NanoTrans: an integrated computational framework for comprehensive transcriptome analysis with nanopore direct RNA sequencing

Ludong Yang; Xinxin Zhang; Fan Wang; Li Zhang; Jing Li; Jia-Xing Yue

doi:10.1016/j.jgg.2024.07.007

Volume 51 Issue 11

Nov. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Genetics and Genomics > 2024 > 51(11): 1300-1309

PDF( 2494 KB)

NanoTrans: an integrated computational framework for comprehensive transcriptome analysis with nanopore direct RNA sequencing

doi: 10.1016/j.jgg.2024.07.007

a. State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, China;
b. Department of Medical Oncology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu 223200, China

Funds:

This work is supported by the National Natural Science Foundation of China (32070592 to JXY, 32000395 to JL, 82272789 to LZ), Guangdong Basic and Applied Basic Research Foundation (2022A1515010717 and 2019A1515110762 to JXY and 2022A1515011873 to JL), Guangdong Pearl River Talents Program (2019QN01Y183 to JXY, 2021QN02Y168 to JL), Guangzhou Municipal Science and Technology Bureau (202102020938 to JL), and Young Talents Program of Sun Yat-sen University Cancer Center (YTP-SYSUCC-0042 to JXY and YTP-SYSUCC-0040 to JL).

Received Date: 2024-01-11
Accepted Date: 2024-07-04
Rev Recd Date: 2024-07-03

Available Online: 2025-06-06

Publish Date: 2024-07-14

Abstract

Abstract

Nanopore direct RNA sequencing (DRS) provides the direct access to native RNA strands with full-length information, shedding light on rich qualitative and quantitative properties of gene expression profiles. Here with NanoTrans, we present an integrated computational framework that comprehensively covers all major DRS-based application scopes, including isoform clustering and quantification, poly(A) tail length estimation, RNA modification profiling, and fusion gene detection. In addition to its merit in providing such a streamlined one-stop solution, NanoTrans also shines in its workflow-orientated modular design, batch processing capability, all-in-one tabular and graphic report output, as well as automatic installation and configuration supports. Finally, by applying NanoTrans to real DRS datasets of yeast, Arabidopsis, as well as human embryonic kidney and cancer cell lines, we further demonstrate its utility, effectiveness, and efficacy across a wide range of DRS-based application settings.
- Direct RNA sequencing,
- Nanopore,
- Transcriptome,
- Long reads,
- DRS

FullText(HTML)

References(20)

References

Byrne, A., Beaudin, A.E., Olsen, H.E., Jain, M., Cole, C., Palmer, T., DuBois, R.M., Forsberg, E.C., Akeson, M., Vollmers, C., 2017. Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nat. Commun. 8, 16027.

Chen, X., Liu, Y., Lv, K., Wang, M., Liu, X., Li, B., 2023. FASTdRNA: a workflow for the analysis of ONT direct RNA sequencing. Bioinform. Adv. 3, vbad099.

Chen, Y., Davidson, N.M., Wan, Y.K., Patel, H., Yao, F., Low, H.M., Hendra, C., Watten, L., Sim, A., Sawyer, C.,et al., 2021. A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines. bioRxiv. https://doi.org/10.1101/2021.04.21.440736.

Cozzuto, L., Liu, H., Pryszcz, L.P., Pulido, T.H., Delgado-Tejedor, A., Ponomarenko, J., Novoa, E.M., 2020. MasterOfPores: A Workflow for the Analysis of Oxford Nanopore Direct RNA Sequencing Datasets. Front. Genet. 11, 211.

Davidson, N.M., Chen, Y., Sadras, T., Ryland, G.L., Blombery, P., Ekert, P.G., Goke, J., Oshlack, A., 2022. JAFFAL: detecting fusion genes with long-read transcriptome sequencing. Genome Biology 23, 10.

Garalde, D.R., Snell, E.A., Jachimowicz, D., Sipos, B., Lloyd, J.H., Bruce, M., Pantic, N., Admassu, T., James, P., Warland, et al., 2018. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15, 201-206.

