Long-read cDNA sequencing identifies functional pseudogenes in the human transcriptome.
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ABSTRACT:
SUBMITTER: Troskie RL
PROVIDER: S-EPMC8108447 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Pseudogenes are gene copies presumed to mainly be functionless relics of evolution due to acquired deleterious mutations or transcriptional silencing. When transcribed, pseudogenes may encode proteins or enact RNA-intrinsic regulatory mechanisms. However, the extent, characteristics and functional relevance of the human pseudogene transcriptome are unclear. Short-read sequencing platforms have limited power to resolve and accurately quantify pseudogene transcripts owing to the high sequence similarity of pseudogenes and their parent genes. Using deep full-length PacBio cDNA sequencing of normal human tissues and cancer cell lines, we identify here hundreds of novel transcribed pseudogenes. Pseudogene transcripts are expressed in tissue-specific patterns, exhibit complex splicing patterns and contribute to the coding sequences of known genes. We survey pseudogene transcripts encoding intact open reading frames (ORFs), representing potential unannotated protein-coding genes, and demonstrate their efficient translation in cultured cells. To assess the impact of noncoding pseudogenes on the cellular transcriptome, we delete the nucleus-enriched pseudogene PDCL3P4 transcript from HAP1 cells and observe hundreds of perturbed genes. This study highlights pseudogenes as a complex and dynamic component of the transcriptional landscape underpinning human biology and disease.
2021-04-26 | GSE160383 | GEO
Project description:Long-read cDNA sequencing identifies functional pseudogenes in the human transcriptome
| PRJNA673144 | ENA
Project description:Normalization of cDNA is widely used to improve the coverage of rare transcripts in analysis of transcriptomes employing next-generation sequencing. Recently, long-read technology has been emerging as a powerful tool for sequencing and construction of transcriptomes, especially for complex genomes containing highly similar transcripts and transcript-spliced isoforms. Here, we analyzed the transcriptome of sugarcane, a highly polyploidy plant genome, by PacBio isoform sequencing (Iso-Seq) of two different cDNA library preparations, with and without a normalization step. The results demonstrated that, while the two libraries included many of the same transcripts, many longer transcripts were removed, and many new generally shorter transcripts were detected by normalization. For the same input cDNA and data yield, the normalized library recovered more total transcript isoforms and number of predicted gene families and orthologous groups, resulting in a higher representation for the sugarcane transcriptome, compared to the non-normalized library. The non-normalized library, on the other hand, included a wider transcript length range with more longer transcripts above ?1.25 kb and more transcript isoforms per gene family and gene ontology terms per transcript. A large proportion of the unique transcripts comprising ?52% of the normalized library were expressed at a lower level than the unique transcripts from the non-normalized library, across three tissue types tested including leaf, stalk, and root. About 83% of the total 5,348 predicted long noncoding transcripts was derived from the normalized library, of which ?80% was derived from the lowly expressed fraction. Functional annotation of the unique transcripts suggested that each library enriched different functional transcript fractions. This demonstrated the complementation of the two approaches in obtaining a complete transcriptome of a complex genome at the sequencing depth used in this study.
| S-EPMC6664245 | biostudies-literature
Project description:Regulation of transcript structure generates transcript diversity and plays an important role in human disease1-7. The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure8-16. In this Article, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from Genotype-Tissue Expression (GTEx) tissues and cell lines, complementing the GTEx resource. We identified just over 70,000 novel transcripts for annotated genes, and validated the protein expression of 10% of novel transcripts. We developed a new computational package, LORALS, to analyse the genetic effects of rare and common variants on the transcriptome by allele-specific analysis of long reads. We characterized allele-specific expression and transcript structure events, providing new insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb the transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we used this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns.
| S-EPMC10337767 | biostudies-literature
Project description:The utility of genome assemblies does not only rely on the quality of the assembled genome sequence, but also on the quality of the gene annotations. The Pacific Biosciences Iso-Seq technology is a powerful support for accurate eukaryotic gene model annotation as it allows for direct readout of full-length cDNA sequences without the need for noisy short read-based transcript assembly. We propose the implementation of the TeloPrime Full Length cDNA Amplification kit to the Pacific Biosciences Iso-Seq technology in order to enrich for genuine full-length transcripts in the cDNA libraries. We provide evidence that TeloPrime outperforms the commonly used SMARTer PCR cDNA Synthesis Kit in identifying transcription start and end sites in Arabidopsis thaliana. Furthermore, we show that TeloPrime-based Pacific Biosciences Iso-Seq can be successfully applied to the polyploid genome of bread wheat (Triticum aestivum) not only to efficiently annotate gene models, but also to identify novel transcription sites, gene homeologs, splicing isoforms and previously unidentified gene loci.
