NDLI: Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching

Content Provider	Springer Nature : BioMed Central
Author	Rehrauer, Hubert Opitz, Lennart Tan, Ge Sieverling, Lina Schlapbach, Ralph
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.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-14-370.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/1471-2105-14-370
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	14
Language	English
Publisher	BioMed Central
Publisher Date	2013-12-24
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Read Count Expression Estimate Read Alignment Short Isoforms Coverage Bias Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching

NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data

Comparative evaluation of full-length isoform quantification from RNA-Seq

Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data

Transcript quantification with RNA-Seq data

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Light-RCV: a lightweight read coverage viewer for next generation sequencing data

Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate

CLASS: constrained transcript assembly of RNA-seq reads

Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching

Similar Documents

Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching

NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data

Comparative evaluation of full-length isoform quantification from RNA-Seq

Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data

Transcript quantification with RNA-Seq data

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Light-RCV: a lightweight read coverage viewer for next generation sequencing data

Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate

CLASS: constrained transcript assembly of RNA-seq reads

Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching