OSCR

Single-cell analysis of inhibitory efferent neurons of the zebrafish lateral line.

Code ↔ Paper

1 match 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 1 match
  1. [1] § Materials and methods › Sequence data analysis › Post mapping QC. ↔ modules/nf-core/custom/multiqccustombiotype/templates/mqc_features_stat.py, lines 123–155 · score 0.62 · rRNA, MultiQC, platform, nf core, genes

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 155 lines · 5.1 KB · MIT · 1 match

  1. #!/usr/bin/env python
  3. # Combine featureCounts biotype counts with a header for MultiQC,
  4. # then calculate feature percentages for the general stats table.
  5. #
  6. # Written by Senthilkumar Panneerselvam and released under the MIT license.
  7. # Adapted for nf-core/modules by Jonathan Manning.
  9. import argparse
  10. import logging
  11. import os
  12. import platform
  13. import shlex
  14. import sys
  16. # Create a logger
  17. logging.basicConfig(format="%(name)s - %(asctime)s %(levelname)s: %(message)s")
  18. logger = logging.getLogger(__file__)
  19. logger.setLevel(logging.INFO)
  21. # Template variables from Nextflow
  22. count_file = "${count}"
  23. header_file = "${header}"
  24. prefix = "${task.ext.prefix}" if "${task.ext.prefix}" != "null" else "${meta.id}"
  25. sample_name = "${meta.id}"
  28. def parse_ext_args(args_string):
  29. """Parse arguments supplied via Nextflow's ext.args."""
  30. if args_string in (None, "", "null"):
  31. args_string = ""
  32. parser = argparse.ArgumentParser()
  33. parser.add_argument(
  34. "--max_biotypes",
  35. type=int,
  36. default=100,
  37. help="Maximum number of biotype rows to write to the MultiQC TSV. "
  38. "Above this, the TSV is dropped so MultiQC does not try to plot a "
  39. "category per gene.",
  40. )
  41. return parser.parse_args(shlex.split(args_string))
  44. ext_args = parse_ext_args("${task.ext.args}")
  45. max_biotypes = ext_args.max_biotypes
  48. def prepare_biotype_counts(count_file, header_file, prefix):
  49. """Cut gene ID and count columns from featureCounts output, prepend header."""
  50. outfile = f"{prefix}.biotype_counts_mqc.tsv"
  52. with open(header_file) as hf:
  53. header = hf.read()
  55. with open(count_file) as cf:
  56. lines = cf.readlines()[2:] # skip featureCounts header lines
  58. n_rows = 0
  59. with open(outfile, "w") as of:
  60. of.write(header)
  61. for line in lines:
  62. fields = line.strip().split("\\t")
  63. if len(fields) >= 7:
  64. of.write(f"{fields[0]}\\t{fields[6]}\\n")
  65. n_rows += 1
  67. return outfile, n_rows
  70. mqc_main = """#id: 'biotype-gs'
  71. #plot_type: 'generalstats'
  72. #pconfig:"""
  74. mqc_pconf = """# percent_{ft}:
  75. # title: '% {ft}'
  76. # namespace: 'Biotype Counts'
  77. # description: '% reads overlapping {ft} features'
  78. # max: 100
  79. # min: 0
  80. # scale: 'RdYlGn-rev'"""
  83. def mqc_feature_stat(bfile, features, outfile, sname=None):
  84. """Calculate features percentage for biotype counts."""
  85. if not sname:
  86. sname = os.path.splitext(os.path.basename(bfile))[0]
  88. fcounts = {}
  89. try:
  90. with open(bfile) as bfl:
  91. for ln in bfl:
  92. if ln.startswith("#"):
  93. continue
  94. parts = ln.strip().split("\\t")
  95. if len(parts) == 2:
  96. ft, cn = parts
  97. fcounts[ft] = float(cn)
  98. except Exception:
  99. logger.error(f"Trouble reading the biocount file {bfile}")
  100. return
  102. total_count = sum(fcounts.values())
  103. if total_count == 0:
  104. logger.error("No biocounts found, exiting")
  105. return
  107. fpercent = {f: (fcounts[f] / total_count) * 100 if f in fcounts else 0 for f in features}
  108. if len(fpercent) == 0:
  109. logger.error(f"None of given features '{', '.join(features)}' found in the biocount file {bfile}")
  110. return
  112. out_head, out_value, out_mqc = ("Sample", f"'{sname}'", mqc_main)
  113. for ft, pt in fpercent.items():
  114. out_head = f"{out_head}\\tpercent_{ft}"
  115. out_value = f"{out_value}\\t{pt}"
  116. out_mqc = f"{out_mqc}\\n{mqc_pconf.format(ft=ft)}"
  118. with open(outfile, "w") as ofl:
  119. out_final = "\\n".join([out_mqc, out_head, out_value]).strip()
  120. ofl.write(out_final + "\\n")
  123. if __name__ == "__main__":
  124. # Step 1: Prepare biotype counts TSV from featureCounts output
  125. biotype_file, n_biotypes = prepare_biotype_counts(count_file, header_file, prefix)
  127. # Step 2: Fail loudly if the first column of the featureCounts output has
  128. # too many unique values. MultiQC cannot render a bar plot with thousands
  129. # of categories, so a high-cardinality group attribute (e.g. a per-gene
  130. # identifier) passed to `featureCounts -g` is almost certainly a config
  131. # mistake upstream.
  132. if n_biotypes > max_biotypes:
  133. logger.error(
  134. f"Too many categories in '{count_file}': {n_biotypes} > "
  135. f"--max_biotypes={max_biotypes}. Column 1 of this file reflects "
  136. "the attribute passed to `featureCounts -g`; if a high-cardinality "
  137. "attribute was used (e.g. a per-gene identifier), MultiQC cannot "
  138. "plot it. Re-run featureCounts with a lower-cardinality attribute "
  139. "(e.g. gene_biotype, gene_type), or raise the limit via "
  140. "`ext.args = '--max_biotypes N'` if this is expected."
  141. )
  142. sys.exit(1)
  144. # Step 3: Calculate rRNA percentage for MultiQC general stats
  145. mqc_feature_stat(
  146. biotype_file,
  147. ["rRNA"],
  148. f"{prefix}.biotype_counts_rrna_mqc.tsv",
  149. sname=sample_name,
  150. )
  152. # Versions
  153. with open("versions.yml", "w") as f:
  154. f.write('"${task.process}":\\n')
  155. f.write(f" python: {platform.python_version()}\\n")

