CHESS 3: an improved, comprehensive catalog of human genes and transcripts based on large-scale expression data, phylogenetic analysis, and protein structure

Varabyou, Ales; Sommer, Markus J.; Erdogdu, Beril; Shinder, Ida; Minkin, Ilia; Chao, Kuan-Hao; Park, Sukhwan; Heinz, Jakob; Pockrandt, Christopher; Shumate, Alaina; Rincon, Natalia; Puiu, Daniela; Steinegger, Martin; Salzberg, Steven L.; Pertea, Mihaela

doi:10.1186/s13059-023-03088-4

Cited by 14 publications

(7 citation statements)

References 40 publications

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“…Even for extensively studied species, gene annotation catalogs are often incomplete, missing both potential gene loci and many transcript isoforms (Amaral et al, 2023; Varabyou et al, 2023). This is one reason why, unlike TranSigner and NanoCount, most existing tools for quantifying transcripts with long-read RNA-seq data prioritize identifying novel isoforms first.…”

Section: Resultsmentioning

confidence: 99%

“…TranSigner requires two inputs: a GTF file containing a reference gene annotation of the target transcriptome and a FASTQ file containing long RNA-seq reads. The reference annotation can be obtained from public sources such as RefSeq (O’Leary et al, 2016), GENCODE (Frankish et al, 2019), or CHESS (Varabyou et al, 2023), or it can be derived from transcriptome assemblies produced by programs like StringTie. The latter annotations have the advantage of including novel isoforms while restricting the annotated transcripts to only those found to be expressed in the analyzed sample.…”

Section: Methodsmentioning

confidence: 99%

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Enhancing transcriptome expression quantification through accurate assignment of long RNA sequencing reads with TranSigner

Ji,

Pertea

2024

Preprint

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Recently developed long–read RNA sequencing technologies promise to provide a more accurate and comprehensive view of transcriptomes compared to short-read sequencers, primarily due to their capability to achieve full–length sequencing of transcripts. However, realizing this potential requires computational tools tailored to process long reads, which exhibit a higher error rate than short reads. Existing methods for assembling and quantifying long–read data often disagree on expressed transcripts and their abundance levels, leading researchers to lack confidence in the transcriptomes produced using this data. One approach to address the uncertainties in transcriptome assembly and quantification is by assigning reads to transcripts, enabling a more detailed characterization of transcript support at the read level. Here, we introduce TranSigner, a versatile tool that assigns long reads to any input transcriptome. TranSigner consists of three consecutive modules performing: read alignment to the given transcripts, transcripts, computation of compatibility scores based on alignment scores and positions, and execution of an expectation–maximization algorithm to probabilistically assign read fractions to transcripts while estimating transcript abundances. Using simulated and experimental datasets from three well studied organisms — Homo Sapiens, Arabidopsis thaliana and Mus musculus — we demonstrate that TranSigner achieves higher accuracy in transcript abundance estimation and read assignment compared to existing tools.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Enhancing transcriptome expression quantification through accurate assignment of long RNA sequencing reads with TranSigner

Ji,

Pertea

2024

Preprint

Self Cite

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“…We collected our experimentally identified splice junctions from a large set of assembled transcripts created as part of the initial construction of the CHESS human annotation (Varabyou, Sommer et al 2023), which is based on assemblies of 9,814 RNA-seq samples collected by the GTEx project (Lonsdale, Thomas et al 2013). Note that most of these splice sites do not appear in the final CHESS catalogue.…”

Section: Methodsmentioning

confidence: 99%

“…Although the human protein-coding gene count has been converging on just under 20,000 genes in recent years (Amaral, Carbonell-Sala et al 2023, Varabyou, Sommer et al 2023), multiple recent studies have suggested the possible presence of thousands of additional short protein-coding genes (Ji, Song et al 2015, Calviello, Mukherjee et al 2016, Raj, Wang et al 2016, van Heesch, Witte et al 2019, Chen, Brunner et al 2020, Gaertner, Van Heesch et al 2020, Martinez, Chu et al 2020, Mudge, Ruiz-Orera et al 2022). Most of these proposed novel genes take the form of short open reading frames (ORFs) that occur just upstream or downstream of existing protein-coding genes, apparently on the same messenger RNA.…”

Section: Introductionmentioning

confidence: 99%

Upstream open reading frames may contain hundreds of novel human exons

Ji,

Salzberg

2024

Preprint

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Several recent studies have presented evidence that the human gene catalogue should be expanded to include thousands of short open reading frames (ORFs) appearing upstream or downstream of existing protein-coding genes, each of which would comprise an additional bicistronic transcript in humans. Here we explore an alternative hypothesis that would explain the translational and evolutionary evidence for these upstream ORFs without the need to create novel genes or bicistronic transcripts. We examined 2,199 upstream ORFs that have been proposed as high-quality candidates for novel genes, to determine if they could instead represent protein-coding exons that can be added to existing genes. We checked for the conservation of these ORFs in four recently sequenced, high-quality human genomes, and found a large majority (87.8%) to be conserved in all four as expected. We then looked for splicing evidence that would connect each upstream ORF to the downstream protein-coding gene at the same locus, thus creating a novel splicing variant using the upstream ORF as its first exon. These protein coding exon candidates were further evaluated using protein structure predictions of the protein sequences that included the proposed new exons. We determined that 582 out of 2,199 upstream ORFs have strong evidence that they can form protein coding exons that are part of an existing gene, and that the resulting protein is predicted to have similar or better structural quality than the currently annotated isoform.

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“…The variations among individuals and between loci can confound computational methods attempting to distinguish correct and incorrect spliced alignments. As we will show below, this can result in the inclusion of transcripts with spurious junctions in human gene catalogs such as CHESS [5], RefSeq [6], and GENCODE [7].…”

Section: Introductionmentioning

confidence: 99%

EASTR: Correcting systematic alignment errors in multi-exon genes

Shinder

Chao

et al. 2023

Preprint

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Accurate alignment of transcribed RNA to reference genomes is a critical step in the analysis of gene expression, which in turn has broad applications in biomedical research and in the basic sciences. We have discovered that widely used splice-aware aligners, such as STAR and HISAT2, can introduce erroneous spliced alignments between repeated sequences, leading to the inclusion of falsely spliced transcripts in RNA-seq experiments. In some cases, the "phantom" introns resulting from these errors have made their way into widely-used genome annotation databases. To address this issue, we have developed EASTR (Emending Alignments of Spliced Transcript Reads), a novel software tool that can detect and remove falsely spliced alignments or transcripts from alignment and annotation files. EASTR improves the accuracy of spliced alignments across diverse species, including human, maize, and Arabidopsis thaliana, by detecting sequence similarity between intron-flanking regions. We demonstrate that applying EASTR before transcript assembly substantially reduces false positive introns, exons, and transcripts, improving the overall accuracy of assembled transcripts. Additionally, we show that EASTR's application to reference annotation databases can detect and correct likely cases of mis-annotated transcripts.

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CHESS 3: an improved, comprehensive catalog of human genes and transcripts based on large-scale expression data, phylogenetic analysis, and protein structure

Cited by 14 publications

References 40 publications

Enhancing transcriptome expression quantification through accurate assignment of long RNA sequencing reads with TranSigner

Enhancing transcriptome expression quantification through accurate assignment of long RNA sequencing reads with TranSigner

Upstream open reading frames may contain hundreds of novel human exons

EASTR: Correcting systematic alignment errors in multi-exon genes

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