SCIENTIA SINICA Vitae, Volume 47, Issue 7: 702-707(2017) https://doi.org/10.1360/N052016-00280

Application of the hyper-accurate mapping algorithm FANSe for next-generation sequencing in non-model organisms

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  • ReceivedApr 30, 2017
  • AcceptedJun 25, 2017
  • PublishedJul 12, 2017


Next-generation sequencing (NGS) has been widely used in biology studies for its high throughput and low cost. However, for the non-model organisms, whose genome has not been accurately sequenced, the traditional mapping algorithms cannot process them efficiently due to the low accuracy, robustness and error tolerance. FANSe series algorithms is the most accurate and error tolerant mapping algorithm for NGS. It solves the problem of the inaccurate reference database, which is the common problem in the NGS for non-model organisms. Therefore, FANSe provides accurate results for genome, transcriptome and proteome studies of non-model organisms. This review summarizes multiple analysis strategy using FANSe in non-model animals, plants, microorganisms and complex symbiosis system as examples for non-model organisms studies.

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[1] Zhang G, Fedyunin I, Kirchner S, et al. FANSe: an accurate algorithm for quantitative mapping of large scale sequencing reads. Nucleic Acids Res, 2012, 40: e83-e83 CrossRef PubMed Google Scholar

[2] Wu X, Xu L, Gu W, et al. Iterative genome correction largely improves proteomic analysis of nonmodel organisms. J Proteome Res, 2014, 13: 2724-2734 CrossRef PubMed Google Scholar

[3] Shi L, Guo Y, Dong C, et al. Long-read sequencing and de novo assembly of a Chinese genome. Nat Commun, 2016, 7: 12065 CrossRef PubMed ADS Google Scholar

[4] Baba T, Bae T, Schneewind O, et al. Genome sequence of Staphylococcus aureus strain newman and comparative analysis of staphylococcal genomes: polymorphism and evolution of two major pathogenicity islands. J Bacteriol, 2008, 190: 300-310 CrossRef PubMed Google Scholar

[5] Martinez J L, Baquero F. Mutation frequencies and antibiotic resistance. Antimicrobial Agents Chemother, 2000, 44: 1771-1777 CrossRef Google Scholar

[6] Otto T D, Sanders M, Berriman M, et al. Iterative Correction of Reference Nucleotides (iCORN) using second generation sequencing technology. Bioinformatics, 2010, 26: 1704-1707 CrossRef PubMed Google Scholar

[7] Kisand V, Lettieri T. Genome sequencing of bacteria: sequencing, de novo assembly and rapid analysis using open source tools. BMC Genomics, 2013, 14: 1. Google Scholar

[8] Storer C G, Pascal C E, Roberts S B, et al. Rank and order: evaluating the performance of SNPs for individual assignment in a non-model organism. PLoS ONE, 2012, 7: e49018 CrossRef PubMed ADS Google Scholar

[9] Carlsson J, Gauthier D T, Carlsson J E L, et al. Rapid, economical single-nucleotide polymorphism and microsatellite discovery based on de novo assembly of a reduced representation genome in a non-model organism: a case study of Atlantic cod Gadus morhua. J Fish Biol, 2013, 82: 944-958 CrossRef PubMed Google Scholar

[10] Yang X, Chockalingam S P, Aluru S. A survey of error-correction methods for next-generation sequencing. Briefings Bioinform, 2013, 14: 56-66 CrossRef PubMed Google Scholar

[11] Glenn T C. Field guide to next-generation DNA sequencers. Mol Ecol Resour, 2011, 11: 759-769 CrossRef PubMed Google Scholar

[12] Li R, Li Y, Fang X, et al. SNP detection for massively parallel whole-genome resequencing. Genome Res, 2009, 19: 1124-1132 CrossRef PubMed Google Scholar

[13] 田洋, 曾严, 张静, 等. 辣木(Moringa oleifera Lam.)的高质量参考基因组. 中国科学: 生命科学, 2015, 45: 488–497. Google Scholar

[14] Haas B J, Papanicolaou A, Yassour M, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc, 2013, 8: 1494-1512 CrossRef PubMed Google Scholar

[15] Luo R, Liu B, Xie Y, et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaSci, 2012, 1: 18 CrossRef PubMed Google Scholar

[16] Zerbino D R, Birney E. Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Genome Res, 2008, 18: 821-829 CrossRef PubMed Google Scholar

