OSCR

A microprotein atlas of the human frontal cortex in Alzheimer's disease.

Code ↔ Paper

29 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 29 matches
  1. [1] § Methods › Spectral validation of MP PSMs using PROSIT ↔ Code/Peptide_TMT_analysis/prosit/prosit_pipeline.py, lines 726–867 · score 0.95 · matched fragment ions, PROSIT predicted fragments, lowest SA, SA rating, matched predicted, Match coverage
  2. [2] § Methods › Cell culture, transient transfection and immunocytochemistry ↔ Code/Miscellaneous/actin_quant_pipeline.py, lines 1–31 · score 0.87 · minor axis, scikit image, actin distribution, intensity profile, SciPy, NumPy
  3. [3] § Results › A subset of smORFs diverges in expression from the main ORF of the encoding gene ↔ Results/microproteins_dashboard.py, lines 395–439 · score 0.84 · altORF, uORF, psORF, lncRNA, iORF, dORF
  4. [4] § Methods › Transcriptional regulatory and RBP motif analyses ↔ Results/generate_supplemental_tables.py, lines 316–375 · score 0.82 · RBP motif, splice junction, gene symbol, Transcription factor, Benjamini Hochberg, Promoter
  5. [5] § Methods › Normalization, covariate adjustment and differential expression analysis of TMT proteomics data ↔ Code/Peptide_TMT_analysis/fragpipe_results_processing_scripts/TMT_regressed_corrected_matrix.R, lines 20–132 · score 0.80 · median coefficients, linear modeling, regressing, Bootstrap, age, matrix
  6. [6] § Results › A subset of smORFs diverges in expression from the main ORF of the encoding gene ↔ Results/generate_supplemental_tables.py, lines 256–315 · score 0.79 · additive model, Pearson correlation, uORF, Benjamini Hochberg, fold change, lncRNA
  7. [7] § Methods › Ribosome profiling re-analysis using Rp3 to assess translation of MS-identified MPs ↔ Results/microproteins_dashboard.py, lines 288–377 · score 0.79 · RiboCode, multi mapping, MS detected, translational evidence, ambiguous, Ribo seq
  8. [8] § Methods › Bulk RNA-seq profiling and differential expression analysis ↔ Code/Shortread_RNA_analysis/Shortread_deseq_processing_scripts/Main_smORF_LRT_analysis.R, lines 1–42 · score 0.76 · Pearson correlation, DESeq2, slope, psych, nested, additive
  9. [9] § Results › Unique MS evidence of MPs in aged human brains ↔ Results/generate_supplemental_tables.py, lines 196–255 · score 0.74 · razor peptides, confidence interval, unique peptides, Protein length, MS evidence, ShortStop
  10. [10] § Methods › DIA MS of human DLPFC tissue ↔ Code/gold_standard_filtering_criteria.py, lines 18–130 · score 0.74 · global pg, RiboCode, ShortStop, proteotypic, DIA, DDA
  11. [11] § Methods › Human postmortem brain cohorts ↔ Code/Shortread_RNA_analysis/Shortread_deseq_processing_scripts/RNA_differential_expression.R, lines 1–84 · score 0.73 · bulk RNA seq, asym AD, ROSMAP DLPFC, Clinical, age, variables
  12. [12] § Methods › Bulk RNA-seq profiling and differential expression analysis ↔ Code/Shortread_RNA_analysis/Shortread_deseq_processing_scripts/RNA_differential_expression.R, lines 1–84 · score 0.72 · DESeq2, limma, RNA seq, hg19, VST, matrices
  13. [13] § Results › Unique MS evidence of MPs in aged human brains ↔ Results/microproteins_dashboard.py, lines 288–377 · score 0.72 · ShortStop, MS detected, UniProt, Protein length, Ribo seq, RPKM
  14. [14] § Methods › Human postmortem brain cohorts ↔ Results/generate_supplemental_tables.py, lines 376–416 · score 0.71 · ROSMAP bulk RNA, DIA proteomics, asym AD, female, RNA seq, UCSD
