Transcriptome assembly strategies for precision medicine

doi:10.1007/s40484-017-0109-2

Quant. Biol.

2017, Vol. 5

Issue (4) : 280-290 https://doi.org/10.1007/s40484-017-0109-2

REVIEW

Transcriptome assembly strategies for precision medicine

Lu Wang¹, Lipi Acharya², Changxin Bai¹, Dongxiao Zhu¹(

)

¹. Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
². Dow AgroSciences, Indianapolis, IN 46268, USA

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Abstract

Background: Precision medicine approach holds great promise to tailored diagnosis, treatment and prevention. Individuals can be vastly different in their genomic information and genetic mechanisms hence having unique transcriptomic signatures. The development of precision medicine has demanded moving beyond DNA sequencing (DNA-Seq) to much more pointed RNA-sequencing (RNA-Seq) [Cell, 2017, 168: 584–599].

Results: Here we conduct a brief survey on the recent methodology development of transcriptome assembly approach using RNA-Seq.

Conclusions: Since transcriptomes in human disease are highly complex, dynamic and diverse, transcriptome assembly is playing an increasingly important role in precision medicine research to dissect the molecular mechanisms of the human diseases.

Author Summary Precision medicine is an emerging area in healthcare that aims to provide personalized diagnosis, treatment, and prevention by taking into account an individual’s genetic information, environment, and lifestyle. Transcriptome assembly approaches enable an in-depth understanding of the cellular mechanisms and cellular processes and thus plays a key role in the precision medicine. In this review article, we survey a number of recently developed transcriptome assembly strategies including de novo transcriptome assembly, reference-based transcriptome assembly, and de novo and reference-based combined strategies.

Keywords precision medicine transcriptome assembly RNA-Seq de novo De Bruijn

Corresponding Author(s): Dongxiao Zhu

Just Accepted Date: 08 June 2017 Online First Date: 31 October 2017 Issue Date: 04 December 2017

Cite this article:

Lu Wang,Lipi Acharya,Changxin Bai, et al. Transcriptome assembly strategies for precision medicine[J]. Quant. Biol., 2017, 5(4): 280-290.

URL:

https://academic.hep.com.cn/qb/EN/10.1007/s40484-017-0109-2
https://academic.hep.com.cn/qb/EN/Y2017/V5/I4/280

Fig.1 De novo transcriptome assembly strategy.

Fig.2 Reference-based transcriptome assembly strategy. We first splice-align reads to the reference genome in order to construct a splice graph to show all possible isoforms at a locus. Then we traverse the constructed graph to obtain the isoforms at the end.

Fig.3 Work flow of the bridge algorithm.

Fig.4 A generative model of the RNA-sequencing process with the notation. Z is used to model the transcripts that have non-zero abundance; e+ is the relative abundance; and F is the joint distribution over n reads conditionally parameterized by m, s and dependent on a set S of m candidate transcripts and transcript index t.

Tab.1 Summary of software for transcriptome assembly.

