{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T03:18:18Z","timestamp":1792034298679,"version":"4.4.0"},"reference-count":56,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Foundation","doi-asserted-by":"publisher","award":["NNF20OC0064467"],"award-info":[{"award-number":["NNF20OC0064467"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Foundation","doi-asserted-by":"publisher","award":["R324-2019-1625"],"award-info":[{"award-number":["R324-2019-1625"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Foundation","doi-asserted-by":"publisher","award":["AUFF-E-2020-9-17"],"award-info":[{"award-number":["AUFF-E-2020-9-17"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003554","name":"Lundbeck Foundation","doi-asserted-by":"publisher","award":["NNF20OC0064467"],"award-info":[{"award-number":["NNF20OC0064467"]}],"id":[{"id":"10.13039\/501100003554","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003554","name":"Lundbeck Foundation","doi-asserted-by":"publisher","award":["R324-2019-1625"],"award-info":[{"award-number":["R324-2019-1625"]}],"id":[{"id":"10.13039\/501100003554","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003554","name":"Lundbeck Foundation","doi-asserted-by":"publisher","award":["AUFF-E-2020-9-17"],"award-info":[{"award-number":["AUFF-E-2020-9-17"]}],"id":[{"id":"10.13039\/501100003554","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Aarhus University Research Foundation","award":["NNF20OC0064467"],"award-info":[{"award-number":["NNF20OC0064467"]}]},{"name":"Aarhus University Research Foundation","award":["R324-2019-1625"],"award-info":[{"award-number":["R324-2019-1625"]}]},{"name":"Aarhus University Research Foundation","award":["AUFF-E-2020-9-17"],"award-info":[{"award-number":["AUFF-E-2020-9-17"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Background\/Objectives: Transcriptome assembly and functional annotation are essential in understanding gene expression and biological function. Nevertheless, many existing pipelines lack the flexibility to integrate both short- and long-read sequencing data or fail to provide a complete, customizable workflow for transcriptome analysis, particularly for non-model organisms. Methods: We present TrAnnoScope, a transcriptome analysis pipeline designed to process Illumina short-read and PacBio long-read data. The pipeline provides a complete, customizable workflow to generate high-quality, full-length (FL) transcripts with broad functional annotation. Its modular design allows users to adapt specific analysis steps for other sequencing platforms or data types. The pipeline encompasses steps from quality control to functional annotation, employing tools and established databases such as SwissProt, Pfam, Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and Eukaryotic Orthologous Groups (KOG). As a case study, TrAnnoScope was applied to RNA-Seq and Iso-Seq data from zebra finch brain, ovary, and testis tissue. Results: The zebra finch transcriptome generated by TrAnnoScope from the brain, ovary, and testis tissue demonstrated strong alignment with the reference genome (99.63%), and it was found that 93.95% of the matched protein sequences in the zebra finch proteome were captured as nearly complete. Functional annotation provided matches to known protein databases and assigned relevant functional terms to the majority of the transcripts. Conclusions: TrAnnoScope successfully integrates short and long sequencing technologies to generate transcriptomes with minimal user input. Its modularity and ease of use make it a valuable tool for researchers analyzing complex datasets, particularly for non-model organisms.<\/jats:p>","DOI":"10.3390\/genes15121547","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T02:57:08Z","timestamp":1732849028000},"page":"1547","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TrAnnoScope: A Modular Snakemake Pipeline for Full-Length Transcriptome Analysis and Functional Annotation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1271-6564","authenticated-orcid":false,"given":"Aysevil","family":"Pektas","sequence":"first","affiliation":[{"name":"Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Panitz","sequence":"additional","affiliation":[{"name":"Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus, Denmark"},{"name":"Applied Statistical Methods, Natural Resources Institute Finland (Luke), 20520 Turku, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Thomsen","sequence":"additional","affiliation":[{"name":"Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, J.W., Shrestha, L., Green, G., Leier, A., and