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Comparison of Next-Generation Sequencing Systems
Open accessAbstract
With fast development and wide applications of next-generation sequencing (NGS) technologies, genomic sequence information is within reach to aid the achievement of goals to decode life mysteries, make better crops, detect pathogens, and improve life qualities. NGS systems are typically represented by SOLiD/Ion Torrent PGM from Life Sciences, Genome Analyzer/HiSeq 2000/MiSeq from Illumina, and GS FLX Titanium/GS Junior from Roche. Beijing Genomics Institute (BGI), which possesses the world’s biggest sequencing capacity, has multiple NGS systems including 137 HiSeq 2000, 27 SOLiD, one Ion Torrent PGM, one MiSeq, and one 454 sequencer. We have accumulated extensive experience in sample handling, sequencing, and bioinformatics analysis. In this paper, technologies of these systems are reviewed, and first-hand data from extensive experience is summarized and analyzed to discuss the advantages and specifics associated with each sequencing system. At last, applications of NGS are summarized.
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Cites
Direct detection of DNA methylation during single-molecule, real-time sequencing
Flusberg, B. A., Webster, D. R., Lee, J. H., Travers, K. J., Olivares, E. C., Clark, T. A., Korlach, J., & Turner, S. W. (2010) · Nature Methods · doi:10.1038/nmeth.1459 · Cited by 1,460
Gnirke, A., Melnikov, A., Maguire, J., Rogov, P., LeProust, E. M., Brockman, W., Fennell, T., Giannoukos, G., Fisher, S., Russ, C., et al. (2009) · Nature Biotechnology · doi:10.1038/nbt.1523 · Cited by 1,433 · cited 2×
“[29] developed a captured method that uses an RNA “baits” to capture target DNA fragments from the “pond” and then uses the Illumina platform to read out the sequence.”
BackgroundMethodThe Human Genome Project: Lessons from Large-Scale Biology
Collins, F. S., Morgan, M., & Patrinos, A. (2003) · Science · doi:10.1126/science.1084564 · Cited by 1,284
Mapping copy number variation by population-scale genome sequencing
1000 Genomes Project, Mills, R. E., Walter, K., Stewart, C., Handsaker, R. E., Chen, K., Alkan, C., Abyzov, A., Yoon, S. C., Ye, K., et al. (2011) · Nature · doi:10.1038/nature09708 · Cited by 1,140 · cited 3×
“[28] constructed a map of unbalanced SVs (genomic structural variants) based on whole genome DNA sequencing data from 185 human”
BackgroundInfluential
Cited by
Coming of age: ten years of next-generation sequencing technologies
Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.49 · Cited by 4,352
Role of the normal gut microbiota
Jandhyala, S. M. (2015) · World Journal of Gastroenterology · doi:10.3748/wjg.v21.i29.8787 · Cited by 3,001
Microbiome definition re-visited: old concepts and new challenges
Berg, G., Rybakova, D., Fischer, D., Cernava, T., Vergès, M.-C. C., Charles, T., Chen, X., Cocolin, L., Eversole, K., Corral, G. H., et al. (2020) · Microbiome · doi:10.1186/s40168-020-00875-0 · Cited by 2,275
PacBio Sequencing and its Applications
