Comparative genomics of human brain and immune gene preservation across species.

Code ↔ Paper

5 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 5 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Materials › Existence patterns in non-primate species ↔ 2_result_pattern_nonprimate_only/nonprimate_pattern_count.py, the whole file · a weak match · score 0.87 · Canis lupus familiaris, Danio rerio, Mus musculus, Orycteropus afer, bit
[2] § Materials › Existence patterns in non-primate species ↔ 1_result_distribution/values_to_binary.py, the whole file · a weak match · score 0.86 · Canis lupus familiaris, Danio rerio, Mus musculus, Orycteropus afer, species, primate
[3] § Materials › Methods ↔ blast_alignment/3_tblastn_summary_thre_cgc.py, lines 12–66 · score 0.54 · coverage thresholds, protein sequences, primate species, identity, BLAST
[4] § Materials › Detecting genes in primate and nonprimate CDS ↔ blast_alignment/3_tblastn_summary_thre_cgc.py, lines 12–66 · score 0.51 · query coverage, primate species, identity, TBLASTN, filtered, sequences
[5] § Materials › Methods ↔ 1_result_distribution/values_to_binary.py, the whole file · a weak match · score 0.51 · Carlito syrichta, Homo sapiens, species, primate

Paper

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The authors' code

Python · 45 lines · 2.3 KB · no license · 2 matches

import pandas as pd
indices_nonprimate = ['Mus musculus', 'Canis lupus familiaris', 'Orycteropus afer afer', 'Danio rerio']
indices_primate = ['Saimiri boliviensis boliviensis', 'Theropithecus gelada', 'Aotus nancymaae', 'Callithrix jacchus',
'Carlito syrichta', 'Cebus capucinus', 'Cercocebus atys', 'Chlorocebus sabaeus',
'Colobus angolensis palliatus', 'Gorilla gorilla', 'Homo sapiens', 'Macaca fascicularis',
'Macaca mulatta', 'Macaca nemestrina', 'Mandrillus leucophaeus', 'Microcebus murinus',
'Nomascus leucogenys', 'Otolemur garnettii', 'Pan paniscus', 'Pan troglodytes', 'Papio anubis',
'Piliocolobus tephrosceles', 'Pongo abelii', 'Prolemur simus', 'Propithecus coquereli',
'Rhinopithecus bieti', 'Rhinopithecus roxellana', 'Cebus imitator', 'Hylobates moloch',
'Lemur catta', 'Sapajus apella', 'Trachypithecus francoisi']
tissue_list = ["brain", "immu", "BI"]
species_list = ["primate", "nonprimate"]
for tissue in tissue_list:
for species in species_list:
filename = f"cds_brainimmu_{species}_genes_{tissue}"
df = pd.read_csv(filename+".csv", index_col=0)
df_new = df.copy()
indices_to_modify = eval(f"indices_{species}")
df_new.loc[indices_to_modify] = df_new.loc[indices_to_modify].applymap(
lambda x: '0' if x in [' ', ''] or pd.isna(x) else '1')
df_new.to_csv(filename+"_distribution.csv", index=True)
# Select all the columns with all 1s
df_ones = df_new.loc[:, (df_new.loc[indices_to_modify] == '1').all()]
df_ones.to_csv(filename+'_all_ones.csv', index=True)
# Select all the columns with all 0s
df_zeros = df_new.loc[:, (df_new.loc[indices_to_modify] == '0').all()]
df_zeros.to_csv(filename+'_all_zeros.csv', index=True)
df_cols = set(df.columns)
df_ones_cols = set(df_ones.columns)
df_zeros_cols = set(df_zeros.columns)
# Find the columns in df that are not in df_ones or df_zeros
remaining_cols = df_cols - (df_ones_cols.union(df_zeros_cols))
df_remaining = df_new[remaining_cols]
df_remaining.to_csv(filename+'_proper_subset.csv', index=True)

values_to_binary.py at commit 1685361, no license · at the source

Overview

Authors: Xiao Liang^1,2, Andrew F. Teich^1,3,4, Lenwood S. Heath²

ORCID iDs: Xiao Liang, Lenwood S. Heath

Department of Pathology and Cell Biology, Columbia University, New York, New York, United States of America
Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
Department of Neurology, Columbia University, New York, New York, United States of America
Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, New York, United States of America

Institutions: Columbia University (United States); Virginia Tech (United States)

Journal: PloS one, volume 21, issue 5, article e0348713

Dates: received 12 May 2025; accepted 20 April 2026; published online 11 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pone.0348713 · PMID 42113807 · PMCID PMC13160339 · OpenAlex W7160847491

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), cellular / molecular (subfield)

MeSH: Brain*, Genomics*, Immune System*, Animals, Evolution, Molecular, Humans, Phylogeny, Primates, Species Specificity (* major topic)

Journal subjects: Biology and Life Sciences, Organisms, Eukaryota, Animals, Vertebrates, Amniotes, Mammals, Primates, Zoology, Genetics, Gene Expression, Immunology, Immune System, Medicine and Health Sciences, Evolutionary Biology, Evolutionary Genetics, Evolutionary Immunology, Anatomy, Brain, Cerebral Cortex, Cerebellum, Social Sciences, Sociology, Human Families

Topic: Artificial Immune Systems Applications (Biomedical Engineering, Engineering), according to OpenAlex

