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| 1 | De novo assembly of a Chinese soybean genome显示文摘Soybean was domesticated in China and has become one of the most important oilseed crops. Due to bottlenecks in their introduction and dissemination, soybeans from different geographic areas exhibit extensive genetic diversity. Asia is the largest soybean market; therefore, a high-quality soybean reference genome from this area is critical for soybean research and breeding.Here, we report the de novo assembly and sequence analysis of a Chinese soybean genome for 'Zhonghuang 13' by a combination of SMRT, Hi-C and optical mapping data. The assembled genome size is 1.025 Gb with a contig N50 of 3.46 Mb and a scaffold N50 of 51.87 Mb. Comparisons between this genome and the previously reported reference genome(cv. Williams82) uncovered more than 250,000 structure variations. A total of 52,051 protein coding genes and 36,429 transposable elements were annotated for this genome, and a gene co-expression network including 39,967 genes was also established. This high quality Chinese soybean genome and its sequence analysis will provide valuable information for soybean improvement in the future. | Yanting Shen Jing Liu Haiying Geng Jixiang Zhang Yucheng Liu Haikuan Zhang Shilai Xing Jianchang Du Shisong Ma Zhixi Tian | 2018 | Science China(Life Sciences)2018，61，8： | 11 |
| 2 | AtGGM2014, an Arabidopsis gene co-expression network for functional studies显示文摘Gene co-expression networks provide an important tool for systems biology studies. Using microarray data from the Array Express database, we constructed an Arabidopsis gene co-expression network, termed At GGM2014, based on the graphical Gaussian model, which contains 102,644 co-expression gene pairs among 18,068 genes. The network was grouped into 622 gene co-expression modules. These modules function in diverse house-keeping, cell cycle, development, hormone response, metabolism, and stress response pathways. We developed a tool to facilitate easy visualization of the expression patterns of these modules either in a tissue context or their regulation under different treatment conditions. The results indicate that at least six modules with tissue-specific expression pattern failed to record modular regulation under various stress conditions. This discrepancy could be best explained by the fact that experiments to study plant stress responses focused mainly on leaves and less on roots, and thus failed to recover specific regulation pattern in other tissues. Overall, the modular structures revealed by our network provide extensive information to generate testable hypotheses about diverse plant signaling pathways. At GGM2014 offers a constructive tool for plant systems biology studies. | MA ShiSong BOHNERT Hans J DINESH-KUMAR Savithramma P | 2015 | Science China(Life Sciences)2015，58，3： | 1 |
| 3 | Loss of TIP1;1 aquaporin in Arabidopsis leads to cell and plant death显示文摘 | Shisong Ma Tanya M Quist Alexander Ulanov | 2004 | The Plant Journal2004，40，6： | 1 |
| 4 | Decoding transcriptional regulation via a human gene expression predictor显示文摘Transcription factors(TFs)regulate cellular activities by controlling gene expression,but a predictive model describing how TFs quantitatively modulate human transcriptomes is lacking.We construct a universal human gene expression predictor named EXPLICIT-Human and utilize it to decode transcriptional regulation.Using the expression of 1613 TFs,the predictor reconstitutes highly accurate transcriptomes for samples derived from a wide range of tissues and conditions.The broad applicability of the predictor indicates that it recapitulates the quantitative relationships between TFs and target genes ubiquitous across tissues.Significant interacting TF-target gene pairs are extracted from the predictor and enable downstream inference of TF regulators for diverse pathways involved in development,immunity,metabolism,and stress response.A detailed analysis of the hematopoiesis process reveals an atlas of key TFs regulating the development of different hematopoietic cell lineages,and a portion of these TFs are conserved between humans and mice.The results demonstrate that our method is capable of delineating the TFs responsible for fate determination.Compared to other existing tools,EXPLICIT-Human shows a better performance in recovering the correct TF regulators. | Yuzhou Wang Yu Zhang Ning Yu Bingyan Li Jiazhen Gong Yide Mei Jianqiang Bao Shisong Ma | 2023 | Journal of Genetics and Genomics2023，50，5： | 0 |
| 5 | EXPLICIT-Kinase:A gene expression predictor for dissecting the functions of the Arabidopsis kinome显示文摘Protein kinases regulate virtually all cellular processes,but it remains challenging to determine the functions of all protein kinases,collectively called the“kinome”,in any species.We developed a computational approach called EXPLICIT-Kinase to predict the functions of the Arabidopsis kinome.Because the activities of many kinases can be regulated transcriptionally,their gene expression patterns provide clues to their functions.A universal gene expression predictor for Arabidopsis was constructed to predict the expression of 30,172 nonkinase genes based on the expression of 994 kinases.The model reconstituted highly accurate transcriptomes for diverse Arabidopsis samples.It identified the significant kinases as predictor kinases for predicting the expression of Arabidopsis genes and pathways.Strikingly,these predictor kinases were often regulators of related pathways,as exemplified by those involved in cytokinesis,tissue development,and stress responses.Comparative analyses revealed that portions of these predictor kinases are shared and conserved between Arabidopsis and maize.As an example,we identified a conserved predictor kinase,RAF6,from a stomatal movement module.We verified that RAF6 regulates stomatal closure.It can directly interact with SLAC1,a key anion channel for stomatal closure,and modulate its channel activity.Our approach enables a systematic dissection of the functions of the Arabidopsis kinome. | Yuming Peng Wanzhu Zuo Hui Zhou Fenfen Miao Yu Zhang Yue Qin Yi Liu Yu Long Shisong Ma | 2022 | Journal of Integrative Plant Biology2022，64，7： | 0 |
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