Rhoads, A., & Au, K. F. (2015) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2015.08.002 · Cited by 2,135
An insight into gut microbiota and its functionalities
Adak, A., & Khan, M. R. (2019) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-018-2943-4 · Cited by 1,485
Characterizing and measuring bias in sequence data
Ross, M. G., Russ, C., Costello, M., Hollinger, A., Lennon, N. J., Hegarty, R., Nusbaum, C., & Jaffe, D. B. (2013) · Genome Biology · doi:10.1186/gb-2013-14-5-r51 · Cited by 909
Adaptive laboratory evolution—Principles and applications for biotechnology
Dragosits, M., & Mattanovich, D. (2013) · Microbial Cell Factories · doi:10.1186/1475-2859-12-64 · Cited by 827
A new view of the fish gut microbiome: advances from next-generation sequencing
Ghanbari, M., Kneifel, W., & Domig, K. J. (2015) · Aquaculture · doi:10.1016/j.aquaculture.2015.06.033 · Cited by 669
Ercolini, D. (2013) · Applied and Environmental Microbiology · doi:10.1128/aem.00256-13 · Cited by 479
Weirather, J. L., de Cesare, M., Wang, Y., Piazza, P., Sebastiano, V., Wang, X.-J., Buck, D., & Au, K. F. (2017) · F1000Research · doi:10.12688/f1000research.10571.2 · Cited by 418
Goldberg, B., Sichtig, H., Geyer, C., Ledeboer, N., & Weinstock, G. M. (2015) · mBio · doi:10.1128/mbio.01888-15 · Cited by 360
Historical perspective, development and applications of next-generation sequencing in plant virology
Barba, M., Czosnek, H., & Hadidi, A. (2014) · Viruses · doi:10.3390/v6010106 · Cited by 317
“Roche 454 has the longest read length, Illumina HiSeq 2500 features the biggest output and lowest sequencing cost, and SOLiD 5500 xlw has the highest accuracy [22].”
BackgroundFouhy, F., Clooney, A. G., Stanton, C., Claesson, M. J., & Cotter, P. D. (2016) · BMC Microbiology · doi:10.1186/s12866-016-0738-z · Cited by 310
Allali, I., Arnold, J. W., Roach, J., Cadenas, M. B., Butz, N., Hassan, H. M., Koci, M., Ballou, A., Mendoza, M., Ali, R., et al. (2017) · BMC Microbiology · doi:10.1186/s12866-017-1101-8 · Cited by 298
Miah, G., Rafii, M., Ismail, M., Puteh, A., Rahim, H., Islam, K., & Latif, M. (2013) · International Journal of Molecular Sciences · doi:10.3390/ijms141122499 · Cited by 241
Li, S., Tighe, S. W., Nicolet, C. M., Grove, D., Levy, S., Farmerie, W., Viale, A., Wright, C., Schweitzer, P. A., Gao, Y., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2972 · Cited by 240
Self-citationA review of modern methods for the detection of foodborne pathogens
Aladhadh, M. (2023) · Microorganisms · doi:10.3390/microorganisms11051111 · Cited by 233
Shameer, K., Badgeley, M. A., Miotto, R., Glicksberg, B. S., Morgan, J. W., & Dudley, J. T. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbv118 · Cited by 198
Impact of next generation sequencing techniques in food microbiology.
Mayo, B., Rachid, C., Alegria, A., Leite, A., Peixoto, R., & Delgado, S. (2014) · Current Genomics · doi:10.2174/1389202915666140616233211 · Cited by 188
Fourth generation biofuel from genetically modified algal biomass: Challenges and future directions.
