bio-metagenomics-abundance

name	bio-metagenomics-abundance
description	Turns shotgun classifier output into a defensible abundance table with Bracken Bayesian re-estimation, then compositional treatment (CLR, zero handling), library-size normalization, reference-frame differential abundance, and optional absolute quantification. Covers why a relative-abundance change is not a change, why Bracken read fractions and MetaPhlAn percentages are different physical quantities, the silent -r read-length bias, the genome-size confound no library-size method fixes, and the rarefaction debate. Use when estimating species abundance from a Kraken2 report, normalizing a community count table, choosing a compositional transform, or converting relative to absolute load. For classification see kraken-classification; for diversity/ordination/DA mechanics see metagenome-visualization.
tool_type	mixed
primary_tool	Bracken

Version Compatibility

Reference examples tested with: Bracken 2.9+, Kraken2 2.1.3+, pandas 2.2+, scikit-bio 0.6+, R zCompositions 1.5+.

Before using code patterns, verify installed versions match. If versions differ:

• CLI: bracken -h, bracken-build -h to confirm flags and defaults
• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('zCompositions') then ?cmultRepl to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

The Bracken databaseRLENmers.kmer_distrib is built per database at a fixed read length and MUST match both the Kraken2 database and the actual (post-trim) read length; -r is not auto-detected and a mismatch silently biases every species fraction. Bracken needs the default Kraken2 report format, not mpa-style.

Abundance Estimation

"How much of each taxon is in my sample?" -> Re-estimate species reads with Bracken, then treat the table as a composition - because the sequencer fixed the total, so the numbers are relative and a change in one forces apparent changes in the rest.

• CLI: bracken -d DB -i kraken.kreport -o out.bracken -w out.bracken.kreport -r 150 -l S -t 10

Scope: Bracken mechanics plus what happens to the numbers afterward - estimand choice, compositional transforms, normalization, reference-frame DA logic, absolute conversion. Read classification -> kraken-classification, metaphlan-profiling. Diversity/ordination/DA tool mechanics -> metagenome-visualization. Generic 16S diversity -> the microbiome category.

The Single Most Important Modern Insight -- A Relative-Abundance Change Is Not a Change

A shotgun abundance table is a composition: the sequencer fixes the total number of reads, not the sample's microbial load. So every number is relative to every other, and "taxon X went up 2-fold" is undefined without a reference frame or an external load anchor. Without one, a single blooming taxon makes every other taxon look depleted (the blooming-taxon illusion), and a Pearson correlation of two taxa's proportions is biased negative by arithmetic, not biology. Bracken is step one of about six: classify -> re-estimate -> compositional transform -> normalize -> reference-frame test -> (optional) absolute conversion. Two corollaries:

1. Bracken percent and MetaPhlAn percent are different physical quantities. Bracken is a fraction of READS (large-genome biased, approximately fraction of DNA); MetaPhlAn is a genome-size-normalized marker abundance (approximately fraction of cells). They legitimately disagree 2-3x for the same sample. Picking one chooses an estimand (DNA vs cells), not the "more accurate tool."
2. No library-size normalizer fixes the genome-size confound. TSS/CSS/TMM/GMPR all operate on the count table and inherit the large-genome bias; only a coverage-based estimand (CoverM) or a genome-normalized profiler (MetaPhlAn) removes it.

What Quantity Is Being Estimated?

Estimand	Definition	Bias / use
Read count	raw reads to a taxon	library-size and genome-size dependent; never compare raw across samples
Relative abundance	read count / total	compositional (sums to 1); still genome-size biased
Coverage abundance	reads x readlen / genome size	removes large-genome bias; ~ fraction of genomes/cells (CoverM)
Cell fraction	fraction of organisms	needs coverage + an assumption of one genome per cell; polyploidy/growth bias it
Absolute load	composition x external total	the only basis for "increased/decreased" (flow/qPCR/spike-in)

Bracken: Step One, Not the Finish Line

Bracken redistributes reads stranded at the genus/family node (where shared k-mers stopped Kraken) down to species, using a database-derived expectation of where length-L reads classify. It does not classify and it does not add precision.

bracken-build -d "$KRAKEN_DB" -t 8 -k 35 -l 150   # one-time; -k MUST equal the Kraken2 build k (35)
bracken -d "$KRAKEN_DB" -i kraken.kreport -o out.bracken -w out.bracken.kreport \
    -r 150 \   # MUST equal the bracken-build -l AND the actual post-trim read length (not auto-detected)
    -l S -t 10 # species level; -t drops taxa with fewer than 10 clade-level reads (strict <) before redistribution

Output columns: name, taxonomy_id, taxonomy_lvl, kraken_assigned_reads, added_reads, new_est_reads, fraction_total_reads. fraction_total_reads is a fraction of classified-and-retained reads, not of all input - so two samples with different unclassified (e.g. host) fractions have non-comparable denominators. combine_bracken_outputs.py --files *.bracken -o matrix.tsv builds a taxa-by-sample matrix.

