Science China Life Sciences

, Volume 62, Issue 7, pp 937–946 | Cite as

QuaPra: Efficient transcript assembly and quantification using quadratic programming with Apriori algorithm

  • Xiangjun Ji
  • Weida Tong
  • Baitang Ning
  • Christopher E. Mason
  • David P. Kreil
  • Pawel P. Labaj
  • Geng ChenEmail author
  • Tieliu ShiEmail author
Research Paper


RNA sequencing (RNA-seq) has greatly facilitated the exploring of transcriptome landscape for diverse organisms. However, transcriptome reconstruction is still challenging due to various limitations of current tools and sequencing technologies. Here, we introduce an efficient tool, QuaPra (Quadratic Programming combined with Apriori), for accurate transcriptome assembly and quantification. QuaPra could detect at least 26.5% more low abundance (0.1–1 FPKM) transcripts with over 2.1% increase of sensitivity and precision on simulated data compared to other currently popular tools. Moreover, around one-quarter more known transcripts were correctly assembled by QuaPra than other assemblers on real sequencing data. QuaPra is freely available at


RNA-Seq transcriptome reconstruction transcript assembly transcript quantification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work was supported by the National High Technology Research and Development Program of China (2015AA020108), the National Key Research and Development Program of China (2016YFC0902100), the China Human Proteome Project (2014DFB30010 and 2014DFB30030), the National Science Foundation of China (31671377, 31401133, 31771460 and 91629103) and the Program of Introducing Talents of Discipline to Universities of China (B14019). We thank Dr. Jiannan Lin, Huanlong Liu, Yimin Ma, Yan Shi, Jiwei Chen, Jun Tang, Qing Zhou for their extensive help with this manuscript. Thanks for the Graduate School and Supercomputer Center of East China Normal University.

Supplementary material

11427_2018_9433_MOESM1_ESM.docx (18 kb)
Supplementary material, approximately 17.9 KB.


