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Long-read cDNA sequencing identifies functional pseudogenes in the human transcriptome.

ABSTRACT: Pseudogenes are gene copies presumed to mainly be functionless relics of evolution due to acquired deleterious mutations or transcriptional silencing. Using deep full-length PacBio cDNA sequencing of normal human tissues and cancer cell lines, we identify here hundreds of novel transcribed pseudogenes expressed in tissue-specific patterns. Some pseudogene transcripts have intact open reading frames and are translated in cultured cells, representing unannotated protein-coding genes. To assess the biological impact of noncoding pseudogenes, we CRISPR-Cas9 delete the nucleus-enriched pseudogene PDCL3P4 and observe hundreds of perturbed genes. This study highlights pseudogenes as a complex and dynamic component of the human transcriptional landscape.

SUBMITTER: Troskie RL

PROVIDER: S-EPMC8108447 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Long-read cDNA sequencing identifies functional pseudogenes in the human transcriptome.

Troskie Robin-Lee RL Jafrani Yohaann Y Mercer Tim R TR Ewing Adam D AD Faulkner Geoffrey J GJ Cheetham Seth W SW

Genome biology 20210510 1

Pseudogenes are gene copies presumed to mainly be functionless relics of evolution due to acquired deleterious mutations or transcriptional silencing. Using deep full-length PacBio cDNA sequencing of normal human tissues and cancer cell lines, we identify here hundreds of novel transcribed pseudogenes expressed in tissue-specific patterns. Some pseudogene transcripts have intact open reading frames and are translated in cultured cells, representing unannotated protein-coding genes. To assess the ...[more]

PMID: 33971925

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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.

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.

Dataset Information

Long-read cDNA sequencing identifies functional pseudogenes in the human transcriptome.

Publications

Long-read cDNA sequencing identifies functional pseudogenes in the human transcriptome.

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