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利用平铺阵列数据和RNA二级结构预测来识别非编码RNA基因。

Use of tiling array data and RNA secondary structure predictions to identify noncoding RNA genes.

作者信息

Weile Christian, Gardner Paul P, Hedegaard Mads M, Vinther Jeppe

机构信息

Molecular Evolution Group, Department of Molecular Biology, University of Copenhagen, Copenhagen N, Denmark.

出版信息

BMC Genomics. 2007 Jul 23;8:244. doi: 10.1186/1471-2164-8-244.

Abstract

BACKGROUND

Within the last decade a large number of noncoding RNA genes have been identified, but this may only be the tip of the iceberg. Using comparative genomics a large number of sequences that have signals concordant with conserved RNA secondary structures have been discovered in the human genome. Moreover, genome wide transcription profiling with tiling arrays indicate that the majority of the genome is transcribed.

RESULTS

We have combined tiling array data with genome wide structural RNA predictions to search for novel noncoding and structural RNA genes that are expressed in the human neuroblastoma cell line SK-N-AS. Using this strategy, we identify thousands of human candidate RNA genes. To further verify the expression of these genes, we focused on candidate genes that had a stable hairpin structures or a high level of covariance. Using northern blotting, we verify the expression of 2 out of 3 of the hairpin structures and 3 out of 9 high covariance structures in SK-N-AS cells.

CONCLUSION

Our results demonstrate that many human noncoding, structured and conserved RNA genes remain to be discovered and that tissue specific tiling array data can be used in combination with computational predictions of sequences encoding structural RNAs to improve the search for such genes.

摘要

背景

在过去十年中,大量非编码RNA基因已被鉴定出来,但这可能只是冰山一角。利用比较基因组学,在人类基因组中发现了大量具有与保守RNA二级结构一致信号的序列。此外,使用平铺阵列进行的全基因组转录谱分析表明,基因组的大部分区域都被转录。

结果

我们将平铺阵列数据与全基因组结构RNA预测相结合,以寻找在人类神经母细胞瘤细胞系SK-N-AS中表达的新型非编码和结构RNA基因。使用这种策略,我们鉴定出数千个人类候选RNA基因。为了进一步验证这些基因的表达,我们聚焦于具有稳定发夹结构或高协方差水平的候选基因。使用Northern印迹法,我们验证了SK-N-AS细胞中3个发夹结构中的2个以及9个高协方差结构中的3个的表达。

结论

我们的结果表明,许多人类非编码、结构化和保守的RNA基因仍有待发现,并且组织特异性平铺阵列数据可与编码结构RNA的序列的计算预测相结合,以改进对此类基因的搜索。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d84f/1949828/1984b86f496d/1471-2164-8-244-1.jpg

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