Is this really the end? Template-switching artifacts resemble alternative polyadenylation

Abstract Background Alternative polyadenylation is commonly examined using cDNA sequencing, which is known to be affected by template-switching artifacts. However, the effects of such template-switching artifacts on alternative polyadenylation are generally disregarded, while alternative polyadenylation artifacts are attributed to internal priming. Results Here, we analyzed both long-read cDNA sequencing and direct RNA sequencing data of two organisms, generated by different sequencing platforms. We developed a filtering algorithm which takes into consideration that template-switching can be a source of artifactual polyadenylation when filtering out spurious polyadenylation sites. The algorithm outperformed the conventional internal priming filters based on comparison to direct RNA sequencing data. We also showed that the polyadenylation artifacts arise in cDNA sequencing at consecutive stretches of as few as three adenines. There was no substantial difference between the lengths of poly(A) tails at the artifactual and the true transcriptional end sites even though it is expected that internal priming artifacts have shorter poly(A) tails than genuine polyadenylated reads. Conclusions Our findings suggest that template switching plays an important role in the generation of spurious polyadenylation and support the need for more rigorous filtering of artifactual polyadenylation sites in cDNA data, or that alternative polyadenylation should be annotated using native RNA sequencing.

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Template-switching artifacts resemble alternative polyadenylation

10.21203/rs.2.12056/v2 ◽

2019 ◽

Author(s):

Zsolt Balázs ◽

Dóra Tombácz ◽

Zsolt Csabai ◽

Norbert Moldován ◽

Michael Snyder ◽

...

Keyword(s):

Rna Sequencing ◽

Alternative Polyadenylation ◽

Template Switching ◽

Cdna Sequencing ◽

Sequencing Data ◽

Filtering Algorithm ◽

Long Read ◽

Polyadenylation Sites ◽

Sequencing Platforms

Abstract Background: Alternative polyadenylation is commonly examined using cDNA sequencing, which is known to be affected by template-switching artifacts. However, the effects of such template-switching artifacts on alternative polyadenylation are generally disregarded, while alternative polyadenylation artifacts are attributed to internal priming. Results: Here, we analyzed both long-read cDNA sequencing and direct RNA sequencing data of two organisms, generated by different sequencing platforms. We developed a filtering algorithm which takes into consideration that template-switching can be a source of artifactual polyadenylation when filtering out spurious polyadenylation sites. The algorithm outperformed the conventional internal priming filters based on comparison to direct RNA sequencing data. We also showed that the polyadenylation artifacts arise in cDNA sequencing at consecutive stretches of as few as three adenines. There was no substantial difference between the lengths of poly(A) tails at the artifactual and the true transcriptional end sites even though it is expected that internal priming artifacts have shorter poly(A) tails than genuine polyadenylated reads. Conclusions: Our findings suggest that template switching plays an important role in the generation of spurious polyadenylation and support the need for more rigorous filtering of artifactual polyadenylation sites in cDNA data, or that alternative polyadenylation should be annotated using native RNA sequencing.

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Template-switching artifacts resemble alternative polyadenylation

10.21203/rs.2.12056/v3 ◽

2019 ◽

Author(s):

Zsolt Balázs ◽

Dóra Tombácz ◽

Zsolt Csabai ◽

Norbert Moldován ◽

Michael Snyder ◽

...

Keyword(s):

Rna Sequencing ◽

Alternative Polyadenylation ◽

Template Switching ◽

Cdna Sequencing ◽

Sequencing Data ◽

Filtering Algorithm ◽

Long Read ◽

Polyadenylation Sites ◽

Sequencing Platforms

Abstract Background: Alternative polyadenylation is commonly examined using cDNA sequencing, which is known to be affected by template-switching artifacts. However, the effects of such template-switching artifacts on alternative polyadenylation are generally disregarded, while alternative polyadenylation artifacts are attributed to internal priming. Results: Here, we analyzed both long-read cDNA sequencing and direct RNA sequencing data of two organisms, generated by different sequencing platforms. We developed a filtering algorithm which takes into consideration that template-switching can be a source of artifactual polyadenylation when filtering out spurious polyadenylation sites. The algorithm outperformed the conventional internal priming filters based on comparison to direct RNA sequencing data. We also showed that the polyadenylation artifacts arise in cDNA sequencing at consecutive stretches of as few as three adenines. There was no substantial difference between the lengths of poly(A) tails at the artifactual and the true transcriptional end sites even though it is expected that internal priming artifacts have shorter poly(A) tails than genuine polyadenylated reads. Conclusions: Our findings suggest that template switching plays an important role in the generation of spurious polyadenylation and support the need for more rigorous filtering of artifactual polyadenylation sites in cDNA data, or that alternative polyadenylation should be annotated using native RNA sequencing.

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Long Read Annotation (LoReAn): automated eukaryotic genome annotation based on long-read cDNA sequencing

10.1101/230359 ◽

2017 ◽

Cited By ~ 1

Author(s):

David E. Cook ◽

Jose Espejo Valle-Inclan ◽

Alije Pajoro ◽

Hanna Rovenich ◽

Bart PHJ Thomma ◽

...

