Evidence map›Paper›PMID 24886553›Full record

ArticleBMC genomics2014

Selective and flexible depletion of problematic sequences from RNA-seq libraries at the cDNA stage.

Stuart K Archer, Nikolay E Shirokikh, Thomas Preiss

Abstract read
In one paragraph

Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  16. RNA-Seq methods for transcriptome analysis.Wiley interdisciplinary reviews. RNA · 2017
    Review
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Stuart K Archer
Nikolay E Shirokikh
Thomas PreissGenome Biology Department, The John Curtin School of Medical Research (JCSMR), The Australian National University, Acton, Canberra, Australian Capital Territory, Australia. thomas.preiss@anu.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA major hurdle to transcriptome profiling by deep-sequencing technologies is that abundant transcripts, such as rRNAs, can overwhelm the libraries, severely reducing transcriptome-wide coverage. Methods for depletion of such unwanted sequences typically require treatment of RNA samples prior to library preparation, are costly and not suited to unusual species and applications. Here we describe Probe-Directed Degradation (PDD), an approach that employs hybridisation to DNA oligonucleotides at the single-stranded cDNA library stage and digestion with Duplex-Specific Nuclease (DSN).

resultsTargeting Saccharomyces cerevisiae rRNA sequences in Illumina HiSeq libraries generated by the split adapter method we show that PDD results in efficient removal of rRNA. The probes generate extended zones of depletion as a function of library insert size and the requirements for DSN cleavage. Using intact total RNA as starting material, probes can be spaced at the minimum anticipated library size minus 20 nucleotides to achieve continuous depletion. No off-target bias is detectable when comparing PDD-treated with untreated libraries. We further provide a bioinformatics tool to design suitable PDD probe sets.

conclusionWe find that PDD is a rapid procedure that results in effective and specific depletion of unwanted sequences from deep-sequencing libraries. Because PDD acts at the cDNA stage, handling of fragile RNA samples can be minimised and it should further be feasible to remediate existing libraries. Importantly, PDD preserves the original RNA fragment boundaries as is required for nucleotide-resolution footprinting or base-cleavage studies. Finally, as PDD utilises unmodified DNA oligonucleotides it can provide a low-cost option for large-scale projects, or be flexibly customised to suit different depletion targets, sample types and organisms.

Indexed as

DNA, ComplementaryDNA ProbesGene LibraryHigh-Throughput Nucleotide SequencingRNASaccharomyces cerevisiaeSequence Analysis, RNADNA, ComplementaryDNA ProbesRNA

Identifiers

PMID24886553
PMCPMC4045971

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.