Transcriptome profiling of mouse samples using nanopore sequencing of cDNA and RNA molecules

Sessegolo, Camille; Cruaud, Corinne; Silva, Corinne Da; Cologne, Audric; Dubarry, Marion; Derrien, Thomas; Lacroix, Vincent; Aury, Jean‐Marc

doi:10.1038/s41598-019-51470-9

Cited by 95 publications

(142 citation statements)

References 29 publications

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“…For example, a correction method that would simply replace a LR by a known transcript sequence to which it was most similar could obtain a high percentage of mapped reads and low error rates but would not correctly represent the sequenced transcriptome. Thus, to further evaluate the 7 correction methods in this paper, we computed two sets of transcriptome-specific indicators which are directed towards the two major applications of RNA-seq experiments: transcript-level quantification (11,47) and isoform recovery (12). For this we considered two types of data: a real ERCC Spike-In dataset and two simulated LR data mimicking the real MCF10A and GM12878 experiments.…”

Section: Transcriptome-specific Indicatorsmentioning

confidence: 99%

“…LR technologies however produce less reads than short read (SR) sequencing approaches (10) for similar costs and have higher error rates. In many cases these higher error rates can prevent the correct identification of isoforms (11)(12)(13). Although several alignment software (14)(15)(16)(17)(18) are optimized to handle these errors, their shortcomings confound transcript identification and annotation.…”

Section: Introductionmentioning

confidence: 99%

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TALC: Transcript-level Aware Long Read Correction

Broseus

Thomas

Oldfield

et al. 2020

Preprint

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Long-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous "hybrid correction" algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data. We have created a novel algorithm called TALC (Transcription Aware Long Read Correction) which models changes in RNA expression and isoform representation in a weighted De-Bruijn graph to correct long reads from transcriptome studies. We tested TALC on a dataset of short and long reads generated for this study. TALC correction results in more accurate reads with less structural errors than existing methods. TALC is implemented in C++ and available at https://gitlab.igh.cnrs.fr/lbroseus/TALC. INTRODUCTIONRecent advances in RNA sequencing (RNA-seq) technologies have revealed that transcription is more pervasive(1), more diverse (2) and more cryptic (3) than expected. Given the major role that RNA processing plays in disease and normal biology, it is crucial to ascertain the existence of novel isoforms and to accurately quantify their abundance. Second generation RNA-seq technologies such as Illumina are well suited to the tasks of assessing gene expression levels and determining proximal exon connectivity. They produce numerous sequencing reads at a low cost ensuring sufficient representation of most transcripts. However, because the RNA or cDNA is fragmented during short RNA-seq protocols, long range connectivity can only be computationally inferred. These predictions based on short reads struggle to correctly identify transcript isoforms that contain multiple alternative exons (4) or that contain retained introns (5). In these cases, long-read (LR) sequencing technologies are invaluable because they can sequence entire molecules in one pass and thus capture long-range connectivity of complex isoforms. LR technologies however produce less reads than short read (SR) sequencing approaches (6) for similar costs and have higher error rates. In many cases these higher error rates can prevent the correct identification of isoforms.Although several alignment software (7-9) are optimized to handle long erroneous sequences, they still display several issues which confound transcript identification and annotation. Many reads fail to be aligned and regions where the sequencing error rates are higher such as UTRs frequently produce ambiguous alignments. Secondly, they struggle to identify splice junctions, notably those flanking small structural variants such as small exons or alternative 5' and 3' splice sites. This impacts the evaluation of exon skipping events and worsens the quality of . CC-BY-NC-ND 4.0

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Section: Transcriptome-specific Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TALC: Transcript-level Aware Long Read Correction

