From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data.
From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data.
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DOI：
10.1371/journal.pone.0125000
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Cheng J
中科院分区：
文献类型：
--
作者：
Li J;Hou J;Sun L;Wilkins JM;Lu Y;Niederhuth CE;Merideth BR;Mawhinney TP;Mossine VV;Greenlief CM;Walker JC;Folk WR;Hannink M;Lubahn DB;Birchler JA;Cheng J
RNA-Seq技术使用下一代测序（NGS）生成数亿个短RNA读数。这些RNA读段可以映射到参考基因组以研究基因表达的变化，但需要改进挖掘大型RNA-Seq数据集以提取有价值的生物学知识的程序。RNAMiner-一个多层次的生物信息学协议和管道-已经开发了这样的数据集。它包括五个步骤：将RNA-Seq读数映射到参考基因组，计算基因表达值，鉴定差异表达基因，预测基因功能，构建基因调控网络。为了证明其实用性，我们将RNAMiner应用于从人类，小鼠，拟南芥和果蝇细胞生成的数据集，并成功地识别出差异表达的基因，将它们聚集成具有凝聚力的功能组，并构建了新的基因调控网络。RNAMiner Web服务可在http://calla.rnet.missouri.edu/rnaminer/index.html上获得。
RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge are needed. RNAMiner—a multi-level bioinformatics protocol and pipeline—has been developed for such datasets. It includes five steps: Mapping RNA-Seq reads to a reference genome, calculating gene expression values, identifying differentially expressed genes, predicting gene functions, and constructing gene regulatory networks. To demonstrate its utility, we applied RNAMiner to datasets generated from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster cells, and successfully identified differentially expressed genes, clustered them into cohesive functional groups, and constructed novel gene regulatory networks. The RNAMiner web service is available at http://calla.rnet.missouri.edu/rnaminer/index.html.
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