SGER: The Transcriptome of Haloferax volcanii

25个完整的基因组序列的可用性提供了丰富的数据的基因内容，代谢潜力和进化相关性的的阿托西亚。 这些数据也使得有可能将联合收割机基因组水平的查询，如转录的DNA阵列分析，与古细菌生理学研究中的生物化学和遗传学方法相结合。 该项目将使用平铺的DNA阵列来生成代谢多样性和实验上易于处理的模式古菌Haloferax volcanii的转录活性图。 一个由专家研究人员组成的国际社区将提供来自在各种条件下生长的细胞和特定突变株的RNA，以组装描述这种生物体转录反应的数据库。 这种方法的优点是，整个基因组，提出的编码和基因间区域，将被查询提供一个公正的和全面的转录活性的地图。 实验完成后，将通过一个指定的公共网站向社区公布结果，以鼓励在分析这些数据方面进行合作，并促进今后对这种生物的实验。 平铺的DNA阵列方法也将解决一个新出现的问题，识别小RNA编码基因，缺乏高度保守的序列，并计划提出使用这些阵列在分析的DNA或RNA相互作用的核酸结合蛋白。最后，这些研究的数据将用于开发新的教学工具，通过与主要本科教学机构的同事进行持续的互动，突出基于基因和平铺阵列的统计分析和数据解释的差异。

The availability of 25 completed Archaea genomic sequences has provided a wealth of data on the gene content, the metabolic potential and the evolutionary relatedness of Archaea. These data have also made it possible to combine genome level enquiries, such as DNA array analysis of transcription, with biochemical and genetic approaches in the study of archaeal physiology. This project will use tiled DNA arrays to generate a map of the transcription activity for the metabolically versatile and experimentally tractable model archaeon, Haloferax volcanii. An international community of expert researchers will provide RNAs from cells grown under a variety of conditions, and from specific mutant strains, to assemble a library of data describing the transcription response of this organism. This approach has the advantage that the entire genome, both proposed coding and intergenic regions, will be queried providing an unbiased and comprehensive map of transcription activity. Results will be released to the community upon completion of the experiments through a designated public web site to encourage collaboration in the analyses of these data and to foster future experimentation with this organism. The tiled DNA array method will also address a new emerging problem, the identification small RNA-encoding genes that lack highly conserved sequences, and plans are proposed to use these arrays in the analyses of the DNA or RNA interactions of nucleic acid binding proteins. Finally, data from these studies will be used to develop new teaching tools, highlighting differences in statistical analyses and data interpretation for gene-based and tiled arrays, through ongoing interactions with colleagues at a predominately undergraduate teaching institution.