Transposase-assisted tagmentation of RNA/DNA hybrid duplexes

Tn5-mediated transposition of double-strand DNA has been widely utilized in various high-throughput sequencing applications. Here, we report that the Tn5 transposase is also capable of direct tagmentation of RNA/DNA hybrids in vitro. As a proof-of-concept application, we utilized this activity to replace the traditional library construction procedure of RNA sequencing, which contains many laborious and time-consuming processes. Results of Transposase-assisted RNA/DNA hybrids Co-tagmEntation (termed ‘TRACE-seq’) are compared to traditional RNA-seq methods in terms of detected gene number, gene body coverage, gene expression measurement, library complexity, and differential expression analysis. At the meantime, TRACE-seq enables a cost-effective one-tube library construction protocol and hence is more rapid (within 6 hr) and convenient. We expect this tagmentation activity on RNA/DNA hybrids to have broad potentials on RNA biology and chromatin research.

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Transposase assisted tagmentation of RNA/DNA hybrid duplexes

10.1101/2020.01.29.926105 ◽

2020 ◽

Cited By ~ 1

Author(s):

Bo Lu ◽

Liting Dong ◽

Danyang Yi ◽

Chenxu Zhu ◽

Meiling Zhang ◽

...

Keyword(s):

Gene Expression Analysis ◽

High Throughput Sequencing ◽

Proof Of Concept ◽

Rna Seq ◽

Library Construction ◽

Construction Procedure ◽

Rna Biology ◽

Concept Application ◽

Tn5 Transposase

AbstractTn5-mediated transposition of double-strand DNA has been widely utilized in various high-throughput sequencing applications. Here, we report that the Tn5 transposase is also capable of direct tagmentation of RNA/DNA hybrids in vitro. As a proof-of-concept application, we utilized this activity to replace the traditional library construction procedure of RNA sequencing, which contains many laborious and time-consuming processes. Results of activity of transposase assisted RNA/DNA hybrids co-tagmentation (termed “ATRAC-seq”) are comparable to traditional RNA-seq methods in terms of gene number, gene body coverage and gene expression analysis; at the meantime, ATRAC-seq enables a one-tube library construction protocol and hence is more rapid (within 8 h) and convenient. We expect this tagmentation activity on RNA/DNA hybrids to have broad potentials on RNA biology and chromatin research.

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HTSeq - A Python framework to work with high-throughput sequencing data

10.1101/002824 ◽

2014 ◽

Cited By ~ 242

Author(s):

Simon Anders ◽

Paul Theodor Pyl ◽

Wolfgang Huber

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Rapid Development ◽

Differential Expression Analysis ◽

Rna Seq ◽

Sequencing Data ◽

Standard Work ◽

Data Formats ◽

High Throughput Sequencing Data ◽

Python Package

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard work flows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data such as genomic coordinates, sequences, sequencing reads, alignments, gene model information, variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability: HTSeq is released as open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index, https://pypi.python.org/pypi/HTSeq

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Complete biosynthetic pathways of ascofuranone and ascochlorin inAcremonium egyptiacum

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1819254116 ◽

2019 ◽

Vol 116 (17) ◽

pp. 8269-8274 ◽

Cited By ~ 18

Author(s):

Yasuko Araki ◽

Takayoshi Awakawa ◽

Motomichi Matsuzaki ◽

Rihe Cho ◽

Yudai Matsuda ◽

...

