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اصول و کاربردهای فناوری توالییابی نسل جدید (NGS) در علوم زیستی (با رویکرد بهنژادی غلات) | ||
بیوتکنولوژی و بیوشیمی غلات | ||
مقاله 7، دوره 3، شماره 1، فروردین 1403، صفحه 110-231 اصل مقاله (2.23 M) | ||
نوع مقاله: مروری | ||
شناسه دیجیتال (DOI): 10.22126/cbb.2024.11011.1080 | ||
نویسندگان | ||
امید محمدعلیزاده1؛ رضا درویش زاده* 2؛ ولی اله محمدی3؛ سمیه صوفی ملکی4؛ دانیال کهریزی5 | ||
1دانش آموخته کارشناسی ارشد، گروه زراعت و اصلاح نباتات، دانشکده علوم و مهندسی کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران. | ||
2نویسنده مسئول مکاتبات، استاد، گروه تولید و ژنتیک گیاهی، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران. | ||
3دانشیار، گروه زراعت و اصلاح نباتات، دانشکده علوم و مهندسی کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران. | ||
4دانش آموخته کارشناسی ارشد، انستیتو علوم اعصاب تولوز، فرانسه. | ||
5استاد، گروه بیوتکنولوژی کشاورزی، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران. | ||
چکیده | ||
مقدمه: از زمان معرفی توالییابی نسل جدید (NGS)، در اوایل دهه ۲۰۰۰، این فناوری به عنوان یکی از تحولات بنیادین در علوم زیستی، باعث پیشرفت چشمگیر در تحقیقات ژنومیک، ترنسکریپتوم ، اپیژنوم و... شده است. اصول فناوری NGS شامل مباحث مربوط به تهیه کتابخانه، توالییابی و تحلیل دادههای حاصل از آن است. این فناوری با توالییابی میلیونها قطعه DNA بهصورت موازی و با دقت بالا و هزینه پایین و همچنین تولید حجم زیادی از دادههای ژنومی در زمان کوتاه، توانسته است جایگزین روشهای قدیمیتری مانند توالییابی سنگر شود و با توالییابی سریع، دقیق و کامل ژنومها و نواحی هدف انقلابی بزرگ در درک پیچیدگیهای ژنتیکی، ساختار ژنوم و تعیین تنوع ژنتیکی ایجاد کند. از مهمترین کاربردهای NGS در علوم زیستی میتوان به شناسایی و مطالعه ژنهای مرتبط با صفات کمی و کیفی، مطالعات تنوع ژنتیکی و ژنتیک جمعیت، تشخیص بیماریهای ژنتیکی، اپیدمیولوژی، میکروبیومشناسی، پزشکی قانونی، فیلوژنتیک، زیستشناسی سامانهای، مهندسی ژنتیک و ویرایش ژنوم و اصلاح نبات و دام اشاره کرد. بااینحال، استفاده مؤثر از دادههای NGS مستلزم توسعه زیرساختهای محاسباتی قوی و الگوریتمهای پیشرفته و همچنین گسترش اطلاعات محققان در رابطه با کاربردها و چالشهای بیوانفورماتیکی مرتبط با دادههای NGS، برای تحلیل و تفسیر این حجم عظیم از اطلاعات است. مواد و روشها: مقاله حاضر یک مقاله مروری میباشد که به شیوه تحلیل محتوا (Content analysis) با جستجوی کلید واژههای توالییابی نسل جدید (NGS)، انواع توالییابی NGS، تجزیه و تحلیل دادههای NGS، کاربردهای توالییابیNGS ، در مقالههای مرتبط در پایگاههای اینترنتی PubMed ،Web of science ،Google scholar و Scopus به دست آمده است. یافتهها: این مطالعه قصد دارد با مرور تفصیلی توالییابیهای نسل اول، دوم و سوم ، بررسی مسیر تجزیه و تحلیل دادههای NGS و کاربردهای گسترده NGS در زمینههای مختلف از جمله تحقیقات غلات، راهنمایی تقریباً کاملی برای تجزیه و تحلیل کارآمد و بهینه داده های حاصل از توالییابی نسل جدید ارائه نماید. به اینمنظور در بخش اول به مرور توالییابیهای نسل اول (ماکسام-گیلبرت و سنگر)، نسلدوم (Illumina، ABI/SOLID، Roche/454 pyrosequencing، Ion Torrent) و نسل سوم (Heliscope، SMRT، Oxford Nanopore) پرداخته شد. سپس در بخش دوم انواع توالییابیهای NGS مانند: Whole genome sequencing; WGS، Whole exome sequencing; WES، Bulk RNA-seq و سایر روش ها معرفی و مسیر تجزیه و تحلیل آنها بررسی شدهاند و در ادامه کاربرد توالییابی نسل جدید در حوزه های مختف مانند شناسایی تنوعات ساختاری ژنومی (SVs)، مطالعه تغییرات اپیژنتیکی، تجزیه و تحلیل جمعیت میکروبی، کشاورزی (با تأکید بر بهنژادی غلات) توضیح داده شد. در نهایت مزایا و چالشهای پیشروی توالییابی نسل جدید بیان گردید. نتیجهگیری: توالییابی نسل جدید به عنوان یک فناوری انقلابی در ژنومیک، تأثیر بسزایی در تحقیقات علوم زیستی داشته است. کاهش هزینهها و افزایش دقت توالییابی به همراه توسعه روشهای جدید، باعث شده است تا NGS به ابزاری کلیدی برای درک بهتر ژنتیک و توسعه راهبردهای درمانی شخصیسازی شده تبدیل شود. با پیشرفتهای مداوم در این حوزه و ترکیب این فناوری با هوش مصنوعی، آیندهی NGS در تحلیل دقیقتر دادههای ژنتیکی و بهبود فرایندهای درمانی بسیار روشن به نظر میرسد. | ||
