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Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › Behavioral assays ↔ sleep_analysis_widget.m, lines 138–181 · score 0.69 · sleep latency, waking activity, sleep length, bouts, 20
  2. [2] § Methods › Behavioral assays ↔ sleep_analysis2020.m, lines 569–643 · score 0.61 · sleep latency, sleep length, tracking, bouts, fish, activity

Paper

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The authors' code

MATLAB · 181 lines · 7.5 KB · CC0-1.0 · 1 match

  1. %Sleep Analysis Widget
  3. % THe purpose of this script is to input a specific window of data to be
  4. % analyzed from the sleep_analysis_2020 code. This will ask for the window,
  5. % which you must manually provide then spit out summary data for that
  6. % window
  10. % Requirement: Place a .mat file to analyze into Perl Batch
  11. % Folder2\Analysis_Window
  13. function window=sleep_analysis_widget()
  15. files = dir('C:\Users\Jason Rihel\Documents\Perl Batch Folder2\Analysis_window');
  17. for zzz = 1:length(files);
  18. if files(zzz).isdir == 0;
  20. %determining the names of files for each run
  21. %determining the names of genotype file for each run-- ASSUMES FORMAT:
  22. %YYMMDD_BBgenotype.txt
  24. filename = files(zzz).name;
  25. load(filename)
  26. end
  27. end
  28. clear zzz files
  30. %for i=1:length(geno.data)
  31. %plot(nanmean(geno.data{i}'))
  32. %hold on
  33. %end
  34. %ylabel('Activity per minute')
  35. %xlabel('Minutes')
  37. %Creating the prompt and assessing the starting and ending points to pull
  39. startP = string('Start');
  40. endP=string('End');
  41. mess = [startP; endP];
  43. dlgtitle = 'Inspect the Graph and Input the start time and end time to analyze';
  44. dims = [1 80];
  45. answer = inputdlg(mess,dlgtitle,dims);
  46. startend = answer;
  47. startW=str2num(startend{1});
  48. endW=str2num(startend{2});
  50. %Now use those to grab the data from all the slots:
  52. for i=1:length(geno.data)
  54. window.name{i}=geno.name{i};
  55. window.fishID{i}=geno.fishID{i};
  56. window.zeitgeber=geno.zeitgeber(startW:endW);
  57. window.time=geno.time(startW:endW);
  58. window.counter=geno.counter(startW:endW);
  59. window.lightschedule=geno.lightschedule(startW:endW);
  60. window.lightboundries=geno.lightboundries(startW:endW);
  61. window.daynumber=geno.daynumber(startW:endW);
  62. window.nightnumber=geno.nightnumber(startW:endW);
  63. window.data{i}=geno.data{i}(startW:endW,:);
  64. window.sleep{i}=geno.sleep{i}(startW:endW,:);
  65. window.sleepconinuity{i}=geno.sleepcontinuity{i}(startW:endW,:);
  66. window.sleepboutstart{i}=geno.sleepboutstart{i}(startW:endW,:);
  67. window.sleepends{i}=geno.sleepends{i}(startW:endW,:);
  68. window.sleeplatencyO{i}=geno.sleeplatency{i}(startW:endW,:);
  69. window.avewaking{i}=geno.avewaking{i}(startW:endW,:);
