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BMI-genome interactions regulate global gene expression with emphasis in brain and gut.

Code ↔ Paper

3 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 3 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] § STAR★Methods › Quantification and statistical analysis › Transcription factor binding site analysis ↔ R/atsnp-package.R, the whole file · a weak match · score 0.93 · transcription factor binding, ComputeMotifScore, atSNP, binding affinity, affinity scores, motif library
  2. [2] § STAR★Methods › Quantification and statistical analysis › Transcription factor binding site analysis ↔ R/motif_analysis.R, lines 667–754 · score 0.79 · ComputeMotifScore, atSNP, binding affinity, motif library, affinity scores, SNPs
  3. [3] § STAR★Methods › Quantification and statistical analysis › Creation of BMI-dynamic predictor models ↔ build_models/create_folds.py, lines 46–156 · score 0.72 · StratifiedKFold, validation fold, cross validation, train, cv, models

Paper

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The authors' code

R · 56 lines · 2.4 KB · no license · 1 match

  1. #' atSNP: affinity tests for regulatory SNP detection
  2. #'
  3. #' @description atSNP implements the affinity test for large sets of SNP-motif
  4. #' interactions using the importance sampling algorithm.
  5. #' Users may identify SNPs that potentially may affect binding affinity of
  6. #' transcription factors.
  7. #' Given a set of SNPs and a library of motif position weight matrices (PWMs),
  8. #' atSNP provides two main functions for analyzing SNP effects:
  9. #' (i) the binding affinity score for each allele and each PWM and
  10. #' the p-values for allele-specific binding affinity scores
  11. #' (ii) the p-values for affinity score changes between the two alleles for each
  12. #' SNP.
  13. #' Compared to other bioinformatics tools that provide similar functionalities,
  14. #' atSNP is highly scalable.
  15. #'
  16. #' The atSNP main functions are:
  17. #' \enumerate{
  18. #' \item \code{\link{LoadMotifLibrary}} - Load position weight matrices
  19. #' \item \code{\link{LoadSNPData}} - Load the SNP information and code the
  20. #' genome sequences around the SNP locations
  21. #' \item \code{\link{LoadFastaData}} - Load the SNP data from fasta files
  22. #' \item \code{\link{ComputeMotifScore}} - Compute the scores for SNP effects on
  23. #' motifs
  24. #' \item \code{\link{ComputePValues}} - Compute p-values for affinity scores
  25. #' }
  26. #'
  27. #' Some helper functions are:
  28. #' \enumerate{
  29. #' \item \code{\link{MatchSubsequence}} - Compute the matching subsequence
  30. #' \item \code{\link{GetIUPACSequence}} - Get the IUPAC sequence of a motif
  31. #' \item \code{\link{dtMotifMatch}} - Compute the augmented matching subsequence
  32. #' on SNP and reference alleles
  33. #' }
  34. #'
  35. #' The composite logo plotting function is:
  36. #' \enumerate{
  37. #' \item \code{\link{plotMotifMatch}} - Plot sequence logos of the position
  38. #' weight matrix of the motif and sequences of its corresponding best matching
  39. #' augmented subsequence on the reference and SNP allele
  40. #' }
  41. #'
  42. #' @references
  43. #' Zuo, Chandler, Shin, Sunyoung, and Keles, Sunduz. (2015). atSNP:
  44. #' Transcription factor binding affinity testing for regulatory SNP detection.
  45. #' Bioinformatics 31 (20): 3353-5.
  46. #'
  47. #' @name atSNP-package
  48. #' @aliases atSNP-package
  49. #' @docType package
  50. #' @author Chandler Zuo Sunyoung Shin \email{sunyoung.shin@@utdallas.edu}
  51. #' @keywords GenomeAnnotation MotifAnnotation LogoPlot
  52. #' @importFrom BiocParallel bpmapply MulticoreParam
  53. #' @importFrom motifStack plotMotifLogo pcm2pfm
  54. #' @import Rcpp data.table BSgenome
  55. #' @seealso atSNP vignette for more information
  56. NULL

atsnp-package.R at commit 2d5f0d8, no license · at the source

Overview

Authors: Rebecca Signer1,2, Carina Seah1,2,3, Hannah Young1,2, Kayla Retallick-Townsley1,2,3, Julia Ciarcia1, Agathe De Pins2, Alanna Cote2, Seoyeon Lee1,3, Meng Jia1,3, Jessica Johnson4, Keira J.A. Johnston1, Jiayi Xu1, Kristen J. Brennand3, Laura M. Huckins1
ORCID iDs: Kayla Retallick-Townsley, Laura M. Huckins
  1. Department of Psychiatry, Yale University School of Medicine, 34 Park Street, New Haven, CT 06520, USA
  2. Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
  3. Department of Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, 300 George Street, New Haven, CT 06520, USA
  4. Department of Psychiatry, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27517, USA
Institutions: Yale University (United States); Icahn School of Medicine at Mount Sinai (United States); University of North Carolina at Chapel Hill (United States)
Journal: Cell genomics, volume 6, issue 7, article 101280
Dates: received 3 January 2025; accepted 22 May 2026; published online 18 June 2026; in print July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.xgen.2026.101280 · PMID 42314667 · PMCID PMC13347945 · OpenAlex W7165122116
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging
Keywords: eQTLs, BMI, functional genomics, genotype-environment interactions
MeSH: Body Mass Index*, Brain*, Gastrointestinal Tract*, Gene Expression Regulation*, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci (* major topic)
Topic: Genetic Associations and Epidemiology (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: HHS | NIH | National Institute of Mental Health (NIMH) (R01MH124839, R01MH118278, R01MH125938, RM1MH132648, R01MH136149); U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS) (R01ES033630); Department of Defense (TP220451); Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai; Clinical and Translational Science Awards (CTSA) (UL1TR004419); U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS); Office of Research Infrastructure of the National Institutes of Health (S10OD026880, S10OD030463)
Citations: not cited yet (Europe PMC); 93 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