  15. [15] § Methods › DIA MS of human DLPFC tissue ↔ Results/microproteins_dashboard.py, lines 1570–1661 · score 0.68 · global pg, RiboCode, proteotypic, DIA, DDA, Human
  16. [16] § Results › Unique MS evidence of MPs in aged human brains ↔ Results/microproteins_dashboard.py, lines 395–439 · score 0.66 · internal ORFs, lncRNA, short isoforms, TrEMBL, upstream, downstream
  17. [17] § Results › Unique MS evidence of MPs in aged human brains ↔ Results/microproteins_dashboard.py, lines 2293–2371 · score 0.65 · Swiss Prot matched, UniProt, TrEMBL, curation, smORFs, aa
  18. [18] § Methods › TMT-based proteomics and protein identification analysis ↔ Code/Peptide_TMT_analysis/fragpipe/fragpipe_round1.sh, lines 1–44 · score 0.64 · rescoring workflow, FragPipe, plex, variable, proteogenomic, batch
  19. [19] § Methods › Statistics and reproducibility ↔ Code/Peptide_TMT_analysis/fragpipe_results_processing_scripts/TMT_ANOVA.R, lines 86–165 · score 0.61 · post hoc, Benjamini Hochberg, broom, diagnoses, TMT
  20. [20] § Methods › Long-read transcriptome assembly and putative ORF database construction ↔ Results/generate_supplemental_tables.py, lines 316–375 · score 0.61 · Nanopore long, GTFtoFASTA, External, TrEMBL, RNA seq, Swiss Prot
  21. [21] § Results › Integrating Ribo-seq, MS and PROSIT provides an evidence framework for confidently identifying brain-expressed MPs ↔ Results/generate_supplemental_tables.py, lines 196–255 · score 0.61 · spectral angle, confidence tiers, protein identification, SA, coverage, grading
  22. [22] § Methods › smORF classification according to genomic context and isoform homology ↔ Results/microproteins_dashboard.py, lines 3916–3973 · score 0.61 · BLASTP alignment, bit scores, classified, smORFs, canonical, models
  23. [23] § Methods › Normalization, covariate adjustment and differential expression analysis of TMT proteomics data ↔ Code/Peptide_TMT_analysis/fragpipe_results_processing_scripts/TAMPOR_combined_rounds.R, lines 55–97 · score 0.60 · Median Polish, GIS, reporter, channel, rounds, batches
  24. [24] § Results › Integrating Ribo-seq, MS and PROSIT provides an evidence framework for confidently identifying brain-expressed MPs ↔ Code/Peptide_TMT_analysis/prosit/prosit_pipeline.py, lines 726–867 · score 0.60 · fragment ion, spectral angle, insufficient, SA, moderate, weak
  25. [25] § Results › Unique MS evidence of MPs in aged human brains ↔ Code/gold_standard_filtering_criteria.py, lines 18–130 · score 0.59 · MS detection, ShortStop, Ribo seq, MS evidence, Swiss Prot, DIA
  26. [26] § Results › Ribosome footprints and MS coverage provide orthogonal evidence for brain-expressed pseudogenes ↔ Code/Miscellaneous/actin_quant_pipeline.py, lines 1–31 · score 0.58 · actin intensity profile, actin distribution, fluorescence, Edge, metric, nuclear
  27. [27] § Methods › Statistics and reproducibility ↔ Code/Shortread_RNA_analysis/Shortread_deseq_processing_scripts/RNA_differential_expression.R, lines 119–176 · score 0.55 · DESeq2, blinded, Braak, CERAD, dplyr, Padj
  28. [28] § Results › Posttranscriptional splicing motif enrichment for genes encoding MPs ↔ Results/generate_supplemental_tables.py, lines 1–30 · score 0.53 · RBP motif enrichment, splice site, junctions, smORF, encoding
  29. [29] § Methods › Differential expression analysis of long-read RNA-seq data ↔ Code/Longread_RNA_analysis/ESPRESSO_data_processing_scripts/deseq_brain_espresso.r, lines 1–48 · score 0.50 · DESeq2, fold change, integers, sex, ESPRESSO, RNA