  70. window.wake{i}=geno.wake{i}(startW:endW,:);
  71. window.wakeconinuity{i}=geno.wakecontinuity{i}(startW:endW,:);
  72. window.wakeboutstart{i}=geno.wakeboutstart{i}(startW:endW,:);
  73. window.wakeends{i}=geno.wakeends{i}(startW:endW,:);
  74. end
  76. % I also want to calculate the sleepLatency from the start of the window:
  77. for i=1:length(geno.data)
  78. for j=1:length(window.sleep{i}(1,1:end));
  79. window.sleeplatencyS{i}(:,j)= [1; zeros(length(window.zeitgeber)-1,1)];
  80. for k = 1: length(window.sleep{i})-1;
  81. if window.sleeplatencyS{i}(k,j)==1;
  82. if window.sleep{i}(k+1,j) ==0;
  83. window.sleeplatencyS{i}(k+1,j) = 1;
  84. end
  85. end
  86. end
  87. end
  88. end
  90. %Now, I collect summary stats within this window:
  92. for i=1:length(geno.data);
  93. window.summarytable.sleep{i} = nansum(geno.sleep{i}(startW:endW,:));
  94. window.summarytable.sleepBout{i} = nansum(geno.sleepboutstart{i}(startW:endW,:));
  95. window.summarytable.sleepLengthmedian{i} = nanmedian(geno.sleepends{i}(startW:endW,:));
  96. window.summarytable.sleepLengthmean{i} = nanmean(geno.sleepends{i}(startW:endW,:));
  97. window.summarytable.sleepLatencyO{i} = nansum(geno.sleeplatency{i}(startW:endW,:));
  98. window.summarytable.sleepLatencyS{i} = nansum(window.sleeplatencyS{i});
  99. window.summarytable.wakeBout{i}= nansum(geno.wakeboutstart{i}(startW:endW,:));
  100. window.summarytable.wakeLengthmedian{i} = nanmedian(geno.wakeends{i}(startW:endW,:));
  101. window.summarytable.wakeLengthmean{i} = nanmean(geno.wakeends{i}(startW:endW,:));
  102. window.summarytable.averageActivity{i} = nanmean(geno.data{i}(startW:endW,:));
  103. window.summarytable.averageWaking{i}= nanmean(geno.avewaking{i}(startW:endW,:));
  104. end
  105. mkdir(strcat('C:\Users\Jason Rihel\Documents\Perl Batch Folder2\Analysis_window\',filename(1:9),'\'));
  106. save(strcat('C:\Users\Jason Rihel\Documents\Perl Batch Folder2\Analysis_window\',filename(1:9),'\',filename(1:9),'_window.mat'),'window')
  108. % Now to make all the plots and stats
  110. % First, I will plot all the summary stats, To do that, I need to create
  111. % strings that put all the data into one vector:
  112. window.stats.sleep=[];
  113. window.stats.sleepBout=[];
  114. window.stats.sleepLength=[];
  115. window.stats.sleepLatencyO=[];
  116. window.stats.sleepLatencyS=[];
  117. window.stats.wakeBout=[];
  118. window.stats.wakeLength=[];
  119. window.stats.averageActivity=[];
  120. window.stats.averageWaking=[];
  122. window.Genomask=[];
  123. for i=1:length(window.data)
  124. window.Genomask=[window.Genomask i*ones(1,length(window.data{i}(1,:)))];
  125. window.stats.sleep=[window.stats.sleep window.summarytable.sleep{i}];
  126. window.stats.sleepBout=[window.stats.sleepBout window.summarytable.sleepBout{i}];