mqc_features_stat.py at commit a1fcddd, under MIT · at the source

Overview

Authors: Remy Manuel1, Aikeremu Ahemaiti1, Melek Umay Tuz-Sasik1, Henrik Boije1
ORCID iDs: Remy Manuel, Melek Umay Tuz-Sasik
  1. Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
Institutions: Uppsala University (Sweden)
Journal: PloS one, volume 21, issue 4, article e0346255
Dates: received 3 October 2025; accepted 17 March 2026; published online 8 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0346255 · PMID 41950195 · PMCID PMC13061224 · OpenAlex W7152133821
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: zebrafish (organism), cellular / molecular (subfield)
Methods: Statistics
MeSH: Lateral Line System*, Neurons, Efferent*, Single-Cell Analysis*, Zebrafish*, Animals, Larva, Single-Cell Gene Expression Analysis (* major topic)
Journal subjects: Biology and Life Sciences, Cell Biology, Cellular Types, Animal Cells, Neurons, Efferent Neurons, Neuroscience, Cellular Neuroscience, Genetics, Gene Expression, Research and Analysis Methods, Animal Studies, Experimental Organism Systems, Model Organisms, Zebrafish, Animal Models, Organisms, Eukaryota, Animals, Vertebrates, Fish, Osteichthyes, Zoology, Developmental Biology, Life Cycles, Larvae, Cognitive Science, Cognitive Psychology, Perception, Sensory Perception, Psychology, Social Sciences, Cell Physiology, Junctional Complexes, Gap Junctions, Anatomy, Nervous System, Synapses, Medicine and Health Sciences, Physiology, Electrophysiology, Neurophysiology, Axon Guidance, Developmental Neuroscience
Topic: Hearing, Cochlea, Tinnitus, Genetics (Sensory Systems, Neuroscience), according to OpenAlex
Funding: Royal Swedish Academy of Sciences; Kjell and Marta Beijers Foundation; Jeanssons Foundation; Carl Tryggers Foundation; Swedish Brain Foundation; Magnus Bergvalls Foundation; Ake Wibergs Foundation; Olle Engkvist Stiftelse; Ragnar Söderberg Foundation; Swedish Foundation for Strategic Research; Knut and Alice Wallenberg Foundation; Swedish Research Council
Citations: cited by 2 papers (Europe PMC); 92 references in the paper