Paper

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The authors' code

Python · 4,247 lines · 242 KB · no license · 7 matches

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It can be read at the source: Results/microproteins_dashboard.py.

Overview

Authors: Brendan Miller1, Eduardo Vieira de Souza1, Calvin Lau1, Joan M. Vaughan1, Victor J. Pai1, Servando Giraldez2, Andréa Rocha1, Jolene K. Diedrich3, Clodagh C. O’Shea2, David A. Bennett4, Alan Saghatelian1
ORCID iDs: Brendan Miller, Eduardo Vieira de Souza, Servando Giraldez, Jolene K. Diedrich, Alan Saghatelian
  1. Clayton Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies,La Jolla, CA USA
  2. Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies,La Jolla, CA USA
  3. Department of Integrative Structural and Computational Biology, The Scripps Research Institute,La Jolla, CA USA
  4. Rush Alzheimer’s Disease Center, Rush University Medical Center,Chicago, IL USA
Institutions: Salk Institute for Biological Studies (United States); Scripps Research Institute (United States); Rush University Medical Center (United States)
Journal: Nature aging, volume 6, issue 9, pages 1979-1997
Dates: received 3 October 2025; accepted 28 July 2026; published online 14 September 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s43587-026-01207-x · PMID 42736449 · PMCID PMC13577902 · OpenAlex W7212863263
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), other (modality), human (organism), Alzheimer's / dementia (population)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, fMRI & imaging
Keywords: Ageing, Alzheimer's disease, Gene expression
MeSH: Alzheimer Disease*, Frontal Lobe*, Proteome*, Humans, Mass Spectrometry, Micropeptides, Open Reading Frames, Proteomics (* major topic)
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: National Institute of General Medical Sciences (R01GM102491)
Citations: not cited yet (Europe PMC); 59 references in the paper

Abstract

Understanding the molecular basis of neurodegeneration requires a comprehensive map of the genome’s protein-coding output. Although thousands of small open reading frames (ORFs) are translated in the human brain, proteomic evidence for their encoded microproteins (MPs) (≤150 amino acids (aa)) remains limited. Here, we present a brain MP atlas that integrates transcriptomics, mass spectrometry and deep-learning-predicted spectra across more than 600 postmortem frontal cortex samples with and without Alzheimer’s disease (AD). We identified 1,067 MPs absent from reviewed UniProtKB entries with high-confidence spectral support. A subset is differentially expressed in AD independently of the annotated main ORF at the same locus. A small ORF expressed by MKKS encodes a 63-amino-acid MP that is the locus’s predominant translation product and is downregulated in AD; its loss impairs microglial mitochondrial respiration, implicating it in microglial bioenergetics. This atlas expands the annotated brain proteome and provides a resource for studying MPs in aging and neurodegeneration.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 29 matches between paragraphs and lines of code.

huggingface.co/spaces/brmiller/brain-microprotein-atlas-app

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 5a558dce1875fa365360e0b8329d9c8bb7e2a952, 25 September 2026
Languages: Python (40), Shell (13), R (12)
Size: 256 files, 65 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (Dockerfile, environment.yml, requirements.txt, Results/requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
At the source: huggingface.co/spaces/brmiller/brain-microprotein-atlas-app

brendan-miller-salk/brain-microprotein-atlas

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: f7428766396fb1108ebc2211a512cfed3c0ad259, 25 September 2026
Languages: Python (37), Shell (12), R (11)
Size: 192 files, 60 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (Dockerfile, environment.yml, requirements.txt, Results/requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: pandas (24 files), tidyverse (10 files), NumPy (7 files), ggplot2 (6 files), DESeq2 (5 files), Matplotlib (3 files), SciPy (3 files), BEDTools (2 files), Biopython (2 files), limma (2 files), patchwork (2 files), Plotly (2 files), broom (1 file), pheatmap (1 file), psych (1 file), reshape2 (1 file), scikit-image (1 file), tifffile (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
61 files, not copied: shown from their source

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At the source: github.com/brendan-miller-salk/brain-microprotein-atlas

Code availability

Code is available on GitHub at https://github.com/brendan-miller-salk/brain-microprotein-atlas. The repository includes analysis scripts, custom pipelines, results, BED/GTF/FASTA files and tools for MP annotation and visualization. The code and data can be further visualized and browsed at https://huggingface.co/spaces/brmiller/brain-microprotein-atlas-app.