  127. window.stats.sleepLength=[window.stats.sleepLength window.summarytable.sleepLengthmedian{i}];
  128. window.stats.sleepLatencyO=[window.stats.sleepLatencyO window.summarytable.sleepLatencyO{i}];
  129. window.stats.sleepLatencyS=[window.stats.sleepLatencyS window.summarytable.sleepLatencyS{i}];
  130. window.stats.wakeBout=[window.stats.wakeBout window.summarytable.wakeBout{i}];
  131. window.stats.wakeLength=[window.stats.wakeLength window.summarytable.wakeLengthmedian{i}];
  132. window.stats.averageActivity=[window.stats.averageActivity window.summarytable.averageActivity{i}];
  133. window.stats.averageWaking=[window.stats.averageWaking window.summarytable.averageWaking{i}];
  134. end
  138. %To make plotting easier, I put this all into one matrix:
  140. window.AllData=[window.stats.sleep' window.stats.sleepBout' window.stats.sleepLength' window.stats.sleepLatencyO' window.stats.sleepLatencyS' window.stats.wakeBout' window.stats.wakeLength' window.stats.averageActivity' window.stats.averageWaking'];
  142. for i=1:9
  143. window.pvalue(i)=anovan(window.AllData(:,i),{window.Genomask})
  144. end
  146. Variablenames(1)=string('sleep Total-min');
  147. Variablenames(2)=string('sleep Bout number');
  148. Variablenames(3)=string('sleepLength-min');
  149. Variablenames(4)=string('sleepLatencyO-min');
  150. Variablenames(5)=string('sleepLatencyS-min');
  151. Variablenames(6)=string('wakeBout number');
  152. Variablenames(7)=string('wakeLength-min');
  153. Variablenames(8)=string('ave Activity-sec');
  154. Variablenames(9)=string('ave Wake Activity-sec');
  156. colorspectra= [57/255 106/255 177/255;218/255 124/255 48/255;62/255 150/255 81/255;204/255 37/255 41/255;83/255 81/255 84/255;107/255 76/255 154/255;146/255 36/255 40/255;148/255 139/255 61/255;148/255 139/255 61/255;148/255 139/255 61/255];
  158. for i=1:length(window.data)
  159. legendname(i) = cellstr(cell2mat(window.name{i}));
  160. end
  162. for i=1:9
  163. figure;plotSpread(window.AllData(:,i),'categoryIdx',window.Genomask,'showMM',4,'distributionIdx',window.Genomask,'categoryColors',colorspectra(1:length(window.data),:))
  164. ax = gca;
  165. ax.XTickLabel=legendname;
  166. title(strcat(Variablenames(i),'-',filename(1:6),'-',filename(8:9),'window'),'FontSize',10)
  167. ylabel(Variablenames(i),'FontSize',14)
  168. A=axis;
  169. ylim([0,A(4)]);
  170. fg = gcf;
  171. for j=1:2+length(window.data)
  172. fg.Children.Children(j).MarkerSize=20;
  173. fg.Children.Children(j).LineWidth=2
  174. end
  175. hgsave(strcat('C:\Users\Jason Rihel\Documents\Perl Batch Folder2\Analysis_window\',filename(1:9),'\',filename(1:9), Variablenames(i),'_window.fig'))
  176. close
  177. end
  179. save(strcat('C:\Users\Jason Rihel\Documents\Perl Batch Folder2\Analysis_window\',filename(1:9),'\',filename(1:9),'_window.mat'),'window')
  180. clear ans
  181. end