Abstract

The zebrafish lateral line system is a sensory network made up of neuromasts, which contain hair cells to detect water flow. Neuromasts signal via sensory afferent neurons and their activity is modulated by efferent neurons. Inhibitory efferent neurons consist of REN, ROLE, and RELL cells and previous work has shown that neuromasts can be innervated by multiple efferent neurons, suggesting potential functional differences. To explore this, we performed single-cell RNA sequencing on REN, ROLE, and RELL neurons in 5-day-old zebrafish larvae. GO analysis across differentially expressed genes did not reveal pathways that suggest differences in cellular function. Comparing markers for neurotransmitter phenotype showed all inhibitory efferent neurons to be cholinergic, but also expressed genes related to other neurotransmitters. Expression of selected genes related to rhombomere location, axon guidance, or gap junctions was similar across efferent neurons. Expression of genes encoding proteins related to membrane potential suggest that REN neurons might be more sensitive to glutamate and may have different action potential dynamics, although functional validation remains to be done. In addition, we assessed neuromast innervation by ROLE and RELL neurons. We found that both ROLE and RELL neurons synapse to approximately 50% of hair cells within a neuromast, compared to approximately 75% innervation by all inhibitory efferent neurons combined. In addition, we did not observe flow polarity bias by innervating efferent axons. However, we did find that RELL neurons had a lower number of synaptic boutons compared to ROLE, which may reflect differences in synaptic output capacity. Taken that our transcriptional analysis did not reveal major intrinsic molecular differences, but we did observe differences in neuromast innervation, raises the possibility that functional differences, if present, may come from upstream inputs. Future work, such as retrograde tracing, could help map these input partners and clarify how different types of efferent neurons contribute to sensory modulation.

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 1 match between paragraphs and lines of code.

Zenodo 1400710

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “Reference genome.”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: DESeq2 (1 file), ggplot2 (1 file), Nextflow (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
15 files
At the source: doi.org/10.5281/zenodo.1400710

nf-core

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Reference genome.”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: github.com/nf-core/

nf-core/rnaseq

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a1fcdddd3b826fe46eb46f0479f2ff8a7815af05, 23 September 2026
Languages: Python (6), R (4), Shell (2), Perl (1)
Size: 851 files, 13 scripts
Software Heritage: archived
Found in: the Zenodo archive record
Holds: README, license file, environment (.devcontainer/devcontainer.json, .devcontainer/setup.sh, modules/local/deseq2_qc/environment.yml, modules/local/preprocess_transcripts_fasta_gencode/environment.yml, modules/local/star_genomeparams_upgrade/environment.yml, modules/nf-core/dupradar/environment.yml, modules/nf-core/fastp/environment.yml, modules/nf-core/fastqc/environment.yml, modules/nf-core/gffread/environment.yml, modules/nf-core/gunzip/environment.yml, modules/nf-core/multiqc/environment.yml, modules/nf-core/ribodetector/environment.gpu.yml), continuous integration, documentation
Not found: CITATION.cff, tests
Tools: DESeq2 (1 file), ggplot2 (1 file), Nextflow (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
15 files
At the source: github.com/nf-core/rnaseq/tree/3.27.0

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 26 scripts, each with its path and the digest of its content;
  • 1 match 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

All relevant data for this study are publicly available from the European Nucleotide Archive (ENA) repository (https://www.ebi.ac.uk/ena/browser/view/PRJEB110328).

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 7 MeSH terms, 12 funders, 89 references.

