scholarly journals RNAseq by Total RNA Library Identifies Additional RNAs Compared to Poly(A) RNA Library

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Guo ◽  
Shilin Zhao ◽  
Quanhu Sheng ◽  
Mingsheng Guo ◽  
Brian Lehmann ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rnas ◽  
Breast Cancer Cell Lines ◽  
Rna Expression ◽  
Library Preparation ◽  
Sequencing Data ◽  
Protein Coding ◽  
Total Rna ◽  
Capture Method ◽  
Highly Correlated

The most popular RNA library used for RNA sequencing is the poly(A) captured RNA library. This library captures RNA based on the presence of poly(A) tails at the 3′ end. Another type of RNA library for RNA sequencing is the total RNA library which differs from the poly(A) library by capture method and price. The total RNA library costs more and its capture of RNA is not dependent on the presence of poly(A) tails. In practice, only ribosomal RNAs and small RNAs are washed out in the total RNA library preparation. To evaluate the ability of detecting RNA for both RNA libraries we designed a study using RNA sequencing data of the same two breast cancer cell lines from both RNA libraries. We found that the RNA expression values captured by both RNA libraries were highly correlated. However, the number of RNAs captured was significantly higher for the total RNA library. Furthermore, we identify several subsets of protein coding RNAs that were not captured efficiently by the poly(A) library. One of the most noticeable is the histone-encode genes, which lack the poly(A) tail.

scholarly journals Massively differential bias between two widely used Illumina library preparation methods for small RNA sequencing

2013 ◽  
Author(s):  
Jeanette Baran-Gale ◽  
Michael R Erdos ◽  
Christina Sison ◽  
Alice Young ◽  
Emily E Fannin ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Small Rnas ◽  
Small Rna Sequencing ◽  
Library Preparation ◽  
Sequencing Technology ◽  
Preparation Methods ◽  
Sequencing Platforms ◽  
Small Rna Library ◽  
Illumina Library

Recent advances in sequencing technology have helped unveil the unexpected complexity and diversity of small RNAs. A critical step in small RNA library preparation for sequencing is the ligation of adapter sequences to both the 5’ and 3’ ends of small RNAs. Two widely used protocols for small RNA library preparation, Illumina v1.5 and Illumina TruSeq, use different pairs of adapter sequences. In this study, we compare the results of small RNA-sequencing between v1.5 and TruSeq and observe a striking differential bias. Nearly 100 highly expressed microRNAs (miRNAs) are >5-fold differentially detected and 48 miRNAs are >10-fold differentially detected between the two methods of library preparation. In fact, some miRNAs, such as miR-24-3p, are over 30-fold differentially detected. The results are reproducible across different sequencing centers (NIH and UNC) and both major Illumina sequencing platforms, GAIIx and HiSeq. While some level of bias in library preparation is not surprising, the apparent massive differential bias between these two widely used adapter sets is not well appreciated. As increasingly more laboratories transition to the newer TruSeq-based library preparation for small RNAs, researchers should be aware of the extent to which the results may differ from previously published results using v1.5.

scholarly journals A low-bias and sensitive small RNA library preparation method using randomized splint ligation

2020 ◽  
Vol 48 (14) ◽  
pp. e80-e80 ◽  
Author(s):  
Sean Maguire ◽  
Gregory J S Lohman ◽  
Shengxi Guan
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Small Rnas ◽  
Small Rna Sequencing ◽  
Preparation Process ◽  
Library Preparation ◽  
Normal Tissues ◽  
Levels Of Detail ◽  
Low Sensitivity ◽  
Next Generation Sequencing Ngs

Abstract Small RNAs are important regulators of gene expression and are involved in human development and disease. Next generation sequencing (NGS) allows for scalable, genome-wide studies of small RNA; however, current methods are challenged by low sensitivity and high bias, limiting their ability to capture an accurate representation of the cellular small RNA population. Several studies have shown that this bias primarily arises during the ligation of single-strand adapters during library preparation, and that this ligation bias is magnified by 2′-O-methyl modifications (2′OMe) on the 3′ terminal nucleotide. In this study, we developed a novel library preparation process using randomized splint ligation with a cleavable adapter, a design which resolves previous challenges associated with this ligation strategy. We show that a randomized splint ligation based workflow can reduce bias and increase the sensitivity of small RNA sequencing for a wide variety of small RNAs, including microRNA (miRNA) and tRNA fragments as well as 2′OMe modified RNA, including Piwi-interacting RNA and plant miRNA. Finally, we demonstrate that this workflow detects more differentially expressed miRNA between tumorous and matched normal tissues. Overall, this library preparation process allows for highly accurate small RNA sequencing and will enable studies of 2′OMe modified RNA with new levels of detail.

scholarly journals SMARTer single cell total RNA sequencing

2018 ◽  
Author(s):  
Verboom Karen ◽  
Everaert Celine ◽  
Bolduc Nathalie ◽  
Livak J. Kenneth ◽  
Yigit Nurten ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Transcript Level ◽  
Cellular Heterogeneity ◽  
Circular Rnas ◽  
Rna Seq ◽  
Sequencing Data ◽  
Total Rna ◽  
Sequencing Experiment