Marquez-Lago, T.T. (2023). The hitchhikers\u2019 guide to RNA sequencing and functional analysis. Brief. Bioinform., 24.","DOI":"10.1093\/bib\/bbac529"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Deshpande, D., Chhugani, K., Chang, Y., Karlsberg, A., Loeffler, C., Zhang, J., Muszynska, A., Munteanu, V., Yang, H., and Rotman, J. (2023). RNA-seq data science: From raw data to effective interpretation. Front. Genet., 14.","DOI":"10.3389\/fgene.2023.997383"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Raghavan, V., Kraft, L., Mesny, F., and Rigerte, L. (2022). A simple guide to de novo transcriptome assembly and annotation. Brief. Bioinform., 23.","DOI":"10.1093\/bib\/bbab563"},{"key":"ref_4","first-page":"71","article-title":"RNA-Seq Data Analysis, Applications and Challenges","volume":"Volume 82","author":"Jaumot","year":"2018","journal-title":"Comprehensive Analytical Chemistry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/978-1-62703-613-9_4","article-title":"RNA-Seq for transcriptome analysis in non-model plants","volume":"1069","author":"Garg","year":"2013","journal-title":"Methods Mol. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dohm, J.C., Peters, P., Stralis-Pavese, N., and Himmelbauer, H. (2020). Benchmarking of long-read correction methods. NAR Genom. Bioinform., 2.","DOI":"10.1093\/nargab\/lqaa037"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Amarasinghe, S.L., Su, S., Dong, X., Zappia, L., Ritchie, M.E., and Gouil, Q. (2020). Opportunities and challenges in long-read sequencing data analysis. Genome Biol., 21.","DOI":"10.1186\/s13059-020-1935-5"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fu, S., Wang, A., and Au, K.F. (2019). A comparative evaluation of hybrid error correction methods for error-prone long reads. Genome Biol., 20.","DOI":"10.1186\/s13059-018-1605-z"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lataretu, M., and Holzer, M. (2020). RNAflow: An Effective and Simple RNA-Seq Differential Gene Expression Pipeline Using Nextflow. Genes, 11.","DOI":"10.3390\/genes11121487"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Jonassen, I. (2020). RASflow: An RNA-Seq analysis workflow with Snakemake. BMC Bioinform., 21.","DOI":"10.1186\/s12859-020-3433-x"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fallon, T.R., Calounova, T., Mokrejs, M., Weng, J.K., and Pluskal, T. (2023). transXpress: A Snakemake pipeline for streamlined de novo transcriptome assembly and annotation. BMC Bioinform., 24.","DOI":"10.1186\/s12859-023-05254-8"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2070","DOI":"10.1111\/1755-0998.13593","article-title":"TransPi-a comprehensive TRanscriptome ANalysiS PIpeline for de novo transcriptome assembly","volume":"22","author":"Conci","year":"2022","journal-title":"Mol. Ecol. Resour."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ortiz, R., Gera, P., Rivera, C., and Santos, J.C. (2021). Pincho: A Modular Approach to High Quality De Novo Transcriptomics. Genes, 12.","DOI":"10.3390\/genes12070953"},{"key":"ref_14","unstructured":"(2024, September 11). FIT: Functional IsoTranscriptomics Analyses. Available online: https:\/\/tappas.org\/."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lienhard, M., van den Beucken, T., Timmermann, B., Hochradel, M., Borno, S., Caiment, F., Vingron, M., and Herwig, R. (2023). IsoTools: A flexible workflow for long-read transcriptome sequencing analysis. Bioinformatics, 39.","DOI":"10.1093\/bioinformatics\/btad364"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5935","DOI":"10.1038\/s41467-023-41649-0","article-title":"TAGET: A toolkit for analyzing full-length transcripts from long-read sequencing","volume":"14","author":"Xia","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Guizard, S., Miedzinska, K., Smith, J., Smith, J., Kuo, R.I., Davey, M., Archibald, A., and Watson, M. (2023). nf-core\/isoseq: Simple gene and isoform annotation with PacBio Iso-Seq long-read sequencing. Bioinformatics, 39.","DOI":"10.1093\/bioinformatics\/btad150"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1186\/s13007-024-01255-7","article-title":"Trans2express\u2014De novo transcriptome assembly pipeline optimized for gene expression analysis","volume":"20","author":"Kasianova","year":"2024","journal-title":"Plant Methods"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, W., Petegrosso, R., Chang, J.W., Sun, J., Yong, J., Chien, J., and Kuang, R. (2020). A large-scale comparative study of isoform expressions measured on four