| S-EPMC4915659 | biostudies-literature
Project description: Not available
| S-EPMC6170618 | biostudies-literature
Project description:BackgroundThe human transcriptome annotation is regarded as one of the most complete of any eukaryotic species. However, limitations in sequencing technologies have biased the annotation toward multi-exonic protein coding genes. Accurate high-throughput long read transcript sequencing can now provide additional evidence for rare transcripts and genes such as mono-exonic and non-coding genes that were previously either undetectable or impossible to differentiate from sequencing noise.ResultsWe developed the Transcriptome Annotation by Modular Algorithms (TAMA) software to leverage the power of long read transcript sequencing and address the issues with current data processing pipelines. TAMA achieved high sensitivity and precision for gene and transcript model predictions in both reference guided and unguided approaches in our benchmark tests using simulated Pacific Biosciences (PacBio) and Nanopore sequencing data and real PacBio datasets. By analyzing PacBio Sequel II Iso-Seq sequencing data of the Universal Human Reference RNA (UHRR) using TAMA and other commonly used tools, we found that the convention of using alignment identity to measure error correction performance does not reflect actual gain in accuracy of predicted transcript models. In addition, inter-read error correction can cause major changes to read mapping, resulting in potentially over 6 K erroneous gene model predictions in the Iso-Seq based human genome annotation. Using TAMA's genome assembly based error correction and gene feature evidence, we predicted 2566 putative novel non-coding genes and 1557 putative novel protein coding gene models.ConclusionsLong read transcript sequencing data has the power to identify novel genes within the highly annotated human genome. The use of parameter tuning and extensive output information of the TAMA software package allows for in depth exploration of eukaryotic transcriptomes. We have found long read data based evidence for thousands of unannotated genes within the human genome. More development in sequencing library preparation and data processing are required for differentiating sequencing noise from real genes in long read RNA sequencing data.
| S-EPMC7596999 | biostudies-literature
Project description:Although shrimp are of great economic importance, few full-length shrimp transcriptomes are available. Here, we used Pacific Biosciences single-molecule real-time (SMRT) long-read sequencing technology to generate transcripts from the Pacific white shrimp (Litopenaeus vannamei). We obtained 322,600 full-length non-chimeric reads, from which we generated 51,367 high-quality unique full-length transcripts. We corrected errors in the SMRT sequences by comparison with Illumina-produced short reads. We successfully annotated 81.72% of all unique SMRT transcripts against the NCBI non-redundant database, 58.63% against Swiss-Prot, 45.38% against Gene Ontology, 32.57% against Clusters of Orthologous Groups of proteins (COG), and 47.83% against Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Across all transcripts, we identified 3,958 long non-coding RNAs (lncRNAs) and 80,650 simple sequence repeats (SSRs). Our study provides a rich set of full-length cDNA sequences for L. vannamei, which will greatly facilitate shrimp transcriptome research.
| S-EPMC6240054 | biostudies-literature
Project description:In cancer, fusions are important diagnostic markers and targets for therapy. Long-read transcriptome sequencing allows the discovery of fusions with their full-length isoform structure. However, due to higher sequencing error rates, fusion finding algorithms designed for short reads do not work. Here we present JAFFAL, to identify fusions from long-read transcriptome sequencing. We validate JAFFAL using simulations, cell lines, and patient data from Nanopore and PacBio. We apply JAFFAL to single-cell data and find fusions spanning three genes demonstrating transcripts detected from complex rearrangements. JAFFAL is available at https://github.com/Oshlack/JAFFA/wiki .
| S-EPMC8739696 | biostudies-literature
Project description:RNA sequencing (RNA-seq) enables characterization and quantification of individual transcriptomes as well as detection of patterns of allelic expression and alternative splicing. Current RNA-seq protocols depend on high-throughput short-read sequencing of cDNA. However, as ongoing advances are rapidly yielding increasing read lengths, a technical hurdle remains in identifying the degree to which differences in read length influence various transcriptome analyses. In this study, we generated two paired-end RNA-seq datasets of differing read lengths (2×75 bp and 2×262 bp) for lymphoblastoid cell line GM12878 and compared the effect of read length on transcriptome analyses, including read-mapping performance, gene and transcript quantification, and detection of allele-specific expression (ASE) and allele-specific alternative splicing (ASAS) patterns. Our results indicate that, while the current long-read protocol is considerably more expensive than short-read sequencing, there are important benefits that can only be achieved with longer read length, including lower mapping bias and reduced ambiguity in assigning reads to genomic elements, such as mRNA transcript. We show that these benefits ultimately lead to improved detection of cis-acting regulatory and splicing variation effects within individuals.
| S-EPMC4176000 | biostudies-literature