Broseus

Thomas

Oldfield

et al. 2020

Preprint

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“…The SIRV genome (SIRVome) is organized into 7 different gene loci, each containing several transcript isoforms with known SIRVome coordinates, sequence and abundance, with a total of 69 isoforms. We used the SIRVs in 5 different sequencing experiments with the Oxford Nanopore Technologies (ONT) MinION platform: cDNA sequencing (cDNA-seq) from human brain (two replicates) and heart tissues (Methods), and direct RNA (RNA-seq) and cDNA-seq from mouse brain 14 . We first used the SIRV transcripts aggregated per gene to evaluate the clustering at gene-level (Methods).…”

mentioning

confidence: 99%

“…The fastest from the other methods was TranscriptClean, which took 43.48-223.00 mins on the same datasets, not taking into account the mapping to the SIRVome, which only took 0.85-3.92 mins ( Supp Table S2). dRNA) from mouse from 14 . Recall was calculated as the fraction of unique annotated introns or intron-chains correctly found by each method with 5 or more supporting reads.…”

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confidence: 99%

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RATTLE: Reference-free reconstruction and quantification of transcriptomes from Nanopore sequencing

Rubia

Indi

Carbonell-Sala

et al. 2020

Preprint

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Single-molecule long-read sequencing provides an unprecedented opportunity to measure the transcriptome from any sample 1-3 . However, current methods for the analysis of transcriptomes from long reads rely on the comparison with a genome or transcriptome reference 2,4,5 , or use multiple sequencing technologies 6,7 .These approaches preclude the cost-effective study of species with no reference available, and the discovery of new genes and transcripts in individuals underrepresented in the reference. Methods for the assembly of DNA long-reads 8-10 cannot be directly transferred to transcriptomes since their consensus sequences lack the interpretability as genes with multiple transcript isoforms. To address these challenges, we have developed RATTLE, the first method for the reference-free reconstruction and quantification of transcripts from long reads. Using simulated data, transcript isoform spike-ins, and sequencing data from human and mouse tissues, we demonstrate that RATTLE accurately performs read clustering and error-correction.Furthermore, RATTLE predicts transcript sequences and their abundances with accuracy comparable to reference-based methods. RATTLE enables rapid and cost-effective long-read transcriptomics in any sample and any species, without the need of a genome or annotation reference and without using additional technologies.

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Nanopore Whole Transcriptome Analysis and Pathogen Surveillance by a Novel Solid‐Phase Catalysis Approach

et al. 2021

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The requirement of a large input amount (500 ng) for Nanopore direct RNA‐seq presents a major challenge for low input transcriptomic analysis and early pathogen surveillance. The high RNA input requirement is attributed to significant sample loss associated with library preparation using solid‐phase reversible immobilization (SPRI) beads. A novel solid‐phase catalysis strategy for RNA library preparation to circumvent the need for SPRI bead purification to remove enzymes is reported here. This new approach leverages concurrent processing of non‐polyadenylated transcripts with immobilized poly(A) polymerase and T4 DNA ligase, followed by directly loading the prepared library onto a flow cell. Whole transcriptome sequencing, using a human pathogen Listeria monocytogenes as a model, demonstrates this new method displays little sample loss, takes much less time, and generates higher sequencing throughput correlated with reduced nanopore fouling compared to the current library preparation for 500 ng input. Consequently, this approach enables Nanopore low‐input direct RNA‐seq, improving pathogen detection and transcript identification in a microbial community standard with spike‐in transcript controls. Besides, as evident in the bioinformatic analysis, the new method provides accurate RNA consensus with high fidelity and identifies higher numbers of expressed genes for both high and low input RNA amounts.

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Transcriptome profiling of mouse samples using nanopore sequencing of cDNA and RNA molecules

Cited by 95 publications

References 29 publications

TALC: Transcript-level Aware Long Read Correction

TALC: Transcript-level Aware Long Read Correction

RATTLE: Reference-free reconstruction and quantification of transcriptomes from Nanopore sequencing

Nanopore Whole Transcriptome Analysis and Pathogen Surveillance by a Novel Solid‐Phase Catalysis Approach

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