Evidence map›Paper›PMID 23294941›Full record

ArticleMicrobial informatics and experimentation2013

An efficient rRNA removal method for RNA sequencing in GC-rich bacteria.

Clelia Peano, Alessandro Pietrelli, Clarissa Consolandi, Elio Rossi, Luca Petiti, Letizia Tagliabue, Gianluca De Bellis, Paolo Landini

Open access · hybridAbstract read
In one paragraph

Article in Microbial informatics and experimentation, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.8field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

22 citing papers in PubMed, 65 citations in OpenAlex.

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  11. Best practices for analysing microbiomes.Nature reviews. Microbiology · 2018
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  15. Ultra-deep sequencing of ribosome-associated poly-adenylated RNA in early Drosophila embryos reveals hundreds of conserved translated sORFs.DNA research : an international journal for rapid publication of reports on genes and genomes · 2016
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  16. Review
  17. Use of Metatranscriptomics in Microbiome Research.Bioinformatics and biology insights · 2016
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors at 4 institutions in 2 countries.

Clelia Peano *Institute of Biomedical Technologies, National Research Council, Segrate, Milan, Italy.
Alessandro Pietrelli *Institute of Biomedical Technologies, National Research Council, Segrate, Milan, Italy.
Clarissa ConsolandiInstitute of Biomedical Technologies, National Research Council, Segrate, Milan, Italy.
Elio RossiDepartment of Biosciences, University of Milan, Milan, Italy.
Luca PetitiDepartment of Medical Biotechnologies and Translational Medicine, University of Milan, Milan, Italy.
Letizia TagliabueDepartment of Biosciences, University of Milan, Milan, Italy.
Gianluca De BellisInstitute of Biomedical Technologies, National Research Council, Segrate, Milan, Italy.
Paolo LandiniDepartment of Biosciences, University of Milan, Milan, Italy.
University of Milan · ITNational Academies of Sciences, Engineering, and Medicine · USInstitute of Biomedical Technologies · ITNational Research Council · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

unlabelled

backgroundNext generation sequencing (NGS) technologies have revolutionized gene expression studies and functional genomics analysis. However, further improvement of RNA sequencing protocols is still desirable, in order to reduce NGS costs and to increase its accuracy. In bacteria, a major problem in RNA sequencing is the abundance of ribosomal RNA (rRNA), which accounts for 95-98% of total RNA and can therefore hinder sufficient coverage of mRNA, the main focus of transcriptomic studies. Thus, efficient removal of rRNA is necessary to achieve optimal coverage, good detection sensitivity and reliable results. An additional challenge is presented by microorganisms with GC-rich genomes, in which rRNA removal is less efficient.

resultsIn this work, we tested two commercial kits for rRNA removal, either alone or in combination, on Burkholderia thailandensis. This bacterium, chosen as representative of the important Burkholderia genus, which includes both pathogenic and environmental bacteria, has a rather large (6.72 Mb) and GC-rich (67.7%) genome. Each enriched mRNA sample was sequenced through paired-end Illumina GAIIx run in duplicate, yielding between 10 and 40 million reads. We show that combined treatment with both kits allows an mRNA enrichment of more than 238-fold, enabling the sequencing of almost all (more than 90%) B. thailandensis transcripts from less than 10 million reads, without introducing any bias in mRNA relative abundance, thus preserving differential expression profile.

conclusionsThe mRNA enrichment protocol presented in this work leads to an increase in detection sensitivity up to 770% compared to total RNA; such increased sensitivity allows for a corresponding reduction in the number of sequencing reads necessary for the complete analysis of whole transcriptome expression profiling. Thus we can conclude that the MICROBExpress/Ovation combined rRNA removal method could be suitable for RNA sequencing of whole transcriptomes of microorganisms with high GC content and complex genomes enabling at the same time an important scaling down of sequencing costs.

Identifiers

PMID23294941
PMCPMC3563489
OpenAlexW2113860998

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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.