools.
      • Cosmos-Hub for the microbial part of the data.
    • Note that this is a more manual approach and can become computationally intensive.