Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data

Nature Biotechnology volume 40, pages 741–750 (2022)Cite this article

Subjects

Abstract

The accuracy of methods for assembling transcripts from short-read RNA sequencing data is limited by the lack of long-range information. Here we introduce Ladder-seq, an approach that separates transcripts according to their lengths before sequencing and uses the additional information to improve the quantification and assembly of transcripts. Using simulated data, we show that a kallisto algorithm extended to process Ladder-seq data quantifies transcripts of complex genes with substantially higher accuracy than conventional kallisto. For reference-based assembly, a tailored scheme based on the StringTie2 algorithm reconstructs a single transcript with 30.8% higher precision than its conventional counterpart and is more than 30% more sensitive for complex genes. For de novo assembly, a similar scheme based on the Trinity algorithm correctly assembles 78% more transcripts than conventional Trinity while improving precision by 78%. In experimental data, Ladder-seq reveals 40% more genes harboring isoform switches compared to conventional RNA sequencing and unveils widespread changes in isoform usage upon m6A depletion by Mettl14 knockout.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Ladder-seq uses mRNA length information to aid transcriptome reconstruction.
Fig. 2: Reduced read assignment ambiguity in Ladder-seq improves transcript quantification.
Fig. 3: Ladder-seq-based transcript assembly.
Fig. 4: Accuracy of de novo transcript assembly from 75 million simulated RNA-seq and matching Ladder-seq reads.
Fig. 5: Ladder-seq improves differential analysis of reconstructed transcriptomes.
Fig. 6: Mettl14 KO leads to isoform switches in m6A methylated genes and leads to loss of protein domains and loss of intron retentions.

Similar content being viewed by others

Data availability

Ladder-seq raw sequencing data from WT and Mettl14 KO mouse NPCs, conventional RNA-seq data from WT mouse NPCs and ONT long-read sequencing from WT and Mettl14 KO mouse NPCs are available in the Gene Expression Omnibus (GSE158985). m6A peaks from two replicates of m6A sequencing in mouse NPCs were downloaded from the Gene Expression Omnibus (GSE104686).

Code availability

The kallisto-ls, StringTie-ls and Trinity-ls programs and workflows are available at:

•https://github.com/canzarlab/ladderseq_quant,

•https://github.com/canzarlab/ladderseq_assembly and

•https://github.com/canzarlab/ladderseq_denovo,

respectively. The results of our benchmark studies can be reproduced via a Snakefile33, available at https://github.com/canzarlab/ladderseq_benchmark.

References

  1. Zhang, C., Zhang, B., Lin, L.-L. & Zhao, S. Evaluation and comparison of computational tools for RNA-seq isoform quantification. BMC Genomics 18, 583 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Teng, M. et al. A benchmark for RNA-seq quantification pipelines. Genome Biol. 17, 74 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Aguiar, D. et al. Bayesian nonparametric discovery of isoforms and individual specific quantification. Nat. Commun. 9, 1681 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Song, L., Sabunciyan, S., Yang, G. & Florea, L. A multi-sample approach increases the accuracy of transcript assembly. Nat. Commun. 10, 5000 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Li, W. V. et al. AIDE: annotation-assisted isoform discovery with high precision. Genome Res. 29, 2056–2072 (2019).

  6. Desrosiers, R. C., Friderici, K. H. & Rottman, F. M. Characterization of novikoff hepatoma mRNA methylation and heterogeneity in the methylated 5′ terminus. Biochemistry 14, 4367–4374 (1975).

    Article  CAS  PubMed  Google Scholar 

  7. Barbosa-Morais, N. L. et al. The evolutionary landscape of alternative splicing in vertebrate species. Science 338, 1587–1593 (2012).

    Article  CAS  PubMed  Google Scholar 

  8. Jelen, N., Ule, J., Živin, M. & Darnell, R. B. Evolution of nova-dependent splicing regulation in the brain. PLoS Genetics 3, e173 (2007).

