scholarly journals Novel methods for rRNA removal and directional, ligation-free RNA-seq library preparation

2010 ◽  
Vol 7 (10) ◽  
pp. i-ii ◽  
Author(s):  
Roy Sooknanan ◽  
Jim Pease ◽  
Ken Doyle
Keyword(s):  
Library Preparation ◽  
Rna Seq ◽  
Novel Methods

scholarly journals Methyltransferase-directed orthogonal tagging and sequencing of miRNAs and bacterial small RNAs

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Milda Mickutė ◽  
Kotryna Kvederavičiūtė ◽  
Aleksandr Osipenko ◽  
Raminta Mineikaitė ◽  
Saulius Klimašauskas ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Regulatory Networks ◽  
Library Preparation ◽  
Rna Seq ◽  
Basic Principles ◽  
Cofactor Binding ◽  
Sequencing Library ◽  
Sequencing Library Preparation ◽  
Target Rna

Abstract Background Targeted installation of designer chemical moieties on biopolymers provides an orthogonal means for their visualisation, manipulation and sequence analysis. Although high-throughput RNA sequencing is a widely used method for transcriptome analysis, certain steps, such as 3′ adapter ligation in strand-specific RNA sequencing, remain challenging due to structure- and sequence-related biases introduced by RNA ligases, leading to misrepresentation of particular RNA species. Here, we remedy this limitation by adapting two RNA 2′-O-methyltransferases from the Hen1 family for orthogonal chemo-enzymatic click tethering of a 3′ sequencing adapter that supports cDNA production by reverse transcription of the tagged RNA. Results We showed that the ssRNA-specific DmHen1 and dsRNA-specific AtHEN1 can be used to efficiently append an oligonucleotide adapter to the 3′ end of target RNA for sequencing library preparation. Using this new chemo-enzymatic approach, we identified miRNAs and prokaryotic small non-coding sRNAs in probiotic Lactobacillus casei BL23. We found that compared to a reference conventional RNA library preparation, methyltransferase-Directed Orthogonal Tagging and RNA sequencing, mDOT-seq, avoids misdetection of unspecific highly-structured RNA species, thus providing better accuracy in identifying the groups of transcripts analysed. Our results suggest that mDOT-seq has the potential to advance analysis of eukaryotic and prokaryotic ssRNAs. Conclusions Our findings provide a valuable resource for studies of the RNA-centred regulatory networks in Lactobacilli and pave the way to developing novel transcriptome and epitranscriptome profiling approaches in vitro and inside living cells. As RNA methyltransferases share the structure of the AdoMet-binding domain and several specific cofactor binding features, the basic principles of our approach could be easily translated to other AdoMet-dependent enzymes for the development of modification-specific RNA-seq techniques.

Transcriptome-wide m6A detection with DART-Seq

2019 ◽  
Author(s):  
Kate D. Meyer
Keyword(s):  
Gene Expression Regulation ◽  
Preparation Method ◽  
Current Knowledge ◽  
Cross Reactivity ◽  
High Variability ◽  
Library Preparation ◽  
Rna Seq ◽  
Generation Sequencing ◽  
Library Preparation Method

Abstract m6A is the most abundant internal mRNA modification and plays diverse roles in gene expression regulation. Much of our current knowledge about m6A has been driven by recent advances in the ability to detect this mark transcriptome-wide. Antibody-based approaches have been the method of choice for global m6A mapping studies. These methods rely on m6A antibodies to immunoprecipitate methylated RNAs, followed by next-generation sequencing to identify m6A-containing transcripts1,2. While these methods enabled the first identification of m6A sites transcriptome-wide and have dramatically improved our ability to study m6A, they suffer from several limitations. These include requirements for high amounts of input RNA, costly and time-consuming library preparation, high variability across studies, and m6A antibody cross-reactivity with other modifications. Here, we describe DART-Seq (deamination adjacent to RNA modification targets), an antibody-free method for global m6A detection. In DART-Seq, the C to U deaminating enzyme, APOBEC1, is fused to the m6A-binding YTH domain. This fusion protein is then introduced to cellular RNA either through overexpression in cells or with in vitro assays, and subsequent deamination of m6A-adjacent cytidines is then detected by RNA sequencing to identify m6A sites. DART-Seq can successfully map m6A sites throughout the transcriptome using as little as 10 nanograms of total cellular RNA, and it is compatible with any standard RNA-seq library preparation method.

scholarly journals Identifying inaccuracies in gene expression estimates from unstranded RNA-seq data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mikhail Pomaznoy ◽  
Ashu Sethi ◽  
Jason Greenbaum ◽  
Bjoern Peters
Keyword(s):  
Gene Expression ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Library Preparation ◽  
Rna Seq ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Machine Learning Model ◽  
Specific Manner ◽  
Library Preparation Protocol

Abstract RNA-seq methods are widely utilized for transcriptomic profiling of biological samples. However, there are known caveats of this technology which can skew the gene expression estimates. Specifically, if the library preparation protocol does not retain RNA strand information then some genes can be erroneously quantitated. Although strand-specific protocols have been established, a significant portion of RNA-seq data is generated in non-strand-specific manner. We used a comprehensive stranded RNA-seq dataset of 15 blood cell types to identify genes for which expression would be erroneously estimated if strand information was not available. We found that about 10% of all genes and 2.5% of protein coding genes have a two-fold or higher difference in estimated expression when strand information of the reads was ignored. We used parameters of read alignments of these genes to construct a machine learning model that can identify which genes in an unstranded dataset might have incorrect expression estimates and which ones do not. We also show that differential expression analysis of genes with biased expression estimates in unstranded read data can be recovered by limiting the reads considered to those which span exonic boundaries. The resulting approach is implemented as a package available at https://github.com/mikpom/uslcount.

scholarly journals Method for improved Illumina sequencing library preparation using NuGEN Ovation RNA-Seq System

BioTechniques ◽  
2011 ◽  
Vol 50 (3) ◽  
pp. 177-181 ◽  
Author(s):  
Steven R. Head ◽  
H.Kiyomi Komori ◽  
G.Traver Hart ◽  
John Shimashita ◽  
Lana Schaffer ◽  
...  
Keyword(s):  
Illumina Sequencing ◽  
Library Preparation ◽  
Rna Seq ◽  
Sequencing Library ◽  
Sequencing Library Preparation

scholarly journals A rapid, directional RNA-seq library preparation workflow for Illumina® sequencing

2012 ◽  
Vol 9 (3) ◽  
pp. i-ii ◽  
Author(s):  
Jim Pease ◽  
Roy Sooknanan
Keyword(s):  
Library Preparation ◽  
Rna Seq

scholarly journals Comparison of Poly-A+ Selection and rRNA Depletion in Detection of lncRNA in Two Equine Tissues Using RNA-seq

2020 ◽  
Vol 6 (3) ◽  
pp. 32 ◽  
Author(s):  
Anna R. Dahlgren ◽  
Erica Y. Scott ◽  
Tamer Mansour ◽  
Erin N. Hales ◽  
Pablo J. Ross ◽  
...  
Keyword(s):  
Ribosomal Rna ◽  
Preparation Method ◽  
Parietal Lobe ◽  
Library Preparation ◽  
Rna Seq ◽  
The Past ◽  
Rrna Depletion ◽  
Non Coding Rnas ◽  
Nucleolar Rna ◽  
Library Preparation Method