Keyword(s):

Differential Expression Analysis ◽

Cost Effective ◽

Drug Candidate ◽

P450 Monooxygenase ◽

Genome Wide ◽

Common Precursor ◽

A Genome ◽

Membrane Bound ◽

The Cost

Ascofuranone (AF) and ascochlorin (AC) are meroterpenoids produced by various filamentous fungi, includingAcremonium egyptiacum(synonym:Acremonium sclerotigenum), and exhibit diverse physiological activities. In particular, AF is a promising drug candidate against African trypanosomiasis and a potential anticancer lead compound. These compounds are supposedly biosynthesized through farnesylation of orsellinic acid, but the details have not been established. In this study, we present all of the reactions and responsible genes for AF and AC biosyntheses inA. egyptiacum, identified by heterologous expression, in vitro reconstruction, and gene deletion experiments with the aid of a genome-wide differential expression analysis. Both pathways share the common precursor, ilicicolin A epoxide, which is processed by the membrane-bound terpene cyclase (TPC) AscF in AC biosynthesis. AF biosynthesis branches from the precursor by hydroxylation at C-16 by the P450 monooxygenase AscH, followed by cyclization by a membrane-bound TPC AscI. All genes required for AC biosynthesis (ascABCDEFG) and a transcriptional factor (ascR) form a functional gene cluster, whereas those involved in the late steps of AF biosynthesis (ascHIJ) are present in another distantly located cluster. AF is therefore a rare example of fungal secondary metabolites requiring multilocus biosynthetic clusters, which are likely to be controlled by the single regulator, AscR. Finally, we achieved the selective production of AF inA. egyptiacumby genetically blocking the AC biosynthetic pathway; further manipulation of the strain will lead to the cost-effective mass production required for the clinical use of AF.

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A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies

Genes ◽

10.3390/genes12121947 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1947

Author(s):

Samarendra Das ◽

Anil Rai ◽

Michael L. Merchant ◽

Matthew C. Cave ◽

Shesh N. Rai

Keyword(s):

Single Cell ◽

Differential Expression ◽

Rna Sequencing ◽

High Throughput Sequencing ◽

Performance Metrics ◽

Differential Expression Analysis ◽

Individual Performance ◽

Rna Seq ◽

Gene Expressions ◽

Single Cell Rna Sequencing

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

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Impact of human gene annotations on RNA-seq differential expression analysis

10.21203/rs.3.rs-301856/v1 ◽

2021 ◽

Author(s):

Yu Hamaguchi ◽

Chao Zeng ◽

Michiaki Hamada

Keyword(s):

Differential Expression ◽

High Throughput ◽

High Throughput Sequencing ◽

Human Gene ◽

Gene Annotation ◽

Differential Expression Analysis ◽

Rna Seq ◽

Gene Annotations ◽

Sequencing Technologies ◽

The Impact

Abstract Background: Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear.Results: Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically.Conclusions: We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

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Molecular Viral Diagnosis and Sanitation of Yam Genetic Resources: Implications for Safe Yam Germplasm Exchange

Viruses ◽

10.3390/v12101101 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1101

Author(s):

Marie Umber ◽

Denis Filloux ◽

Suzia Gélabale ◽

Rose-Marie Gomez ◽

Armelle Marais ◽

...

Keyword(s):

High Throughput Sequencing ◽

Cost Effective ◽

Meristem Culture ◽

Germplasm Exchange ◽

Tropical Plants ◽

High Prevalence ◽

Clean Seed ◽

High Level ◽

Viral Diagnosis

Yam (Dioscorea spp.) is an important crop in tropical and subtropical regions. Many viruses have been recently identified in yam, hampering genetic conservation and safe international exchanges of yam germplasm. We report on the implementation of reliable and cost-effective PCR-based detection tools targeting eight different yam-infecting viruses. Viral indexing of the in vitro yam collection maintained by the Biological Resources Center for Tropical Plants (BRC-TP) in Guadeloupe (French West Indies) unveiled a high prevalence of potyviruses, badnaviruses, Dioscorea mosaic associated virus (DMaV) and yam asymptomatic virus 1 (YaV1) and a high level of coinfections. Infected yam accessions were subjected to a combination of thermotherapy and meristem culture. Sanitation levels were monitored using PCR-based and high-throughput sequencing-based diagnosis, confirming the efficacy and reliability of PCR-based detection tools. Sanitation rates were highly variable depending on viruses. Sixteen accessions were successfully sanitized, paving the way to safe yam germplasm exchanges and the implementation of clean seed production programs worldwide.

