An Integrated RNA-seq Atlas of the Murine T-Helper Cell Transcriptome

Andrew Deonarine


T-helper cells play an important role in mediating the immune response, and with the advent of next generation sequencing, significant insights can be gained into the T-helper cell transcriptome. One of the barriers to analyzing next-generation sequencing data, such as that generated by RNA-seq analyses, is that many of the statistical properties concerning quantification (ie. RPKM [1] vs. FPKM [2]), normalization [3], and differential expression (using methods such as edger [4], DESeq [5], and Cuffdiff [6]) of the data are still not clearly understood. Building on previous investigations into the bimodality of transcript expression [7], a computational pipeline was created to integrate various methods of expression quantification, cell type clustering, differential expression analyses, gene annotation methods, and novel transcript identification into a murine T-helper cell expression atlas. By integrating these various analyses, it was possible to identify key signature genes (transcription factors, cytokines, receptors, and other molecules) that distinguish the various T-helper cell types from each other, in addition to novel transcripts. This expression atlas, which is easily accessible as a user-friendly online resource at, will form the basis for future investigations into immune regulation and function using network-based analyses.

This work is relevant to the goals of SEQAHEAD because it represents a major step forward in the integration and comparison of various methods of expression quantification, differential expression analysis, and annotation of RNA-seq data. The computational principles presented here could potentially be applicable to many other fields of molecular biology and medicine.

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Relevant Web sites


next generation sequencing; COST; RNA-seq;

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