کلیدواژهها | ||
آنالیز دادههای حجیم؛ بیوانفورماتیک؛ پلتفرمهای توالییابی؛ توالییابی نسل جدید (NGS) | ||
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مراجع | ||
Abubucker, S., Segata, N., Goll, J., Schubert, A. M., Izard, J., Cantarel, B. L., Rodriguez-Mueller, B., Zucker, J., Thiagarajan, M., Henrissat, B., White, O., Kelley, S. T., Methé, B., Schloss, P. D., Gevers, D., Mitreva, M., & Huttenhower, C. 2012. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Computational Biology, 8(6), e1002358. https://doi.org/10.1371/journal.pcbi.1002358 Adessi, C. 2000. Solid phase DNA amplification: characterisation of primer attachment and amplification mechanisms. Nucleic Acids Research, 28(20), 87e–887. https://doi.org/10.1093/nar/28.20.e87 Agarwal, V., Bell, G. W., Nam, J.-W., & Bartel, D. P. 2015. Predicting effective microRNA target sites in mammalian mRNAs. ELife, 4. CLOCKSS. https://doi.org/10.7554/elife.05005 Ahmadian, A., Ehn, M., & Hober, S. 2006. Pyrosequencing: history, biochemistry and future. Clinica Chimica Acta, 363(1–2), 83–94. https://doi.org/10.1016/j.cccn.2005.04.038 Akalin, A., Kormaksson, M., Li, S., Garrett-Bakelman, F. E., Figueroa, M. E., Melnick, A., & Mason, C. E. 2012. MethylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biology, 13(10), R87. https://doi.org/10.1186/gb-2012-13-10-r87 Allendorf, F. W., Hohenlohe, P. A., & Luikart, G. 2010. Genomics and the future of conservation genetics. Nature Reviews Genetics, 11(10), 697–709. https://doi.org/10.1038/nrg2844 Alles, J., Karaiskos, N., Praktiknjo, S. D., Grosswendt, S., Wahle, P., Ruffault, P.-L., Ayoub, S., Schreyer, L., Boltengagen, A., Birchmeier, C., Zinzen, R., Kocks, C., & Rajewsky, N. 2017. Cell fixation and preservation for droplet-based single-cell transcriptomics. BMC Biology, 15(1). https://doi.org/10.1186/s12915-017-0383-5 Alves, L. de F., Westmann, C. A., Lovate, G. L., de Siqueira, G. M. V., Borelli, T. C., & Guazzaroni, M.-E. 2018. Metagenomic approaches for understanding new concepts in microbial science. International Journal of Genomics, 2018, 1–15. https://doi.org/10.1155/2018/2312987 Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q. 2020. Opportunities and challenges in long-read sequencing data analysis. Genome Biology, 21(1). https://doi.org/10.1186/s13059-020-1935-5 Ambardar, S., Gupta, R., Trakroo, D., Lal, R., & Vakhlu, J. 2016. High throughput sequencing: an overview of sequencing chemistry. Indian Journal of Microbiology, 56(4), 394–404. https://doi.org/10.1007/s12088-016-0606-4 Anaparthy, N., Ho, Y.-J., Martelotto, L., Hammell, M., & Hicks, J. 2019. Single-cell applications of next-generation sequencing. Cold Spring Harbor Perspectives in Medicine, 9(10), a026898. https://doi.org/10.1101/cshperspect.a026898 Andrews, S. 2010. FastQC: a quality control tool for high throughput sequence data. Available online at: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Andrews, S. 2010. FastQC: a quality control tool for high throughput sequence data. https://doi.org/10.7554/elife.33070.019 Angelini, C., & Costa, V. 2014. Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems. Frontiers in Cell and Developmental Biology, 2. https://doi.org/10.3389/fcell.2014.00051 Angermueller, C., Lee, H. J., Reik, W., & