Experimental Characterization of Novel Coding Small ORFs in the Arabidopsis thaliana Genome

拟南芥基因组中新编码小 ORF 的实验表征

基本信息

  • 批准号:
    0749634
  • 负责人:
  • 金额:
    $ 50万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2008
  • 资助国家:
    美国
  • 起止时间:
    2008-03-15 至 2012-02-29
  • 项目状态:
    已结题

项目摘要

The major goal of this project is to provide experimental evidence for the transcription, translation, and functions of novel small Open Reading Frames (sORFs, 90-300 bp) in the Arabidopsis thaliana genome. Advances in whole genome expression profiling have led to the discovery that thousands of genes are not annotated in several eukaryotic genomes. The question of whether these novel genes code for proteins remains largely unresolved because they are mainly verified by evidence of transcription but not translation. Small protein coding genes are particularly difficult to uncover due to their lower levels of expression and the lack of statistical power to predict them. This is unfortunate because small protein coding genes have been found to play important functions in eukaryotes. To globally assess the transcription and translation of these sORFs and other potential novel coding genes, polysome-associated transcripts will be sequenced to determine the identity of transcripts that are destined to be translated. To uncover potential roles of sORFs, differential regulation under stress conditions will be examined using tiling array data. In addition, sORF genes with evidence of translation will be targeted for determining tissue-specific expression patterns and gain- and loss-of-function phenotypes. To overcome current limitations in coding sequence prediction methods, the sequence properties of novel sORF genes will be identified and used for improving measures of coding potential. A revised coding potential measure will then be applied to the prediction of novel protein genes in plant sequences.Although the scientific community has gained much knowledge of the functional elements within genomes, we still refer to substantial parts of genomes as "dark matter" due to the fact that much of the genome displays various types of functional signatures but are not identified as genes. In a broad sense, this project will provide important insights into genome "dark matter". Given the availability of a large amount of sequences from agriculturally and economically important plants, this project will contribute significantly to a better understanding of the parts list in genomes and eventually aid efforts to improve crops. This project also provides a unique training environment for students of all levels. In addition, the planned outreach activities introduce concepts of genomes to high school students that will increase science literacy and enhance pre-college biology education
本项目的主要目的是为拟南芥基因组中新的小开放阅读框架(sORFs,90-300 bp)的转录、翻译和功能提供实验依据。全基因组表达谱研究的进展已经发现,在几个真核生物基因组中,数千个基因没有注释。这些新基因是否编码蛋白质的问题在很大程度上仍然没有得到解决,因为它们主要通过转录而不是翻译的证据来验证。小的蛋白质编码基因特别难以发现,因为它们的表达水平较低,并且缺乏预测它们的统计能力。这是不幸的,因为已经发现小的蛋白质编码基因在真核生物中发挥重要功能。为了全面评估这些sORF和其他潜在的新型编码基因的转录和翻译,将对多核糖体相关转录物进行测序,以确定注定要翻译的转录物的身份。为了揭示sORF的潜在作用,将使用平铺阵列数据检查胁迫条件下的差异调节。此外,具有翻译证据的sORF基因将被靶向用于确定组织特异性表达模式以及功能获得和丧失表型。为了克服当前编码序列预测方法的局限性,将鉴定新型sORF基因的序列特性并用于改进编码潜力的测量。尽管科学界已经对基因组中的功能元件有了很多了解,但我们仍然把基因组的相当大的部分称为“暗物质”,因为事实上,基因组的大部分显示出各种类型的功能特征,但并没有被确定为基因。从广义上讲,该项目将为基因组“暗物质”提供重要见解。鉴于农业和经济上重要植物的大量序列的可用性,该项目将大大有助于更好地理解基因组中的部分列表,并最终帮助改善作物的努力。该项目还为各级学生提供了独特的培训环境。此外,计划中的外联活动向高中生介绍基因组概念,这将提高科学素养,加强大学预科生物学教育

项目成果

期刊论文数量(0)
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会议论文数量(0)
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Shin-Han Shiu其他文献

Machine learning reveals genes impacting oxidative stress resistance across yeasts
机器学习揭示了影响酵母氧化应激抗性的基因
  • DOI:
    10.1038/s41467-025-60189-3
  • 发表时间:
    2025-07-01
  • 期刊:
  • 影响因子:
    15.700
  • 作者:
    Katarina Aranguiz;Linda C. Horianopoulos;Logan Elkin;Kenia Segura Abá;Drew Jordahl;Katherine A. Overmyer;Russell L. Wrobel;Joshua J. Coon;Shin-Han Shiu;Antonis Rokas;Chris Todd Hittinger
  • 通讯作者:
    Chris Todd Hittinger
Selection-enriched genomic loci (SEGL) reveals genetic loci for environmental adaptation and photosynthetic productivity in emChlamydomonas reinhardtii/em
选择富集基因组位点(SEGL)揭示了莱茵衣藻环境适应和光合生产力的遗传位点
  • DOI:
    10.1016/j.algal.2022.102709
  • 发表时间:
    2022-05-01
  • 期刊:
  • 影响因子:
    4.500
  • 作者:
    Ben F. Lucker;Joshua A. Temple;Nicolas L. Panchy;Urs F. Benning;Jacob D. Bibik;Peter G. Neofotis;Joseph C. Weissman;Ivan R. Baxter;Shin-Han Shiu;David M. Kramer
  • 通讯作者:
    David M. Kramer
CLAVATA signalling shapes barley inflorescence by controlling activity and determinacy of shoot meristem and rachilla
CLAVATA 信号通过控制茎尖分生组织和小穗轴的活性和确定性来塑造大麦花序。
  • DOI:
    10.1038/s41467-025-59330-z
  • 发表时间:
    2025-04-26
  • 期刊:
  • 影响因子:
    15.700
  • 作者:
    Isaia Vardanega;Jan Eric Maika;Edgar Demesa-Arevalo;Tianyu Lan;Gwendolyn K. Kirschner;Jafargholi Imani;Ivan F. Acosta;Katarzyna Makowska;Götz Hensel;Thilanka Ranaweera;Shin-Han Shiu;Thorsten Schnurbusch;Maria von Korff;Rüdiger Simon
  • 通讯作者:
    Rüdiger Simon
PTEMD: a novel method for identifyingpolymorphic transposable elements via scanning of high-throughput short reads
PTEMD:一种通过扫描高通量短读段来识别多态性转座元件的新方法
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    4.1
  • 作者:
    Stephen Obol Opiyo;Ning Jiang;Shin-Han Shiu;Guo-Liang Wang
  • 通讯作者:
    Guo-Liang Wang
Computational prediction of plant metabolic pathways
  • DOI:
    10.1016/j.pbi.2021.102171
  • 发表时间:
    2022-04-01
  • 期刊:
  • 影响因子:
    7.500
  • 作者:
    Peipei Wang;Ally M. Schumacher;Shin-Han Shiu
  • 通讯作者:
    Shin-Han Shiu

Shin-Han Shiu的其他文献

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{{ truncateString('Shin-Han Shiu', 18)}}的其他基金

RESEARCH-PGR: Combining machine learning and experimental analysis to define trichome and root-specific gene regulatory networks in cultivated tomato and related Solanaceae species
RESEARCH-PGR:结合机器学习和实验分析来定义栽培番茄和相关茄科物种中的毛状体和根特异性基因调控网络
  • 批准号:
    2218206
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Research: Assessing the connections between genetic interactions, environments, and phenotypes in Arabidopsis thaliana
合作研究:评估拟南芥遗传相互作用、环境和表型之间的联系
  • 批准号:
    2210431
  • 财政年份:
    2022
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
TRTech-PGR: Connecting sequences to functions within and between species through computational modeling and experimental studies
TRTech-PGR:通过计算模型和实验研究将序列与物种内部和物种之间的功能连接起来
  • 批准号:
    2107215
  • 财政年份:
    2021
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
NRT-HDR: Intersecting computational and data science to address grand challenges in plant biology
NRT-HDR:交叉计算和数据科学以应对植物生物学的巨大挑战
  • 批准号:
    1828149
  • 财政年份:
    2018
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Research: Fitness effects of loss-of-function mutations in duplicate genes
合作研究:重复基因功能丧失突变的适应性影响
  • 批准号:
    1655386
  • 财政年份:
    2017
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Computational and Experimental Studies of Plastid Functional Networks
质体功能网络的计算和实验研究
  • 批准号:
    1119778
  • 财政年份:
    2011
  • 资助金额:
    $ 50万
  • 项目类别:
    Continuing Grant

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