platforms. BMC Genom., 21.","DOI":"10.1186\/s12864-020-6643-8"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"33","DOI":"10.12688\/f1000research.29032.2","article-title":"Sustainable data analysis with Snakemake","volume":"10","author":"Molder","year":"2021","journal-title":"F1000Research"},{"key":"ref_21","unstructured":"Babraham Bioinformatics (2024, August 08). FastQC. Available online: https:\/\/www.bioinformatics.babraham.ac.uk\/projects\/fastqc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3047","DOI":"10.1093\/bioinformatics\/btw354","article-title":"MultiQC: Summarize analysis results for multiple tools and samples in a single report","volume":"32","author":"Ewels","year":"2016","journal-title":"Bioinformatics"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.12688\/f1000research.15931.1","article-title":"FastQ Screen: A tool for multi-genome mapping and quality control","volume":"7","author":"Wingett","year":"2018","journal-title":"F1000Research"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"i884","DOI":"10.1093\/bioinformatics\/bty560","article-title":"fastp: An ultra-fast all-in-one FASTQ preprocessor","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_25","unstructured":"Pacific Biosciences (2024, August 08). Iso-Seq\u2014Scalable De Novo Isoform Discovery from Pacbio HiFi Reads. Available online: https:\/\/isoseq.how\/."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101054","DOI":"10.1016\/j.imu.2022.101054","article-title":"Computational methods for analyzing RNA-sequencing contaminated samples and its impact on cancer genome studies","volume":"32","author":"Mortezaei","year":"2022","journal-title":"Inform. Med. Unlocked"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.3390\/cimb45030120","article-title":"Revealing the History and Mystery of RNA-Seq","volume":"45","author":"Gondane","year":"2023","journal-title":"Curr. Issues Mol. Biol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.12688\/f1000research.12232.1","article-title":"BlobTools: Interrogation of genome assemblies","volume":"6","author":"Laetsch","year":"2017","journal-title":"F1000Research"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/nmeth.1923","article-title":"Fast gapped-read alignment with Bowtie 2","volume":"9","author":"Langmead","year":"2012","journal-title":"Nat. Methods"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e323","DOI":"10.1002\/cpz1.323","article-title":"BUSCO: Assessing Genomic Data Quality and Beyond","volume":"1","author":"Manni","year":"2021","journal-title":"Curr. Protoc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., and Madden, T.L. (2009). BLAST+: Architecture and applications. BMC Bioinform., 10.","DOI":"10.1186\/1471-2105-10-421"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, J.R., Holt, J., McMillan, L., and Jones, C.D. (2018). FMLRC: Hybrid long read error correction using an FM-index. BMC Bioinform., 19.","DOI":"10.1186\/s12859-018-2051-3"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","article-title":"Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"key":"ref_34","unstructured":"Gilbert, D. (2024, October 29). EvidentialGene: tr2aacds, mRNA Transcript Assembly Software. Available online: http:\/\/arthropods.eugenes.org\/EvidentialGene\/about\/EvidentialGene_trassembly_pipe.html."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2666","DOI":"10.1093\/bioinformatics\/bty149","article-title":"NanoPack: Visualizing and processing long-read sequencing data","volume":"34","author":"Schultz","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_36","unstructured":"Trinity (2024, August 08). Counting Full Length Trinity Transcripts. Available online: https:\/\/github.com\/trinityrnaseq\/trinityrnaseq\/wiki."},{"key":"ref_37","unstructured":"(2024, October 21). Trinotate: Transcriptome Functional Annotation and Analysis. Available online: https:\/\/github.com\/Trinotate\/Trinotate\/wiki."