The effect of human genome annotation complexity on RNA-Seq gene expression quantification

Wu, Po-Yen; Phan, John H.; Wang, May D.

doi:10.1109/bibmw.2012.6470224

Cited by 12 publications

(13 citation statements)

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“…For thrombin study samples (SRA: SRP008482), we use OSA alignment with htseq-count quantification to prepare input read count data for the edgeR package. Most of the top 20 differentially expressed genes are detected in at least two of the six annotations, while several others are unique and annotation-specific [ 16 ]. Even though the functions of these annotation-specific genes are unclear, more complex annotations are still preferable since they provide an opportunity to identify novel genomic elements that may be functionally important.…”

Section: Resultsmentioning

confidence: 99%

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Assessing the impact of human genome annotation choice on RNA-seq expression estimates

Phan

Wang

2013

BMC Bioinformatics

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BackgroundGenome annotation is a crucial component of RNA-seq data analysis. Much effort has been devoted to producing an accurate and rational annotation of the human genome. An annotated genome provides a comprehensive catalogue of genomic functional elements. Currently, at least six human genome annotations are publicly available, including AceView Genes, Ensembl Genes, H-InvDB Genes, RefSeq Genes, UCSC Known Genes, and Vega Genes. Characteristics of these annotations differ because of variations in annotation strategies and information sources. When performing RNA-seq data analysis, researchers need to choose a genome annotation. However, the effect of genome annotation choice on downstream RNA-seq expression estimates is still unclear. This study (1) investigates the effect of different genome annotations on RNA-seq quantification and (2) provides guidelines for choosing a genome annotation based on research focus.ResultsWe define the complexity of human genome annotations in terms of the number of genes, isoforms, and exons. This definition facilitates an investigation of potential relationships between complexity and variations in RNA-seq quantification. We apply several evaluation metrics to demonstrate the impact of genome annotation choice on RNA-seq expression estimates. In the mapping stage, the least complex genome annotation, RefSeq Genes, appears to have the highest percentage of uniquely mapped short sequence reads. In the quantification stage, RefSeq Genes results in the most stable expression estimates in terms of the average coefficient of variation over all genes. Stable expression estimates in the quantification stage translate to accurate statistics for detecting differentially expressed genes. We observe that RefSeq Genes produces the most accurate fold-change measures with respect to a ground truth of RT-qPCR gene expression estimates.ConclusionsBased on the observed variations in the mapping, quantification, and differential expression calling stages, we demonstrate that the selection of human genome annotation results in different gene expression estimates. When conducting research that emphasizes reproducible and robust gene expression estimates, a less complex genome annotation may be preferred. However, simpler genome annotations may limit opportunities for identifying or characterizing novel transcriptional or regulatory mechanisms. When conducting research that aims to be more exploratory, a more complex genome annotation may be preferred.

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Section: Resultsmentioning

confidence: 99%

“…The information sources and annotating strategies of each human genome annotation are summarized in [ 16 ]. The procedures for improving cross-annotation comparability are briefly described below:…”

Section: Methodsmentioning

confidence: 99%

Assessing the impact of human genome annotation choice on RNA-seq expression estimates

Phan

Wang

2013

BMC Bioinformatics

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“…Furthermore, the approximate number of genes with detectable expression in porcine peripheral blood is still unknown. The lack of a detailed catalog and annotation of the pig genome and transcriptome hinders annotation-based high throughput studies [ 24 , 25 ]. Fortunately, two independent assemblies of the pig genome have been recently initiated using a PacBio long read-based approach [ 22 , 26 ] and are currently being annotated.…”

Section: Introductionmentioning

confidence: 99%

A high-quality annotated transcriptome of swine peripheral blood

Smith

Nonneman

et al. 2017

BMC Genomics

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BackgroundHigh throughput gene expression profiling assays of peripheral blood are widely used in biomedicine, as well as in animal genetics and physiology research. Accurate, comprehensive, and precise interpretation of such high throughput assays relies on well-characterized reference genomes and/or transcriptomes. However, neither the reference genome nor the peripheral blood transcriptome of the pig have been sufficiently assembled and annotated to support such profiling assays in this emerging biomedical model organism. We aimed to assemble published and novel RNA-seq data to provide a comprehensive, well-annotated blood transcriptome for pigs by integrating a de novo assembly with a genome-guided assembly.ResultsA de novo and a genome-guided transcriptome of porcine whole peripheral blood was assembled with ~162 million pairs of paired-end and ~183 million single-end, trimmed and normalized Illumina RNA-seq reads (~6 billion initial reads from 146 RNA-seq libraries) from five independent studies by using the Trinity and Cufflinks software, respectively. We then removed putative transcripts (PTs) of low confidence from both assemblies and merged the remaining PTs into an integrated transcriptome consisting of 132,928 PTs, with 126,225 (~95%) PTs from the de novo assembly and more than 91% of PTs spliced. In the integrated transcriptome, ~90% and 63% of PTs had significant sequence similarity to sequences in the NCBI NT and NR databases, respectively; 68,754 (~52%) PTs were annotated with 15,965 unique gene ontology (GO) terms; and 7618 PTs annotated with Enzyme Commission codes were assigned to 134 pathways curated by the Kyoto Encyclopedia of Genes and Genomes (KEGG). Full exon-intron junctions of 17,528 PTs were validated by PacBio IsoSeq full-length cDNA reads from 3 other porcine tissues, NCBI pig RefSeq mRNAs and transcripts from Ensembl Sscrofa10.2 annotation. Completeness of the 5’ termini of 37,569 PTs was validated by public cap analysis of gene expression (CAGE) data. By comparison to the Ensembl transcripts, we found that (1) the deduced precursors of 54,402 PTs shared at least one intron or exon with those of 18,437 Ensembl transcripts; (2) 12,262 PTs had both longer 5’ and 3’ termini than their maximally overlapping Ensembl transcripts; and (3) 41,838 spliced PTs were totally missing from the Sscrofa10.2 annotation. Similar results were obtained when the PTs were compared to the pig NCBI RefSeq mRNA collection.ConclusionsWe built, validated and annotated a comprehensive porcine blood transcriptome with significant improvement over the annotation of Ensembl Sscrofa10.2 and the pig NCBI RefSeq mRNAs, and laid a foundation for blood-based high throughput transcriptomic assays in pigs and for advancing annotation of the pig genome.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3863-7) contains supplementary material, which is available to authorized users.

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“…Our results show that the increasing complexity of gene annotation adversely affected DE analysis. Wu et al[23] evaluated the impact of human gene annotation choice on RNA-seq expression estimates. They defined the complexities of gene annotations in terms of the relative rank of the number of genes, isoforms, and exons and demonstrated that more complex annotation results in a smaller correlation between RNA-seq fold-change and qRT-PCR fold-change.…”

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Impact of human gene annotations on RNA-seq differential expression analysis

Hamaguchi

Zeng

Hamada

2021

Preprint

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Background: Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear.Results: Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically.Conclusions: We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

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The effect of human genome annotation complexity on RNA-Seq gene expression quantification

Cited by 12 publications

References 15 publications

Assessing the impact of human genome annotation choice on RNA-seq expression estimates

Assessing the impact of human genome annotation choice on RNA-seq expression estimates

A high-quality annotated transcriptome of swine peripheral blood

Impact of human gene annotations on RNA-seq differential expression analysis

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