A global molecular code for birth order and neuronal identity in Drosophila.

Code ↔ Paper

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The 2 matches

[1] § Methods › Atlas annotation › Hemilineage ↔ vignettes/articles/accessing-graphene-server.Rmd, lines 20–91 · score 0.53 · electron microscopy, machine learning, components, transferring, neuronal, mapping
[2] § Methods › Connectomics data analysis › Identification of 14A types matching light-level stainings ↔ R/annotations.R, lines 186–314 · score 0.52 · soma_side, RHS, Clio, IDs, hemilineage, match

Paper

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The authors' code

R Markdown · 116 lines · 6.2 KB · GPL-3.0 · 1 match

---
title: "Accessing the chunked graph server"
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## tl;dr
To set up programmatic access to FlyWire, use `flywire_set_token()` as follows:
```{r, eval=FALSE}
library(fafbseg)
flywire_set_token()
```
and follow the on-screen prompts.
## Background
The main goal of connectomics is to trace (segment) the branching morphology of
neurons in any 3D volume (typically based on electron microscopy) and to
identify the connections (synapses) between them that together define a
connectome.
Neuron segmentation can be either skeleton-based
(in which only the centre line defining each part of the neuronal arbour is traced out)
or voxel-based,
where a filled 3D structure is defined by labelling every voxel with an integer id,
representing its membership of particular object;
this is typically then displayed as a 3D mesh surface. Although skeleton-based
segmentation is more efficient for manual segmentation by humans,
voxel-wise segmentation is more natural for machine learning algorithms.
Furthermore, it generates a richer representation of the neuron
(capturing more of its structural features) and, crucially,
also resolves ambiguities about object identity.
For example when two users manually trace a neuron skeleton,
they will place nodes in different positions and it is not trivial
computationally to determine if they are part of the same object or how to join tracing of independent pieces together.
In contrast in a voxel-wise segmentation every xyz location in the image is
explicitly defined to be part of a specified object.
In a voxel-wise segmentation, each neuron can be considered as a collection of voxels grouped together as connected components. Machine learning based algorithms try to identify such connected components (e.g. using flood filling networks). However machines are not perfect and many segments are wrongly connected. Hence we need a step where humans proofread the segments and alters the connections. These connected components can be readily represented as vertices and the connections between them as edges. The human observer essentially modifies these edges in the graph structure. Note that this graph representation of object segmentation is distinct from the binary tree that represents the neuronal morphology or the connection graph of neurons within a
neural network.
This graph based representation of segmentation also has advantages for synchronous
editing, which can be a major technical issue if many users are to generate or
proof-read segmentation at the same time.
The `CATMAID` web application (for skeleton based tracing) approaches this by
allowing multiple users to edit all neurons using a shared database.
This implementation reduces conflicts by the simple expedient of allowing users
to see each other's work in real time.
However an explicit merge step is needed when conflicting annotations are created
(usually because editing has happened in different database) and this can be computationally complex. The [Seung Lab](https://seunglab.org/) has developed a data structure system called `chunked graph` that lets users modify segmentations (3D voxel-wise) in real-time and allows access to earlier versions of the segmentation. A brief overview of the system is published in [medium](https://medium.com/princeton-systems-course/building-a-multi-user-platform-for-real-time-very-large-graph-editing-ee37268025ad).
In practice an initial *over-segmentation* is generated, consisting of
many "supervoxels". These supervoxels, which are small collections of perhaps
tens to thousands of individual voxels, are effectively immutable but are almost
certain to belong to the same object. It is these supervoxels which are the leaves
on the graph database representation of segmented objects.
The use of supervoxels reduces the size of the graph database compared with the
situation in which individual voxel has to be represented.
So long as human proof-reading is based on the same base segmentation, there are
strategies to merge proof-reading even if this happens asynchronously.
However merging proof-reading based on different *base segmentations* defined by
different underlying supervoxels is still complex;
a similar challenge exists if it is necessary to map proof-reading based on one
base segmentation to a newer (presumably improved) base segmentation.
## Goal
One of the goals in the **fafbseg** package is to provide access between different 3D segmentations of the `FAFB` dataset. Currently available segmentations include `FlyWire` (based on 3D CNNs : [U-Net](https://arxiv.org/abs/1709.02974)) and `GoogleBrain` (based on recursive CNNs : [flood-filling networks](https://www.nature.com/articles/s41592-018-0049-4)). Furthermore to enable location transfer and comparison with `CATMAID` (skeleton based) manual reconstruction. In order to achieve that one needs access to the 3D segmentations which are stored in a graph database [Graphene/PyChunkedGraph](https://github.com/seung-lab/PyChunkedGraph).
They also provide a Python tool called
[CloudVolume](https://github.com/seung-lab/cloud-volume) which provides
programmatic access to the graph database.
## ChunkedGraph token
### Simple Instructions
You gain access to the `Graphene/Chunkedgraph` server by using a *token* which is
granted to you after you authenticate via a Google account. The simplest way to
do this is by doing
```{r, eval=FALSE}
library(fafbseg)
flywire_set_token()
```
This will offer to open your browser, generate a new token and save it in
a standard location. **Note that this will invalidate your previous token**!
### Details
The manual steps that `flywire_set_token()` tries to automate are are as follows
1. Visit https://globalv1.flywire-daf.com/auth/api/v1/refresh_token,
2. Copy the token present there, let's say it was `"xxyyzz"`
3. create a file named `cave-secret.json` at location `~/.cloudvolume/secrets/cave-secret.json`. For example, in Terminal:
```bash
mkdir -p .cloudvolume/secrets/
touch ~/.cloudvolume/secrets/cave-secret.json
open ~/.cloudvolume/secrets/cave-secret.json -e
```
4. Paste in the following contents
```json
{
"token": "xxyyzz"
}
```
5. Save the file
6. Now you have access to the chunked graph server

