OSCR

Distinct gut and oral microbiome patterns associated with dyslexia in a family-based cohort: A preliminary exploratory study.

Code ↔ Paper

8 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 8 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Sequence data processing ↔ 01_Scripts/01_Microbiome_Analysis/QIIME2_Microbiome_Pipeline.sh, lines 1–63 · score 0.71 · dada2 denoise, imported, trunc, Adapter, quality, QIIME2
  2. [2] § Materials and methods › Predicted function analysis with PICRUSt2 ↔ 01_Scripts/04_PICRUSt_Analysis/PICRUSt-script.sh, the whole file · a weak match · score 0.67 · picrust2_pipeline.py, MetaCyc, KO, Predicted, EC, pathways
  3. [3] § Materials and methods › Relative abundance analysis ↔ 01_Scripts/01_Microbiome_Analysis/stacked_bar_plots.R, lines 1–34 · score 0.65 · stacked bar, relative abundance, saliva microbiome, species
  4. [4] § Materials and methods › Microbiome alpha and beta diversity analyses ↔ 01_Scripts/01_Microbiome_Analysis/QIIME2_Microbiome_Pipeline.sh, lines 119–172 · score 0.64 · beta diversity, matrices, Aitchison, PCoA, distance, metric
  5. [5] § Materials and methods › Taxonomic classification ↔ 01_Scripts/01_Microbiome_Analysis/QIIME2_Microbiome_Pipeline.sh, lines 65–117 · score 0.56 · scikit-learn, sklearn, collapsed, taxa, QIIME2, classification
  6. [6] § Materials and methods › Sequence data processing ↔ 01_Scripts/01_Microbiome_Analysis/QIIME2_Microbiome_Pipeline.sh, lines 1–63 · score 0.55 · minquality, removal, trimns, trimqualities, raw, adapter
  7. [7] § Materials and methods › Participant information ↔ 01_Scripts/01_Microbiome_Analysis/QIIME2_Microbiome_Pipeline.sh, lines 119–172 · score 0.52 · beta diversity, alpha diversity, saliva samples, QIIME2, genus, species
  8. [8] § Materials and methods › Differential abundance analysis ↔ 01_Scripts/02_Maaslin2_analysis/Maaslin2_script.R, lines 1–45 · score 0.51 · species abundance, Maaslin2, seed, Apraxia, speech, metadata

