Transcriptomics is a major platform to study organismal biology. The advent of new parallel sequencing technologies has opened up a new avenue of transcriptomics with ever deeper and deeper sequencing to identify and quantify each and every transcript in a sample. However, this may not be the best usage of the parallel sequencing technology for all transcriptomics experiments. I utilized the Shannon Entropy approach to estimate the information contained within a transcriptomics experiment and tested the ability of shallow RNAseq to capture the majority of this information. This analysis showed that it was possible to capture nearly all of the network or genomic information present in a variety of transcriptomics experiments using a subset of the most abundant 5000 transcripts or less within any given sample. Thus, it appears that it should be possible and affordable to conduct large scale factorial analysis with a high degree of replication using parallel sequencing technologies.
Keywords: transcriptomics, information content, microarray, RNAseq, sequencing depth, factorial genomics, eQTL, genetical genomics
Citation: Kliebenstein DJ (2012) Exploring the shallow end; estimating information content in transcriptomics studies. Front. Plant Sci. 3:213. doi: 10.3389/fpls.2012.00213
Received: 01 August 2012; Accepted: 23 August 2012;
Published online: 10 September 2012.
Edited by:Alisdair Fernie, Max Planck Institute for Plant Physiology, Germany
Reviewed by:Alisdair Fernie, Max Planck Institute for Plant Physiology, Germany
Copyright: © 2012 Kliebenstein. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
*Correspondence: Daniel J. Kliebenstein, Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616 USA. e-mail: firstname.lastname@example.org