accessing-graphene-server.Rmd at commit 7dad86d, under GPL-3.0 · at the source

Overview

Authors: Sebastian Cachero¹, Myrto Mitletton¹, Isabella R. Beckett¹, Elizabeth C. Marin², Laia Serratosa Capdevila³, Marina Gkantia², Jelly H. M. Soffers⁴, Haluk Lacin⁴, Gregory S. X. E. Jefferis^1,2, Erika Donà^1,5

ORCID iDs: Sebastian Cachero, Elizabeth C. Marin, Jelly H. M. Soffers, Haluk Lacin, Gregory S. X. E. Jefferis, Erika Donà

Institutions: MRC Laboratory of Molecular Biology (United Kingdom); University of Cambridge (United Kingdom); University of Missouri–Kansas City (United States); National Research Council (Italy)

Journal: Nature, volume 657, issue 8130, pages 202-212

Dates: received 19 August 2025; accepted 11 June 2026; published online 22 July 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41586-026-10797-w · PMID 42486976 · PMCID PMC13538039 · OpenAlex W7170051355

Open access: hybrid, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), drosophila (organism), cellular / molecular (subfield)

Methods: Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Machine learning

Keywords: Cellular neuroscience, Molecular neuroscience, Neural patterning, Cell type diversity, Sexual dimorphism

MeSH: Drosophila melanogaster*, Neurons*, Animals, Apoptosis, Atlases as Topic, Cell Lineage, Connectome, Female, Gene Expression Regulation, Developmental, Larva, Male, Neurogenesis, Sex Characteristics, Transcription Factors, Transcriptome (* major topic)

Topic: Neurobiology and Insect Physiology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex

Funding: European Research Council (649111)

Citations: cited by 3 papers (Europe PMC); 117 references in the paper

Abstract

The assembly of functional neural circuits relies on the generation of diverse neural types with precise molecular identity and connectivity. Unlocking general principles of neuronal specification and wiring across the nervous system requires a systematic and high-resolution characterization of its diversity, recently enabled by advances in single-cell transcriptomics and connectomics. However, linking the molecular identity of neurons to circuit architecture remains a key challenge. Here we present a high-resolution developmental transcriptional atlas for the Drosophila melanogaster nerve cord, the central hub for sensory–motor circuits. With a considerable 38× aggregate coverage relative to its reference connectome1,2, our atlas captures extensive molecular diversity and enables robust alignment to the adult connectome. We identified three developmental principles underlying neuronal diversity in the nerve cord. First, the timing of neurogenesis shapes diversification of molecular identity: embryonic-born neurons diverge faster than larval-born neurons, as also observed in the adult connectome. Second, 17 transcription factors common to neurons from all lineages provide a global molecular identity code for birth order. Lastly, by mapping sex-specific transcriptional profiles to the connectome, we identified female-specific apoptosis and transcriptional divergence as key global drivers of sex specification. By revealing key organizational axes of molecular identity, this atlas opens avenues to dissect the molecular mechanisms underpinning the development and evolution of neural circuits.