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rsigner/BMI_dynamic_models

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9f7b7188f3976d84bf5e91f8d8d93c7ef99d59b4, 2 February 2026
Languages: R (5), Python (1)
Size: 47 files, 6 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, environment (requirements.txt), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: data.table (5 files), tidyverse (5 files), NumPy (1 file), pandas (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
8 files
At the source: github.com/rsigner/BMI_dynamic_models

Zenodo 20215573

License: none: the authors keep all their rights
State: the link is dead, verified on 27 September 2026
Evidence: found in the paper
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link is dead (HTTP 404)
  • 27 September 2026: the link is dead (HTTP 404)
At the source: doi.org/10.5281/zenodo.20215573

predictdb.org/post/2021

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: predictdb.org/post/2021/07/21/gtex-v8-models-on-eqtl-and-sqtl/

hakyimlab/metaxcan

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e069063a10539fe92adadb645fc667a40c2cf885, 8 September 2026
Languages: Python (118), R (2), Shell (2)
Size: 318 files, 122 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, environment (software/conda_env.yaml, software/requirements.txt, software/setup.cfg, software/setup.py), tests, documentation, 1 notebook
Not found: CITATION.cff, continuous integration
Tools: NumPy (52 files), pandas (40 files), SciPy (7 files), h5py (4 files), statsmodels (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
124 files
At the source: github.com/hakyimlab/MetaXcan?tab=readme-ov-file

chandlerzuo/atSNP

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2d5f0d8e2645c737c8ce597377f584691f34bfc5, 30 October 2020
Languages: R (16), C++ (6), C/C++ (6)
Size: 65 files, 28 scripts
Software Heritage: not archived
Found in: the text, “Key resources table”
Holds: README, environment (DESCRIPTION), tests, documentation, 1 notebook
Not found: license file, CITATION.cff, continuous integration
Tools: data.table (2 files), ggplot2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
29 files
At the source: github.com/chandlerzuo/atSNP

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

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  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1016/j.xgen.2026.101280.

Versions

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Version 2, 28 September 2026

  • Authors: added Laura M. Huckins (0000-0002-5369-6502); removed Laura M. Huckins

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 4 keywords, 10 MeSH terms, 7 funders, 92 references.

Cite

This paper

Signer, R., Seah, C., Young, H., Retallick-Townsley, K., Ciarcia, J., De Pins, A., Cote, A., Lee, S., Jia, M., Johnson, J., Johnston, K. J., Xu, J., Brennand, K. J., & Huckins, L. M. (2026). BMI-genome interactions regulate global gene expression with emphasis in brain and gut. Cell genomics, 6(7), 101280. https://doi.org/10.1016/j.xgen.2026.101280

BibTeX

@article{signer2026bmi,
author = {Signer, Rebecca and Seah, Carina and Young, Hannah and Retallick-Townsley, Kayla and Ciarcia, Julia and De Pins, Agathe and Cote, Alanna and Lee, Seoyeon and Jia, Meng and Johnson, Jessica and Johnston, Keira J.A. and Xu, Jiayi and Brennand, Kristen J. and Huckins, Laura M.},
title = {{BMI-genome interactions regulate global gene expression with emphasis in brain and gut}},
journal = {Cell genomics},
year = {2026},
month = jun,
volume = {6},
number = {7},
pages = {101280},
publisher = {Elsevier},
issn = {2666-979X},
doi = {10.1016/j.xgen.2026.101280},
url = {https://doi.org/10.1016/j.xgen.2026.101280},
pmid = {42314667},
pmcid = {PMC13347945}
}

RIS

TY - JOUR
AU - Signer, Rebecca
AU - Seah, Carina
AU - Young, Hannah
AU - Retallick-Townsley, Kayla
AU - Ciarcia, Julia
AU - De Pins, Agathe
AU - Cote, Alanna
AU - Lee, Seoyeon
AU - Jia, Meng
AU - Johnson, Jessica
AU - Johnston, Keira J.A.
AU - Xu, Jiayi
AU - Brennand, Kristen J.
AU - Huckins, Laura M.
TI - BMI-genome interactions regulate global gene expression with emphasis in brain and gut
T2 - Cell genomics
J2 - Cell Genom
PY - 2026
DA - 2026/06/18
VL - 6
IS - 7
SP - 101280
SN - 2666-979X
PB - Elsevier
DO - 10.1016/j.xgen.2026.101280
UR - https://doi.org/10.1016/j.xgen.2026.101280
LA - en
ER -

CSL-JSON

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