sleep_analysis_widget.m at commit 9deed26, under CC0-1.0 · at the source

Overview

Authors: Meilin Fernandez Garcia1, Kayla Retallick-Townsley1,2, April Pruitt3, Elizabeth A. Davidson4, Novin Balafkan1,5, Jonathan Warrell6, Tzu-Chieh Huang1,7, Alfred Kibowen1, Zhiyuan Chu6, Yi Dai8, Sarah E. Fitzpatrick3,9, Ran Meng6, Annabel Sen1, Sophie Cohen1, Olivia Livoti1, Suha Khan4, Charlotte Becker1, Andre Luiz Teles e Silva1,10, Jenny Liu1, Grace Dossou4
and 13 other authorsJen Cheung1, Susanna Liu6, Sadaf Ghorbani1,5, P. J. Michael Deans1, Marisa DeCiucis7,11,12, Prashant Emani6, Huanyao Gao1, Hongying Shen7,11,12, Mark Gerstein6,13, Zuoheng Wang8,13, Laura M. Huckins1,2,3, Ellen J. Hoffman4,14,15, Kristen Brennand1,2,3,7,15,16
ORCID iDs: Meilin Fernandez Garcia, Kayla Retallick-Townsley, April Pruitt, Elizabeth A. Davidson, Novin Balafkan, Jonathan Warrell, Tzu-Chieh Huang, Alfred Kibowen, Zhiyuan Chu, Yi Dai, Sarah E. Fitzpatrick, Ran Meng, Annabel Sen, Charlotte Becker, Andre Luiz Teles e Silva, Jenny Liu, Susanna Liu, Sadaf Ghorbani, P. J. Michael Deans, Marisa DeCiucis, Prashant Emani, Huanyao Gao, Hongying Shen, Mark Gerstein, Zuoheng Wang, Laura M. Huckins, Ellen J. Hoffman, Kristen Brennand
16 affiliations
  1. Division of Molecular Psychiatry, Department of Psychiatry, Yale University School of Medicine,New Haven, CT USA
  2. Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai,New York, NY USA
  3. Interdepartmental Neuroscience Program, Yale School of Medicine,New Haven, CT USA
  4. Child Study Center, Yale University School of Medicine,New Haven, CT USA
  5. Mohn Research Centre for Regenerative Medicine, Haukeland University Hospital,Bergen, Norway
  6. Department of Molecular Biophysics & Biochemistry and Program in Computational Biology and Bioinformatics, Yale University,New Haven, CT USA
  7. BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder,Easton, MD USA
  8. Department of Biostatistics, Yale University School of Public Health,New Haven, CT USA
  9. Medical Scientist Training Program, Yale School of Medicine,New Haven, CT USA
  10. Hospital Israelita Albert Einstein,São Paulo, Brazil
  11. Department of Cellular & Molecular Physiology, Yale School of Medicine,New Haven, CT USA
  12. Systems Biology Institute, Yale West Campus,West Haven, CT USA
  13. Department of Biomedical Informatics and Data Science, Yale School of Medicine,New Haven, CT USA
  14. Department of Neuroscience, Yale University School of Medicine,New Haven, CT USA
  15. Wu Tsai Institute, Yale University School of Medicine,New Haven, CT USA
  16. Department of Genetics, Yale University School of Medicine,New Haven, CT USA
Institutions: Yale University (United States); Icahn School of Medicine at Mount Sinai (United States); Haukeland University Hospital (Norway); Breakthrough Discoveries for thriving with Bipolar Disorder (United States); Hospital Israelita Albert Einstein (Brazil); Institute for Systems Biology (United States)
Journal: Nature neuroscience, volume 29, issue 5, pages 1079-1094
Dates: received 23 August 2024; accepted 25 February 2026; published online 24 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02247-7 · PMID 42032432 · PMCID PMC13156037 · OpenAlex W7155539368
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), zebrafish (organism), other condition (population), autism (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Connectivity
Keywords: Functional genomics, Autism spectrum disorders, Molecular neuroscience
MeSH: Neurodevelopmental Disorders*, Transcriptome*, Animals, Genetic Predisposition to Disease, Humans, Induced Pluripotent Stem Cells, Neural Stem Cells, Neurons, Phenotype, Zebrafish (* major topic)
Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute of Mental Health (R01MH123155, RM1MH132648, R01MH121074, T32MH014276, F31MH130122, R01MH124839, R01MH118278, R01MH116002, R21MH133245); Howard Hughes Medical Institute (HHMI) (HHMI Gilliams Fellowship); NIGMS (T32GM136651); U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS) (R01ES033630)
Citations: cited by 1 paper (Europe PMC); 160 references in the paper
Research resources: RRID:SCR_002105, RRID:SCR_002798, Using WebGestalt RRID:SCR_006786, RRID:SCR_007322, RRID:SCR_007370, Flow cytometry data were analyzed RRID:SCR_008520, RRID:SCR_010943, Raw fastq files were quality-checked RRID:SCR_014583, we trained a random forest model32 RRID:SCR_015718, RRID:SCR_016884, RRID:SCR_017344, RRID:SCR_025016

Abstract

Diverse risk genes have been identified for neurodevelopmental disorders (NDDs), but how these genes converge on similar biological pathways in neurons, and thus give rise to similar phenotypes, is unclear. Here we apply a pooled CRISPR approach to successfully target 23 NDD loss-of-function genes with roles in chromatin biology and examine convergent effects on gene expression across human induced pluripotent stem cell-derived neural progenitor cells, glutamatergic neurons and GABAergic neurons. Points of convergence vary between these cell types, with the greatest number of convergent genes and strongest convergent networks in mature glutamatergic neurons, where they broadly represent synaptic, epigenetic and, unexpectedly, mitochondrial pathways. The most convergent networks were observed between NDD genes with shared biological annotations, clinical associations and co-expression patterns in human post-mortem brain. Drugs that were predicted to reverse convergent transcriptomic signatures and/or arousal and sensory processing behaviors ameliorated behavioral phenotypes in zebrafish NDD gene mutants. These results suggest that convergent effects of NDD risk genes could provide clinically useful insights.