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.

natverse/malevnc

License: GPL-3.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: d24c79554a58f7cf985e71d2578ad86423667413, 4 June 2026

Languages: R (41)

Size: 123 files, 41 scripts

Software Heritage: not archived

Found in: the text, “Connectomics data analysis”

Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation

Not found: CITATION.cff

Tools: tidyverse (10 files), reticulate (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

43 files

R/annotate.R, R, 567 lines
R/annotations.R, R, 480 lines, 1 match
R/bodies.R, R, 221 lines
R/clio-auth.R, R, 271 lines
R/data.R, R, 107 lines
R/datasets.R, R, 144 lines
R/dvid.R, R, 207 lines
R/dvidtools.R, R, 348 lines
R/fetch.R, R, 33 lines
R/malevnc-package.R, R, 31 lines
R/meshes.R, R, 149 lines
R/neuprint.R, R, 339 lines
R/rgl.R, R, 59 lines
R/rois.R, R, 100 lines
R/soma.R, R, 151 lines
R/surfaces.R, R, 17 lines
R/svg.R, R, 78 lines
R/url-shortening.R, R, 116 lines
R/urls.R, R, 172 lines
R/utils.R, R, 92 lines
R/xform.R, R, 250 lines
R/xyzpos.R, R, 75 lines
R/zzz.R, R, 7 lines
data-raw/DATASET.R, R, 11 lines
data-raw/manc-meshes.R, R, 67 lines
data-raw/mancsomapos.R, R, 13 lines
tests/spelling.R, R, 3 lines
tests/testthat.R, R, 5 lines
tests/testthat/test-annotations.R, R, 31 lines
tests/testthat/test-bodies.R, R, 12 lines
tests/testthat/test-clio.R, R, 244 lines
tests/testthat/test-dvid.R, R, 46 lines
tests/testthat/test-neuprint.R, R, 28 lines
tests/testthat/test-rgl.R, R, 15 lines
tests/testthat/test-rois.R, R, 11 lines
tests/testthat/test-soma.R, R, 13 lines
tests/testthat/test-svg.R, R, 17 lines
tests/testthat/test-url-shortening.R, R, 7 lines
tests/testthat/test-urls.R, R, 35 lines
tests/testthat/test-xform.R, R, 20 lines
tests/testthat/test-xyzpos.R, R, 7 lines
LICENSE.md, License, 595 lines
README.md, Text, 128 lines

At the source: github.com/natverse/malevnc

flyconnectome/fancr

License: GPL-3.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 7b3d429729627d83dad9387f54294272640e87f9, 28 August 2024

Languages: R (24)

Size: 71 files, 24 scripts

Software Heritage: not archived

Found in: the references

Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 1 notebook

Not found: CITATION.cff

Tools: tidyverse (3 files), reticulate (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

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26 files

R/cave.R, R, 114 lines
R/cloudvolume.R, R, 78 lines
R/coords.R, R, 70 lines
R/data.R, R, 59 lines
R/fancr-package.R, R, 11 lines
R/ids.R, R, 287 lines
R/l2.R, R, 81 lines
R/meshes.R, R, 22 lines
R/partners.R, R, 96 lines
R/urls.R, R, 161 lines
R/utils.R, R, 5 lines
R/xform.R, R, 180 lines
R/zetta-api.R, R, 56 lines
R/zzz.R, R, 13 lines
data-raw/BANC.R, R, 54 lines
data-raw/mirror-banc.R, R, 17 lines
tests/spelling.R, R, 3 lines
tests/testthat.R, R, 4 lines
tests/testthat/test-ids.R, R, 44 lines
tests/testthat/test-partners.R, R, 4 lines
tests/testthat/test-urls.R, R, 16 lines
tests/testthat/test-xform.R, R, 17 lines
tests/testthat/test-zetta-api.R, R, 8 lines
vignettes/BANC.Rmd, R, 130 lines
LICENSE.md, License, 595 lines
README.md, Text, 72 lines

At the source: github.com/flyconnectome/fancr

natverse/fafbseg

License: GPL-3.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 7dad86dabb8d4dee07bb5e49aeabc13a4628036f, 27 September 2026

Languages: R (89)

Size: 220 files, 89 scripts

Software Heritage: archived

Found in: the text, “Connectomics data analysis”

Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 6 notebooks

Not found: CITATION.cff

Tools: reticulate (20 files), tidyverse (19 files), data.table (3 files), igraph (3 files), ggplot2 (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

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R/autosyn.R, R, 1,314 lines
R/braincircuits.R, R, 293 lines
R/brainmaps-api.R, R, 592 lines
R/catmaid-ngl.R, R, 58 lines
R/cave.R, R, 1,073 lines
R/choose-segmentation.R, R, 161 lines
R/cloudvolume-reticulate.R, R, 230 lines
R/cloudvolume.R, R, 162 lines
R/coord-xforms.R, R, 118 lines
R/fafbseg-package.R, R, 69 lines
R/fafbseg-py.R, R, 193 lines
R/fetch-all.R, R, 154 lines
R/flytable-cache.R, R, 464 lines
R/flytable-cam.R, R, 216 lines
R/flytable.R, R, 1,720 lines
R/flywire-api.R, R, 1,352 lines
R/flywire-arrow.R, R, 358 lines
R/flywire-coord.R, R, 167 lines
R/flywire-fetch.R, R, 143 lines
R/flywire-key-point.R, R, 73 lines
R/flywire-mesh.R, R, 93 lines
R/flywire-nuclei.R, R, 226 lines
R/flywire-princeton.R, R, 14 lines
R/flywire-skeleton.R, R, 888 lines
R/flywire-urls.R, R, 354 lines
R/flywire-user.R, R, 32 lines
R/ids.R, R, 532 lines
R/l2cache.R, R, 267 lines
R/merge-groups.R, R, 78 lines
R/meshparty-reticulate.R, R, 80 lines
R/ng-annotations.R, R, 312 lines
R/ng-mesh.R, R, 59 lines
R/ng-raw-io.R, R, 105 lines
R/ng-scene.R, R, 126 lines
R/nginfo.R, R, 97 lines
R/plot.R, R, 72 lines
R/read_merge_info.R, R, 143 lines
R/release-data.R, R, 272 lines
R/skeleton-io.R, R, 223 lines
R/test-utils.R, R, 15 lines
R/urls.R, R, 621 lines
R/utils-pipe.R, R, 11 lines
R/utils-urls.R, R, 21 lines
R/utils.R, R, 1,085 lines
R/vcache.R, R, 41 lines
R/zip.R, R, 83 lines
R/zzz.R, R, 34 lines
data-raw/DATASET.R, R, 29 lines
inst/app/app.R, R, 85 lines
tests/spelling.R, R, 3 lines
tests/testthat.R, R, 12 lines
tests/testthat/api/nglstate/123.R, R, 18 lines
tests/testthat/configure_httptests.R, R, 36 lines
tests/testthat/helper.R, R, 1 line
tests/testthat/test-autosyn.R, R, 182 lines
tests/testthat/test-brainmaps-api.R, R, 28 lines
tests/testthat/test-cave-leaves.R, R, 245 lines
tests/testthat/test-cave.R, R, 179 lines
tests/testthat/test-cloudvolume.R, R, 32 lines
tests/testthat/test-fafbseg-py.R, R, 24 lines
tests/testthat/test-fetch-all.R, R, 7 lines
tests/testthat/test-flytable-cam.R, R, 79 lines
tests/testthat/test-flytable.R, R, 483 lines
tests/testthat/test-flywire-arrow.R, R, 171 lines
tests/testthat/test-flywire-key-point.R, R, 66 lines
tests/testthat/test-flywire-nuclei.R, R, 16 lines
tests/testthat/test-flywire-user.R, R, 14 lines
tests/testthat/test-flywire.R, R, 347 lines
tests/testthat/test-ids.R, R, 192 lines
tests/testthat/test-l2cache.R, R, 106 lines
tests/testthat/test-merge-groups.R, R, 84 lines
tests/testthat/test-ng-annotations.R, R, 43 lines
tests/testthat/test-ng-raw-io.R, R, 24 lines
tests/testthat/test-ng-scene.R, R, 72 lines
tests/testthat/test-nginfo.R, R, 17 lines
tests/testthat/test-release-data.R, R, 7 lines
tests/testthat/test-sirepo.R, R, 8 lines
tests/testthat/test-skeleton.R, R, 11 lines
tests/testthat/test-skeletor.R, R, 18 lines
tests/testthat/test-synapse-query.R, R, 48 lines
tests/testthat/test-urls.R, R, 187 lines
tests/testthat/test-utils.R, R, 320 lines
tests/testthat/test-vcache.R, R, 20 lines
vignettes/articles/FAFB-FlyWire.Rmd, R, 150 lines
vignettes/articles/accessing-graphene-server.Rmd, R, 116 lines, 1 match
vignettes/articles/capturing-ngl-meshes.Rmd, R, 50 lines
vignettes/articles/installing-cloudvolume-meshparty.Rmd, R, 283 lines
vignettes/articles/mesh-to-mesh.Rmd, R, 253 lines
vignettes/fafbseg-catmaid-legacy-info.Rmd, R, 136 lines
LICENSE.md, License, 595 lines
README.md, Text, 111 lines

