Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Automatic image analysis for gene expression patterns of fly embryos.

Hanchuan Peng1, Fuhui Long, Jie Zhou

  • 1Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA. pengh@janelia.hhmi.org

BMC Cell Biology
|August 23, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The Vertebrate Genomes Project Phase I: A global reference genome resource.

bioRxiv : the preprint server for biology·2026
Same author

Complete sequencing of medaka genomes reveals the architecture of centromeric satellites, giant mobile elements, and sex chromosomes.

Genome research·2026
Same author

The genome sequence of <i>Saccopteryx leptura, Schreber, 1774</i> (Chiroptera, Emballonuridae, Saccopteryx).

Wellcome open research·2026
Same author

Modeling 3D mesoscaled neuronal complexity through learning-based dynamic morphometric convolution.

Brain informatics·2026
Same author

AISleep: Automated and interpretable sleep staging from single-channel EEG data.

Patterns (New York, N.Y.)·2025
Same author

Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases.

Nature methods·2025
Same journal

Shikonin sensitizes A549 cells to TRAIL-induced apoptosis through the JNK, STAT3 and AKT pathways.

BMC cell biology·2018
Same journal

Mitotic activity patterns and cytoskeletal changes throughout the progression of diapause developmental program in Daphnia.

BMC cell biology·2018
Same journal

Post-treatment de-phosphorylation of p53 correlates with dasatinib responsiveness in malignant melanoma.

BMC cell biology·2018
Same journal

Comparative evaluation of mesenchymal stromal cells from umbilical cord and amniotic membrane in xeno-free conditions.

BMC cell biology·2018
Same journal

The STRIPAK complex components FAM40A and FAM40B regulate endothelial cell contractility via ROCKs.

BMC cell biology·2018
Same journal

Kif17 phosphorylation regulates photoreceptor outer segment turnover.

BMC cell biology·2018
See all related articles

Automated computational methods analyze gene expression patterns from in situ hybridization images in Drosophila melanogaster embryos. These tools accurately cluster co-regulated genes and classify developmental stages, aiding large-scale genomic analysis.

Area of Science:

  • Developmental Biology
  • Computational Biology
  • Genomics

Background:

  • In situ hybridization (ISH) in Drosophila melanogaster embryos reveals gene expression patterns crucial for understanding gene regulation.
  • Analyzing these spatial-temporal patterns is key for identifying co-expressed and co-regulated genes, and transcription factor binding motifs.
  • The growing volume of ISH data necessitates automated computational approaches for efficient analysis.

Purpose of the Study:

  • To develop automated computational methods for analyzing gene expression patterns from ISH images.
  • To extract feature representations, cluster genes by expression patterns, suggest transcription factor binding site motifs, and identify expressing anatomical regions.
  • To create a suite of tools for large-scale computational analysis of Drosophila embryonic gene expression.

Related Experiment Videos

Main Methods:

  • Developed three feature representations: Gaussian Mixture Models (GMM), Principal Component Analysis (PCA), and wavelet functions.
  • Employed a minimum spanning tree method (MSTCUT) for clustering expression patterns.
  • Utilized motif finders with cross-species conservation for transcription factor binding site identification and trained binary classifiers for anatomical region identification.

Main Results:

  • Algorithms extract feature representations from ISH images.
  • Genes with similar spatio-temporal expression patterns were clustered.
  • Identified potential transcription factor binding motifs and accurately classified developmental stages with over 99% accuracy.
  • Successfully applied methods to the Berkeley Drosophila Genome Project (BDGP) gene expression database.

Conclusions:

  • Automated image analysis methods effectively recapitulate known co-regulated genes.
  • The developed techniques achieve high accuracy in developmental stage classification, robust to image variations.
  • These computational tools provide a valuable framework for large-scale analysis of Drosophila embryonic gene expression patterns.