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

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Abstract

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 https://doi.org/www.megabionet.org/QuaPra/.

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Acknowledgements

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.

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Authors and Affiliations

  1. The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China

    Xiangjun Ji, Geng Chen & Tieliu Shi

  2. National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR RD, Jefferson, AR, 72079, USA

    Weida Tong & Baitang Ning

  3. Department of Physiology and Biophysics, Weill Cornell Medicine, New York, 10065, USA

    Christopher E. Mason

  4. The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, 10021, USA

    Christopher E. Mason

  5. Feil Family Brain & Mind Research Institute, New York, 10065, USA

    Christopher E. Mason

  6. Chair of Bioinformatics Research Group, Boku University, Vienna, A-1190, Austria

    David P. Kreil & Pawel P. Labaj

  7. Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, 30-387, Poland

    Pawel P. Labaj

  8. APART Fellow, Austrian Academy of Science, Vienna, A-1190, Austria

    Pawel P. Labaj

  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 Therapy, Guangxi Medical University, Nanning, 530021, China

    Tieliu Shi

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  1. Xiangjun Ji
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  2. Weida Tong
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  3. Baitang Ning
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  4. Christopher E. Mason
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  6. Pawel P. Labaj
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  7. Geng Chen
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  8. Tieliu Shi
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Correspondence to Geng Chen or Tieliu Shi.

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Ji, X., Tong, W., Ning, B. et al. QuaPra: Efficient transcript assembly and quantification using quadratic programming with Apriori algorithm. Sci. China Life Sci. 62, 937–946 (2019). https://doi.org/10.1007/s11427-018-9433-3

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  • DOI: https://doi.org/10.1007/s11427-018-9433-3

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