At the source: github.com/natverse/fafbseg

google/neuroglancer

License: Apache-2.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: da443d25610b23c40c4a46ed9b92129e6203e68e, 22 September 2026

Languages: TypeScript (702), Python (133), C++ (23), JavaScript (20), Shell (11), C/C++ (6), Go (3), NEURON (1), Jupyter (1), Rust (1), C (1)

Size: 2,060 files, 902 scripts

Software Heritage: archived

Found in: the text, “Identification of neurons belonging to fru MARCM”

Holds: README, license file, environment (pyproject.toml, setup.py, uv.lock, docs/pyproject.toml, python/Dockerfile, src/mesh/draco/Dockerfile, src/sliceview/compresso/Dockerfile, src/sliceview/crackle/Dockerfile, src/sliceview/jxl/compile.Dockerfile, src/sliceview/jxl/optimize.Dockerfile, src/sliceview/png/Dockerfile), tests, continuous integration, documentation, 1 notebook

Not found: CITATION.cff

Tools: NumPy (67 files), Pillow (3 files), SciPy (3 files), pandas (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

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build_tools/after-version-change.ts, TypeScript, 48 lines
build_tools/build-package.ts, TypeScript, 247 lines
build_tools/cli.ts, TypeScript, 266 lines
build_tools/postpack.ts, TypeScript, 9 lines
build_tools/rspack/configuration_with_define.js, JavaScript, 13 lines
build_tools/rspack/rspack_config_from_cli.js, JavaScript, 12 lines
build_tools/update-conditions.ts, TypeScript, 147 lines
build_tools/update-example-dependencies.ts, TypeScript, 30 lines
build_tools/vitest/build_fake_gcs_server.ts, TypeScript, 57 lines
build_tools/vitest/fake_ngauth_server.ts, TypeScript, 98 lines
build_tools/vitest/polyfill-browser-globals-in-node.ts, TypeScript, 16 lines
build_tools/vitest/python_tools.ts, TypeScript, 26 lines
build_tools/vitest/test_data_server.ts, TypeScript, 51 lines
build_tools/vitest/vitest-environment-jsdom-patched/index.ts, TypeScript, 50 lines
cors_webserver.py, Python, 62 lines
docs/concepts/data_source_coordinate_space.screenshot.py, Python, 59 lines
docs/concepts/data_source_coordinate_transform.screenshot.py, Python, 59 lines
docs/conf.py, Python, 344 lines
docs/generate_logo.py, Python, 122 lines
docs/update_intersphinx_inventories.py, Python, 42 lines
docs/user-guide/navigation_mouse_cross_section_translate.video.py, Python, 28 lines
docs/user-guide/navigation_mouse_cross_section_zoom.video.py, Python, 30 lines
eslint.config.js, JavaScript, 124 lines
examples/parcel/parcel-project-built/src/index.js, JavaScript, 3 lines
examples/parcel/parcel-project-source/src/index.js, JavaScript, 3 lines
examples/rsbuild/rsbuild-project-built/rsbuild.config.ts, TypeScript, 45 lines
examples/rsbuild/rsbuild-project-built/src/index.js, JavaScript, 3 lines
examples/rsbuild/rsbuild-project-source/rsbuild.config.ts, TypeScript, 60 lines
examples/rsbuild/rsbuild-project-source/src/index.js, JavaScript, 3 lines
examples/rspack/rspack-project-built/rspack.config.js, JavaScript, 46 lines
examples/rspack/rspack-project-built/src/index.js, JavaScript, 3 lines
examples/rspack/rspack-project-source/rspack.config.js, JavaScript, 61 lines
examples/rspack/rspack-project-source/src/index.js, JavaScript, 3 lines
examples/vite/vite-project-built/vite.config.ts, TypeScript, 47 lines
examples/vite/vite-project-source/vite.config.ts, TypeScript, 51 lines
examples/webpack/webpack-project-built/src/index.js, JavaScript, 3 lines
examples/webpack/webpack-project-built/webpack.config.js, JavaScript, 48 lines
examples/webpack/webpack-project-source/src/index.js, JavaScript, 3 lines
examples/webpack/webpack-project-source/webpack.config.js, JavaScript, 58 lines
ngauth_server/auth.go, Go, 521 lines
ngauth_server/bounded_access_token.go, Go, 95 lines
ngauth_server/go.mod, NEURON, 30 lines
ngauth_server/main.go, Go, 52 lines
noxfile.py, Python, 195 lines
playwright.config.ts, TypeScript, 22 lines
python/build_tools/cibuildwheel_linux_cache_setup.sh, Shell, 12 lines
python/copy_openmesh_deps.py, Python, 49 lines
python/examples/agglomeration_split_tool_csv_to_sqlite.py, Python, 83 lines
python/examples/example.py, Python, 70 lines
python/examples/example_action.py, Python, 30 lines
python/examples/example_add_cube.py, Python, 55 lines
python/examples/example_annotation_properties.py, Python, 66 lines
python/examples/example_cdf.py, Python, 58 lines
python/examples/example_coordinate_arrays.py, Python, 58 lines
python/examples/example_coordinate_transform.py, Python, 46 lines
python/examples/example_cross_section.py, Python, 40 lines
python/examples/example_dask.py, Python, 53 lines
python/examples/example_fixed_token.py, Python, 20 lines
python/examples/example_grid_layout.py, Python, 39 lines
python/examples/example_layer_side_panels.py, Python, 39 lines
python/examples/example_local_volume_coordinate_arrays.py, Python, 63 lines
python/examples/example_overlay.py, Python, 49 lines
python/examples/example_partial_viewport.py, Python, 87 lines
python/examples/example_precomputed_gcs.py, Python, 16 lines
python/examples/example_read_precomputed_annotations.py, Python, 23 lines
python/examples/example_row_layout.py, Python, 29 lines
python/examples/example_signed_int.py, Python, 45 lines
