Hamid Beiki, Brenda M Murdoch, Carissa A Park, Chandlar Kern, Denise Kontechy, Gabrielle Becker, Gonzalo Rincon, Honglin Jiang, Huaijun Zhou, Jacob Thorne, James E Koltes, Jennifer J Michal, Kimberly Davenport, Monique Rijnkels, Pablo J Ross, Rui Hu, Sarah Corum, Stephanie McKay, Timothy P L Smith, Wansheng Liu, Wenzhi Ma, Xiaohui Zhang, Xiaoqing Xu, Xuelei Han, Zhihua Jiang, Zhi-Liang Hu, James M Reecy
{"title":"Enhanced bovine genome annotation through integration of transcriptomics and epi-transcriptomics datasets facilitates genomic biology","authors":"Hamid Beiki, Brenda M Murdoch, Carissa A Park, Chandlar Kern, Denise Kontechy, Gabrielle Becker, Gonzalo Rincon, Honglin Jiang, Huaijun Zhou, Jacob Thorne, James E Koltes, Jennifer J Michal, Kimberly Davenport, Monique Rijnkels, Pablo J Ross, Rui Hu, Sarah Corum, Stephanie McKay, Timothy P L Smith, Wansheng Liu, Wenzhi Ma, Xiaohui Zhang, Xiaoqing Xu, Xuelei Han, Zhihua Jiang, Zhi-Liang Hu, James M Reecy","doi":"10.1093/gigascience/giae019","DOIUrl":null,"url":null,"abstract":"Background The accurate identification of the functional elements in the bovine genome is a fundamental requirement for high-quality analysis of data informing both genome biology and genomic selection. Functional annotation of the bovine genome was performed to identify a more complete catalog of transcript isoforms across bovine tissues. Results A total of 160,820 unique transcripts (50% protein coding) representing 34,882 unique genes (60% protein coding) were identified across tissues. Among them, 118,563 transcripts (73% of the total) were structurally validated by independent datasets (PacBio isoform sequencing data, Oxford Nanopore Technologies sequencing data, de novo assembled transcripts from RNA sequencing data) and comparison with Ensembl and NCBI gene sets. In addition, all transcripts were supported by extensive data from different technologies such as whole transcriptome termini site sequencing, RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin using sequencing. A large proportion of identified transcripts (69%) were unannotated, of which 86% were produced by annotated genes and 14% by unannotated genes. A median of two 5′ untranslated regions were expressed per gene. Around 50% of protein-coding genes in each tissue were bifunctional and transcribed both coding and noncoding isoforms. Furthermore, we identified 3,744 genes that functioned as noncoding genes in fetal tissues but as protein-coding genes in adult tissues. Our new bovine genome annotation extended more than 11,000 annotated gene borders compared to Ensembl or NCBI annotations. The resulting bovine transcriptome was integrated with publicly available quantitative trait loci data to study tissue–tissue interconnection involved in different traits and construct the first bovine trait similarity network. Conclusions These validated results show significant improvement over current bovine genome annotations.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":null,"pages":null},"PeriodicalIF":11.8000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GigaScience","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/gigascience/giae019","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Background The accurate identification of the functional elements in the bovine genome is a fundamental requirement for high-quality analysis of data informing both genome biology and genomic selection. Functional annotation of the bovine genome was performed to identify a more complete catalog of transcript isoforms across bovine tissues. Results A total of 160,820 unique transcripts (50% protein coding) representing 34,882 unique genes (60% protein coding) were identified across tissues. Among them, 118,563 transcripts (73% of the total) were structurally validated by independent datasets (PacBio isoform sequencing data, Oxford Nanopore Technologies sequencing data, de novo assembled transcripts from RNA sequencing data) and comparison with Ensembl and NCBI gene sets. In addition, all transcripts were supported by extensive data from different technologies such as whole transcriptome termini site sequencing, RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin using sequencing. A large proportion of identified transcripts (69%) were unannotated, of which 86% were produced by annotated genes and 14% by unannotated genes. A median of two 5′ untranslated regions were expressed per gene. Around 50% of protein-coding genes in each tissue were bifunctional and transcribed both coding and noncoding isoforms. Furthermore, we identified 3,744 genes that functioned as noncoding genes in fetal tissues but as protein-coding genes in adult tissues. Our new bovine genome annotation extended more than 11,000 annotated gene borders compared to Ensembl or NCBI annotations. The resulting bovine transcriptome was integrated with publicly available quantitative trait loci data to study tissue–tissue interconnection involved in different traits and construct the first bovine trait similarity network. Conclusions These validated results show significant improvement over current bovine genome annotations.
期刊介绍:
GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.