Shin Han Shiu
Michigan State University
88Publications
42H-index
7,180Citations
Publications 88
Newest
Bethany M. Moore1
Estimated H-index: 1
(Michigan State University),
Peipei Wang1
Estimated H-index: 1
(Michigan State University)
+ 6 AuthorsShin Han Shiu42
Estimated H-index: 42
(Michigan State University)
Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. Using Arabidopsis thaliana as a model, we identified distinguishing characteristics of SM and GM (general metabolism, traditionally referred to as primary metabolism) genes through a detailed study of features including duplication pattern, sequence conservation, transcription, protein domain content, and gene network properties. Analysis of m...
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Yao-Ming Chang6
Estimated H-index: 6
(Academia Sinica),
Hsin-Hung Lin5
Estimated H-index: 5
(Academia Sinica)
+ 12 AuthorsMei-Yeh Jade Lu9
Estimated H-index: 9
(Academia Sinica)
Time-series transcriptomes of a biological process obtained under different conditions are useful for identifying the regulators of the process and their regulatory networks. However, such data are 3D (gene expression, time, and condition), and there is currently no method that can deal with their full complexity. Here, we developed a method that avoids time-point alignment and normalization between conditions. We applied it to analyze time-series transcriptomes of developing maize leaves under ...
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Published on Mar 24, 2019in bioRxiv
Christina B Azodi2
Estimated H-index: 2
(Michigan State University),
Jeremy Pardo3
Estimated H-index: 3
(Michigan State University)
+ 2 AuthorsShin Han Shiu42
Estimated H-index: 42
(Michigan State University)
The ability to predict traits from genome-wide sequence information (Genomic Prediction, GP), has improved our understanding of the genetic basis of complex traits and transformed breeding practices. Transcriptome data may also be useful for GP. However, it remains unclear how well transcript levels can predict traits, particularly when traits are scored at different development stages. Using maize genetic markers and transcript levels from seedlings to predict mature plant traits, we found tran...
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Published on Oct 1, 2018in Genome Biology and Evolution 3.94
Peipei Wang1
Estimated H-index: 1
(Michigan State University),
Bethany M. Moore1
Estimated H-index: 1
(Michigan State University)
+ 3 AuthorsShin Han Shiu42
Estimated H-index: 42
(Michigan State University)
1 Citations Source Cite
Published on Nov 15, 2018in bioRxiv
John P Lloyd2
Estimated H-index: 2
(University of Michigan),
Megan J. Bowman2
Estimated H-index: 2
(Michigan State University)
+ 4 AuthorsShin Han Shiu42
Estimated H-index: 42
(Michigan State University)
Extensive transcriptional activity occurring in unannotated, intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To address this, we evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions (ITRs) in four Poaceae species. Most ITR sequences are species-specific. Those found across species tend to be more divergent in expression and have more re...
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Published on Nov 8, 2018in bioRxiv
Sahra Uygun5
Estimated H-index: 5
(Michigan State University),
Christina B Azodi2
Estimated H-index: 2
(Michigan State University),
Shin Han Shiu42
Estimated H-index: 42
(Michigan State University)
Multicellular organisms have diverse cell types with distinct roles in development and responses to the environment. At the transcriptional level, the differences in environmental response between cell types are due to differences in regulatory programs. In plants, although cell-type environmental responses have been examined, details on how these responses are regulated remain spotty. Here, we identify a set of putative cis-regulatory elements (pCREs) enriched in the promoters of genes responsi...
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Published on Feb 23, 2018in bioRxiv
Peipei Wang1
Estimated H-index: 1
(Michigan State University),
Bethany M. Moore1
Estimated H-index: 1
(Michigan State University)
+ 3 AuthorsShin Han Shiu42
Estimated H-index: 42
(Michigan State University)
Gene duplication and loss contribute to gene content differences as well as phenotypic divergence across species. However, the extent to which gene content varies among closely related plant species and the factors responsible for such variation remain unclear. Here, we used the Solanaceae family as a model to investigate differences in gene family size and the likely factors contributing to these differences. We found that genes in highly variable families have high turnover rate and tend to be...
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Published on Jun 1, 2018in Molecular Biology and Evolution 10.22
John P Lloyd2
Estimated H-index: 2
(Michigan State University),
Zing Tsung-Yeh Tsai6
Estimated H-index: 6
(University of Michigan)
+ 2 AuthorsShin Han Shiu42
Estimated H-index: 42
(Michigan State University)
1 Citations Source Cite
Published on Jan 1, 2018in The Plant Cell 8.23
Ming-Jung Liu2
Estimated H-index: 2
(Academia Sinica),
Koichi Sugimoto8
Estimated H-index: 8
(Michigan State University)
+ 5 AuthorsShin Han Shiu42
Estimated H-index: 42
(Michigan State University)
The evolution of transcriptional regulatory mechanisms is central to how stress response and tolerance differ between species. However, it remains largely unknown how divergence in cis-regulatory sites and, subsequently, transcription factor (TF) binding specificity contribute to stress-responsive expression divergence, particularly between wild and domesticated spe-cies. By profiling wound-responsive gene transcriptomes in wild Solanum pennellii and do-mesticated S. lycopersicum, we found exten...
2 Citations Source Cite
Published on Aug 7, 2018in bioRxiv
Nicholas Panchy7
Estimated H-index: 7
(University of Tennessee),
John P Lloyd2
Estimated H-index: 2
(University of Michigan),
Shin Han Shiu42
Estimated H-index: 42
(Michigan State University)
The collection all TFs, target genes and their interactions in an organism form a gene regulatory network (GRN), which underly complex patterns of transcription even in unicellular species. However, identifying which interactions regulate expression in a specific temporal context remains a challenging task. With multiple experimental and computational approaches to characterize GRNs, we predicted general and phase-specific cell-cycle expression in Saccharomyces cerevisiae using four regulatory d...
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