Microbiome data analysis feels mature now. Many pipelines are published, tools well-maintained, and reviewers no longer ask what is Kraken2 or HUMAnN3. However, behind this polished appearance, the problems persist. Even in top journals, we still see overpredicted pathways, misassigned species, and batch-driven clusters misread as biology.
The problem is not lack of tools. It is that these tools are sensitive, sometimes too sensitive, and easily manipulated by noise, contamination, or hidden assumptions. When teams trust outputs blindly - or apply “standard pipelines” without deep understanding - the analysis may proceed smoothly but the conclusion may collapse.
In this 2-part series, we don’t explain what 16S is or how to choose between MetaPhlAn and Bracken. Instead, we focus on the common but hard-to-detect errors - and how experienced analysts avoid them. These examples come from real consulting cases, some during peer review rescue, some before disaster strikes.
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The Problem
Samples like placenta, blood, skin, and lung often have low microbial biomass. In such context, even trace contaminants from reagents, lab surfaces, or kits can dominate the observed signal.
Why It Happens
Many pipelines - even those with DADA2 or Deblur - assume that whatever remains after denoising is real. But in low-biomass samples, kit contaminants like Ralstonia, Cutibacterium, or Pelomonas routinely appear as dominant taxa.
How It Shows Up
You see taxa that shouldn’t be there - oral species in placenta, skin species in blood. But because they’re not completely absurd, people include them in the narrative.
Common Mistakes
- No negative controls used
- Failing to run Decontam or equivalent
- Believing taxa based on relative abundance alone
- Assuming “high confidence” from classifier means “real”
What We Do Instead
We always use negative controls - both lab and sequencing-level. We track batch-level signals. We use Decontam with both prevalence and frequency models. And we never include taxa in interpretation unless there’s context-specific plausibility.
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The Problem
Taxonomic profiling tools disagree. Kraken2 might say Escherichia coli, MetaPhlAn says Shigella, and GTDB-Tk gives something else. Often, these conflicts are invisible - unless someone compares tools side-by-side.
Why It Happens
Different tools use different markers, different trees, and different naming conventions. Some lump species; others split too finely. And updates are frequent - what was Bacteroides last year might now be Phocaeicola.
How It Shows Up
Species-level claims that don’t replicate. An “enriched strain” that isn’t found when re-run. Or worse - conflicting results depending on which tool was used.
Common Mistakes
- Trusting tool output without question
- Mixing databases without matching versions
- Overinterpreting species-level calls from 16S
What We Do Instead
We cross-check using multiple tools. For strain-level claims, we often require shotgun data and corroborating evidence. And we always version-lock the database and classifier.
The Problem
Functional profiling tools like PICRUSt2 or HUMAnN3 give lists of pathways - often dozens or hundreds. These are tempting to interpret as “real,” but many are artifacts of database structure or taxonomic assumptions.
Why It Happens
Most tools rely on inferred functions from taxonomy. If a species has certain genes in the reference genome, all similar ASVs are assumed to have them. This logic breaks down in complex or novel environments.
How It Shows Up
Studies claim enriched vitamin B12 biosynthesis, sulfur metabolism, or methanogenesis based only on predicted function. But without metagenome-assembled genomes (MAGs) or expression data, these are speculative.
Common Mistakes
- Treating predicted pathway as observed activity
- Not filtering low-confidence pathways
- Failing to validate with real data (MAGs, expression, phenotype)
What We Do Instead
We use function prediction cautiously. If needed, we validate against known biology or MAGs. We never draw major conclusions solely from predicted function, especially in shotgun metagenomics analysis.
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The Problem
Total-sum normalization (i.e. converting to relative abundance) introduces compositional artifacts. Rarefaction reduces data. CLR or log transforms assume pseudo-counts. All of these change interpretation.
Why It Happens
Because tools often require specific formats. HUMAnN3 uses relative abundances. DESeq2 expects counts. People convert blindly, without realizing they are distorting the relationships.
How It Shows Up
Two groups appear different - but only because one has a spike in one taxon. Other taxa look depleted but it’s an artifact. Or functional profiles differ due to scaling, not biology.
Common Mistakes
- Mixing normalized and raw counts across tools
- Using rarefied data for differential testing
- Misinterpreting compositional bias as biological change
What We Do Instead
We match normalization to downstream method. For statistical tests, we often use raw counts with models that handle overdispersion (e.g. DESeq2). For compositional data, we use ALDEx2 or Songbird with proper interpretation.
The Problem
Different extraction kits yield different microbial profiles - even from the same sample. PCR cycles, primer choice, and sequencing lanes also introduce variability.
Why It Happens
Microbiome composition is sensitive to every step. Yet many researchers assume batch effects are sequencing-related only.
How It Shows Up
PCA plots show sample groups clustering by kit lot or operator. Or taxa enriched in one batch but not another.
Common Mistakes
- Ignoring kit, operator, or date in metadata
- Confusing batch effect with biological signal
- Applying statistical correction without randomization
What We Do Instead
We design for balance: randomize across batches when possible. We visualize clustering by metadata. And we always include extraction/PCR metadata in statistical models.
Continue Reading Part 2