    Article  PubMed Central  Google Scholar 

  9. Merkin, J., Russell, C., Chen, P. & Burge, C. B. Evolutionary dynamics of gene and isoform regulation in mammalian tissues. Science 338, 1593–1599 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tardaguila, M. et al. SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification. Genome Res. 28, 396–411 (2018).

    Article  CAS  PubMed Central  Google Scholar 

  11. Chen, K. et al. Genome-wide binding and mechanistic analyses of Smchd1-mediated epigenetic regulation. Proc. Natl Acad. Sci. USA 112, E3535–E3544 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Soneson, C. et al. A comprehensive examination of Nanopore native RNA sequencing for characterization of complex transcriptomes. Nat. Commun. 10, 3359 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hurowitz, E. H. & Brown, P. O. Genome-wide analysis of mRNA lengths in Saccharomyces cerevisiae. Genome Biol. 5, R2 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525 (2016).

    Article  CAS  PubMed  Google Scholar 

  15. Heber, S., Alekseyev, M., Sze, S.-H., Tang, H. & Pevzner, P. A. Splicing graphs and EST assembly problem. Bioinformatics 18, S181–S188 (2002).

    Article  PubMed  Google Scholar 

  16. Pachter, L. Models for transcript quantification from RNA-seq. Preprint at https://arxiv.org/abs/1104.3889 (2011).

  17. Kovaka, S. et al. Transcriptome assembly from long-read RNA-seq alignments with Stringtie2. Genome Biol. 20, 278 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ebrahim Sahraeian, S. M. et al. Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis. Nat. Commun. 8, 59 (2017).

  19. Glinos, D. A. et al. Transcriptome variation in human tissues revealed by long-read sequencing. Preprint at https://www.biorxiv.org/content/10.1101/2021.01.22.427687v1 (2021).

  20. Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chang, Z., Wang, Z. & Li, G. The impacts of read length and transcriptome complexity for de novo assembly: a simulation study. PLoS ONE 9, 1–8 (2014).

    Google Scholar 

  22. Dong, X. et al. The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools. NAR Genom. Bioinform. 3, lqab028 (2021).

  23. Wang, Y. et al. N6-methyladenosine RNA modification regulates embryonic neural stem cell self-renewal through histone modifications. Nat. Neurosci. 21, 195–206 (2018).

    PubMed  PubMed Central  Google Scholar 

  24. Canzar, S., Andreotti, S., Weese, D., Reinert, K. & Klau, G. W. CIDANE: comprehensive isoform discovery and abundance estimation. Genome Biol. 17, 16 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Alqassem, I., Sonthalia, Y., Klitzke-Feser, E., Shim, H. & Canzar, S. McSplicer: a probabilistic model for estimating splice site usage from RNA-seq data. Bioinformatics 37, 2004–2011 (2021).

    Article  CAS  PubMed Central  Google Scholar 

  26. Batista, P. J. et al. m6a RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ke, S. et al. A majority of m6a residues are in the last exons, allowing the potential for 3′ UTR regulation. Genes Dev. 29, 2037–2053 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yamauchi, T., Nishiyama, M., Moroishi, T., Kawamura, A. & Nakayama, K. I. FBXL5 inactivation in mouse brain induces aberrant proliferation of neural stem progenitor cells. Mol. Cell. Biol. 37, e00470-16 (2017).

  29. Kuboyama, K., Fujikawa, A., Suzuki, R. & Noda, M. Inactivation of protein tyrosine phosphatase receptor type Z by pleiotrophin promotes remyelination through activation of differentiation of oligodendrocyte precursor cells. J. Neurosci. 35, 12162–12171 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kurosaki, T., Popp, M. W. & Maquat, L. E. Quality and quantity control of gene expression by nonsense-mediated mRNA decay. Nat. Rev. Mol. Cell Biol. 20, 406–420 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lianoglou, S., Garg, V., Yang, J. L., Leslie, C. S. & Mayr, C. Ubiquitously transcribed genes use alternative polyadenylation to achieve tissue-specific expression. Genes Dev. 27, 2380–2396 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Tang, A. D. et al. Full-length transcript characterization of SF3B1 mutation in chronic lymphocytic leukemia reveals downregulation of retained introns. Nat. Commun. 11, 1438 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).

