{"doi":"10.21203/rs.3.rs-421080/v1","title":"Impact of Gene Annotation Choice on the Quantification of RNA-Seq Data","abstract":"<jats:title>Abstract</jats:title>\n        <jats:p><jats:bold>Background</jats:bold>: RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis.<jats:bold>Results</jats:bold>: In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEquencing Quality Control (SEQC) consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from &gt;800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.<jats:bold>Conclusion</jats:bold>: In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.</jats:p>","journal":null,"year":null,"id":16672,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":36694,"name":"Yang Liao","orcid":null,"position":1,"is_corresponding":false},{"id":36696,"name":"Wei Shi","orcid":"0000-0003-1182-7735","position":2,"is_corresponding":false},{"id":122145,"name":"David Chisanga","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"36284789","pmcid":null,"openalex_id":"https://openalex.org/W3118378687","authors":[],"funders":[{"funder_name":"National Health and Medical Research Council (NHMRC)","grant_id":"1128609","title":"Computational reconstruction and validation of a gene regulatory network controlling differentiation of B cells to antibody-secreting plasma cells"},{"funder_name":"National Health and Medical Research Council (NHMRC)","grant_id":"1023454","title":"Computational and statistical methods for the analysis of RNA-Seq data"},{"funder_name":"National Centre for the Replacement, Refinement and Reduction of Animals in Research","grant_id":"NC/P001076/1","title":null},{"funder_name":"Biotechnology and Biological Sciences Research Council","grant_id":"BBS/E/T/000PR9817","title":null},{"funder_name":"Wellcome Trust","grant_id":"209558/Z/17/Z","title":null},{"funder_name":"HCRW_","grant_id":"RFPPB-18-1497(T)","title":null},{"funder_name":"Medical Research Council","grant_id":"MR/S036954/1","title":null},{"funder_name":"Medical Research Council","grant_id":"MR/K010468/1","title":null}],"total_grants":8,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"cc-by","oa_locations":[{"url":"https://www.researchsquare.com/article/rs-421080/latest.pdf","host_type":"repository"},{"url":"https://www.researchsquare.com/article/rs-421080/latest.pdf","host_type":"repository"},{"url":"https://www.researchsquare.com/article/rs-421080/v1","host_type":"publisher"},{"url":"https://www.researchsquare.com/article/rs-421080/v1.html","host_type":"publisher"},{"url":"https://doi.org/10.21203/rs.3.rs-421080/v1","host_type":"repository"},{"url":"https://figshare.com/articles/journal_contribution/Impact_of_gene_annotation_choice_on_the_quantification_of_RNA-seq_data/19703575","host_type":"repository"},{"url":"https://doi.org/10.26181/19703575.v2","host_type":"repository"},{"url":"https://doi.org/10.1101/2021.01.07.425794","host_type":""},{"url":"https://www.biorxiv.org/content/biorxiv/early/2021/01/08/2021.01.07.425794.full.pdf","host_type":""},{"url":"https://doi.org/10.1186/s12859-022-04644-8","host_type":""},{"url":"https://www.researchsquare.com/article/rs-421080/v1.pdf?c=1619547111000","host_type":""},{"url":"https://dx.doi.org/10.26181/19703575.v2","host_type":""},{"url":"https://dx.doi.org/10.26181/19703575","host_type":""},{"url":"https://dx.doi.org/10.26181/19703575.v1","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/35354358","host_type":""},{"url":"http://dx.doi.org/10.1186/s12859-022-04644-8","host_type":""},{"url":"https://doaj.org/article/c0bffa1168654d2aacd3885eeede2c86","host_type":""},{"url":"https://dx.doi.org/10.21203/rs.3.rs-421080/v1","host_type":""},{"url":"https://doi.org/https://doi.org/10.1186/s12859-022-04644-8","host_type":""}],"fields_of_study":["Genomics and Phylogenetic Studies","Cancer-related molecular mechanisms research","Molecular Biology Techniques and Applications"],"mesh_terms":[],"keywords":["RefSeq","Ensembl","Annotation","Gene nomenclature","Gene Annotation","Computational biology","Genome project","Gene","RNA-Seq","Gene expression","Human genome","Genome","Data mining","Genetics","Genomics","Biology","Computer science","Transcriptome","570","Base Sequence","QH301-705.5","Sequence Analysis, RNA","Research","Computer applications to medicine. Medical informatics","R858-859.7","Mathematical sciences","Molecular Sequence Annotation","Bioinformatics and computational biology","576","Normalization","Biological sciences","Gene expression quantification","FOS: Biological sciences","Exome Sequencing","Information and computing sciences","Humans","Biology (General)","carotid-cavernous fistula","endoscopy","endovascular treatment","transsphenoidal surgery","video"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-02T12:31:30.299967Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}