Evidence map›Paper›PMID 30241496›Full record

ArticleBMC genomics2018

A comparative analysis of library prep approaches for sequencing low input translatome samples.

Yang Song, Beatrice Milon, Sandra Ott, Xuechu Zhao, Lisa Sadzewicz, Amol Shetty, Erich T Boger, Luke J Tallon, Robert J Morell, Anup Mahurkar and 1 more

Abstract readComparative Study
In one paragraph

Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed
–field-weighted citation impact
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

57 citing papers in PubMed.

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  14. Comparison of RNA-Sequencing Methods for Degraded RNA.International journal of molecular sciences · 2024
    Article
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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

11 authors.

Yang SongInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Beatrice MilonDepartment of Otorhinolaryngology-Head & Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Sandra OttInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Xuechu ZhaoInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Lisa SadzewiczInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Amol ShettyInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Erich T BogerGenomics and Computational Biology Core, National Institute on Deafness and Other Communication Disorders, Bethesda, MD, 20892, USA.
Luke J TallonInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Robert J MorellGenomics and Computational Biology Core, National Institute on Deafness and Other Communication Disorders, Bethesda, MD, 20892, USA.
Anup MahurkarInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Ronna HertzanoInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA. rhertzano@som.umaryland.edu.

Funding

Genomic and Computational Biology Support for NIDCD Intramural ResearchZICDC000086 · NIDCD · NATIONAL INSTITUTE ON DEAFNESS AND OTHER COMMUNICATION DISORDERS · PI MORELL, ROBERT J · 2015 to 2025
$19.8M
AUTOSOMAL DOMINANT NONSYNDROMIC HEARING LOSSR01DC003544 · NIDCD · UNIVERSITY OF IOWA · PI SMITH, RICHARD J.H. · 1998 to 2017
$7.8M
Cell Type Specific Transcriptional Cascades in Inner Ear DevelopmentR01DC013817 · NIDCD · UNIVERSITY OF MARYLAND BALTIMORE · PI HERTZANO, RONNA · 2015 to 2022
$3.6M
Cell Type Specific Transcriptional Cascades in Inner Ear DevelopmentU01DC013817 · NIDCD · UNIVERSITY OF MARYLAND BALTIMORE · PI AHMED, ZUBAIR M. · 2023 to 2025
$1.6M
Congressionally Directed Medical Research Programs MR130240NIDCD NIH HHS R01 DC003544NIDCD NIH HHS R01 DC013817NIDCD NIH HHS R01DC013817NIDCD NIH HHS R01DC03544NIDCD NIH HHS U01 DC013817
6 · The paper itself

Abstract

backgroundCell type-specific ribosome-pulldown has become an increasingly popular method for analysis of gene expression. It allows for expression analysis from intact tissues and monitoring of protein synthesis in vivo. However, while its utility has been assessed, technical aspects related to sequencing of these samples, often starting with a smaller amount of RNA, have not been reported. In this study, we evaluated the performance of five library prep protocols for ribosome-associated mRNAs when only 250 pg-4 ng of total RNA are used.

resultsWe obtained total and RiboTag-IP RNA, in three biological replicates. We compared 5 methods of library preparation for Illumina Next Generation sequencing: NuGEN Ovation RNA-Seq system V2 Kit, TaKaRa SMARTer Stranded Total RNA-Seq Kit, TaKaRa SMART-Seq v4 Ultra Low Input RNA Kit, Illumina TruSeq RNA Library Prep Kit v2 and NEBNext® Ultra™ Directional RNA Library Prep Kit using slightly modified protocols each with 4 ng of total RNA. An additional set of samples was processed using the TruSeq kit with 70 ng, as a 'gold standard' control and the SMART-Seq v4 with 250 pg of total RNA. TruSeq-processed samples had the best metrics overall, with similar results for the 4 ng and 70 ng samples. The results of the SMART-Seq v4 processed samples were similar to TruSeq (Spearman correlation > 0.8) despite using lower amount of input RNA. All RiboTag-IP samples had an increase in the intronic reads compared with the corresponding whole tissue, suggesting that the IP captures some immature mRNAs. The SMARTer-processed samples had a higher representation of ribosomal and non-coding RNAs leading to lower representation of protein coding mRNA. The enrichment or depletion of IP samples compared to corresponding input RNA was similar across all kits except for SMARTer kit.

conclusionRiboTag-seq can be performed successfully with as little as 250 pg of total RNA when using the SMART-Seq v4 kit and 4 ng when using the modified protocols of other library preparation kits. The SMART-Seq v4 and TruSeq kits resulted in the highest quality libraries. RiboTag IP RNA contains some immature transcripts.

Indexed as

Gene LibraryProtein BiosynthesisTranscriptomeAnimalsHigh-Throughput Nucleotide SequencingImmunoprecipitationMiceQuality ControlRibosomesRNA, MessengerSequence Analysis, RNARNA, MessengerCoverage biasLibrary preparation kitsLow-input RNA-seqRiboTagRNA-seq

Identifiers

PMID30241496
PMCPMC6151020

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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.