Keyword(s):

Single Molecule ◽

Genome Annotation ◽

Cdna Sequencing ◽

Sequencing Data ◽

Automated Annotation ◽

Long Read ◽

Transcript Structure ◽

Genome Annotations ◽

Sequencing Platforms ◽

Splice Forms

AbstractSingle-molecule full-length cDNA sequencing can aid genome annotation by revealing transcript structure and alternative splice-forms, yet current annotation pipelines do not incorporate such information. Here we present LoReAn (Long Read Annotation) software, an automated annotation pipeline utilizing short- and long-read cDNA sequencing, protein evidence, and ab initio prediction to generate accurate genome annotations. Based on annotations of two fungal and two plant genomes, we show that LoReAn outperforms popular annotation pipelines by integrating single-molecule cDNA sequencing data generated from either the PacBio or MinION sequencing platforms, and correctly predicting gene structure and capturing genes missed by other annotation pipelines.

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Aptardi predicts polyadenylation sites in sample-specific transcriptomes using high-throughput RNA sequencing and DNA sequence

Nature Communications ◽

10.1038/s41467-021-21894-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ryan Lusk ◽

Evan Stene ◽

Farnoush Banaei-Kashani ◽

Boris Tabakoff ◽

Katerina Kechris ◽

...

Keyword(s):

Rna Sequencing ◽

Dna Sequence ◽

Mammalian Species ◽

Alternative Polyadenylation ◽

Sequence Information ◽

Rna Seq ◽

Average Precision ◽

Polyadenylation Sites ◽

Dna Nucleotide Sequence

AbstractAnnotation of polyadenylation sites from short-read RNA sequencing alone is a challenging computational task. Other algorithms rooted in DNA sequence predict potential polyadenylation sites; however, in vivo expression of a particular site varies based on a myriad of conditions. Here, we introduce aptardi (alternative polyadenylation transcriptome analysis from RNA-Seq data and DNA sequence information), which leverages both DNA sequence and RNA sequencing in a machine learning paradigm to predict expressed polyadenylation sites. Specifically, as input aptardi takes DNA nucleotide sequence, genome-aligned RNA-Seq data, and an initial transcriptome. The program evaluates these initial transcripts to identify expressed polyadenylation sites in the biological sample and refines transcript 3′-ends accordingly. The average precision of the aptardi model is twice that of a standard transcriptome assembler. In particular, the recall of the aptardi model (the proportion of true polyadenylation sites detected by the algorithm) is improved by over three-fold. Also, the model—trained using the Human Brain Reference RNA commercial standard—performs well when applied to RNA-sequencing samples from different tissues and different mammalian species. Finally, aptardi’s input is simple to compile and its output is easily amenable to downstream analyses such as quantitation and differential expression.

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TagSeqTools: a flexible and comprehensive analysis pipeline for NAD tagSeq data

10.1101/2020.03.09.982934 ◽

2020 ◽

Cited By ~ 1

Author(s):

Huan Zhong ◽

Zongwei Cai ◽

Zhu Yang ◽

Yiji Xia

Keyword(s):

Rna Sequencing ◽

Comprehensive Analysis ◽

Enzymatic Reactions ◽

Computational Tool ◽

Sequencing Data ◽

Analysis Pipeline ◽

Oxford Nanopore ◽

Long Read ◽

Identification And Characterization

AbstractNAD tagSeq has recently been developed for the identification and characterization of NAD+-capped RNAs (NAD-RNAs). This method adopts a strategy of chemo-enzymatic reactions to label the NAD-RNAs with a synthetic RNA tag before subjecting to the Oxford Nanopore direct RNA sequencing. A computational tool designed for analyzing the sequencing data of tagged RNA will facilitate the broader application of this method. Hence, we introduce TagSeqTools as a flexible, general pipeline for the identification and quantification of tagged RNAs (i.e., NAD+-capped RNAs) using long-read transcriptome sequencing data generated by NAD tagSeq method. TagSeqTools comprises two major modules, TagSeek for differentiating tagged and untagged reads, and TagSeqQuant for the quantitative and further characterization analysis of genes and isoforms. Besides, the pipeline also integrates some advanced functions to identify antisense or splicing, and supports the data reformation for visualization. Therefore, TagSeqTools provides a convenient and comprehensive workflow for researchers to analyze the data produced by the NAD tagSeq method or other tagging-based experiments using Oxford nanopore direct RNA sequencing. The pipeline is available at https://github.com/dorothyzh/TagSeqTools, under Apache License 2.0.

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NanoGalaxy: Nanopore long-read sequencing data analysis in Galaxy

GigaScience ◽

10.1093/gigascience/giaa105 ◽

2020 ◽

Vol 9 (10) ◽

Cited By ~ 1

Author(s):

Willem de Koning ◽

Milad Miladi ◽

Saskia Hiltemann ◽

Astrid Heikema ◽

John P Hays ◽

...

