ArticleNature communications2016
Deep phenotyping unveils hidden traits and genetic relations in subtle mutants.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- An automated high-resolution screening platform identifies regulators of anchor cell invasion inScience advances · 2026Article
- Network modularity reveals context and state-dependent reorganization of time-varying functional connectivity in single-cell resolved neural activity recordings.bioRxiv : the preprint server for biology · 2025Article
- WormSNAP: A software for fast, accurate, and unbiased detection of fluorescent puncta in C. elegans.PLoS computational biology · 2025Article
- Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Article
- Morphological hallmarks of dopaminergic neurodegeneration are associated with altered neuron function in Caenorhabditis elegans.Neurotoxicology · 2024Article
- Automated scoring of nematode nictation on a textured background.bioRxiv : the preprint server for biology · 2023Article
- Article
- IdentifyingiScience · 2022Article
- Microfluidics for understanding model organisms.Nature communications · 2022Review
- Reciprocal EGFR signaling in the anchor cell ensures precise inter-organ connection during Caenorhabditis elegans vulval morphogenesis.Development (Cambridge, England) · 2022Article
- An Overview of Organs-on-Chips Based on Deep Learning.Research (Washington, D.C.) · 2022Review
- A Deep Learning Analysis Reveals Nitrogen-Doped Graphene Quantum Dots Damage Neurons of NematodeNanomaterials (Basel, Switzerland) · 2021Article
- Microswimmer Combing: Controlling Interfacial Dynamics for Open-Surface Multifunctional Screening of Small Animals.Advanced healthcare materials · 2021Article
- Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock.BMC biology · 2020Article
- Multimodal Stimulation in a Microfluidic Device Facilitates Studies of Interneurons in Sensory Integration in C. elegans.Small (Weinheim an der Bergstrasse, Germany) · 2020Article
- Systematic phenomics analysis of autism-associated genes reveals parallel networks underlying reversible impairments in habituation.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Microfluidic Technologies for High Throughput Screening Through Sorting and On-Chip Culture ofMolecules (Basel, Switzerland) · 2019Review
- Digging deeper: methodologies for high-content phenotyping in Caenorhabditis elegans.Lab animal · 2019Review
- In vivo measurements reveal a single 5'-intron is sufficient to increase protein expression level in Caenorhabditis elegans.Scientific reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Discovering mechanistic insights from phenotypic information is critical for the understanding of biological processes. For model organisms, unlike in cell culture, this is currently bottlenecked by the non-quantitative nature and perceptive biases of human observations, and the limited number of reporters that can be simultaneously incorporated in live animals. An additional challenge is that isogenic populations exhibit significant phenotypic heterogeneity. These difficulties limit genetic approaches to many biological questions. To overcome these bottlenecks, we developed tools to extract complex phenotypic traits from images of fluorescently labelled subcellular landmarks, using C. elegans synapses as a test case. By population-wide comparisons, we identified subtle but relevant differences inaccessible to subjective conceptualization. Furthermore, the models generated testable hypotheses of how individual alleles relate to known mechanisms or belong to new pathways. We show that our model not only recapitulates current knowledge in synaptic patterning but also identifies novel alleles overlooked by traditional methods.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.