    Article  PubMed  Google Scholar 

  34. DeAngelis, M. M., Wang, D. G. & Hawkins, T. L. Solid-phase reversible immobilization for the isolation of PCR products. Nucleic Acids Res. 23, 4742–4743 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sobczak, K. & Krzyzosiak, W. J. RNA structure analysis assisted by capillary electrophoresis. Nucleic Acids Res. 30, e124 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Azarani, A. & Hecker, K. H. RNA analysis by ion-pair reversed-phase high performance liquid chromatography. Nucleic Acids Res. 29, e7 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wang, Y. et al. High-resolution profile of transcriptomes reveals a role of alternative splicing for modulating response to nitrogen in maize. BMC Genomics 21, 353 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Li, R. et al. Direct full-length RNA sequencing reveals unexpected transcriptome complexity during Caenorhabditis elegans development. Genome Res. 30, 287–298 (2020).

  39. Haussmann, I. U. et al. m6a potentiates Sxl alternative pre-mRNA splicing for robust Drosophila sex determination. Nature 540, 301–304 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Bartosovic, M. et al. N6-methyladenosine demethylase FTO targets pre-mRNAs and regulates alternative splicing and 3′-end processing. Nucleic Acids Res. 45, 11356–11370 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Xiao, W. et al. Nuclear m6a reader YTHDC1 regulates mRNA splicing. Mol. Cell 61, 507–519 (2016).

    Article  CAS  PubMed  Google Scholar 

  42. Zhou, K. I. et al. Regulation of co-transcriptional pre-mRNA splicing by m6a through the low-complexity protein hnRNPG. Mol. Cell 76, 70–81 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Jacob, A. G. & Smith, C. W. J. Intron retention as a component of regulated gene expression programs. Hum. Genet. 136, 1043–1057 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Braunschweig, U. et al. Widespread intron retention in mammals functionally tunes transcriptomes. Genome Res. 24, 1774–1786 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Yoon, K.-J. et al. Temporal control of mammalian cortical neurogenesis by m6a methylation. Cell 171, 877–889 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Eckmann, C. R., Rammelt, C. & Wahle, E. Control of poly(A) tail length. Wiley Interdiscip. Rev. RNA 2, 348–361 (2011).

    Article  CAS  PubMed  Google Scholar 

  47. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  48. Conforti, L. et al. Kif1Bβ isoform is enriched in motor neurons but does not change in a mouse model of amyotrophic lateral sclerosis. J. Neurosci. Res. 71, 732–739 (2003).

    Article  CAS  PubMed  Google Scholar 

  49. Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

    Article  PubMed Central  Google Scholar 

  52. Chang, Z. et al. Bridger: a new framework for de novo transcriptome assembly using RNA-seq data. Genome Biol. 16, 30 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Shao, M. & Kingsford, C. Accurate assembly of transcripts through phase-preserving graph decomposition. Nat. Biotechnol. 35, 1167–1169 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Pertea, M., Kim, D., Pertea, G. M., Leek, J. T. & Salzberg, S. L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protocols 11, 1650 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Kent, W. J. BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Xie, Y. et al. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics 30, 1660–1666 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Liu, J., Yu, T., Mu, Z. & Li, G. TransLiG: a de novo transcriptome assembler that uses line graph iteration. Genome Biol. 20, 81 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Roberts, A. & Pachter, L. Streaming fragment assignment for real-time analysis of sequencing experiments. Nat. Methods 10, 71–73 (2013).

    Article  CAS  PubMed  Google Scholar 

  60. Li, B., Ruotti, V., Stewart, R. M., Thomson, J. A. & Dewey, C. N. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 26, 493–500 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Vitting-Seerup, K. & Sandelin, A. The landscape of isoform switches in human cancers. Mol. Cancer Res. 15, 1206–1220 (2017).