Paper

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The authors' code

Shell · 177 lines · 4.6 KB · no license · 5 matches

  1. #!/bin/bash
  3. # QIIME 2 Microbiome Analysis Pipeline
  4. # Author: Sterling L. Wright
  5. # Date: May 22nd, 2025
  6. # Description: This script performs quality control, taxonomic classification, diversity analysis,
  7. # and data export using QIIME 2 from raw paired-end fastq files.
  9. ##############################
  10. # 1. Preprocessing: AdapterRemoval
  11. ##############################
  13. OUTPUT_DIR="adapter_removal_output"
  14. mkdir -p $OUTPUT_DIR
  16. cat SampleNames.txt | while read sample; do
  17. R1="${sample}_L001_R1_001.fastq.gz"
  18. R2="${sample}_L001_R2_001.fastq.gz"
  20. AdapterRemoval --file1 $R1 \
  21. --file2 $R2 \
  22. --output1 ${OUTPUT_DIR}/${sample}_unmerged_R1.fastq.gz \
  23. --output2 ${OUTPUT_DIR}/${sample}_unmerged_R2.fastq.gz \
  24. --outputcollapsed ${OUTPUT_DIR}/${sample}_merged.fastq.gz \
  25. --minlength 30 \
  26. --threads 4 \
  27. --trimqualities \
  28. --trimns \
  29. --collapse \
  30. --minquality 20
  32. done
  34. ##############################
  35. # 2. Import and DADA2 Denoising
  36. ##############################
  38. qiime tools import \
  39. --type 'SampleData[PairedEndSequencesWithQuality]' \
  40. --input-path manifest.txt \
  41. --output-path paired-end-demux.qza \
  42. --input-format PairedEndFastqManifestPhred33
  44. qiime tools validate paired-end-demux.qza
  46. qiime demux summarize \
  47. --i-data paired-end-demux.qza \
  48. --o-visualization paired-end-demux.qzv
  50. qiime dada2 denoise-paired \
  51. --i-demultiplexed-seqs paired-end-demux.qza \
  52. --p-trim-left-f 0 \
  53. --p-trim-left-r 0 \
  54. --p-trunc-len-f 250 \
  55. --p-trunc-len-r 200 \
  56. --o-table feature-table.qza \
  57. --o-representative-sequences rep-seqs.qza \
  58. --o-denoising-stats denoising-stats.qza \
  59. --p-n-threads 6
  61. qiime metadata tabulate \
  62. --m-input-file denoising-stats.qza \
  63. --o-visualization denoising-dada2-stats.qzv
  65. ##############################
  66. # 3. Taxonomy Assignment
  67. ##############################
  69. qiime feature-classifier classify-sklearn \
  70. --i-classifier $DATABASE \
  71. --i-reads rep-seqs.qza \
  72. --o-classification taxonomy.qza
  74. qiime metadata tabulate \
  75. --m-input-file taxonomy.qza \
  76. --o-visualization taxonomy.qzv
  78. qiime taxa barplot \
  79. --i-table feature-table.qza \
  80. --i-taxonomy taxonomy.qza \
  81. --m-metadata-file $METADATA \
  82. --o-visualization taxa-barplot.qzv
  84. ##############################
  85. # 4. Collapse to Genus and Species
  86. ##############################
  88. # Genus
  89. qiime taxa collapse \
  90. --i-table feature-table.qza \
  91. --i-taxonomy taxonomy.qza \
  92. --p-level 6 \
  93. --o-collapsed-table genus-table.qza
  95. # Species
  96. qiime taxa collapse \
  97. --i-table feature-table.qza \
  98. --i-taxonomy taxonomy.qza \
  99. --p-level 7 \
  100. --o-collapsed-table species-table.qza
  102. ##############################
  103. # 5. Export Collapsed Tables
  104. ##############################
  106. for LEVEL in genus species; do
  107. NAME="${LEVEL}-table"
  108. qiime tools export \
  109. --input-path ${NAME}.qza \
  110. --output-path exported-${LEVEL}
  112. biom convert \
  113. -i exported-${LEVEL}/feature-table.biom \
  114. -o exported-${LEVEL}/${NAME}.tsv \
  115. --to-tsv
  117. done
  119. ##############################
  120. # 6. Alpha Diversity Analysis
  121. ##############################
  123. mkdir -p ALPHA_DIVERSITY
  125. for METRIC in observed_features shannon simpson; do
  126. qiime diversity alpha \
  127. --i-table species-table.qza \
  128. --p-metric $METRIC \
  129. --o-alpha-diversity ALPHA_DIVERSITY/alpha-${METRIC}_vector.qza
  131. qiime diversity alpha-group-significance \
  132. --i-alpha-diversity ALPHA_DIVERSITY/alpha-${METRIC}_vector.qza \
  133. --m-metadata-file $METADATA \
  134. --o-visualization ALPHA_DIVERSITY/alpha-${METRIC}.qzv
  136. done
  138. ##############################
  139. # 7. Beta Diversity (Optional Example)
  140. ##############################
  142. # Filter genus table to saliva samples
  143. qiime feature-table filter-samples \
  144. --i-table genus-table.qza \
  145. --m-metadata-file $METADATA \
  146. --p-where "SampleType IN ('saliva')" \
  147. --o-filtered-table genus-saliva.qza
  149. qiime diversity beta \
  150. --i-table genus-saliva.qza \
  151. --p-metric aitchison \
  152. --p-pseudocount 1 \
  153. --o-distance-matrix genus-saliva-aitchison-distance.qza
  155. qiime diversity pcoa \
  156. --i-distance-matrix genus-saliva-aitchison-distance.qza \
  157. --o-pcoa pcoa-genus-saliva.qza
  159. qiime emperor plot \
  160. --i-pcoa pcoa-genus-saliva.qza \
  161. --m-metadata-file $METADATA \
  162. --o-visualization pcoa-genus-saliva-emperor.qzv
  164. declare -a StringArray=("SampleType" "State" "City")
  165. for category in ${StringArray[@]}; do
  166. qiime diversity beta-group-significance \
  167. --i-distance-matrix genus-saliva-aitchison-distance.qza \
  168. --m-metadata-file $METADATA \
  169. --m-metadata-column "$category" \
  170. --o-visualization ${NAME}-$category-significance.qzv \
  171. --p-pairwise
  172. done

QIIME2_Microbiome_Pipeline.sh at commit 9dbb79a, no license · at the source

Overview

Authors: Sterling L Wright1,2, Mia Joslin3,4, Yookyung Kim1, Magdalena Olson1,2, Ayden Hall2,4, Beate Peter1, Corrie M Whisner1,2
ORCID iDs: Sterling L Wright, Magdalena Olson
  1. College of Health Solutions, Arizona State University, Phoenix, Arizona, United States of America
  2. Center for Health Through Microbiomes, The Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America
  3. School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, United States of America
  4. School of Life Sciences, The College of the Liberal Arts and Sciences, Arizona State University, Tempe, Arizona, United States of America
Institutions: Arizona State University (United States)
Journal: PloS one, volume 21, issue 7, article e0353463
Dates: received 3 November 2025; accepted 23 June 2026; published online 21 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0353463 · PMID 42479725 · PMCID PMC13387549 · OpenAlex W7169873485
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population)
Methods: Statistics, Machine learning
MeSH: Dyslexia*, Gastrointestinal Microbiome*, Microbiota*, Mouth*, Adolescent, Child, Cohort Studies, Family, Feces, Female, Humans, Male, RNA, Ribosomal, 16S, Saliva (* major topic)
Topic: Reading and Literacy Development (Developmental and Educational Psychology, Psychology), according to OpenAlex
Funding: College of Liberal Arts and Sciences, Arizona State University; National Institute of Diabetes and Digestive and Kidney Diseases (T32DK137525); NIDDK NIH HHS (T32 DK137525)
Citations: not cited yet (Europe PMC); 105 references in the paper

