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dc.contributor.author
Rehrauer, Hubert
dc.contributor.author
Opitz, Lennart
dc.contributor.author
Tan, Ge
dc.contributor.author
Sieverling, Lina
dc.contributor.author
Schlapbach, Ralph
dc.date.accessioned
2018-09-04T12:48:44Z
dc.date.available
2017-06-11T04:50:10Z
dc.date.available
2018-09-04T12:48:44Z
dc.date.issued
2013-12
dc.identifier.other
10.1186/1471-2105-14-370
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/80421
dc.identifier.doi
10.3929/ethz-b-000080421
dc.description.abstract
Background RNA-seq is now widely used to quantitatively assess gene expression, expression differences and isoform switching, and promises to deliver results for the entire transcriptome. However, whether the transcriptional state of a gene can be captured accurately depends critically on library preparation, read alignment, expression estimation and the tests for differential expression and isoform switching. There are comparisons available for the individual steps but there is not yet a systematic investigation which specific genes are impacted by biases throughout the entire analysis workflow. It is especially unclear whether for a given gene, with current methods and protocols, expression changes and isoform switches can be detected. Results For the human genes, we report their detectability under various conditions using different approaches. Overall, we find that the input material has the biggest influence and may, depending on the protocol and RNA degradation, exhibit already strong length-dependent over- and underrepresentation of transcripts. The alignment step aligns for 50% of the isoforms up to 99% of the reads correctly; only in the presence of transcript modifications mainly short isoforms will have a low alignment rate. In our dataset, we found that, depending on the aligner and the input material used, the expression estimation of up to 93% of the genes being accurate within a factor of two; with the deviations being due to ambiguous alignments. Detection of differential expression using a negative-binomial count model works reliably for our simulated data but is dependent on the count accuracy. Interestingly, using the fold-change instead of the p-value as a score for differential expression yields the same performance in the situation of three replicates and the true change being two-fold. Isoform switching is harder to detect and for at least 109 genes the isoform differences evade detection independent of the method used. Conclusions RNA-seq is a reliable tool but the repetitive nature of the human genome makes the origin of the reads ambiguous and limits the detectability for certain genes. RNA-seq does not equally well represent isoforms independent of their size which may range from ~200nt to ~100′000nt. Researchers are advised to verify that their target genes do not have extreme properties with respect to repeated regions, GC content, and isoform length and complexity.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/2.0/
dc.subject
Read Count
en_US
dc.subject
Expression Estimate
en_US
dc.subject
Read Alignment
en_US
dc.subject
Short Isoforms
en_US
dc.subject
Coverage Bias
en_US
dc.title
Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
ethz.journal.title
BMC bioinformatics
ethz.journal.volume
14
en_US
ethz.pages.start
370
en_US
ethz.size
10 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
004240301
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02207 - Functional Genomics Center Zürich / Functional Genomics Center Zürich
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::08828 - Schlapbach, Ralph (Tit.-Prof.)
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::08828 - Schlapbach, Ralph (Tit.-Prof.)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02207 - Functional Genomics Center Zürich / Functional Genomics Center Zürich
ethz.date.deposited
2017-06-11T04:50:40Z
ethz.source
ECIT
ethz.identifier.importid
imp5936519daad2d57934
ethz.ecitpid
pub:126268
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-26T19:16:14Z
ethz.rosetta.lastUpdated
2018-11-08T01:32:29Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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