Full-length transcriptome reconstruction reveals a large diversity of RNA and protein isoforms in rat hippocampus

Wang, Xi; You, Xintian; Langer, Julian D.; Hou, Jingyi; Rupprecht, Fiona; Vlatkovic, Irena; Quedenau, Claudia; Tushev, Georgi; Epstein, Irina; Schaefke, Bernhard; Sun, Wei; Fang, Liang; Li, Guipeng; Hu, Yuhui; Schuman, Erin M.; Chen, Wei

doi:10.1038/s41467-019-13037-0

Cited by 53 publications

(50 citation statements)

References 72 publications

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“…Given the recent discovery of numerous novel functional transcripts in multiple organs (3,58), the re-evaluation of transcript diversity and complexity in model organisms (4,5,59,60) and the growing interest in Oxford Nanopore direct-RNA multiple assets in viral transcriptome research (61-64), TALC's capacity to correct the entire gamut of transcripts, and preserve the correct transcript structure may prove increasingly valuable. Table 1: Usual alignment-based statistics for the 7 correction methods tested: primary alignment rates and base accuracy (real LR data corrected by real SR data).…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

TALC: Transcript-level Aware Long Read Correction

Broseus

Thomas

Oldfield

et al. 2020

Preprint

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“…Each scenario that we tested predicted genes that are not annotated in Araport11. Compelling RNA-seq evidence from wide range of phyla, ranging from bacteria, to yeast, to humans, shows that many sequences that are not annotated as genes are highly transcribed and translated 1,55,56,62,[86][87][88][89][90] . For example, in a study of RNA-Seq data from over 3000 diverse yeast…”

Section: Discussionmentioning

confidence: 99%

“…A more straightforward approach to identify genes is to directly align RNA-Seq data to genomes 51 . Although this approach is important for predicting non-coding RNAs 52, 53 and young genes 1,51,[54][55][56] , with some exceptions it has not been widely adopted for genome annotation 33,57 . This may be in part because direct inference predicts many genes that have not been annotated 55 .…”

Section: Introductionmentioning

confidence: 99%

Foster thy young: Enhanced prediction of orphan genes in assembled genomes

Singh

Bhandary

et al. 2019

Preprint

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The evolutionary rapid emergence of new genes gives rise to "orphan genes" that share no sequence homology to genes in closely related genomes. These genes provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Gene annotation pipelines that combine ab initio machine-learning with sequence homology-based searches are efficient in identifying basal genes with a long evolutionary history. However, their ability to identify orphan genes and other young genes has not been systematically evaluated. Here, we classify the phylostrata of curated Arabidopsis thaliana genes and use these to assess the ability of two of the most prevalent annotation pipelines, MAKER and BRAKER, to predict orphans and other young genes. MAKER predictions are highly dependent on the RNA-Seq evidence, predicting between 11% and 60% of the orphan-genes and 95% to 98% of basal-genes in the annotated genome of Arabidopsis. In contrast, BRAKER consistently predicts 33% of orphan-genes and 98% of basal-genes. A less used method to identify genes is by directly aligning RNA-Seq data to the genome sequence. We present a Findable, Accessible, Interoperable and Reusable (FAIR) approach, called BIND, that mitigates the under-prediction of orphan genes. BIND combines BRAKER predictions with direct evidence-based inference of transcripts based on RNA-Seq alignments to the genome. BIND increases the number and accuracy of orphan gene predictions, identifying 68% of Araport11-annotated orphan genes and 99% of the conserved genes.

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“…Transcript assembly from SR can be problematic (Wang et al 2019), especially if the transcripts contain transposable elements (Babarinde et al 2019). This is reflected in the large number of singleexon fragments from the SR assembly (72,902; 27%).…”

Section: Transcript Assembly From Short and Long Readsmentioning

confidence: 99%

Transposable Element-Gene Splicing Modulates the Transcriptional Landscape of Human Pluripotent Stem Cells

Babarinde

et al. 2020

Preprint

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Transposable elements (TEs) occupy nearly 50% of mammalian genomes and are both potential dangers to genome stability and functional genetic elements. TEs can be expressed and exonised as part of a transcript, however, their full contribution to the transcript splicing remains unresolved. Here, guided by long and short read sequencing of RNAs, we show that 26% of coding and 65% of non-coding transcripts of human pluripotent stem cells (hPSCs) contain TEs. Different TE families have unique integration patterns with diverse consequences on RNA expression and function. We identify hPSC-specific splicing of endogenous retroviruses (ERVs) as well as LINE L1 elements into protein coding genes that generate TE-derived peptides. Finally, single cell RNA-seq reveals that proliferating hPSCs are dominated by ERV-containing transcripts, and subpopulations express SINE or LINE-containing transcripts. Overall, we demonstrate that TE splicing modulates the pluripotency transcriptome by enhancing and impairing transcript expression and generating novel transcripts and peptides.

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Full-length transcriptome reconstruction reveals a large diversity of RNA and protein isoforms in rat hippocampus

Cited by 53 publications

References 72 publications

TALC: Transcript-level Aware Long Read Correction

TALC: Transcript-level Aware Long Read Correction

Foster thy young: Enhanced prediction of orphan genes in assembled genomes

Transposable Element-Gene Splicing Modulates the Transcriptional Landscape of Human Pluripotent Stem Cells

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