    Article  CAS  PubMed  Google Scholar 

  62. Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008–2017 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Park, H. J. et al. CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Res. 41, e74 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014).

  65. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, 1–9 (2008).

    Article  Google Scholar 

  66. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Alexa, A., Rahnenführer, J. & Lengauer, T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22, 1600–1607 (2006).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank A. A. Kha for helpful discussions and the members of the Canzar laboratory for comments and suggestions. We thank P. J. Shaw and M. Halic for constructive feedback on mRNA length separation. We thank S. Esser for excellent technical assistance. S. Chakraborty was supported by a Deutsche Forschungsgemeinschaft (DFG) fellowship through the Graduate School of Quantitative Biosciences Munich. F.R.R. was supported, in part, by the Bavarian Gender Equality Grant. This work was funded by the TTIC Research fund (to S. Canzar) and by grants from the National Institutes of Health (R35NS097370 to G.M. and R35NS116843 to H.S), the Simons Foundation (575050 to H.S.), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) SFB-TRR 338/1 2021-452881907 (to S. Canzar), DFG Sachbeihilfe (417912216 to M.L.S.), Deutsche Gesellschaft für Muskelkranke e.V. Forschungsförderung (awarded in 2019 to M.L.S.), the Institute for Basic Science (IBS-R008-D1 to A.H. and H.C.), the National Research Foundation of Korea (2019R1C1C1006600 and 2020M3A9E4039670 to K.-J.Y. and 2021R1C1C1010758 to A.H. and H.C.), funded by the Ministry of Science, and by ICT of Korea, Young Investigator Grant, from the Suh Kyungbae Foundation (to K.-J.Y.).

Author information

Author notes

  1. These authors contributed equally: Francisca Rojas Ringeling, Shounak Chakraborty.

Authors and Affiliations

  1. Gene Center, Ludwig-Maximilians-Universität München, Munich, Germany

    Francisca Rojas Ringeling, Shounak Chakraborty, Akshay M. Patel, Tim Reska & Stefan Canzar

  2. Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA

    Caroline Vissers

  3. Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA

    Derek Reiman

  4. Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

    Ki-Heon Lee, Chan-Woo Park & Ki-Jun Yoon

  5. Center for RNA Research, Institute for Basic Science (IBS), Seoul, Republic of Korea

    Ari Hong & Hyeshik Chang

  6. Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea

    Ari Hong & Hyeshik Chang

  7. Department of Informatics, Technical University of Munich, Garching, Germany

    Julien Gagneur

  8. Institute of Human Genetics, Technical University of Munich, Munich, Germany

    Julien Gagneur

  9. Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany

    Julien Gagneur

  10. School of Biological Sciences, Seoul National University, Seoul, Republic of Korea

    Hyeshik Chang

  11. Biomedical Center, Department of Physiological Chemistry, Ludwig-Maximilians-Universität München, Martinsried-Planegg, Germany

    Maria L. Spletter

  12. Department of Neuroscience and Mahoney Institute for Neurosciences, University of Pennsylvania, Philadelphia, PA, USA

    Guo-li Ming & Hongjun Song

Authors
  1. Francisca Rojas Ringeling
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shounak Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Caroline Vissers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Derek Reiman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Akshay M. Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ki-Heon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ari Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chan-Woo Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Tim Reska
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Julien Gagneur
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hyeshik Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Maria L. Spletter
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ki-Jun Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Guo-li Ming
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Hongjun Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Stefan Canzar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F.R.R. and S. Canzar conceived the original idea. S. Chakraborty and S. Canzar designed the kallisto-ls, StringTie-ls and Trintity-ls methods, with input from F.R.R. S. Chakraborty implemented the kallisto-ls, StringTie-ls and Trinity-ls methods and conducted all experiments and evaluations on simulated data. F.R.R. and S. Canzar contributed to the evaluation on simulated data. D.R. performed the initial computational study on simulated data (not shown). F.R.R. performed differential splicing analysis in NPCs, with input from S. Canzar. F.R.R. developed and performed Ladder-seq experiments, with input from H.S., G.M., M.L.S. and J.G. C.V. conducted RT–qPCR experiments. K.-J.Y. bred mice and harvested NPCs. K.-J.Y., H.C., K.-H.L., A.H. and C.-W.P. performed ONT long-read sequencing experiments. A.M.P., S. Chakraborty, F.R.R. and T.R. contributed to analysis of ONT long-read datasets. F.R.R. and S. Canzar wrote the manuscript, with input from all authors. S. Canzar supervised the entire project. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Stefan Canzar.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Reduced (effective) gene complexity in Ladder-seq.