python/examples/example_single_mesh_layer.py, Python, 16 lines
python/examples/example_skeletons.py, Python, 76 lines
python/examples/example_toggle_visibility.py, Python, 51 lines
python/examples/example_tool.py, Python, 24 lines
python/examples/extend_segments_tool.py, Python, 290 lines
python/examples/flood_filling_simulation.py, Python, 250 lines
python/examples/interactive_inference.py, Python, 115 lines
python/examples/jupyter-notebook-demo.ipynb, Jupyter, 177 lines
python/examples/synaptic_partners.py, Python, 192 lines
python/examples/webdriver_example.py, Python, 32 lines
python/examples/write_annotations.py, Python, 207 lines
python/ext/src/_neuroglancer.cc, C++, 208 lines
python/ext/src/compress_segmentation.cc, C++, 232 lines
python/ext/src/compress_segmentation.h, C/C++, 173 lines
python/ext/src/compress_segmentation_test.cc, C++, 229 lines
python/ext/src/generate_marching_cubes_tables.py, Python, 72 lines
python/ext/src/mesh_objects.cc, C++, 147 lines
python/ext/src/mesh_objects.h, C/C++, 38 lines
python/ext/src/on_demand_object_mesh_generator.cc, C++, 226 lines
python/ext/src/on_demand_object_mesh_generator.h, C/C++, 61 lines
python/ext/src/openmesh_dependencies.cc, C++, 22 lines
python/ext/src/voxel_mesh_generator.cc, C++, 479 lines
python/ext/src/voxel_mesh_generator.h, C/C++, 246 lines
python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Core/Mesh/ArrayKernel.cc, C++, 260 lines
python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Core/Mesh/ArrayKernelT.cc, C++, 315 lines
python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Core/Mesh/PolyConnectivity.cc, C++, 1,233 lines
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python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Core/Mesh/TriConnectivity.cc, C++, 500 lines
python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Core/Mesh/TriMeshT.cc, C++, 93 lines
python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Core/System/config.h, C/C++, 111 lines
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python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Core/Utils/BaseProperty.cc, C++, 59 lines
python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Core/Utils/SingletonT.cc, C++, 85 lines
python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Tools/Decimater/BaseDecimaterT.cc, C++, 310 lines
python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Tools/Decimater/DecimaterT.cc, C++, 369 lines
python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Tools/Decimater/ModQuadricT.cc, C++, 159 lines
python/ext/third_party/openmesh/OpenMesh/src/OpenMesh/Tools/Decimater/Observer.cc, C++, 98 lines
python/neuroglancer/__init__.py, Python, 122 lines
python/neuroglancer/async_util.py, Python, 86 lines
python/neuroglancer/boss_credentials.py, Python, 55 lines
python/neuroglancer/chunks.py, Python, 39 lines
python/neuroglancer/cli.py, Python, 93 lines
python/neuroglancer/coordinate_space.py, Python, 320 lines
python/neuroglancer/credentials_provider.py, Python, 69 lines
python/neuroglancer/default_credentials_manager.py, Python, 56 lines
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python/neuroglancer/dvid_credentials.py, Python, 57 lines
python/neuroglancer/equivalence_map.py, Python, 193 lines
python/neuroglancer/futures.py, Python, 63 lines
python/neuroglancer/google_credentials.py, Python, 85 lines
python/neuroglancer/json_utils.py, Python, 59 lines
python/neuroglancer/json_wrappers.py, Python, 649 lines
python/neuroglancer/local_volume.py, Python, 386 lines
python/neuroglancer/random_token.py, Python, 21 lines
python/neuroglancer/read_precomputed_annotations.py, Python, 642 lines
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python/neuroglancer/read_precomputed_skeletons.py, Python, 102 lines
python/neuroglancer/screenshot.py, Python, 51 lines
python/neuroglancer/segment_colors.py, Python, 91 lines
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python/neuroglancer/tool/edit_states.py, Python, 139 lines
python/neuroglancer/tool/filter_bodies.py, Python, 217 lines
python/neuroglancer/tool/mask_tool.py, Python, 319 lines
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python/neuroglancer/tool/save_meshes.py, Python, 92 lines
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python/neuroglancer/trackable_state.py, Python, 217 lines
python/neuroglancer/url_state.py, Python, 158 lines
python/neuroglancer/viewer.py, Python, 56 lines
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python/tests/on_demand_mesh_generator_test.py, Python, 56 lines
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python/tests/skeleton_options_test.py, Python, 79 lines
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testdata/codec/crackle/generate_crackle_examples.py, Python, 94 lines
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testdata/datasource/n5/generate_n5.py, Python, 69 lines
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typings/index.d.ts, TypeScript, 2 lines
typings/nifti-reader-js.d.ts, TypeScript, 341 lines
typings/raw.d.ts, TypeScript, 4 lines
vitest.workspace.ts, TypeScript, 130 lines
LICENSE, License, 202 lines
README.md, Text, 177 lines