AbstractSingle cell RNA sequencing methods have been increasingly used to understand cellular heterogeneity. Nevertheless, most of these methods suffer from one or more limitations, such as focusing only on polyadenylated RNA, sequencing of only the 3’ end of the transcript, an exuberant fraction of reads mapping to ribosomal RNA, and the unstranded nature of the sequencing data. Here, we developed a novel single cell strand-specific total RNA library preparation method addressing all the aforementioned shortcomings. Our method was validated on a microfluidics system using three different cancer cell lines undergoing a chemical or genetic perturbation. We demonstrate that our total RNA-seq method detects an equal or higher number of genes compared to classic polyA[+] RNA-seq, including novel and non-polyadenylated genes. The obtained RNA expression patterns also recapitulate the expected biological signal. Inherent to total RNA-seq, our method is also able to detect circular RNAs. Taken together, SMARTer single cell total RNA sequencing is very well suited for any single cell sequencing experiment in which transcript level information is needed beyond polyadenylated genes.

scholarly journals A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Simon Haile ◽  
Richard D. Corbett ◽  
Veronique G. LeBlanc ◽  
Lisa Wei ◽  
Stephen Pleasance ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Single Cells ◽  
Cell Types ◽  
Full Length ◽  
Sequencing Data ◽  
Total Rna ◽  
Specific Protocol

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

scholarly journals Telomere-to-telomere genome assembly of asparaginase-producing Trichoderma simmonsii

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dawoon Chung ◽  
Yong Min Kwon ◽  
Youngik Yang
Keyword(s):  
Rna Sequencing ◽  
Draft Genome ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Terrestrial Plants ◽  
Protein Coding ◽  
Oxford Nanopore ◽  
Wide Range ◽  
The Family ◽  
Encoding Genes

Abstract Background Trichoderma is a genus of fungi in the family Hypocreaceae and includes species known to produce enzymes with commercial use. They are largely found in soil and terrestrial plants. Recently, Trichoderma simmonsii isolated from decaying bark and decorticated wood was newly identified in the Harzianum clade of Trichoderma. Due to a wide range of applications in agriculture and other industries, genomes of at least 12 Trichoderma spp. have been studied. Moreover, antifungal and enzymatic activities have been extensively characterized in Trichoderma spp. However, the genomic information and bioactivities of T. simmonsii from a particular marine-derived isolate remain largely unknown. While we screened for asparaginase-producing fungi, we observed that T. simmonsii GH-Sj1 strain isolated from edible kelp produced asparaginase. In this study, we report a draft genome of T. simmonsii GH-Sj1 using Illumina and Oxford Nanopore technologies. Furthermore, to facilitate biotechnological applications of this species, RNA-sequencing was performed to elucidate the transcriptional profile of T. simmonsii GH-Sj1 in response to asparaginase-rich conditions. Results We generated ~ 14 Gb of sequencing data assembled in a ~ 40 Mb genome. The T. simmonsii GH-Sj1 genome consisted of seven telomere-to-telomere scaffolds with no sequencing gaps, where the N50 length was 6.4 Mb. The total number of protein-coding genes was 13,120, constituting ~ 99% of the genome. The genome harbored 176 tRNAs, which encode a full set of 20 amino acids. In addition, it had an rRNA repeat region consisting of seven repeats of the 18S-ITS1–5.8S-ITS2–26S cluster. The T. simmonsii genome also harbored 7 putative asparaginase-encoding genes with potential medical applications. Using RNA-sequencing analysis, we found that 3 genes among the 7 putative genes were significantly upregulated under asparaginase-rich conditions. Conclusions The genome and transcriptome of T. simmonsii GH-Sj1 established in the current work represent valuable resources for future comparative studies on fungal genomes and asparaginase production.

scholarly journals Combining DGE and RNA-sequencing data to identify new polyA+ non-coding transcripts in the human genome

2013 ◽  
Vol 42 (5) ◽  
pp. 2820-2832 ◽  
Author(s):  
Nicolas Philippe ◽  
Elias Bou Samra ◽  
Anthony Boureux ◽  
Alban Mancheron ◽  
Florence Rufflé ◽  
...  
Keyword(s):  
Human Genome ◽  
Rna Sequencing ◽  
Dynamic Range ◽  
Tiling Array ◽  
Expression Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Data Set ◽  
Protein Coding ◽  
Protein Coding Genes

Abstract Recent sequencing technologies that allow massive parallel production of short reads are the method of choice for transcriptome analysis. Particularly, digital gene expression (DGE) technologies produce a large dynamic range of expression data by generating short tag signatures for each cell transcript. These tags can be mapped back to a reference genome to identify new transcribed regions that can be further covered by RNA-sequencing (RNA-Seq) reads. Here, we applied an integrated bioinformatics approach that combines DGE tags, RNA-Seq, tiling array expression data and species-comparison to explore new transcriptional regions and their specific biological features, particularly tissue expression or conservation. We analysed tags from a large DGE data set (designated as ‘TranscriRef’). We then annotated 750 000 tags that were uniquely mapped to the human genome according to Ensembl. We retained transcripts originating from both DNA strands and categorized tags corresponding to protein-coding genes, antisense, intronic- or intergenic-transcribed regions and computed their overlap with annotated non-coding transcripts. Using this bioinformatics approach, we identified ∼34 000 novel transcribed regions located outside the boundaries of known protein-coding genes. As demonstrated using sequencing data from human pluripotent stem cells for biological validation, the method could be easily applied for the selection of tissue-specific candidate transcripts. DigitagCT is available at http://cractools.gforge.inria.fr/softwares/digitagct.

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