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intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data

BMC Genomics ◽

10.1186/s12864-020-07205-6 ◽

2020 ◽

Vol 21 (S11) ◽

Author(s):

Yingying Cao ◽

Simo Kitanovski ◽

Daniel Hoffmann

Keyword(s):

Gene Expression ◽

Expression Analysis ◽

High Throughput Sequencing ◽

Differential Expression Analysis ◽

Cell Types ◽

R Package ◽

Integrative Approach ◽

Integrated Analysis ◽

Data Sets ◽

Rna Seq

Abstract Background RNA-Seq, the high-throughput sequencing (HT-Seq) of mRNAs, has become an essential tool for characterizing gene expression differences between different cell types and conditions. Gene expression is regulated by several mechanisms, including epigenetically by post-translational histone modifications which can be assessed by ChIP-Seq (Chromatin Immuno-Precipitation Sequencing). As more and more biological samples are analyzed by the combination of ChIP-Seq and RNA-Seq, the integrated analysis of the corresponding data sets becomes, theoretically, a unique option to study gene regulation. However, technically such analyses are still in their infancy. Results Here we introduce intePareto, a computational tool for the integrative analysis of RNA-Seq and ChIP-Seq data. With intePareto we match RNA-Seq and ChIP-Seq data at the level of genes, perform differential expression analysis between biological conditions, and prioritize genes with consistent changes in RNA-Seq and ChIP-Seq data using Pareto optimization. Conclusion intePareto facilitates comprehensive understanding of high dimensional transcriptomic and epigenomic data. Its superiority to a naive differential gene expression analysis with RNA-Seq and available integrative approach is demonstrated by analyzing a public dataset.

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High-resolution Expression Profiling of Selected Gene Sets during Plant Immune Activation

10.1101/775973 ◽

2019 ◽

Cited By ~ 1

Author(s):

Pingtao Ding ◽

Bruno Pok Man Ngou ◽

Oliver J. Furzer ◽

Toshiyuki Sakai ◽

Ram Krishna Shrestha ◽

...

Keyword(s):

Gene Expression ◽

High Resolution ◽

Differential Gene Expression ◽

Time Course ◽

High Throughput Sequencing ◽

Cost Effective ◽

Biological Processes ◽

Rna Seq ◽

Sequence Capture ◽

Differential Gene

SUMMARYSequence capture followed by next-generation sequencing has broad applications in cost-effective exploration of biological processes at high resolution [1, 2]. Genome-wide RNA sequencing (RNA-seq) over a time course can reveal the dynamics of differential gene expression. However, in many cases, only a limited set of genes are of interest, and are repeatedly used as markers for certain biological processes. Sequence capture can help generate high-resolution quantitative datasets to assess changes in abundance of selected genes. We previously used sequence capture to accelerate Resistance gene cloning [1, 3, 4], investigate immune receptor gene diversity [5] and investigate pathogen diversity and evolution [6, 7].The plant immune system involves detection of pathogens via both cell-surface and intracellular receptors. Both receptor classes can induce transcriptional reprogramming that elevates disease resistance [8]. To assess differential gene expression during plant immunity, we developed and deployed quantitative sequence capture (CAP-I). We designed and synthesized biotinylated single-strand RNA bait libraries targeted to a subset of defense genes, and generated sequence capture data from 99 RNA-seq libraries. We built a data processing pipeline to quantify the RNA-CAP-I-seq data, and visualize differential gene expression. Sequence capture in combination with quantitative RNA-seq enabled cost-effective assessment of the expression profile of a specified subset of genes. Quantitative sequence capture is not limited to RNA-seq or any specific organism and can potentially be incorporated into automated platforms for high-throughput sequencing.