At the source: github.com/google/neuroglancer/

FlyNeuroAtlas/DevSeqVNC

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: bed37b484f2ef27fa52bec22e947f5770b4e3a38, 20 July 2026

Languages: R (1)

Size: 14 files, 1 script

Software Heritage: not archived

Found in: “Code availability”

Holds: README, 1 notebook

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

Tools: ggplot2 (1 file), Seurat (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

2 files

Code/File_Download_and_RDS_Data_Inspection.Rmd, R, 299 lines
README.md, Text, 38 lines

At the source: github.com/FlyNeuroAtlas/DevSeqVNC

Zenodo 17185431

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: the references

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 410)

27 September 2026: the link is dead (HTTP 410)

At the source:

Code availability

Code with instructions to download and inspect data from GEO and SCope is available at GitHub (https://github.com/FlyNeuroAtlas/DevSeqVNC). Additional code is available on request.

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:

6 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
1,057 scripts, each with its path and the digest of its content;
2 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

bioproject:PRJNA1297747, at NCBI BioProject; found in “Data availability”
geo:GSE304221, at NCBI GEO; found in “Data availability”
zenodo:17183028, at Zenodo; found in “Data availability”
zenodo:17183029, at Zenodo; found in the references
zenodo:17184089, at Zenodo; found in “Data availability”
zenodo:17185432, at Zenodo; found in “Data availability”