We estimate transcript expression in Mettl14 KO sample 1 using kallisto on Ladder-seq reads pooled across bands and show the histogram of gene complexity measured as the number of expressed transcripts. In Ladder-seq, we partition the set of expressed transcripts into 7 bands and count the number of transcripts contained in each band according to their annotated length (plus 200 nt average poly(A) tail size46), assuming cuts at 1000 bp, 1500 bp, 2000 bp, 3000 bp, 4000 bp and 6000 bp. The resulting histogram of effective gene complexity shows an increased fraction of gene bands with low complexity.

Extended Data Fig. 2 In silico gel for WT and KO NPC samples.

For every annotated transcript the intensity of a point with y- coordinate equal to its annotated length (plus 200 nt average poly(A) tail size) shows the fraction of reads obtained from each band (x-axis) that can be assigned unambiguously to it. As expected, each band contains predominantly reads from transcripts of a distinct length range.

Extended Data Fig. 3 Overview of the benchmark strategy.

1. The ground truth transcriptome including abundances and error profile is calculated by RSEM from GEUVADIS sample NA12716_7. 2. Reads are simulated from the ground truth transcriptome by RSEM to obtain RNA-seq samples of different sequencing depths. 3. A matching Ladder-seq sample is obtained by separating reads in silico according to probability mass functions estimated from our NPC Ladder-seq sample (and variants thereof). 4. Transcripts are quantified and assembled by our Ladder-seq tailored transcript analysis methods kallisto-ls, StringTie-ls, and Trinity-ls from the Ladder-seq sample, while their conventional counterparts are run on the corresponding RNA-seq sample. 5. The results are compared to the ground truth to evaluate and compare their accuracy.

Extended Data Fig. 4 Quantification accuracy of kallisto-ls on 75 million simulated reads.

Mean values across 20 simulations are reported. Pearson correlation of estimated and ground truth abundance in log2 transformed transcripts per million (TPM) and mean absolute relative difference (MARD) are shown as a function of gene complexity, that is the number of transcripts expressed by a gene. For ease of visualization, we omit genes expressing a single transcript, many of which are estimated to be lowly expressed in GEUVADIS sample NA12716_7 by RSEM.

Extended Data Fig. 5 Accuracy of transcript assembly from 75 million simulated reads.

RNA-seq and Ladder-seq reads were aligned identically to the reference genome (GRCh38) using STAR. Sensitivity and precision of StringTie-ls and its conventional counterpart StringTie2 are shown as a function of gene complexity measured as the number of expressed transcripts. Sensitivity and precision are calculated with respect to the same set of ground truth transcripts as in the smaller 30 million read pairs data set.

Extended Data Fig. 6 Accuracy of de novo transcript assembly from 30 million (top row) and 75 million (bottom row) simulated reads.

(a) Sensitivity of Trinity-ls and its conventional counterpart Trinity at 90% transcript length cut-off is shown as a function of gene complexity measured as the number of expressed transcripts. (b) Total number of correctly assembled transcripts at different transcript length cut-offs. (c) Precision at different transcript length cut-offs.

Extended Data Fig. 7 Ladder-seq improves differential analysis of reconstructed transcriptomes.