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 60 scripts, each with its path and the digest of its content;
  • 29 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

Datasets cited

Data availability

The data used in this study were in part obtained through the AD Knowledge Portal (https://doi.org/10.7303/9618238). Transcriptomics and associative data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center; additional phenotypic data can be requested from www.radc.rush.edu. Transcriptomics data were also generated from postmortem brain tissue collected through the Mount Sinai VA Medical Center Brain Bank and provided by E. Schadt from Mount Sinai School of Medicine. The TMT proteomics data were based on samples provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center. The raw and processed data used in this study are available through multiple public repositories. Data from the AMP-AD Knowledge Portal (https://adknowledgeportal.synapse.org), including the raw and processed datasets from ROSMAP (Synapse ID: syn3219045 (http://www.synapse.org/Synapse:syn3219045)) and MSBB (Synapse ID: syn3159438 (http://www.synapse.org/Synapse:syn3159438)), and methods developed by the AMP-AD Target Discovery Program (supported by the NIA), are shared without publication embargo and are available for secondary analysis; access requires registration and adherence to data use and attribution guidelines (https://adknowledgeportal.synapse.org/#/DataAccess/Instructions). ROSMAP resources can also be requested directly through the Rush Alzheimer’s Disease Center (www.radc.rush.edu). All raw long-read sequencing data generated from 12 postmortem human brain samples are available through Synapse (ID: syn52047893 (http://www.synapse.org/Synapse:syn52047893/wiki/622953)) under restricted access, requiring a free Synapse account. The Ribo-seq data from Duffy et al.5 are available through dbGaP under accession no. phs002489.v1.p1 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002489.v1.p1). The UniProtKB human proteome database (Swiss-Prot and TrEMBL reference sequences, UP000005640 (http://www.uniprot.org/proteomes/UP000005640), downloaded on 20 January 2025) used for the spectral searches is available at https://ftp.uniprot.org/pub/databases/uniprot/previous_releases/. Raw MS data for the HMC3 studies and the UCSD DIA-MS cohort have been deposited at the PRIDE Archive under accession no. PXD071500 (http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD071500).

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 3, 28 September 2026

  • Publisher: — → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 3 keywords, 8 MeSH terms, 1 funder, 59 references.

Cite

This paper

Miller, B., Vieira de Souza, E., Lau, C., Vaughan, J. M., Pai, V. J., Giraldez, S., Rocha, A., Diedrich, J. K., O’Shea, C. C., Bennett, D. A., & Saghatelian, A. (2026). A microprotein atlas of the human frontal cortex in Alzheimer's disease. Nature aging, 6(9), 1979-1997. https://doi.org/10.1038/s43587-026-01207-x

BibTeX

@article{miller2026microprotein,
author = {Miller, Brendan and Vieira de Souza, Eduardo and Lau, Calvin and Vaughan, Joan M. and Pai, Victor J. and Giraldez, Servando and Rocha, Andréa and Diedrich, Jolene K. and O’Shea, Clodagh C. and Bennett, David A. and Saghatelian, Alan},
title = {{A microprotein atlas of the human frontal cortex in Alzheimer's disease}},
journal = {Nature aging},
year = {2026},
month = sep,
volume = {6},
number = {9},
pages = {1979--1997},
publisher = {Nature Portfolio},
issn = {2662-8465},
doi = {10.1038/s43587-026-01207-x},
url = {https://doi.org/10.1038/s43587-026-01207-x},
pmid = {42736449},
pmcid = {PMC13577902}
}

RIS

TY - JOUR
AU - Miller, Brendan
AU - Vieira de Souza, Eduardo
AU - Lau, Calvin
AU - Vaughan, Joan M.
AU - Pai, Victor J.
AU - Giraldez, Servando
AU - Rocha, Andréa
AU - Diedrich, Jolene K.
AU - O’Shea, Clodagh C.
AU - Bennett, David A.
AU - Saghatelian, Alan
TI - A microprotein atlas of the human frontal cortex in Alzheimer's disease
T2 - Nature aging
J2 - Nat Aging
PY - 2026
DA - 2026/09/14
VL - 6
IS - 9
SP - 1979
EP - 1997
SN - 2662-8465
PB - Nature Portfolio
DO - 10.1038/s43587-026-01207-x
UR - https://doi.org/10.1038/s43587-026-01207-x
LA - en
ER -

CSL-JSON

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