1	J. S. Buguliskis, (2015) Could rna-seq become the workhorse of precision medicine? Genet. Eng. Biotech. N. 35, 8–9
2	R. Chen, and M. Snyder, (2013) Promise of personalized omics to precision medicine. Wiley Interdiscip. Rev. Syst. Biol. Med., 5, 73–82 https://doi.org/10.1002/wsbm.1198 pmid: 23184638
3	F. S. Collins, and H. Varmus, (2015) A new initiative on precision medicine. N. Engl. J. Med., 372, 793–795 https://doi.org/10.1056/NEJMp1500523 pmid: 25635347
4	F. Klauschen,, M. Andreeff,, U. Keilholz, , M. Dietel, and A. Stenzinger, (2014) The combinatorial complexity of cancer precision medicine. Oncoscience, 1, 504–509 https://doi.org/10.18632/oncoscience.66 pmid: 25594052
5	Ö. Çakır, , N. Turgut-Kara, , Ş. Arı, and B. Zhang, (2015) De novo transcriptome assembly and comparative analysis elucidate complicated mechanism regulating Astragalus chrysochlorus response to selenium stimuli. PLoS One, 10, e0135677 https://doi.org/10.1371/journal.pone.0135677 pmid: 26431547
6	Nayak L., Ray I., De R. K. (2016) Precision medicine with electronic medical records: from the patients and for the patients, Ann. Transl. Med. 4 (Suppl 1), S61
7	K. Kourou, , T. P. Exarchos, , K. P. Exarchos, , M. V. Karamouzis, and D. I. Fotiadis, (2015) Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J., 13, 8–17 https://doi.org/10.1016/j.csbj.2014.11.005 pmid: 25750696
8	S. Vural, , X. Wang, and C. Guda, (2016) Classification of breast cancer patients using somatic mutation profiles and machine learning approaches. BMC Syst. Biol., 10, 62 https://doi.org/10.1186/s12918-016-0306-z pmid: 27587275
9	D. M. Hyman, , B. S. Taylor, and J. Baselga, (2017) Implementing genome-driven oncology. Cell, 168, 584–599 https://doi.org/10.1016/j.cell.2016.12.015 pmid: 28187282
10	A. Conesa,, P. Madrigal,, S. Tarazona,, D. Gomez-Cabrero,, A. Cervera, , A. McPherson, , M. W. Szcześniak, , D. J. Gaffney,, L. L. Elo, , X. Zhang, , et al. (2016) A survey of best practices for RNA-seq data analysis. Genome Biol., 17, 13 https://doi.org/10.1186/s13059-016-0881-8 pmid: 26813401
11	J. A. Martin, and Z. Wang, (2011) Next-generation transcriptome assembly. Nat. Rev. Genet., 12, 671–682 https://doi.org/10.1038/nrg3068 pmid: 21897427
12	D. R. Zerbino, and E. Birney, (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res., 18, 821–829 https://doi.org/10.1101/gr.074492.107 pmid: 18349386
13	J. T. Simpson, , K. Wong, , S. D. Jackman, , J. E. Schein, , S. J. Jones, and I. Birol, (2009) ABySS: a parallel assembler for short read sequence data. Genome Res., 19, 1117–1123 https://doi.org/10.1101/gr.089532.108 pmid: 19251739
14	P. A. Pevzner, , H. Tang, and M. S. Waterman, (2001) An Eulerian path approach to DNA fragment assembly. Proc. Natl. Acad. Sci. USA, 98, 9748–9753 https://doi.org/10.1073/pnas.171285098 pmid: 11504945
15	M. Fumagalli, (2013) Assessing the effect of sequencing depth and sample size in population genetics inferences. PLoS One, 8, e79667 https://doi.org/10.1371/journal.pone.0079667 pmid: 24260275
16	M. G. Grabherr,, B. J. Haas,, M. Yassour, , J. Z. Levin,, D. A. Thompson,, I. Amit, , X. Adiconis,, L. Fan,, R. Raychowdhury,, Q. Zeng,, et al. (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol., 29, 644–652 https://doi.org/10.1038/nbt.1883 pmid: 21572440
17	N. Bruijn, (1946) A Combinatorial Problem. In Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen. Series A, 49, 758–764.
18	M. H. Schulz, , D. R. Zerbino, , M. Vingron, and E. Birney, (2012) Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics, 28, 1086–1092 https://doi.org/10.1093/bioinformatics/bts094 pmid: 22368243
19	Y. Xie, , G. Wu, , J. Tang, , R. Luo, , J. Patterson, , S. Liu, , W. Huang, , G. He, , S. Gu, , S. Li, , et al. (2014) SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics, 30, 1660–1666 https://doi.org/10.1093/bioinformatics/btu077 pmid: 24532719
20	R. Li, , C. Yu, , Y. Li, , T.-W. Lam, , S.-M. Yiu, , K. Kristiansen, and J. Wang, (2009) SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics, 25, 1966–1967 https://doi.org/10.1093/bioinformatics/btp336 pmid: 19497933
21	C.-Y. Shi, , H. Yang,, C.-L. Wei,, O. Yu,, Z.-Z. Zhang,, C.-J. Jiang,, J. Sun,, Y.-Y. Li,, Q. Chen, , T. Xia, , et al. (2011) Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds. BMC Genomics, 12, 131 https://doi.org/10.1186/1471-2164-12-131 pmid: 21356090
22	R. Garg, , R. K. Patel, , A. K. Tyagi, and M. Jain, (2011) De novo assembly of chickpea transcriptome using short reads for gene discovery and marker identification. DNA Res., 18, 53–63 https://doi.org/10.1093/dnares/dsq028 pmid: 21217129
