ArticleFrontiers in plant science2012
Exploring the shallow end; estimating information content in transcriptomics studies.
Article in Frontiers in plant science, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Cancer prognosis with shallow tumor RNA sequencing.Nature medicine · 2020Article
- Targeting individual cells by barcode in pooled sequence libraries.Nucleic acids research · 2019Article
- Tradict enables accurate prediction of eukaryotic transcriptional states from 100 marker genes.Nature communications · 2017Article
- An integrated RNAseq-BMC plant biology · 2017Article
- Low Dimensionality in Gene Expression Data Enables the Accurate Extraction of Transcriptional Programs from Shallow Sequencing.Cell systems · 2016Article
- Learning from Co-expression Networks: Possibilities and Challenges.Frontiers in plant science · 2016Review
- A high-throughput RNA-seq approach to profile transcriptional responses.Scientific reports · 2015Article
- The plant transcriptome-from integrating observations to models.Frontiers in plant science · 2013Article
- The utility of shallow RNA-Seq for documenting differential gene expression in genes with high and low levels of expression.PloS one · 2013Article
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
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.
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Registered trials
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