Bracken's defining failure mode

Bracken can only redistribute among species already in the database. A true organism absent from the database has its reads parked by Kraken at the shared genus node, and Bracken hands them to the database-present congeners - confidently fabricating or inflating those species. Distrust any species with high added_reads but tiny kraken_assigned_reads; gate presence on upstream unique-minimizer evidence (kraken-classification) and a coverage breadth check.

Treat the Table as a Composition

Goal: Make the abundance table valid for multivariate stats and correlation by removing closure with a centered log-ratio, after handling zeros (log of zero is undefined).

Approach: Impute count zeros with Bayesian-multiplicative replacement (preserves ratios), then CLR-transform; use Aitchison distance (Euclidean on CLR) downstream, never raw-proportion Pearson or Bray-Curtis for correlation.

import pandas as pd
import numpy as np
from skbio.stats.composition import clr, multi_replace   # multiplicative_replacement was renamed multi_replace in skbio 0.6

counts = pd.read_csv('matrix.tsv', sep='\t', index_col=0)   # taxa x samples (new_est_reads)
mat = counts.T.values.astype(float)                          # samples x taxa for transform
mat_nozero = multi_replace(mat / mat.sum(axis=1, keepdims=True))
clr_mat = clr(mat_nozero)                                    # closure removed; rows are CLR coordinates
clr_df = pd.DataFrame(clr_mat, index=counts.columns, columns=counts.index)

For sparse tables prefer R zCompositions::cmultRepl() (Bayesian-multiplicative, posterior-imputed) over a fixed +1 pseudocount, which is arbitrary, distorts ratios, and re-opens closure. Structural zeros (taxon genuinely absent) and sampling zeros (present below detection) are usually indistinguishable from the table - disclose the assumption rather than pretend otherwise.

Library-Size Normalization

Method	What it does	Shotgun applicability
TSS (proportions)	divide by library size	IS the compositional closure; fine for viz, biased for DA/correlation
Rarefaction	subsample to common depth	discards data; defensible for diversity, contested for DA (see below)
CSS (metagenomeSeq)	scale by a cumulative-sum quantile	robust to dominant taxa; designed for marker surveys, usable on counts
TMM (edgeR)	trimmed mean of M-values	"most features unchanged" often violated in microbiome; use with caution
RLE (DESeq median-of-ratios)	geometric-mean reference	breaks on zeros (geometric mean -> 0); needs poscounts workaround
GMPR	pairwise median ratios then geometric-mean	purpose-built for zero-inflated counts; good default size factor

None of these fixes the genome-size confound - that needs a coverage estimand or a genome-normalized profiler.

The rarefaction debate (do not take a side; decide per analysis)

McMurdie & Holmes 2014 (PLoS Comput Biol 10:e1003531) showed rarefying for DIFFERENTIAL ABUNDANCE is statistically wasteful versus modeling library size. Schloss 2024 (mSphere 9:e00354-23 and the companion e00355-23) argues rarefaction is currently the best control of uneven effort for RICHNESS and COMMUNITY-DISTANCE analyses, where scaling alone does not remove the depth effect. They concern different downstream analyses: for DA do not rarefy (use a CoDA/reference-frame method or a modeled size factor); for alpha/beta diversity rarefaction is defensible. Treating "rarefy: yes/no" as one global switch is the mistake.

Reference-Frame Differential Abundance (logic here; mechanics in metagenome-visualization)

"Taxon X increased" needs a reference frame because the total is fixed. Methods differ chiefly by their implicit frame: total-sum (naive, wrong), the geometric mean of all taxa (CLR / ALDEx2), or an estimated per-sample sampling fraction (ANCOM-BC). ALDEx2 (Fernandes 2014 Microbiome 2:15) Monte-Carlo samples a Dirichlet posterior, CLR-transforms, and tests each draw; ANCOM-BC (Lin & Peddada 2020 Nat Commun 11:3514) estimates and corrects each sample's sampling fraction. Do not run naive t-tests or Wilcoxon on TSS proportions. Run the tools and read their output in metagenome-visualization.

Absolute Quantification: the Only Basis for "Increased/Decreased"

Relative methods recover the composition; absolute load = composition x an external total. Anchors: flow-cytometry cell counts (QMP, Vandeputte 2017 Nature 551:507, which showed apparent Crohn's "increases" were a microbial-load artifact - the absolute trajectory was opposite); per-taxon flow density (Props 2017 ISME J 11:584); a known spike-in organism added before extraction (SCML, Stammler 2016 Microbiome 4:28; a cellular spike also captures extraction bias a DNA spike misses); or total 16S qPCR copies (cheap, copy-number biased). Any "X increased" claim without an anchor or a reference-frame method is unsupported.

Per-Method Failure Modes

Bracken -r read-length mismatch

Trigger: -r 150 on trimmed ~120 bp reads, or a database built only for a different length. Mechanism: the redistribution prior is fragment-length specific and not auto-detected. Symptom: biased species fractions with no error if the .kmer_distrib exists, hard crash if not. Fix: -r = actual post-trim read length and a matching built distribution.