  1. Bradford, J.R., Cox, A., Bernard, P., and Camp, N.J. (2016). Consensus analysis of whole transcriptome profiles from two breast cancer patient cohorts reveals long non-coding RNAs associated with intrinsic subtype and the tumour microenvironment. PLoS ONE 11, e0163238.CrossRefGoogle Scholar
  2. Bray, N.L., Pimentel, H., Melsted, P., and Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34, 525–527.CrossRefGoogle Scholar
  3. Chan, M.C., Ilott, N.E., Schödel, J., Sims, D., Tumber, A., Lippl, K., Mole, D.R., Pugh, C.W., Ratcliffe, P.J., Ponting, C.P., et al. (2016). Tuning the transcriptional response to hypoxia by inhibiting hypoxia-inducible factor (HIF) prolyl and asparaginyl hydroxylases. J Biol Chem 291, 20661–20673.CrossRefGoogle Scholar
  4. Chen, G., Shi, T., and Shi, L. (2017). Characterizing and annotating the genome using RNA-seq data. Sci China Life Sci 60, 116–125.CrossRefGoogle Scholar
  5. Chen, J., and Xue, Y. (2016). Emerging roles of non-coding RNAs in epigenetic regulation. Sci China Life Sci 59, 227–235.CrossRefGoogle Scholar
  6. Derrien, T., Johnson, R., Bussotti, G., Tanzer, A., Djebali, S., Tilgner, H., Guernec, G., Martin, D., Merkel, A., Knowles, D.G., et al. (2012). The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res 22, 1775–1789.CrossRefGoogle Scholar
  7. Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., and Gingeras, T.R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21.CrossRefGoogle Scholar
  8. Dong, C., Zhao, G., Zhong, M., Yue, Y., Wu, L., and Xiong, S. (2013). RNA sequencing and transcriptomal analysis of human monocyte to macrophage differentiation. Gene 519, 279–287.CrossRefGoogle Scholar
  9. Griebel, T., Zacher, B., Ribeca, P., Raineri, E., Lacroix, V., Guigó, R., and Sammeth, M. (2012). Modelling and simulating generic RNA-Seq experiments with the flux simulator. Nucl Acids Res 40, 10073–10083.CrossRefGoogle Scholar
  10. Hipp J., Myka A., Wirth R., Güntzer U. (1998) A new algorithm for faster mining of generalized association rules. Lect Notes Artif Int, 1510, 74–82.Google Scholar
  11. Kim, D., Langmead, B., and Salzberg, S.L. (2015). HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12, 357–360.CrossRefGoogle Scholar
  12. Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., and Salzberg, S. L. (2013). TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14, R36.CrossRefGoogle Scholar
  13. Labaj, P.P., Leparc, G.G., Linggi, B.E., Markillie, L.M., Wiley, H.S., and Kreil, D.P. (2011). Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics 27, i383–i391.CrossRefGoogle Scholar
  14. Leinonen, R., Sugawara, H., Shumway, M., and Shumway, M. (2011). The sequence read archive. Nucl Acids Res 39, D19–D21.CrossRefGoogle Scholar
  15. Li, B., and Dewey, C.N. (2011). RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC BioInf 12, 323.CrossRefGoogle Scholar
  16. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., and Durbin, R. (2009). The sequence alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079.CrossRefGoogle Scholar
  17. Li, W., and Jiang, T. (2012). Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads. Bioinformatics 28, 2914–2921.CrossRefGoogle Scholar
  18. Magistri, M., Velmeshev, D., Makhmutova, M., and Faghihi, M.A. (2015). Transcriptomics profiling of Alzheimer’s disease reveal neurovascular defects, altered amyloid-β homeostasis, and deregulated expression of long noncoding RNAs. J Alzheimer’s Disease 48, 647–665.CrossRefGoogle Scholar
  19. Mollet, I.G., Ben-Dov, C., Felicio-Silva, D., Grosso, A.R., Eleutério, P., Alves, R., Staller, R., Silva, T.S., and Carmo-Fonseca, M. (2010). Unconstrained mining of transcript data reveals increased alternative splicing complexity in the human transcriptome. Nucl Acids Res 38, 4740–4754.CrossRefGoogle Scholar
  20. Parkinson, H., Sarkans, U., Kolesnikov, N., Abeygunawardena, N., Burdett, T., Dylag, M., Emam, I., Farne, A., Hastings, E., Holloway, E., et al. (2011). ArrayExpress update—an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucl Acids Res 39, D1002–D1004.CrossRefGoogle Scholar
  21. Pertea, M., Pertea, G.M., Antonescu, C.M., Chang, T.C., Mendell, J.T., and Salzberg, S.L. (2015). Stringtie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol 33, 290–295.CrossRefGoogle Scholar
  22. Schiano, C., Costa, V., Aprile, M., Grimaldi, V., Maiello, C., Esposito, R., Soricelli, A., Colantuoni, V., Donatelli, F., Ciccodicola, A., et al. (2017). Heart failure: pilot transcriptomic analysis of cardiac tissue by RNA-sequencing. Cardiol J 24, 539–553.CrossRefGoogle Scholar
  23. Song, L., Sabunciyan, S., and Florea, L. (2016). CLASS2: accurate and efficient splice variant annotation from RNA-seq reads. Nucl Acids Res 44, e98.CrossRefGoogle Scholar
  24. Sun, T. T., He, J., Liang, Q., Ren, L. L., Yan, T. T., Yu, T. C., Tang, J. Y., Bao, Y.J., Hu, Y., Lin, Y., et al. (2016). LncRNA GClnc1 promotes gastric carcinogenesis and may act as a modular scaffold of WDR5 and KAT2A complexes to specify the histone modification pattern. Cancer Discov 6, 784–801.CrossRefGoogle Scholar
  25. The ENCODE Project Consortium (2012). An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74.CrossRefGoogle Scholar
  26. Tomescu, A.I., Kuosmanen, A., Rizzi, R., Mäkinen, V. (2013). A novel min-cost flow method for estimating transcript expression with RNA-Seq. BMC Bioinformatics 14, S15.CrossRefGoogle Scholar
  27. Trapnell, C., Pachter, L., and Salzberg, S.L. (2009). TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111.CrossRefGoogle Scholar
  28. Trapnell, C., Williams, B.A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M.J., Salzberg, S.L., Wold, B.J., and Pachter, L. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28, 511–515.CrossRefGoogle Scholar
  29. Volders, P.J., Helsens, K., Wang, X., Menten, B., Martens, L., Gevaert, K., Vandesompele, J., and Mestdagh, P. (2013). LNCipedia: a database for annotated human lncRNA transcript sequences and structures. Nucl Acids Res 41, D246–D251.CrossRefGoogle Scholar
  30. Wang, E.T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S.F., Schroth, G.P., and Burge, C.B. (2008). Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476.CrossRefGoogle Scholar
  31. Wang, K., Singh, D., Zeng, Z., Coleman, S.J., Huang, Y., Savich, G.L., He, X., Mieczkowski, P., Grimm, S.A., Perou, C.M., et al. (2010). MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucl Acids Res 38, e178.CrossRefGoogle Scholar
  32. Wang, Z., Gerstein, M., and Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10, 57–63.CrossRefGoogle Scholar
  33. Zhu, Y., Orre, L.M., Johansson, H.J., Huss, M., Boekel, J., Vesterlund, M., Fernandez-Woodbridge, A., Branca, R.M.M., and Lehtiö, J. (2018). Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow. Nat Commun 9, 903.CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xiangjun Ji
    • 1
  • Weida Tong
    • 2
  • Baitang Ning
    • 2
  • Christopher E. Mason
    • 3
    • 4
    • 5
  • David P. Kreil
    • 6
  • Pawel P. Labaj
    • 6
    • 7
    • 8
  • Geng Chen
    • 1
    Email author
  • Tieliu Shi
    • 1
    • 9
    Email author
  1. 1.The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghaiChina
  2. 2.National Center for Toxicological ResearchU.S. Food and Drug AdministrationJeffersonUSA
  3. 3.Department of Physiology and BiophysicsWeill Cornell MedicineNew YorkUSA
  4. 4.The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational BiomedicineNew YorkUSA
  5. 5.Feil Family Brain & Mind Research InstituteNew YorkUSA
  6. 6.Chair of Bioinformatics Research GroupBoku UniversityViennaAustria
  7. 7.Malopolska Centre of BiotechnologyJagiellonian UniversityKrakowPoland
  8. 8.APART FellowAustrian Academy of ScienceViennaAustria
  9. 9.National Center for International Research of Biological Targeting Diagnosis and Therapy, Guangxi Key Laboratory of Biological Targeting Diagnosis and Therapy Research, Collaborative Innovation Center for Targeting Tumor Diagnosis and TherapyGuangxi Medical UniversityNanningChina

Personalised recommendations