Long non-coding RNAs (lncRNAs) are untranslated regulatory transcripts longer than 200 nucleotides that can play a role in transcriptional, post-translational, and epigenetic regulation. Traditionally, RNA-sequencing (RNA-seq) libraries have been created by isolating transcriptomic RNA via poly-A+ selection. In the past 10 years, methods to perform ribosomal RNA (rRNA) depletion of total RNA have been developed as an alternative, aiming for better coverage of whole transcriptomic RNA, both polyadenylated and non-polyadenylated transcripts. The purpose of this study was to determine which library preparation method is optimal for lncRNA investigations in the horse. Using liver and cerebral parietal lobe tissues from two healthy Thoroughbred mares, RNA-seq libraries were prepared using standard poly-A+ selection and rRNA-depletion methods. Averaging the two biologic replicates, poly-A+ selection yielded 327 and 773 more unique lncRNA transcripts for liver and parietal lobe, respectively. More lncRNA were found to be unique to poly-A+ selected libraries, and rRNA-depletion identified small nucleolar RNA (snoRNA) to have a higher relative expression than in the poly-A+ selected libraries. Overall, poly-A+ selection provides a more thorough identification of total lncRNA in equine tissues while rRNA-depletion may allow for easier detection of snoRNAs.

scholarly journals Comparative evaluation of RNA-Seq library preparation methods for strand-specificity and low input

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Dimitra Sarantopoulou ◽  
Soon Yew Tang ◽  
Emanuela Ricciotti ◽  
Nicholas F. Lahens ◽  
Damien Lekkas ◽  
...  
Keyword(s):  
Comparative Evaluation ◽  
Differential Expression Analysis ◽  
Functional Results ◽  
Starting Material ◽  
The Other ◽  
Library Preparation ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Preparation Methods ◽  
Low Input

Abstract Library preparation is a key step in sequencing. For RNA sequencing there are advantages to both strand specificity and working with minute starting material, yet until recently there was no kit available enabling both. The Illumina TruSeq stranded mRNA Sample Preparation kit (TruSeq) requires abundant starting material while the Takara Bio SMART-Seq v4 Ultra Low Input RNA kit (V4) sacrifices strand specificity. The SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Pico) by Takara Bio claims to overcome these limitations. Comparative evaluation of these kits is important for selecting the appropriate protocol. We compared the three kits in a realistic differential expression analysis. We prepared and sequenced samples from two experimental conditions of biological interest with each of the three kits. We report differences between the kits at the level of differential gene expression; for example, the Pico kit results in 55% fewer differentially expressed genes than TruSeq. Nevertheless, the agreement of the observed enriched pathways suggests that comparable functional results can be obtained. In summary we conclude that the Pico kit sufficiently reproduces the results of the other kits at the level of pathway analysis while providing a combination of options that is not available in the other kits.

scholarly journals A High-Throughput Method for Illumina RNA-Seq Library Preparation

2012 ◽  
Vol 3 ◽  
Author(s):  
Ravi Kumar ◽  
Yasunori Ichihashi ◽  
Seisuke Kimura ◽  
Daniel H. Chitwood ◽  
Lauren R. Headland ◽  
...  
Keyword(s):  
High Throughput ◽  
Library Preparation ◽  
Rna Seq ◽  
High Throughput Method

scholarly journals Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols

2015 ◽  
Author(s):  
Peter A Combs ◽  
Michael B Eisen
Keyword(s):  
Low Cost ◽  
Critical Evaluation ◽  
Standard Protocol ◽  
Library Preparation ◽  
Rna Seq ◽  
High Input ◽  
Simple Modification ◽  
Input Sample ◽  
Low Volume ◽  
The Cost

Recently, a number of protocols extending RNA-sequencing to the single-cell regime have been published. However, we were concerned that the additional steps to deal with such minute quantities of input sample would introduce serious biases that would make analysis of the data using existing approaches invalid. In this study, we performed a critical evaluation of several of these low-volume RNA-seq protocols, and found that they performed slightly less well in metrics of interest to us than a more standard protocol, but with at least two orders of magnitude less sample required. We also explored a simple modification to one of these protocols that, for many samples, reduced the cost of library preparation to approximately $20/sample.

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