23	Q.-Y. Zhao, , Y. Wang, , Y.-M. Kong, , D. Luo, , X. Li, and P. Hao, (2011) Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study. BMC Bioinformatics, 12, S2 https://doi.org/10.1186/1471-2105-12-S14-S2 pmid: 22373417
24	G. Robertson,, J. Schein,, R. Chiu, , R. Corbett, , M. Field,, S. D. Jackman, , K. Mungall, , S. Lee, , H. M. Okada, , J. Q. Qian, , et al. (2010) De novo assembly and analysis of RNA-seq data. Nat. Methods, 7, 909–912 https://doi.org/10.1038/nmeth.1517 pmid: 20935650
25	C. Trapnell, , A. Roberts, , L. Goff, , G. Pertea, , D. Kim, , D. R. Kelley, , H. Pimentel, , S. L. Salzberg, , J. L. Rinn, and L. Pachter, (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc., 7, 562–578 https://doi.org/10.1038/nprot.2012.016 pmid: 22383036
26	C. Trapnell, , L. Pachter, and S. L. Salzberg, (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25, 1105–1111 https://doi.org/10.1093/bioinformatics/btp120 pmid: 19289445
27	M. Yandell, and D. Ence, (2012) A beginner’s guide to eukaryotic genome annotation. Nat. Rev. Genet., 13, 329–342 https://doi.org/10.1038/nrg3174 pmid: 22510764
28	Z. Chang, , G. Li,, J. Liu, , Y. Zhang, , C. Ashby, , D. Liu, , C. L. Cramer, and X. Huang, (2015) Bridger: a new framework for de novo transcriptome assembly using RNA-seq data. Genome Biol., 16, 30. https://doi.org/10.1186/s13059-015-0596-2 pmid: 25723335
29	L. Maretty, , J. A. Sibbesen, and A. Krogh, ( 2014) Bayesian transcriptome assembly. Genome Biol., 15, 501 https://doi.org/10.1186/s13059-014-0501-4 pmid: 25367074
30	D. Kim, , G. Pertea, , C. Trapnell, , H. Pimentel, , R. Kelley, and S. L. Salzberg, (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol., 14, R36 https://doi.org/10.1186/gb-2013-14-4-r36 pmid: 23618408
31	J. Martin, , V. M. Bruno, , Z. Fang, , X. Meng, , M. Blow, , T. Zhang, , G. Sherlock, , M. Snyder, and Z. Wang, (2010) Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads. BMC Genomics, 11, 663 https://doi.org/10.1186/1471-2164-11-663 pmid: 21106091
32	B. J. Haas, , A. Papanicolaou, , M. Yassour, , M. Grabherr, , P. D. Blood, , J. Bowden, , M. B. Couger, , D. Eccles, , B. Li, , M. Lieber, , et al. (2013) De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc., 8, 1494–1512 https://doi.org/10.1038/nprot.2013.084 pmid: 23845962
33	C. Trapnell, , B. A. Williams, , G. Pertea, , A. Mortazavi, , G. Kwan, , M. J. van Baren, , S. L. Salzberg, , B. J. Wold, and L. Pachter, (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol., 28, 511–515 https://doi.org/10.1038/nbt.1621 pmid: 20436464
34	M. Guttman, , M. Garber, , J. Z. Levin, , J. Donaghey, , J. Robinson, , X. Adiconis, , L. Fan, , M. J. Koziol, , A. Gnirke, , C. Nusbaum, , et al. (2010) Ab initio reconstruction of cell type–specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat. Biotechnol., 28, 503–510 https://doi.org/10.1038/nbt.1633 pmid: 20436462
35	J. Liu, , T. Yu, , T. Jiang, and G. Li, (2016) TransComb: genome-guided transcriptome assembly via combing junctions in splicing graphs. Genome Biol., 17, 213 https://doi.org/10.1186/s13059-016-1074-1 pmid: 27760567
36	E. W. Myers, (1995) Toward simplifying and accurately formulating fragment assembly. J. Comput. Biol., 2, 275–290 https://doi.org/10.1089/cmb.1995.2.275 pmid: 7497129
37	S. Kumar, and M. L. Blaxter, (2010) Comparing de novo assemblers for 454 transcriptome data. BMC Genomics, 11, 571 https://doi.org/10.1186/1471-2164-11-571 pmid: 20950480
38	V. Zeng, , K. E. Villanueva,, B. S. Ewen-Campen,, F. Alwes, , W. E. Browne, and C. G. Extavour, (2011) De novo assembly and characterization of a maternal and developmental transcriptome for the emerging model crustacean Parhyale hawaiensis. BMC Genomics, 12, 581 https://doi.org/10.1186/1471-2164-12-581 pmid: 22118449
39	J. Zhu, , F. He, , J. Wang, and J. Yu, (2008) Modeling transcriptome based on transcript-sampling data. PLoS One, 3, e1659 https://doi.org/10.1371/journal.pone.0001659 pmid: 18286206
40	B. Li, , N. Fillmore, , Y. Bai,, M. Collins, , J. A. Thomson,, R. Stewart, and C. N. Dewey, (2014) Evaluation of de novo transcriptome assemblies from RNA-Seq data. Genome Biol., 15, 553 https://doi.org/10.1186/s13059-014-0553-5 pmid: 25608678
41	M. Garber, , M. G. Grabherr,, M. Guttman, and C. Trapnell, (2011) Computational methods for transcriptome annotation and quantification using RNA-seq. Nat. Methods, 8, 469–477 https://doi.org/10.1038/nmeth.1613 pmid: 21623353

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