Shokravi, H., Shokravi, Z., Heidarrezaei, M., Ong, H. C., Rahimian Koloor, S. S., Petrů, M., Lau, W. J., & Ismail, A. F. (2021) · Chemosphere · doi:10.1016/j.chemosphere.2021.131535 · Cited by 127
Dotterud, C. K., Avershina, E., Sekelja, M., Simpson, M. R., Rudi, K., Storrø, O., Johnsen, R., & Øien, T. (2015) · Journal of Pediatric Gastroenterology and Nutrition · doi:10.1097/mpg.0000000000000781 · Cited by 112
Andrews, P., Baker, D., Benvinisty, N., Miranda, B., Bruce, K., Br…#x00FC;stle, O., Choi, M., Choi, Y.-M., Crook, J., de Sousa, P., et al. (2015) · Regenerative Medicine · doi:10.2217/rme.14.93 · Cited by 113
Association of dietary fibre intake and gut microbiota in adults
Lin, D., Peters, B. A., Friedlander, C., Freiman, H. J., Goedert, J. J., Sinha, R., Miller, G., Bernstein, M. A., Hayes, R. B., & Ahn, J. (2018) · British Journal of Nutrition · doi:10.1017/s0007114518002465 · Cited by 109
Self-citationMatsa, E., Ahrens, J. H., & Wu, J. C. (2016) · Physiological Reviews · doi:10.1152/physrev.00036.2015 · Cited by 105
High Throughput Sequencing for Detection of Foodborne Pathogens
Sekse, C., Holst-Jensen, A., Dobrindt, U., Johannessen, G. S., Li, W., Spilsberg, B., & Shi, J. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02029 · Cited by 98
Genes, behavior and next‐generation RNA sequencing
Hitzemann, R., Bottomly, D., Darakjian, P., Walter, N., Iancu, O., Searles, R., Wilmot, B., & McWeeney, S. (2013) · Genes, Brain and Behavior · doi:10.1111/gbb.12007 · Cited by 88
Technical guide for genetic advancement of underdeveloped and intractable Clostridium
Pyne, M. E., Bruder, M., Moo-Young, M., Chung, D. A., & Chou, C. P. (2014) · Biotechnology Advances · doi:10.1016/j.biotechadv.2014.04.003 · Cited by 78
First insights into the metagenome of Egyptian mummies using next-generation sequencing
Khairat, R., Ball, M., Chang, C.-C. H., Bianucci, R., Nerlich, A. G., Trautmann, M., Ismail, S., Shanab, G. M. L., Karim, A. M., Gad, Y. Z., et al. (2013) · Journal of Applied Genetics · doi:10.1007/s13353-013-0145-1 · Cited by 72
Taylor, S. D., Ericson, N. G., Burton, J. N., Prolla, T. A., Silber, J. R., Shendure, J., & Bielas, J. H. (2014) · Aging Cell · doi:10.1111/acel.12146 · Cited by 67
Sequence data for Clostridium autoethanogenum using three generations of sequencing technologies
Utturkar, S. M., Klingeman, D. M., Bruno-Barcena, J. M., Chinn, M. S., Grunden, A. M., Köpke, M., & Brown, S. D. (2015) · Scientific Data · doi:10.1038/sdata.2015.14 · Cited by 46
Leishman, E., Murphy, M., Mackie, K., & Bradshaw, H. B. (2018) · Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids · doi:10.1016/j.bbalip.2018.02.001 · Cited by 42
Microbiome: paediatricians’ perspective
Arora, S., Dewan, P., & Gupta, P. (2015) · Indian Journal of Medical Research · doi:10.4103/0971-5916.171275 · Cited by 39
Α Quantum Pattern Recognition Method for Improving Pairwise Sequence Alignment
Prousalis, K., & Konofaos, N. (2019) · Scientific Reports · doi:10.1038/s41598-019-43697-3 · Cited by 33
Exploring explainable AI features in the vocal biomarkers of lung disease
Chen, Z., Liang, N., Li, H., Zhang, H., Li, H., Yan, L., Hu, Z., Chen, Y., Zhang, Y., Wang, Y., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108844 · Cited by 26
Impact of Next-Generation Sequencing Technology in Plant–Microbe Interaction Study
Kumari, A., Sumer, S., Jalan, B., Nongbri, P. L., & Laskar, M. A. (2017) · Microbial Applications Vol.1 · doi:10.1007/978-3-319-52666-9_13 · Cited by 13
Metadata
- Authors
- Liu, L., Li, Y., Li, S., Hu, N., He, Y., Pong, R., Lin, D., Lu, L., & Law, M.
- Venue
- Journal of Biomedicine and Biotechnology
- Publisher
- Wiley
- Published
- 2012
- Volume
- 2012
- Pages
- 1-11
- ISSN
- 1110-7243 (ISSN-L)
- 1110-7251
- OpenAlex
- W2120048439
- Cited by
- 1,533
- References
- 4
- FWCI
- 55.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
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