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Yang, C.-L.

Publications and source records attributed to Yang, C.-L..

2 recordsLinked to original sources

Single-cell transcriptomics unveils xylem cell development and evolution

As the most abundant tissue on Earth1, xylem is responsible for lateral growth in plants. Typical xylem has a radial system composed of ray parenchyma cells and an axial system of fusiform cells2. In most angiosperms, fusiform cells are a combination of vessel elements for water transportation and libriform fibers for mechanical support, while both functions are performed together by tracheids in other vascular plants2. However, little is known about the developmental programs and evolutionary relationships of these xylem cell types. Through both single-cell and laser-capture microdissection transcriptomic profiling, here we demonstrate the developmental lineages of ray and fusiform cells in stem-differentiating xylem across four divergent woody angiosperms. Cross-species analyses of single-cell trajectories reveal highly conserved ray, yet variable fusiform, lineages across angiosperms. Core eudicots Populus trichocarpa and Eucalyptus grandis share nearly identical fusiform lineages. The tracheids in the basal eudicot Trochodendron aralioides, an evolutionarily reversed character3, 4, exhibit strong transcriptomic similarity to vessel elements but not libriform fibers, suggesting that water transportation, instead of mechanical support, is the major feature. We also found that the more basal angiosperm Liriodendron chinense has a fusiform lineage distinct from that in core eudicots. This evo-developmental framework provides a comprehensive understanding of the formation of xylem cell lineages across multiple plant species spanning over a hundred million years of evolutionary history5.

plant biology↗

ezGeno: An Automatic Model Selection Package for Genomic Data Analysis

To facilitate the process of tailor-making a deep neural network for exploring the dynamics of genomic DNA, we have developed a hands-on package called ezGeno that automates the search process of various parameters and network structure. ezGeno considers three different sets of search spaces, namely, the number of filters, dilation factors, and the connectivity between different layers. ezGeno can be applied to any kind of 1D genomic input such as genomic sequences, histone modifications, DNase feature data and so on. Combinations of multiple abovementioned 1D features are also applicable. Specifically, for the task of predicting TF binding using genomic sequences as the input, ezGeno can consistently return the best performing set of parameters and network structure, as well as highlight the important segments within the original sequences. For the task of predicting tissue-specific enhancer activity using both sequence and DNase feature data as the input, ezGeno also regularly outperforms the hand-designed models. In this study, we demonstrate that ezGeno is superior in efficiency and accuracy when compared to AutoKeras, a general open-source AutoML package. The average AUC of ezGeno is also consistently higher than the result of using a one-layer DeepBind model. With the flexibility of ezGeno, we expect that this package can provide future researchers not only support of model design in their analysis of genomic studies but also more insights into the regulatory landscape. AvailabilityThe ezGeno package can be freely accessed at https://github.com/ailabstw/ezGeno. ContactDr. Chien-Yu Chen, chienyuchen@ntu.edu.tw

bioinformatics↗