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 2 matches between paragraphs and lines of code.

ehoffmanlab/Weinschutz-Mendes-et-al-2023-behavior

License: GPL-3.0
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Evidence: files inventoried
Commit: 874f8448d1732161d0b5a3296f38cacfc535abe3, 17 February 2023
Languages: MATLAB (30), Shell (3)
Size: 59 files, 33 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, tests, documentation
Not found: CITATION.cff, environment file, continuous integration
Tools: Statistics and Machine Learning Toolbox (4 files), Parallel Computing Toolbox (3 files), boundedline (1 file), Curve Fitting Toolbox (1 file)
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  • 30 September 2026: the link answers
35 files
At the source: github.com/ehoffmanlab/Weinschutz-Mendes-et-al-2023-behavior

jrihel/sleep-analysis

License: CC0-1.0
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Commit: 9deed26f429b306851210d792e48e69a4d0ae97d, 15 November 2023
Languages: MATLAB (7), Perl (1)
Size: 17 files, 8 scripts
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Found in: the text, “Behavioral assays”
Holds: README, license file
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Tools: Statistics and Machine Learning Toolbox (3 files), shadedErrorBar (1 file)
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10 files
At the source: github.com/JRihel/Sleep-Analysis/tree/Sleep-Analysis-Code

Zenodo 7644898

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Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
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35 files
At the source:

Zenodo 7644073

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At the source:

synapse.org/synapse:syn72039767

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At the source: synapse.org/Synapse:syn72039767

cran.r-project.org/package=webgestaltr

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At the source: CRAN.R-project.org/package=WebGestaltR

Zenodo 7644897

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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
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Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
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35 files
At the source:

Code availability

The full analysis pipeline (including code and processed data objects) used for analysis of single-cell CRISPR-KO data, evaluation and characterization of gene-level and network-level convergence, and predictive modeling using random forests are publicly available from Synpase (syn72039767 (http://www.synapse.org/Synapse:syn72039767)). Custom MATLAB software developed by the Hoffman Lab to analyze visual-startle response parameters is available from GitHub via https://github.com/ehoffmanlab/Weinschutz-Mendes-et-al-2023-behavior; 10.5281/zenodo.7644898 (ref. 156). Custom MATLAB software developed by J. Rihel to analyze sleep–wake assays is available from GitHub via https://github.com/JRihel/Sleep-Analysis/tree/Sleep-Analysis-Code; 10.5281/zenodo.7644073 (ref. 159).

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

Tracing map

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What the map holds:

  • 7 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

Key Resource Table available at 10.5281/zenodo.19457279. All hiPSCs are available from the Rutgers University Cell and DNA Repository (study 160; http://www.nimhstemcells.org/). Protocols are available from https://www.protocols.io/workspaces/brennand-laboratory. scRNA-seq data reported in this paper are publicly available from the Gene Expression Omnibus (GSE319096 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE319096)). Previously published SCZ CRISPRa screen datasets that were used for external validation of random forest models are available from the GEO (GSE200774 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE200774)) and from Synapse (syn27819129 (http://www.synapse.org/Synapse:syn51105550)). Secondary summary statistics from the main analysis are available from Synapse (syn72039767 (http://www.synapse.org/Synapse:syn72039767)). Source data are provided with this paper.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 33 authors, 3 keywords, 10 MeSH terms, 4 funders, 159 references, 12 RRIDs.