Gewartowska, O., Aranaz-Novaliches, G., Krawczyk, P.S., Mroczek, S., Kusio-Kobialka, M., Tarkowski, B., Spoutil, F., Benada, O., Kofronova, O., Szwedziak, P., et al., 2021. Cytoplasmic polyadenylation by TENT5A is required for proper bone formation. Cell Reports 35(3):109015.

Ghandi, M., Huang, F.W., Jane-Valbuena, J., Kryukov, G.V., Lo, C.C., McDonald, E.R., Barretina, J., Gelfand, E.T., Bielski, C.M., Li, H., et al., 2019. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503-508.

Gleeson, J., Leger, A., Prawer, Y.D.J., Lane, T.A., Harrison, P.J., Haerty, W., Clark, M.B., 2022. Accurate expression quantification from nanopore direct RNA sequencing with NanoCount. Nucleic Acids Res. 50, e19.

Krause, M., Niazi, A.M., Labun, K., Cleuren, Y.N.T., Muller, F.S., Valen, E., 2019. tailfindr: alignment-free poly(A) length measurement for Oxford Nanopore RNA and DNA sequencing. RNA 25, 1229-1241.

Liu, H., Begik, O., Lucas, M.C., Ramirez, J.M., Mason, C.E., Wiener, D., Schwartz, S., Mattick, J.S., Smith, M.A., Novoa, E.M., 2019. Accurate detection of m⁶A RNA modifications in native RNA sequences. Nat. Commun. 10, 4079.

Liu, Q., Hu, Y., Stucky, A., Fang, L., Zhong, J.F., Wang, K., 2020. LongGF: computational algorithm and software tool for fast and accurate detection of gene fusions by long-read transcriptome sequencing. BMC Genomics 21, 793.

Logsdon, G.A., Vollger, M.R., Eichler, E.E., 2020. Long-read human genome sequencing and its applications. Nature Reviews Genetics 1-18.

Loman, N.J., Quick, J., Simpson, J.T., 2015. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat. Methods 12, 733-735.

Mroczek, S., Chlebowska, J., Kulinski, T.M., Gewartowska, O., Gruchota, J., Cysewski, D., Liudkovska, V., Borsuk, E., Nowis, D., Dziembowski, A., 2017. The non-canonical poly(A) polymerase FAM46C acts as an onco-suppressor in multiple myeloma. Nat. Commun. 8, 619.

Parker, M.T., Knop, K., Sherwood, A.V., Schurch, N.J., Mackinnon, K., Gould, P.D., Hall, A.J., Barton, G.J., Simpson, G.G., 2020. Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m⁶A modification. eLife 9, e49658.

Pratanwanich, P.N., Yao, F., Chen, Y., Koh, C.W.Q., Wan, Y.K., Hendra, C., Poon, P., Goh, Y.T., Yap, P.M.L., Chooi, J.Y., et al., 2021. Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore. Nat. Biotechnol. 39, 1394-1402.

Sheikh, A.H., Tabassum, N., Rawat, A., Almeida Trapp, M., Nawaz, K., Hirt, H., 2024. m⁶A RNA methylation counteracts dark-induced leaf senescence in Arabidopsis. Plant Physiol. 194, 2663-2678.

Tang, A.D., Soulette, C.M., van Baren, M.J., Hart, K., Hrabeta-Robinson, E., Wu, C.J., Brooks, A.N., 2020. Full-length transcript characterization of SF3B1 mutation in chronic lymphocytic leukemia reveals downregulation of retained introns. Nat. Commun. 11, 1438.

Tudek, A., Krawczyk, P.S., Mroczek, S., Tomecki, R., Turtola, M., Matylla-Kulinska, K., Jensen, T.H., Dziembowski, A., 2021. Global view on the metabolism of RNA poly(A) tails in yeast Saccharomyces cerevisiae. Nat. Commun. 12, 4951.

Relative Articles

Supplements(0)

Cited By

Proportional views