(a) Computational pipeline for differential isoform usage analysis with conventional RNA-seq and Ladder-seq. Reads were aligned using STAR aligner prior to transcript assembly for both pipelines. (b) Venn diagram showing overlap between switching genes identified by Ladder-seq and conventional RNA-seq. (c and e) Isoform switches identified only by Ladder- seq in genes Exo1 and Tram1l1 (between n=4 WT and n=4 KO samples). Red arrows show location of m6A methylation. TCONS_00000541 and TCONS_00000542 are novel isoforms of Exo1 detected only by Ladder-seq. TCONS_00006855 is a novel isoform of Tram1l1 that was assembled by both methods, but conventional RNA-seq failed to identify the isoform switch. Without length information, conventional RNA-seq reads in KO bands 2 and 3 were predominantly assigned to the annotated transcript in band 4. Barplots represent mean ± SEM; ***FDR corrected p<0.001. (d and f) Coverage plots for switching genes Exo1 and Tram1l1 showing separation of reads from transcripts of different lengths. (g) Jensen Shannon divergence for Ladder-seq and conventional RNA-seq for all identified transcripts grouped by relative difference in abundance estimation by both methods (n=18761 for <0.5, n=12722 for 0.5-1, n=7918 for 1-1.5, n=6292 for 1.5-2 relative difference). Relative difference is defined as the absolute difference in estimated transcript abundance (in TPM) divided by the average of the two. Boxplot definition: Bottom and top of the box correspond to lower and upper quartiles of the data, bar is the median and whiskers are median ± 1.5x interquartile range.

Extended Data Fig. 8 Mettl14 KO leads to isoform switches in m6A methylated genes.

(a) Gene Ontology for m6A methylated genes containing isoform switches. (b) Isoform switch in Ptprz1 (between n=4 WT and n=4 KO samples). Red arrow shows location of m6A methylation. Barplots represent mean ± SEM; ***FDR corrected p<0.001. (c) Gene Ontology analysis for genes with loss of protein domains in KO NPCs. (d) Splicing analysis: Number of gains and losses of each splicing event in KO NPCs. A3: Alternative 3’ acceptor site; A5: Alternative 5’ acceptor site; ES: Exon skipping; IR: Intron retention; MEE: Mutually exclusive exon; MES: Multiple exon skipping. (e) Gene Ontology enrichment analysis of genes with intron retention loss in KO NPCs.

Extended Data Fig. 9 Comparison of Ladder-seq and ONT-cDNA sequencing on mouse NPCs.

(a) Orange bars show validation by StringTie2 (left panel) or by an independent ONT dataset (Dong et al.) (right panel) of transcripts found by both Ladder-seq and ONT-cDNA while light blue bars show validation values for transcripts reported only by ONT-cDNA. In comparison, 32.5% of transcripts uniquely identified by Ladder-seq (average TPM ≥ 1) were also identified in the dataset by Dong et al. (b) Boxplots showing expression levels (TPM) for transcripts identified both by long- reads and Ladder-seq (green boxes) and for transcripts identified only by Ladder-seq (grey boxes). Left panel shows values for all Ladder-seq transcripts with TPM higher than 1 (n=6169 identified only by Ladder-seq, n=15099 by both). Right panel shows values for Ladder-seq switching transcripts with TPM higher than 1 (n=905 identified only by Ladder-seq, n=2012 by both). Boxplot definition: Bottom and top of the box correspond to lower and upper quartiles of the data, bar is the median and whiskers are median ± 1.5x interquartile range.

Extended Data Fig. 10 Cumulative percentage of Ladder-seq transcripts identified by long-read sequencing.

Bars show percentage of Ladder-seq transcripts identified by FLAIR (green), plus those additionally identified by StringTie2 (blue), plus transcripts additionally found in a recently published long-read mouse NPC transcriptome (light blue).

Supplementary information

Supplementary Information

Supplementary Figs. 1–14 and Tables 1–23

Reporting Summary

Supplementary Table

Supplementary Table 19, switching isoforms in Mettl14 KO NPCs.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ringeling, F.R., Chakraborty, S., Vissers, C. et al. Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data. Nat Biotechnol 40, 741–750 (2022). https://doi.org/10.1038/s41587-021-01136-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-021-01136-7

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics