Validation / Papers / El Masri 2026

Early-life microbiota in the LIMIT cohort: unveiling meconium microbiota types beyond maternal lifestyle

Genomics and transcriptomics · research paper · MaAsLin 2 (R), through the microbiome adapter

How to read this page. In this validation, a script plays the scientist. It gives the answers that we wrote before the run, from the methods of the paper. A known value comes from the paper, from a tutorial or from a check that we ran. This page has no combined run of the paper yet.

No run is scored for this paper yet.

The paper

El Masri D et al. Early-life microbiota in the LIMIT cohort: unveiling meconium microbiota types beyond maternal lifestyle. Gut Microbes 18(1) (2026). doi:10.1080/19490976.2026.2712811

Related sources:

Mallick H et al. Multivariable association discovery in population-scale meta-omics studies. PLOS Comput Biol 17:e1009442 (2021). Source of the MaAsLin 2 method. doi:10.1371/journal.pcbi.1009442

What it measured

The study sequenced the 16S rRNA gene (V1 to V8, long reads) in 168 meconium samples of newborns from the LIMIT cohort in Pavia. The authors tested the association of each OTU with gestational age, gestational weight gain (reference adequate), maternal age and pre-pregnancy BMI in the babies born by vaginal delivery, with MaAsLin 2. The model uses total sum scaling, removes features that occur in fewer than 5 percent of the samples, and applies the default log transform. One OTU (Klebsiella quasivariicola) is associated with gestational age.

Data

Zenodo record 10.5281/zenodo.18376726 (files otutable.csv and metadata.csv), the study database of the paper Size: Two CSV files, 267 kB and 21 kB. 538 OTUs by 168 samples, and 168 sample rows with 28 columns..

License: CC BY 4.0 (Zenodo record). Pseudonymous mother-infant data without direct identifiers.

Data source

The instruction

A script sends this message as the scientist.

ScientistI have a 16S study of 168 newborn meconium samples. Which OTUs are associated with gestational age in the babies born by vaginal delivery, when I adjust for gestational weight gain, maternal age and pre-pregnancy BMI?

Basis: The Methods and the Results section on MaAsLin 2. The paper does not name the column that holds the delivery type. The benchmark uses the column birth_type with the value natural, which gives the 132 participants of the paper.

The decisions

The model asks questions during a run. A script gives these answers to the questions of the model. We wrote the answers before the run.

**Table 1 |** Answers that a script gives to the questions of the model.
Decision	Value	Source
Research question	`Which OTUs are associated with gestational age in babies born by vaginal delivery?`	Results, MaAsLin 2 on the observed bacterial OTUs.
Unit of replication	`subjects, one sample for each subject`	One meconium sample for each mother-infant pair (168 pairs).
Filter that keeps a subset of the samples	`birth_type=natural`	Results, Figure 2 and Figure 3 cover infants born via vaginal delivery. The number 132 matches vaginal births with complete data.
Samples or features to exclude	`none`	The paper removes features in fewer than 5 percent of the samples. That is the prevalence limit.
Smallest share of samples that must contain a feature	`0.05`	Methods. All features detected in fewer than 5 percent of the samples were excluded.
Smallest share of samples that must contain a feature, for the distance	`0`	Figure 2 uses the relative abundance table. The paper states no feature filter for the distance.
Rarefaction depth	`none`	Methods. The abundances were normalized with total sum scaling. No rarefaction is stated for MaAsLin 2.
Alpha diversity index	`shannon`	Figure 2e shows Shannon, Simpson and inverse Simpson. The MaAsLin 2 result does not use it.
Column that defines the groups for alpha diversity	`GWG`	Figure 2e compares the GWG categories.
Normalization before the distance	`relative`	Figure 2a uses Bray-Curtis distance on the relative abundance table. The MaAsLin 2 result does not use it.
Distance measure	`bray`	Figure 2a, Bray-Curtis.
Pseudo-count for CLR	`1`	Not used in the paper. The value is only an entry for the decision record.
PERMANOVA terms	`GWG`	The PERMANOVA of GWG in Figure 2. The MaAsLin 2 result does not use it.
Number of permutations	`999`	Not stated in the text. The value is the default of adonis2.
Strata	`none`	Not used in the paper.
Random seed	`1`	Not stated. The linear model has no random step.
Differential abundance method	`maaslin2`	Methods, MaAsLin2 v1.16.0.
Columns that are tested	`gestational_age, GWG, age_mother, pre_pregnancy_BMI`	Methods. Gestational age, GWG, maternal age and pre-BMI as fixed effects.
Random effects	`none`	Methods. The paper names fixed effects only. Each mother-infant pair has one sample.
Reference level	`GWG, adequate`	Methods. Adequate GWG as the reference category.
Normalization for MaAsLin 2	`TSS`	Methods. The OTU abundances were normalized with total sum scaling.
Transform for MaAsLin 2	`LOG`	Methods. The default MaAsLin 2 transformation, which is the log transform.
Standardize continuous columns	`True`	Not stated. The default of MaAsLin 2 is true.
Adjusted p value limit	`0.05`	Results. Significant after false discovery rate correction, q below 0.05.
Monte Carlo draws for ALDEx2	`128`	Not used in the paper. ALDEx2 default.