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"D412","DOI":"10.1093\/nar\/gkaa913","article-title":"Pfam: The protein families database in 2021","volume":"49","author":"Mistry","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"D480","DOI":"10.1093\/nar\/gkaa1100","article-title":"UniProt: The universal protein knowledgebase in 2021","volume":"49","author":"UniProt","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1038\/s41587-021-01156-3","article-title":"SignalP 6.0 predicts all five types of signal peptides using protein language models","volume":"40","author":"Teufel","year":"2022","journal-title":"Nat. Biotechnol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1006\/jmbi.2000.4315","article-title":"Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes","volume":"305","author":"Krogh","year":"2001","journal-title":"J. Mol. Biol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"D309","DOI":"10.1093\/nar\/gky1085","article-title":"eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses","volume":"47","author":"Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1093\/bioinformatics\/btt509","article-title":"Infernal 1.1: 100-fold faster RNA homology searches","volume":"29","author":"Nawrocki","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"iyad031","DOI":"10.1093\/genetics\/iyad031","article-title":"The Gene Ontology knowledgebase in 2023","volume":"224","author":"Aleksander","year":"2023","journal-title":"Genetics"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1093\/nar\/28.1.27","article-title":"KEGG: Kyoto encyclopedia of genes and genomes","volume":"28","author":"Kanehisa","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1093\/nar\/28.1.33","article-title":"The COG database: A tool for genome-scale analysis of protein functions and evolution","volume":"28","author":"Tatusov","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1101\/pdb.emo084574","article-title":"The zebra finch, Taeniopygia guttata: An avian model for investigating the neurobiological basis of vocal learning","volume":"2014","author":"Mello","year":"2014","journal-title":"Cold Spring Harb. Protoc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e61849","DOI":"10.7554\/eLife.61849","article-title":"The Natural History of Model Organisms: Neurogenomic insights into the behavioral and vocal development of the zebra finch","volume":"10","author":"Hauber","year":"2021","journal-title":"eLife"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"D19","DOI":"10.1093\/nar\/gkq1019","article-title":"International Nucleotide Sequence Database Collaboration","volume":"39","author":"Leinonen","year":"2011","journal-title":"Seq. Read Archive. Nucleic Acids Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"D590","DOI":"10.1093\/nar\/gks1219","article-title":"The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools","volume":"41","author":"Quast","year":"2013","journal-title":"Nucleic Acids Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1038\/s41587-021-01108-x","article-title":"Nanopore sequencing technology, bioinformatics and applications","volume":"39","author":"Wang","year":"2021","journal-title":"Nat. Biotechnol."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ferrarini, M., Moretto, M., Ward, J.A., Surbanovski, N., Stevanovic, V., Giongo, L., Viola, R., Cavalieri, D., Velasco, R., and Cestaro, A. (2013). An evaluation of the PacBio RS platform for sequencing and de novo assembly of a chloroplast genome. BMC Genom., 14.","DOI":"10.1186\/1471-2164-14-670"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"jkab083","DOI":"10.1093\/g3journal\/jkab083","article-title":"Comparison of long-read sequencing technologies in interrogating bacteria and fly genomes","volume":"11","author":"Tvedte","year":"2021","journal-title":"G3"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sacristan-Horcajada, E., Gonzalez-de la Fuente, S., Peiro-Pastor, R., Carrasco-Ramiro, F., Amils, R., Requena, J.M., Berenguer, J., and Aguado, B. (2021). ARAMIS: From systematic errors of NGS long reads to accurate assemblies. Brief. Bioinform., 22.","DOI":"10.1093\/bib\/bbab170"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1515\/ijb-2021-0091","article-title":"Error analysis of the PacBio sequencing CCS reads","volume":"19","author":"Pourmohammadi","year":"2023","journal-title":"Int. J. Biostat."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1093\/molbev\/msx319","article-title":"BUSCO Applications from Quality Assessments to Gene Prediction and Phylogenomics","volume":"35","author":"Waterhouse","year":"2018","journal-title":"Mol. Biol. Evol."}],"container-title":["Genes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4425\/15\/12\/1547\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:42:18Z","timestamp":1760114538000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4425\/15\/12\/1547"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"references-count":56,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["genes15121547"],"URL":"https:\/\/doi.org\/10.3390\/genes15121547","relation":{},"ISSN":["2073-4425"],"issn-type":[{"value":"2073-4425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,29]]}}}