Keyword(s):

Genome Assembly ◽

Bioinformatics Analysis ◽

De Novo ◽

Sequence Data ◽

Ease Of Use ◽

Easy Access ◽

Complex Data ◽

Sequencing Data ◽

Long Read ◽

Sequencing Platforms

Abstract Background Long-read sequencing can be applied to generate very long contigs and even completely assembled genomes at relatively low cost and with minimal sample preparation. As a result, long-read sequencing platforms are becoming more popular. In this respect, the Oxford Nanopore Technologies–based long-read sequencing “nanopore" platform is becoming a widely used tool with a broad range of applications and end-users. However, the need to explore and manipulate the complex data generated by long-read sequencing platforms necessitates accompanying specialized bioinformatics platforms and tools to process the long-read data correctly. Importantly, such tools should additionally help democratize bioinformatics analysis by enabling easy access and ease-of-use solutions for researchers. Results The Galaxy platform provides a user-friendly interface to computational command line–based tools, handles the software dependencies, and provides refined workflows. The users do not have to possess programming experience or extended computer skills. The interface enables researchers to perform powerful bioinformatics analysis, including the assembly and analysis of short- or long-read sequence data. The newly developed “NanoGalaxy" is a Galaxy-based toolkit for analysing long-read sequencing data, which is suitable for diverse applications, including de novo genome assembly from genomic, metagenomic, and plasmid sequence reads. Conclusions A range of best-practice tools and workflows for long-read sequence genome assembly has been integrated into a NanoGalaxy platform to facilitate easy access and use of bioinformatics tools for researchers. NanoGalaxy is freely available at the European Galaxy server https://nanopore.usegalaxy.eu with supporting self-learning training material available at https://training.galaxyproject.org.

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A benchmark of structural variation detection by long reads through a realistic simulated model

Genome Biology ◽

10.1186/s13059-021-02551-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Nicolas Dierckxsens ◽

Tong Li ◽

Joris R. Vermeesch ◽

Zhi Xie

Keyword(s):

Structural Variation ◽

New Method ◽

Sequencing Data ◽

Simulated Model ◽

Long Reads ◽

Long Read ◽

Sequencing Platforms

AbstractAccurate simulations of structural variation distributions and sequencing data are crucial for the development and benchmarking of new tools. We develop Sim-it, a straightforward tool for the simulation of both structural variation and long-read data. These simulations from Sim-it reveal the strengths and weaknesses for current available structural variation callers and long-read sequencing platforms. With these findings, we develop a new method (combiSV) that can combine the results from structural variation callers into a superior call set with increased recall and precision, which is also observed for the latest structural variation benchmark set developed by the GIAB Consortium.

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RNA sequencing data: hitchhiker's guide to expression analysis

10.7287/peerj.preprints.27283 ◽

2018 ◽

Author(s):

Koen Van Den Berge ◽

Katharina Hembach ◽

Charlotte Soneson ◽

Simone Tiberi ◽

Lieven Clement ◽

...

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Large Scale ◽

Science Studies ◽

Rna Seq ◽

Sequencing Data ◽

Data Types ◽

The Past ◽

Long Read ◽

Statistical Approaches

Gene expression is the fundamental level at which the result of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq datasets as well as the performance of the myriad of methods developed. In this review, we give an overall view of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

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Comparative assessment of long-read error correction software applied to Nanopore RNA-sequencing data

Briefings in Bioinformatics ◽

10.1093/bib/bbz058 ◽

2019 ◽

Vol 21 (4) ◽

pp. 1164-1181 ◽

Cited By ~ 9

Author(s):

Leandro Lima ◽

Camille Marchet ◽

Ségolène Caboche ◽

Corinne Da Silva ◽

Benjamin Istace ◽

...

Keyword(s):

Error Correction ◽

Rna Sequencing ◽

Gene Families ◽

Error Rates ◽

Open Reading Frames ◽

Sequencing Data ◽

Isoform Diversity ◽

Long Reads ◽

Long Read ◽

Read Error Correction

Abstract Motivation Nanopore long-read sequencing technology offers promising alternatives to high-throughput short read sequencing, especially in the context of RNA-sequencing. However this technology is currently hindered by high error rates in the output data that affect analyses such as the identification of isoforms, exon boundaries, open reading frames and creation of gene catalogues. Due to the novelty of such data, computational methods are still actively being developed and options for the error correction of Nanopore RNA-sequencing long reads remain limited. Results In this article, we evaluate the extent to which existing long-read DNA error correction methods are capable of correcting cDNA Nanopore reads. We provide an automatic and extensive benchmark tool that not only reports classical error correction metrics but also the effect of correction on gene families, isoform diversity, bias toward the major isoform and splice site detection. We find that long read error correction tools that were originally developed for DNA are also suitable for the correction of Nanopore RNA-sequencing data, especially in terms of increasing base pair accuracy. Yet investigators should be warned that the correction process perturbs gene family sizes and isoform diversity. This work provides guidelines on which (or whether) error correction tools should be used, depending on the application type. Benchmarking software https://gitlab.com/leoisl/LR_EC_analyser

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