[17] Trapnell C, Salzberg S L. How to map billions of short reads onto genomes. Nat Biotechnol, 2009, 27: 455-457 CrossRef PubMed Google Scholar

[18] Li H, Homer N. A survey of sequence alignment algorithms for next-generation sequencing. Briefings Bioinform, 2010, 11: 473-483 CrossRef PubMed Google Scholar

[19] Xiao C L, Mai Z B, Lian X L, et al. FANSe2: a robust and cost-efficient alignment tool for quantitative next-generation sequencing applications. PLoS ONE, 2014, 9: e94250 CrossRef PubMed ADS Google Scholar

[20] Project I R G S. The map-based sequence of the rice genome. Nature, 2005, 436: 793-800 CrossRef PubMed ADS Google Scholar

[21] Huang X, Kurata N, Wei X, et al. A map of rice genome variation reveals the origin of cultivated rice. Nature, 2012, 490: 497-501 CrossRef PubMed ADS Google Scholar

[22] Hussain S, Yin H, Peng S, et al. Comparative transcriptional profiling of primed and non-primed rice seedlings under submergence stress. Front Plant Sci, 2016, 7: 1125. Google Scholar

[23] Zhong J, Cui Y, Guo J, et al. Resolving chromosome-centric human proteome with translating mRNA analysis: a strategic demonstration. J Proteome Res, 2013, 13: 50–59. Google Scholar

[24] Chang C, Li L, Zhang C, et al. Systematic analyses of the transcriptome, translatome, and proteome provide a global view and potential strategy for the C-HPP. J Proteome Res, 2013, 13: 38–49. Google Scholar

[25] Li S, Han Y, Lei H, et al. In vitro biomimetic platforms featuring a perfusion system and 3D spheroid culture promote the construction of tissue-engineered corneal endothelial layers. Sci Rep, 2017, 7: 777 CrossRef PubMed Google Scholar

[26] Liu L, Li Y, Li S, et al. Comparison of next-generation sequencing systems. Biomed Res Int, 2012, 2012: 251364. Google Scholar

[27] Wang X, Slebos R J, Wang D, et al. Protein identification using customized protein sequence databases derived from RNA-Seq data. J Proteome Res, 2011, 11: 1009–1017. Google Scholar

[28] Lunter G, Goodson M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res, 2011, 21: 936-939 CrossRef PubMed Google Scholar

[29] Hu R, Zhu X, Xiang S, et al. Comparative transcriptome analysis revealed the genotype specific cold response mechanism in tobacco. Biochem Biophys Res Commun, 2016, 469: 535-541 CrossRef PubMed Google Scholar

[30] Yu Q, Xiong Y, Liu J, et al. Transcriptome analysis of the SL221 cells at the early stage during Spodoptera litura nucleopolyhedrovirus infection. PLoS ONE, 2016, 11: e0147873 CrossRef PubMed ADS Google Scholar

[31] Blissard G W. Baculovirus-insect cell interactions. In: Insect Cell Culture: Fundamental and Applied Aspects. Berlin: Springer, 1996. 73–93. Google Scholar

[32] Fang Z, Shao J, Weng Q. De novo transcriptome analysis of Spodoptera exigua multiple nucleopolyhedrovirus (SeMNPV) genes in latently infected Se301 cells. Virol Sin, 2016, 31: 425-436 CrossRef PubMed Google Scholar

[33] Sharon I, Banfield J F. Genomes from metagenomics. Science, 2013, 342: 1057-1058 CrossRef PubMed ADS Google Scholar

[34] Schatz M C, Witkowski J, McCombie W R. Current challenges in de novo plant genome sequencing and assembly. Genome Biol, 2012, 13: 1. Google Scholar

[35] Luo B, Gu W, Zhong J, et al. Revealing crosstalk of plant and fungi in the symbiotic roots of sewage-cleaning Eichhornia crassipes using direct de novo metatranscriptomic analysis. Sci Rep, 2015, 5: 15407 CrossRef PubMed ADS Google Scholar

[36] Singhal S. De novo transcriptomic analyses for non-model organisms: an evaluation of methods across a multi-species data set. Mol Ecol Resour, 2013, 13: 403-416 CrossRef PubMed Google Scholar

[37] Nekrutenko A, Taylor J. Next-generation sequencing data interpretation: enhancing reproducibility and accessibility. Nat Rev Genet, 2012, 13: 667-672 CrossRef PubMed Google Scholar

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