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Generation and analysis of a mouse multi-tissue genome annotation atlas.

ABSTRACT: Generating an accurate and complete genome annotation for an organism is complex because the cells within each tissue can express a unique set of transcript isoforms from a unique set of genes. A comprehensive genome annotation should contain information on what tissues express what transcript isoforms at what level. This tissue-level isoform information can then inform a wide range of research questions as well as experiment designs. Long-read sequencing technology combined with advanced full-length cDNA library preparation methods has now achieved throughput and accuracy where generating these types of annotations is achievable. Here, we show this by generating a genome annotation of the mouse (Mus musculus). We used the nanopore-based R2C2 long-read sequencing method to generate 64 million highly accurate full length cDNA consensus reads - averaging 5.4 million reads per tissue for a dozen tissues. Using the Mandalorion tool we processed these reads to generate the Tissue-level Atlas of Mouse Isoforms (TAMI - available at https://genome.ucsc.edu/s/vollmers/TAMI) which we believe will be a valuable complement to conventional, manually curated reference genome annotations.

SUBMITTER: Adams M

PROVIDER: S-EPMC10862843 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Generation and analysis of a mouse multi-tissue genome annotation atlas.

Adams Matthew M Vollmers Christopher C

bioRxiv : the preprint server for biology 20240201

Generating an accurate and complete genome annotation for an organism is complex because the cells within each tissue can express a unique set of transcript isoforms from a unique set of genes. A comprehensive genome annotation should contain information on what tissues express what transcript isoforms at what level. This tissue-level isoform information can then inform a wide range of research questions as well as experiment designs. Long-read sequencing technology combined with advanced full-l ...[more]

PMID: 38352519

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Project description:Efforts to obtain a comprehensive genome sequence for rainbow trout are ongoing and will be complemented by transcriptome information that will enhance genome assembly and annotation. Previously, transcriptome reference sequences were reported using data from different sources. Although the previous work added a great wealth of sequences, a complete and well-annotated transcriptome is still needed. In addition, gene expression in different tissues was not completely addressed in the previous studies. In this study, non-normalized cDNA libraries were sequenced from 13 different tissues of a single doubled haploid rainbow trout from the same source used for the rainbow trout genome sequence. A total of ~1.167 billion paired-end reads were de novo assembled using the Trinity RNA-Seq assembler yielding 474,524 contigs > 500 base-pairs. Of them, 287,593 had homologies to the NCBI non-redundant protein database. The longest contig of each cluster was selected as a reference, yielding 44,990 representative contigs. A total of 4,146 contigs (9.2%), including 710 full-length sequences, did not match any mRNA sequences in the current rainbow trout genome reference. Mapping reads to the reference genome identified an additional 11,843 transcripts not annotated in the genome. A digital gene expression atlas revealed 7,678 housekeeping and 4,021 tissue-specific genes. Expression of about 16,000-32,000 genes (35-71% of the identified genes) accounted for basic and specialized functions of each tissue. White muscle and stomach had the least complex transcriptomes, with high percentages of their total mRNA contributed by a small number of genes. Brain, testis and intestine, in contrast, had complex transcriptomes, with a large numbers of genes involved in their expression patterns. This study provides comprehensive de novo transcriptome information that is suitable for functional and comparative genomics studies in rainbow trout, including annotation of the genome.

Project description:BackgroundMalaria is a tropical disease caused by protozoan parasite, Plasmodium, which is transmitted to humans by various species of female anopheline mosquitoes. Anopheles stephensi is one such major malaria vector in urban parts of the Indian subcontinent. Unlike Anopheles gambiae, an African malaria vector, transcriptome of A. stephensi midgut tissue is less explored. We have therefore carried out generation, annotation, and analysis of expressed sequence tags from sugar-fed and Plasmodium yoelii infected blood-fed (post 24 h) adult female A. stephensi midgut tissue.ResultsWe obtained 7061 and 8306 ESTs from the sugar-fed and P. yoelii infected mosquito midgut tissue libraries, respectively. ESTs from the combined dataset formed 1319 contigs and 2627 singlets, totaling to 3946 unique transcripts. Putative functions were assigned to 1615 (40.9%) transcripts using BLASTX against UniProtKB database. Amongst unannotated transcripts, we identified 1513 putative novel transcripts and 818 potential untranslated regions (UTRs). Statistical comparison of annotated and unannotated ESTs from the two libraries identified 119 differentially regulated genes. Out of 3946 unique transcripts, only 1387 transcripts were mapped on the A. gambiae genome. These also included 189 novel transcripts, which were mapped to the unannotated regions of the genome. The EST data is available as ESTDB at http://mycompdb.bioinfo-portal.cdac.in/cgi-bin/est/index.cgi.Conclusion3946 unique transcripts were successfully identified from the adult female A. stephensi midgut tissue. These data can be used for microarray development for better understanding of vector-parasite relationship and to study differences or similarities with other malaria vectors. Mapping of putative novel transcripts from A. stephensi on the A. gambiae genome proved fruitful in identification and annotation of several genes. Failure of some novel transcripts to map on the A. gambiae genome indicates existence of substantial genomic dissimilarities between these two potent malaria vectors.

Dataset Information

Generation and analysis of a mouse multi-tissue genome annotation atlas.

Publications

Generation and analysis of a mouse multi-tissue genome annotation atlas.

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