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Nanopore sequencing of RNA and cDNA molecules in Escherichia coli

RNA ◽

10.1261/rna.078937.121 ◽

2021 ◽

pp. rna.078937.121

Author(s):

Felix Grünberger ◽

Sébastien Ferreira-Cerca ◽

Dina Grohmann

Keyword(s):

Escherichia Coli ◽

Rna Sequencing ◽

Single Molecule ◽

High Throughput Sequencing ◽

Model Organism ◽

Cost Effective ◽

Rna Seq ◽

Sequencing Platform ◽

Quantitative Measurements ◽

Oxford Nanopore

High-throughput sequencing dramatically changed our view of transcriptome architectures and allowed for ground-breaking discoveries in RNA biology. Recently, sequencing of full-length transcripts based on the single-molecule sequencing platform from Oxford Nanopore Technologies (ONT) was introduced and is widely employed to sequence eukaryotic and viral RNAs. However, experimental approaches implementing this technique for prokaryotic transcriptomes remain scarce. Here, we present an experimental and bioinformatic workflow for ONT RNA-seq in the bacterial model organism Escherichia coli, which can be applied to any microorganism. Our study highlights critical steps of library preparation and computational analysis and compares the results to gold standards in the field. Furthermore, we comprehensively evaluate the applicability and advantages of different ONT-based RNA sequencing protocols, including direct RNA, direct cDNA, and PCR-cDNA. We find that (PCR)-cDNA-seq offers improved yield and accuracy compared to direct RNA sequencing. Notably, (PCR)-cDNA-seq is suitable for quantitative measurements and can be readily used for simultaneous and accurate detection of transcript 5'and 3' boundaries, analysis of transcriptional units and transcriptional heterogeneity. In summary, based on our comprehensive study, we show that Nanopore RNA-seq to be a ready-to-use tool allowing rapid, cost-effective, and accurate annotation of multiple transcriptomic features. Thereby Nanopore RNA-seq holds the potential to become a valuable alternative method for RNA analysis in prokaryotes.

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Nanopore sequencing of RNA and cDNA molecules expands the transcriptomic toolbox in prokaryotes

10.1101/2021.06.14.448286 ◽

2021 ◽

Author(s):

Felix Gruenberger ◽

Sebastien Ferreira-Cerca ◽

Dina Grohmann

Keyword(s):

Rna Sequencing ◽

Single Molecule ◽

High Throughput Sequencing ◽

Model Organism ◽

Cost Effective ◽

Rna Seq ◽

Sequencing Platform ◽

Transcript Quantification ◽

Oxford Nanopore ◽

Bacterial Model

High-throughput sequencing dramatically changed our view of transcriptome architectures and allowed for ground-breaking discoveries in RNA biology. Recently, sequencing of full-length transcripts based on the single-molecule sequencing platform from Oxford Nanopore Technologies (ONT) was introduced and is widely employed to sequence eukaryotic and viral RNAs. However, experimental approaches implementing this technique for prokaryotic transcriptomes remain scarce. Here, we present an experimental and bioinformatic workflow for ONT RNA-seq in the bacterial model organism Escherichia coli, which can be applied to any microorganism. Our study highlights critical steps of library preparation and computational analysis and compares the results to gold standards in the field. Furthermore, we comprehensively evaluate the applicability and advantages of different ONT-based RNA sequencing protocols, including direct RNA, direct cDNA, and PCR-cDNA. We find that cDNA-seq offers improved yield and accuracy without bias in quantification compared to direct RNA sequencing. Notably, cDNA-seq can be readily used for simultaneous transcript quantification, accurate detection of transcript 5 ′ and 3′ boundaries, analysis of transcriptional units and transcriptional heterogeneity. In summary, we establish Nanopore RNA-seq to be a ready-to-use tool allowing rapid, cost-effective, and accurate annotation of multiple transcriptomic features thereby advancing it to become a standard method for RNA analysis in prokaryotes.

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