Citations: not cited yet (Europe PMC); 71 references in the paper

Abstract

The study of human gene evolution along primate and non-primate lineages has attracted increasing attention. Previous research demonstrated associations between the origin of genes and their expression in various tissues, including human-specific genes contributing to the brain. However, the relationship between gene tissue expression and their existence in evolutionary history has rarely been systematically examined from a phylogenetic perspective. In this study, we analyzed 1360 human genes highly expressed in the brain and/or the immune system, along with their distribution in 31 non-human primate species and 4 non-primate species. Two control sets were included for comparison: a randomly selected set of 295 human genes, and a set of 369 human genes each representing a distinct HGNC gene family. We discovered that compared to random and immune-related genes (genes highly expressed in immune system), brain genes (genes highly expressed in brain) have earlier origins, predating primates, and have been preserved across various primate species. We also show that these earlier origins are unlikely to be due to genes that are widely highly expressed in many tissues. Moreover, genes highly expressed in both the brain and immune system display a tendency toward early origin, consistent with other brain genes. This observation indicates that genes highly expressed in both systems are more likely to begin with high expression in the brain, subsequently acquiring high expression in immune tissues, rather than vice versa. We investigated the brain and immune-related genes that are estimated to have emerged among primates, as well as genes that originated before primates but are absent in certain primate clades. Genes in the latter group were either highly expressed across more than ten tissues or specifically expressed in no more than two organ systems, suggesting that these genes may be either broadly essential or highly specialized, performing specific functions in a few systems.

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 5 matches between paragraphs and lines of code.

xlxlxlx/humanmouse_brainimmune_genes

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 168536181028e5742e6b3deb4505c5946db18673, 27 July 2026

Languages: Python (17)

Size: 28 files, 17 scripts

Software Heritage: not archived

Found in: “Data Availability”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: pandas (13 files), NumPy (2 files)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

18 files

0_result_map/1_protein2gene_name.py, Python, 19 lines
0_result_map/2_map_gene2tissue.py, Python, 44 lines
0_result_map/3_overlap_filter_BI.py, Python, 21 lines
1_result_distribution/values_to_binary.py, Python, 45 lines, 2 matches
2_result_pattern_PandNP/primate_nonprimate_matching.py, Python, 39 lines
2_result_pattern_nonprimate_only/nonprimate_pattern_count.py, Python, 40 lines, 1 match
3_result_clades/1_clade_assigner.py, Python, 90 lines
3_result_clades/2_clade_pattern_filter_BI.py, Python, 18 lines
3_result_clades/2_clade_pattern_filter_brain.py, Python, 18 lines
3_result_clades/2_clade_pattern_filter_immu.py, Python, 18 lines
4_result_tissue_count/1_gene_tissue_count.py, Python, 21 lines
4_result_tissue_count/2_df_list_count.py, Python, 24 lines
4_result_tissue_count/3_basic_mean_mode.py, Python, 18 lines
blast_alignment/1_tblastn_protein_cds.py, Python, 60 lines
blast_alignment/2_tblastn_result2sql_cgc.py, Python, 61 lines
blast_alignment/3_tblastn_summary_thre_cgc.py, Python, 76 lines, 2 matches
fasta2genelist.py, Python, 32 lines
README.md, Text, 109 lines

At the source: github.com/xlxlxlx/humanmouse_brainimmune_genes

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

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17 scripts, each with its path and the digest of its content;
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Data

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

Data Availability

All relevant data for this study are publicly available from the GitHub repository (https://github.com/xlxlxlx/humanmouse_brainimmune_genes).

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 9 MeSH terms, 67 references.

Cite

This paper

Liang, X., Teich, A. F., & Heath, L. S. (2026). Comparative genomics of human brain and immune gene preservation across species. PloS one, 21(5), e0348713. https://doi.org/10.1371/journal.pone.0348713

BibTeX

@article{liang2026comparative, author = {Liang, Xiao and Teich, Andrew F. and Heath, Lenwood S.}, title = {{Comparative genomics of human brain and immune gene preservation across species}}, journal = {PloS one}, year = {2026}, month = may, volume = {21}, number = {5}, pages = {e0348713}, publisher = {PLOS}, issn = {1932-6203}, doi = {10.1371/journal.pone.0348713}, url = {https://doi.org/10.1371/journal.pone.0348713}, pmid = {42113807}, pmcid = {PMC13160339} }

RIS

TY - JOUR AU - Liang, Xiao AU - Teich, Andrew F. AU - Heath, Lenwood S. TI - Comparative genomics of human brain and immune gene preservation across species T2 - PloS one J2 - PLoS One PY - 2026 DA - 2026/05/11 VL - 21 IS - 5 SP - e0348713 SN - 1932-6203 PB - PLOS DO - 10.1371/journal.pone.0348713 UR - https://doi.org/10.1371/journal.pone.0348713 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pone.0348713", "type": "article-journal", "title": "Comparative genomics of human brain and immune gene preservation across species", "container-title": "PloS one", "author": [ { "family": "Liang", "given": "Xiao" }, { "family": "Teich", "given": "Andrew F." }, { "family": "Heath", "given": "Lenwood S." } ], "container-title-short": "PLoS One", "volume": "21", "issue": "5", "page": "e0348713", "DOI": "10.1371/journal.pone.0348713", "PMID": "42113807", "PMCID": "PMC13160339", "ISSN": "1932-6203", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pone.0348713", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 11 ] ] } }

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