Known values

The tolerance is the largest difference from the known value that we accept. We set it before the run. Exact: the number must be the same.

**Table 2 |** Known values for El Masri 2026.
Value	Known value	Tolerance	Source
`samples_in_study`Meconium samples in the study Source of the known value Printed in the paper. Independent check: yesWhere: Methods. "168 meconium samples were analyzed" and "168 mother-infant pairs were selected".Check: check_limit_maaslin.py reads the table and the sample table with pandas.Note in the list of known values: El Masri 2026, Methods: 168 meconium samples	168	exact	Printed in the paper. Independent check: yes
`samples_in_model`Samples in the model after the filter for vaginal delivery and the removal of missing values Source of the known value Printed in the paper. Independent check: yesWhere: Results. "this OTU was present in only 7 of the 132 study participants".Check: check_limit_maaslin.py keeps the 142 vaginal births and drops the 10 with missing values, which leaves 132.Note in the list of known values: El Masri 2026, Results: 7 of the 132 study participants; check_limit_maaslin.py	132	exact	Printed in the paper. Independent check: yes
`significant_otus`Associations with q at or below 0.05 Source of the known value Printed in the paper. Independent check: yesWhere: Results. "identified a single significant association after multiple-testing correction".Check: check_limit_maaslin.py counts 1 result with q at or below 0.05 among 325 tests.Note in the list of known values: El Masri 2026, Results: a single significant association; check_limit_maaslin.py	1	exact	Printed in the paper. Independent check: yes
`top_q`Adjusted p value (q) of the most significant OTU Source of the known value Printed in the paper. Independent check: yesWhere: Results. "OTU RS_GCF_002269255.1 (Klebsiella quasivariicola) was negatively associated with gestational age (q = 0.043)".Check: check_limit_maaslin.py fits ordinary least squares models in statsmodels with the MaAsLin 2 steps and gives q 0.042647. The paper value 0.043 is this value rounded.Note in the list of known values: El Masri 2026, Results: q = 0.043; check_limit_maaslin.py gives 0.042647	0.043	± 0.0005	Printed in the paper. Independent check: yes
`top_otu_samples`Samples that contain the most significant OTU Source of the known value Printed in the paper. Independent check: yesWhere: Results. "this OTU was present in only 7 of the 132 study participants, i.e. 5.3%".Check: check_limit_maaslin.py counts 7 samples with a read of this OTU.Note in the list of known values: El Masri 2026, Results: 7 of the 132; check_limit_maaslin.py	7	exact	Printed in the paper. Independent check: yes
`otus_tested`OTUs tested after the prevalence limit (not printed in the paper) Source of the known value Independent check: we calculated itTool: MaAsLin2 1.26.0 through the microbiome adapterWhere: Not printed in the paper. The benchmark authors ran the model.Check: check_limit_maaslin.py applies the 5 percent prevalence limit and keeps 65 OTUs (325 tests).Note in the list of known values: check_limit_maaslin.py: 65 OTUs, 325 tests	65	exact	Independent check: we calculated it
`shannon_gwg_p`p value of the Kruskal-Wallis test of Shannon diversity by GWG category Source of the known value Independent check: we calculated itTool: vegan 2.7.6 and R wilcox and kruskal tests through the microbiome adapterWhere: Not printed. The paper states that alpha diversity does not differ between the GWG categories (Figure 2e, Kruskal-Wallis). The p value is in a figure only.Check: check_limit_diversity.py computes Shannon with numpy and the Kruskal-Wallis test with SciPy. It gives p 0.991662.Note in the list of known values: computed: check_limit_diversity.py; the paper says no difference (Figure 2e)	0.9917	± 0.005	Independent check: we calculated it
`permanova_gwg_r2`PERMANOVA R2 of GWG (Bray-Curtis, relative abundances) Source of the known value Independent check: we calculated itTool: vegan 2.7.6 adonis2 through the microbiome adapterWhere: Not printed. The paper states no effect of GWG on beta diversity (Supplementary Table S1).Check: check_limit_diversity.py implements the PERMANOVA of Anderson (2001) in numpy on Bray-Curtis distances. It gives R2 0.010403.Note in the list of known values: computed: check_limit_diversity.py; the paper says no effect	0.0104	± 0.0005	Independent check: we calculated it
`permanova_gwg_f`PERMANOVA F of GWG Source of the known value Independent check: we calculated itTool: vegan 2.7.6 adonis2 through the microbiome adapterWhere: Not printed. Same as the R2 item.Check: check_limit_diversity.py gives F 0.678065. The F value does not depend on the seed.Note in the list of known values: computed: check_limit_diversity.py	0.678	± 0.005	Independent check: we calculated it
`permanova_gwg_p`PERMANOVA p of GWG, 999 permutations Source of the known value Independent check: we calculated itTool: vegan 2.7.6 adonis2, 999 permutationsWhere: Not printed. The p value changes with the seed.Check: The numpy permutation test with seed 1 gives 0.910. The tolerance 0.06 covers the change with the seed.Note in the list of known values: computed: check_limit_diversity.py, numpy seed 1 gives 0.910; the p value changes with the seed	0.91	± 0.06	Independent check: we calculated it