Cite

This paper

Manuel, R., Ahemaiti, A., Tuz-Sasik, M. U., & Boije, H. (2026). Single-cell analysis of inhibitory efferent neurons of the zebrafish lateral line. PloS one, 21(4), e0346255. https://doi.org/10.1371/journal.pone.0346255

BibTeX

@article{manuel2026single,
author = {Manuel, Remy and Ahemaiti, Aikeremu and Tuz-Sasik, Melek Umay and Boije, Henrik},
title = {{Single-cell analysis of inhibitory efferent neurons of the zebrafish lateral line}},
journal = {PloS one},
year = {2026},
month = apr,
volume = {21},
number = {4},
pages = {e0346255},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0346255},
url = {https://doi.org/10.1371/journal.pone.0346255},
pmid = {41950195},
pmcid = {PMC13061224}
}

RIS

TY - JOUR
AU - Manuel, Remy
AU - Ahemaiti, Aikeremu
AU - Tuz-Sasik, Melek Umay
AU - Boije, Henrik
TI - Single-cell analysis of inhibitory efferent neurons of the zebrafish lateral line
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/04/08
VL - 21
IS - 4
SP - e0346255
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0346255
UR - https://doi.org/10.1371/journal.pone.0346255
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pone.0346255",
"type": "article-journal",
"title": "Single-cell analysis of inhibitory efferent neurons of the zebrafish lateral line",
"container-title": "PloS one",
"author": [
{
"family": "Manuel",
"given": "Remy"
},
{
"family": "Ahemaiti",
"given": "Aikeremu"
},
{
"family": "Tuz-Sasik",
"given": "Melek Umay"
},
{
"family": "Boije",
"given": "Henrik"
}
],
"container-title-short": "PLoS One",
"volume": "21",
"issue": "4",
"page": "e0346255",
"DOI": "10.1371/journal.pone.0346255",
"PMID": "41950195",
"PMCID": "PMC13061224",
"ISSN": "1932-6203",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pone.0346255",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
8
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1172/jci196689 [code]
AAV-mediated gene therapy demonstrates phenotypic rescue in a mouse model of Cockayne syndrome.
Journal: The Journal of clinical investigation
In common: Nextflow, DESeq2, pheatmap, 1 other tool, 2 references
[2] doi:10.1080/19490976.2026.2659400 [code]
Microbiome signatures correlate with diet-mediated ADHD symptom reduction.
Journal: Gut microbes
In common: Nextflow, DESeq2, pheatmap, 1 other tool, 1 reference
[3] doi:10.1186/s12915-026-02643-2 [code]
Circulating microRNAs reveal egg-brain crosstalk and a brain-predominant microRNA linked to the onset of the next reproductive cycle in iteroparous salmonids.
Journal: BMC biology
In common: Nextflow, DESeq2, pheatmap, 1 other tool, cellular / molecular
[4] doi:10.1002/hed.70290 [code]
Cigarette Smoke Exposure Attenuates T2R-Mediated Apoptosis in Head and Neck Squamous Cell Carcinoma.
Journal: Head & neck
In common: Nextflow, DESeq2, pheatmap, 1 other tool, cellular / molecular
[5] doi:10.21203/rs.3.rs-9927928/v1 [code]
Genome-wide and allele-resolved maps of the radial architecture of the mouse genome
Journal: Research Square (preprint)
In common: Nextflow, DESeq2, pheatmap, 1 other tool
[6] doi:10.1371/journal.pgen.1012090 [code]
Delta family protocadherins contribute to protoglomerular targeting of olfactory sensory neuron axons in the olfactory bulb.
Journal: PLoS genetics
In common: DESeq2, pheatmap, ggplot2, zebrafish, cellular / molecular, 1 reference
[7] doi:10.1038/s41514-026-00397-3 [code]
Nasal administration of Protollin enhances monocyte phagocytosis and decreases CD8<sup>+</sup> T cell cytotoxicity in subjects with early Alzheimer's disease: a Phase 1 clinical trial.
Journal: npj aging
In common: DESeq2, pheatmap, ggplot2, 2 references
[8] doi:10.1038/s42003-026-10252-6 [code]
RET signaling as a mediator of estrogen receptor positive breast cancer brain metastasis.
Journal: Communications biology
In common: DESeq2, pheatmap, ggplot2, cellular / molecular, 1 reference
[9] doi:10.1111/adb.70179 [code]
Transcriptional Response to Chronic Long-Access Fentanyl Self-Administration in Rat Habenula and Amygdala.
Journal: Addiction biology
In common: Nextflow, pheatmap, ggplot2, cellular / molecular
[10] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: DESeq2, pheatmap, ggplot2, zebrafish, cellular / molecular

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.