Pearson/Bray-Curtis on proportions

Trigger: correlating two taxa's relative abundances, or Bray-Curtis distance for a "who-co-occurs" claim. Mechanism: closure biases proportion correlations negative and makes Euclidean/Bray-Curtis sub-compositionally incoherent. Symptom: spurious negative interactions; unstable clustering. Fix: CLR + Aitchison distance, or SparCC/proportionality for co-occurrence.

Relative fold-change reported as absolute

Trigger: "taxon X doubled" from a relative table. Mechanism: one bloom deflates every other proportion. Symptom: whole-community "depletion" that is really one taxon rising. Fix: anchor to load (flow/qPCR/spike-in) or use ANCOM-BC; state which frame.

RLE/DESeq on a sparse table

Trigger: DESeq2/edgeR median-of-ratios on a species count table. Mechanism: the geometric-mean reference collapses to 0 when any feature has a zero. Symptom: degenerate size factors, errors, or nonsense fold-changes. Fix: GMPR or CSS; or DESeq2 with a poscounts estimator.

Quantitative Thresholds

Threshold	Source	Rationale
Bracken -k = 35	Lu 2017 PeerJ Comput Sci 3:e104	must equal the Kraken2 database k-mer length
Bracken -r = post-trim read length	Bracken docs	redistribution prior is fragment-length specific; silent bias otherwise
Bracken -t 10 default	Bracken docs	redistribution floor; raise to suppress noise, too high deletes real rare taxa
Multiplicative/Bayesian zero replacement over +1	Martin-Fernandez 2015 Stat Modelling 15:134	preserves ratios; fixed pseudocount distorts them and re-opens closure
Rarefy for diversity, not for DA	McMurdie 2014; Schloss 2024 mSphere 9:e00354-23	decision is per-analysis, not global
Coverage breadth (covered_fraction) presence gate	Aroney 2025 Bioinformatics 41:btaf147	high mean coverage over a few % of a genome is a conserved-region artifact

Common Errors

Error / symptom	Cause	Solution
"kmer_distrib not found"	-r has no matching built distribution	bracken-build -l <readlen> or pick a DB shipping it
Bracken rejects input	mpa-style or per-read file passed to -i	give the default Kraken2 --report
Species with huge added_reads, tiny assigned	redistribution into a DB-present relative of an absent taxon	gate presence on unique minimizers + coverage breadth
CLR returns -inf / NaN	zeros not replaced before log-ratio	multiplicative or cmultRepl replacement first
Cross-sample fractions not comparable	differing unclassified (host) fractions in the denominator	track classified fraction as a covariate; host-deplete upstream
MetaPhlAn and Bracken disagree	different estimands (cells vs reads)	do not merge; pick one estimand and state it

References

• Lu J, Breitwieser FP, Thielen P, Salzberg SL. 2017. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci 3:e104.
• Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. 2017. Microbiome datasets are compositional: and this is not optional. Front Microbiol 8:2224.
• Quinn TP, Erb I, Richardson MF, Crowley TM. 2018. Understanding sequencing data as compositions: an outlook and review. Bioinformatics 34:2870-2878.
• Fernandes AD, Reid JN, Macklaim JM, et al. 2014. Unifying the analysis of high-throughput sequencing datasets. Microbiome 2:15.
• Lin H, Peddada SD. 2020. Analysis of compositions of microbiomes with bias correction. Nat Commun 11:3514.
• Martin-Fernandez JA, Hron K, Templ M, Filzmoser P, Palarea-Albaladejo J. 2015. Bayesian-multiplicative treatment of count zeros in compositional data sets. Stat Modelling 15:134-158.
• McMurdie PJ, Holmes S. 2014. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol 10:e1003531.
• Schloss PD. 2024. Rarefaction is currently the best approach to control for uneven sequencing effort in amplicon sequence analyses. mSphere 9:e00354-23.
• Weiss S, Xu ZZ, Peddada S, et al. 2017. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5:27.
• Chen L, Reeve J, Zhang L, et al. 2018. GMPR: a robust normalization method for zero-inflated count data. PeerJ 6:e4600.
• Vandeputte D, Kathagen G, D'hoe K, et al. 2017. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551:507-511.
• Stammler F, Glasner J, Hiergeist A, et al. 2016. Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4:28.
• Aroney STN, Newell RJP, Nissen JN, Camargo AP, Tyson GW, Woodcroft BJ. 2025. CoverM: read alignment statistics for metagenomics. Bioinformatics 41:btaf147.

Related Skills

• kraken-classification - Generates the Kraken2 report and owns the read-count-is-not-abundance reframe
• metaphlan-profiling - Genome-size-normalized cell-fraction abundance; a different estimand
• metagenome-visualization - Diversity, ordination, and differential-abundance tool mechanics
• contamination-controls - Host-fraction and blank handling that affect the denominator
• genome-assembly/metagenome-assembly - Coverage-based MAG abundance via read mapping
• workflows/metagenomics-pipeline - End-to-end profiling and abundance