Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Background:
Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.
Results:
We present a computational method to annotate gene expression pattern images automatically. The proposed method uses the bag-of-words scheme to utilize the existing information on pattern annotation and annotates images using a model that exploits correlations among terms. The proposed method can annotate images individually or in groups (e.g., according to the developmental stage). In addition, the proposed method can integrate information from different two-dimensional views of embryos. Results on embryonic patterns from BDGP data demonstrate that our method significantly outperforms other methods.
Conclusion:
The proposed bag-of-words scheme is effective in representing a set of annotations assigned to a group of images, and the model employed to annotate images successfully captures the correlations among different controlled vocabulary terms. The integration of existing annotation information from multiple embryonic views improves annotation performance.
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
Aristotle studied developing organisms, among other things, in ancient Greece, and his writings shaped Western philosophy and natural science for greater than two thousand years. He spent much of his life in Greece and studied with Plato at Plato's Academy in Athens, where he later established his own school called the Lyceum. Aristotle wrote greater than 150 treatises on subjects ranging from aesthetics, politics, ethics, and natural philosophy, which include physics and biology. Less than fifty of Aristotle's treatises persisted into the twenty-first century. In natural philosophy, later called natural science, Aristotle established methods for investigation and reasoning and provided a theory on how embryos generate and develop. He originated the theory that an organism develops gradually from undifferentiated material, later called epigenesis.
Lysogenic bacteria, or virus-infected bacteria, were the primary experimental models used by scientists working in the laboratories of the Pasteur Institute in Paris, France, during the 1950s and 1960s. Historians of science have noted that the use of lysogenic bacteria as a model in microbiological research influenced the scientific achievements of the Pasteur Institute's scientists. Francois Jacob and Jacques Monod used lysogenic bacteria to develop their operon model of gene regulation, to investigate the cellular regulatory mechanisms of the lysogenic life cycle, and to infer the process of cellular differentiation in the development of more complex eukaryotes.
In 2008 researchers Daniel Warner and Richard Shine tested the Charnov-Bull model by conducting experiments on the Jacky dragon (Amphibolurus muricatus), in Australia. Their results showed that temperature-dependent sex determination(TSD) evolved in this species as an adaptation to fluctuating environmental temperatures. The Charnov-Bull model, proposed by Eric Charnov and James Bull in 1977, described the evolution of TSD, although the model was, for many years, untested. Many reptiles and some fish exhibit non-genetic sex determination, in which an embryos' environment can influence the sex of the adult organism. Environmental conditions such as humidity or population density can alter sex in some organisms, and a widespread form of non-genetic sex determination is temperature-dependent sex determination. TSD reveals how embryonic development can contribute to the evolution of physiological processes. Researchers have documented TSD in a wide range of species, and they continue to investigate how such a sex determining system has evolved.
In 1830, a dispute erupted in the halls of lÕAcad mie des Sciences in Paris between the two most prominent anatomists of the nineteenth century. Georges Cuvier and tienne Geoffroy Saint-Hilaire, once friends and colleagues at the Paris Museum, became arch rivals after this historical episode. Like many important disputes in the history of science, this debate echoes several points of contrasts between the two thinkers. The two French Ânaturalists not only disagreed about what sorts of comparisons between vertebrates were acceptable, but also about which principles ought to underlie a rational system of animal taxonomy and guide the study of animal anatomy. Digging deeper into their differences, their particular disagreements over specific issues within zoology and anatomy culminated in the articulation of two competing and divergent philosophical views on the aims and methods of the life sciences. The emergence of these two distinct positions has had a lasting impact in the development of evolutionary and developmental biology. This essay will provide an overview of the conceptual themes of the debate, its implications for the development of the life sciences, and its role in the history of embryology and developmental biology.