Latest scored run

No run is scored for this paper yet.

Notes

Triage notes by the maintainers

The text below is from the triage notes. We show it as the maintainers wrote it.

Classes: a = tool or adapter fault, b = harness fault, c = benchmark spec fault, d = model fault.

claude:claude-haiku-5-5, run 1: 20261009-034501-3dac
claude:claude-haiku-5-5, run 2: 20261009-034911-c4a1
claude:claude-haiku-5-5, run 3 (final): 20261009-035258-57f6

Model	Item or call	Expected	Got	Class	Cause	Fix
haiku	permanova_gwg_f and permanova_gwg_r2 in run 2	0.678 and 0.0104	0.537 and 0.0103	a	One decision (prevalence limit 0.05) was linked to both MaAsLin 2 and the distance. The tool dropped rare OTUs before the distance.	The new decision `beta_min_prevalence` (default 0) sets the limit for the distance. Run 3 passes.
haiku	samples_in_study in run 2	168	142	d	The model reported the vaginal births as the samples in the study. The prompt gives 168.	None. Run 3 reports 168.
haiku	inspect_data	ok	"can't open file src/tools/inspect_data.py"	b	Blind mode hides the repository from child processes. The built-in tool could not start.	None. The model used inspect_feature_table.
haiku	beta_diversity with strata subject in run 3	ok	"strata is not a column of the metadata: subject"	d	The sample table has no subject column. The error named the column.	None. The model repeated the call without strata.
haiku	final answer, the number 0.104875	sourced	unsourced	d	A value that the model derived from logged results.	None. No scored item depends on it.
spec	otus_tested	not scored	65	c	The paper does not print the number of tested OTUs.	The item is a reference (no score). `check_limit_maaslin.py` gives 65.
spec	the four diversity items	not scored in the paper	scored	c	The paper prints no number for the Shannon test or for the PERMANOVA.	The values come from `check_limit_diversity.py` (numpy, SciPy). `case.yaml` marks them as computed.

Commits in this branch for this paper: see git log -- bench/papers/elmasri2026-limit-maaslin2.