Abstract

Background: Many neurodevelopmental disorders, including dyslexia and childhood apraxia of speech (CAS), have genetic predispositions that are understood to varying degrees. However, the microbiome in individuals with dyslexia and CAS remains underexplored. The goal of this exploratory study was to determine whether fecal and saliva microbiome diversity and composition are associated with dyslexia or CAS.

Methods: To this end, we examined the fecal and saliva microbiota of individuals with dyslexia, CAS, and their neurotypical family members using 16S rRNA gene amplicon sequencing in a family-based cohort composing of 7 individuals with dyslexia, 11 with CAS, and 10 neurotypical family members (n = 28). Participants with dyslexia and CAS were drawn from separate families, with neurotypical relatives serving as within-family controls. A total of 19 fecal and 29 saliva samples were collected, with paired fecal-saliva samples available for 19 individuals. Taxonomic classification was performed using four 16S rRNA reference databases, and microbial diversity, composition, and functional potential were analyzed.

Results: Individuals with dyslexia consistently showed distinct fecal microbiome alpha and beta diversity patterns at the species level compared to neurotypical family members and participants with CAS histories, irrespective of taxonomic database employed. Both fecal and saliva datasets identified key taxa associated with dyslexia, but not with CAS. Predicted functional profiling further identified dyslexia-associated pathways in the fecal microbiome, whereas no functional differences were detected in saliva.

Conclusion: Although these results suggest that individuals with dyslexia may harbor distinct fecal and saliva microbiomes, the findings are exploratory and should be considered as hypothesis-generating. Future studies leveraging larger, independent cohorts will be essential to validate these findings and to more rigorously examine the oral-gut-brain axis in language-based syndromes.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

asu-htm/Neuro-Microbiome-Exploration-Dyslexia-and-Apraxia-Focus

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9dbb79ab5a11d4643d9ddb308ad4db8044db9898, 17 September 2025
Languages: R (3), Shell (2)
Size: 36 files, 5 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (3 files), tidyverse (3 files), cowplot (1 file), ggpubr (1 file), patchwork (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/asu-htm/Neuro-Microbiome-Exploration-Dyslexia-and-Apraxia-Focus/

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 5 scripts, each with its path and the digest of its content;
  • 8 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

The adapter removed, short-read sequenced raw fastq files are available on the NCBI Sequence Read Archive (SRA) with accession ID: PRJNA1246995. Scripts for this project can be found on GitHub: https://github.com/asu-htm/Neuro-Microbiome-Exploration-Dyslexia-and-Apraxia-Focus/. The STORMS reporting checklist for this study is available both as a supplemental file and on the project’s GitHub page. We have provided the deidentified demographic information in the S1 Table.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 14 MeSH terms, 3 funders, 97 references.

Cite

This paper

Wright, S. L., Joslin, M., Kim, Y., Olson, M., Hall, A., Peter, B., & Whisner, C. M. (2026). Distinct gut and oral microbiome patterns associated with dyslexia in a family-based cohort: A preliminary exploratory study. PloS one, 21(7), e0353463. https://doi.org/10.1371/journal.pone.0353463

BibTeX

@article{wright2026distinct,
author = {Wright, Sterling L and Joslin, Mia and Kim, Yookyung and Olson, Magdalena and Hall, Ayden and Peter, Beate and Whisner, Corrie M},
title = {{Distinct gut and oral microbiome patterns associated with dyslexia in a family-based cohort: A preliminary exploratory study}},
journal = {PloS one},
year = {2026},
month = jul,
volume = {21},
number = {7},
pages = {e0353463},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0353463},
url = {https://doi.org/10.1371/journal.pone.0353463},
pmid = {42479725},
pmcid = {PMC13387549}
}

RIS

TY - JOUR
AU - Wright, Sterling L
AU - Joslin, Mia
AU - Kim, Yookyung
AU - Olson, Magdalena
AU - Hall, Ayden
AU - Peter, Beate
AU - Whisner, Corrie M
TI - Distinct gut and oral microbiome patterns associated with dyslexia in a family-based cohort: A preliminary exploratory study
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/07/21
VL - 21
IS - 7
SP - e0353463
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0353463
UR - https://doi.org/10.1371/journal.pone.0353463
LA - en
ER -

CSL-JSON

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