Data availability

10x scRNA-seq data collected in this study (FASTQ files, CellRanger outputs and processed and annotated Seurat objects) are available at the Gene Expression Omnibus (GEO) under accession number GSE304221 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE304221), and at BioProject under accession number PRJNA1297747 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1297747). Annotated loom files are also accessible at Zenodo (full dataset115: https://zenodo.org/records/17183028; neurons116: https://zenodo.org/records/17184089; glia117: https://zenodo.org/records/17185432) and at SCope (https://scope.aertslab.org/#/HundredDrills/*/welcome), an interactive web platform for exploring single-cell datasets. The SCope interface allows rapid inspection of marker genes and cell-level information, making it straightforward to interact with the data. Data presented here, including links between the atlas and the connectome, can also be browsed on a dedicated webpage (https://flyem.mrc-lmb.cam.ac.uk/VNCatlas). Previously published 10x scRNA-seq data used in this study26 are available at GEO under accession number GSE141807 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE141807). Additional imaging data are available on request.

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 2, 28 September 2026

Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 5 keywords, 15 MeSH terms, 1 funder, 116 references.

Cite

This paper

Cachero, S., Mitletton, M., Beckett, I. R., Marin, E. C., Capdevila, L. S., Gkantia, M., Soffers, J. H. M., Lacin, H., Jefferis, G. S. X. E., & Donà, E. (2026). A global molecular code for birth order and neuronal identity in Drosophila. Nature, 657(8130), 202-212. https://doi.org/10.1038/s41586-026-10797-w

BibTeX

@article{cachero2026global, author = {Cachero, Sebastian and Mitletton, Myrto and Beckett, Isabella R. and Marin, Elizabeth C. and Capdevila, Laia Serratosa and Gkantia, Marina and Soffers, Jelly H. M. and Lacin, Haluk and Jefferis, Gregory S. X. E. and Donà, Erika}, title = {{A global molecular code for birth order and neuronal identity in Drosophila}}, journal = {Nature}, year = {2026}, month = jul, volume = {657}, number = {8130}, pages = {202--212}, publisher = {Nature Portfolio}, issn = {0028-0836}, doi = {10.1038/s41586-026-10797-w}, url = {https://doi.org/10.1038/s41586-026-10797-w}, pmid = {42486976}, pmcid = {PMC13538039} }

RIS

TY - JOUR AU - Cachero, Sebastian AU - Mitletton, Myrto AU - Beckett, Isabella R. AU - Marin, Elizabeth C. AU - Capdevila, Laia Serratosa AU - Gkantia, Marina AU - Soffers, Jelly H. M. AU - Lacin, Haluk AU - Jefferis, Gregory S. X. E. AU - Donà, Erika TI - A global molecular code for birth order and neuronal identity in Drosophila T2 - Nature J2 - Nature PY - 2026 DA - 2026/07/22 VL - 657 IS - 8130 SP - 202 EP - 212 SN - 0028-0836 PB - Nature Portfolio DO - 10.1038/s41586-026-10797-w UR - https://doi.org/10.1038/s41586-026-10797-w LA - en ER -

CSL-JSON

{ "id": "10.1038/s41586-026-10797-w", "type": "article-journal", "title": "A global molecular code for birth order and neuronal identity in Drosophila", "container-title": "Nature", "author": [ { "family": "Cachero", "given": "Sebastian" }, { "family": "Mitletton", "given": "Myrto" }, { "family": "Beckett", "given": "Isabella R." }, { "family": "Marin", "given": "Elizabeth C." }, { "family": "Capdevila", "given": "Laia Serratosa" }, { "family": "Gkantia", "given": "Marina" }, { "family": "Soffers", "given": "Jelly H. M." }, { "family": "Lacin", "given": "Haluk" }, { "family": "Jefferis", "given": "Gregory S. X. E." }, { "family": "Donà", "given": "Erika" } ], "container-title-short": "Nature", "volume": "657", "issue": "8130", "page": "202-212", "DOI": "10.1038/s41586-026-10797-w", "PMID": "42486976", "PMCID": "PMC13538039", "ISSN": "0028-0836", "publisher": "Nature Portfolio", "URL": "https://doi.org/10.1038/s41586-026-10797-w", "language": "en", "issued": { "date-parts": [ [ 2026, 7, 22 ] ] } }

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A global molecular code for birth order and neuronal identity in Drosophila.

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Paper

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Abstract

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natverse/malevnc

flyconnectome/fancr

natverse/fafbseg

google/neuroglancer

FlyNeuroAtlas/DevSeqVNC

Zenodo 17185431

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