Cite

This paper

Fernandez Garcia, M., Retallick-Townsley, K., Pruitt, A., Davidson, E. A., Balafkan, N., Warrell, J., Huang, T.-C., Kibowen, A., Chu, Z., Dai, Y., Fitzpatrick, S. E., Meng, R., Sen, A., Cohen, S., Livoti, O., Khan, S., Becker, C., Luiz Teles e Silva, A., Liu, J., . . . Brennand, K. (2026). Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo. Nature neuroscience, 29(5), 1079-1094. https://doi.org/10.1038/s41593-026-02247-7

BibTeX

@article{fernandezgarcia2026transcriptomic,
author = {Fernandez Garcia, Meilin and Retallick-Townsley, Kayla and Pruitt, April and Davidson, Elizabeth A. and Balafkan, Novin and Warrell, Jonathan and Huang, Tzu-Chieh and Kibowen, Alfred and Chu, Zhiyuan and Dai, Yi and Fitzpatrick, Sarah E. and Meng, Ran and Sen, Annabel and Cohen, Sophie and Livoti, Olivia and Khan, Suha and Becker, Charlotte and Luiz Teles e Silva, Andre and Liu, Jenny and Dossou, Grace and Cheung, Jen and Liu, Susanna and Ghorbani, Sadaf and Deans, P. J. Michael and DeCiucis, Marisa and Emani, Prashant and Gao, Huanyao and Shen, Hongying and Gerstein, Mark and Wang, Zuoheng and Huckins, Laura M. and Hoffman, Ellen J. and Brennand, Kristen},
title = {{Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo}},
journal = {Nature neuroscience},
year = {2026},
month = apr,
volume = {29},
number = {5},
pages = {1079--1094},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02247-7},
url = {https://doi.org/10.1038/s41593-026-02247-7},
pmid = {42032432},
pmcid = {PMC13156037}
}

RIS

TY - JOUR
AU - Fernandez Garcia, Meilin
AU - Retallick-Townsley, Kayla
AU - Pruitt, April
AU - Davidson, Elizabeth A.
AU - Balafkan, Novin
AU - Warrell, Jonathan
AU - Huang, Tzu-Chieh
AU - Kibowen, Alfred
AU - Chu, Zhiyuan
AU - Dai, Yi
AU - Fitzpatrick, Sarah E.
AU - Meng, Ran
AU - Sen, Annabel
AU - Cohen, Sophie
AU - Livoti, Olivia
AU - Khan, Suha
AU - Becker, Charlotte
AU - Luiz Teles e Silva, Andre
AU - Liu, Jenny
AU - Dossou, Grace
AU - Cheung, Jen
AU - Liu, Susanna
AU - Ghorbani, Sadaf
AU - Deans, P. J. Michael
AU - DeCiucis, Marisa
AU - Emani, Prashant
AU - Gao, Huanyao
AU - Shen, Hongying
AU - Gerstein, Mark
AU - Wang, Zuoheng
AU - Huckins, Laura M.
AU - Hoffman, Ellen J.
AU - Brennand, Kristen
TI - Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/04/24
VL - 29
IS - 5
SP - 1079
EP - 1094
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02247-7
UR - https://doi.org/10.1038/s41593-026-02247-7
LA - en
ER -

CSL-JSON

{
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"type": "article-journal",
"title": "Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo",
"container-title": "Nature neuroscience",
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{
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"given": "Elizabeth A."
},
{
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"family": "Warrell",
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{
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},
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{
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},
{
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},
{
"family": "DeCiucis",
"given": "Marisa"
},
{
"family": "Emani",
"given": "Prashant"
},
{
"family": "Gao",
"given": "Huanyao"
},
{
"family": "Shen",
"given": "Hongying"
},
{
"family": "Gerstein",
"given": "Mark"
},
{
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},
{
"family": "Huckins",
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{
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],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "5",
"page": "1079-1094",
"DOI": "10.1038/s41593-026-02247-7",
"PMID": "42032432",
"PMCID": "PMC13156037",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02247-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
24
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]
}
}

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