groHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data

BMC Bioinformatics

Minho Chae¹  Charles G. Danko²  W. Lee Kraus¹ 

[1] Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, 75390, TX, USA

[2] Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, 14853, NY, USA

关键词: ChIP-seq;    Enhancer RNAs (eRNAs);    Long non-coding RNAs (lncRNAs);    Primary miRNAs;    Enhancer;    Cell type specificity;    Peak calling;    Gene regulation;    Primary transcript;    Transcription unit;    Transcription;    groHMM;    GRO-seq;   

Others  :  1230948
DOI  :  10.1186/s12859-015-0656-3

 received in 2014-11-18, accepted in 2015-06-30,  发布年份 2015

【 摘 要 】

Background

Global run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units.

Results

Here, we present groHMM, a computational tool in R, which defines the boundaries of transcription units de novo using a two state hidden-Markov model (HMM). A systematic comparison of the performance between groHMM and two existing peak-calling methods tuned to identify broad regions (SICER and HOMER) favorably supports our approach on existing GRO-seq data from MCF-7 breast cancer cells. To demonstrate the broader utility of our approach, we have used groHMM to annotate a diverse array of transcription units (i.e., primary transcripts) from four GRO-seq data sets derived from cells representing a variety of different human tissue types, including non-transformed cells (cardiomyocytes and lung fibroblasts) and transformed cells (LNCaP and MCF-7 cancer cells), as well as non-mammalian cells (from flies and worms). As an example of the utility of groHMM and its application to questions about the transcriptome, we show how groHMM can be used to analyze cell type-specific enhancers as defined by newly annotated enhancer transcripts.

Conclusions

Our results show that groHMM can reveal new insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.

【 授权许可】

   
2015 Chae et al.

【 图 表 】

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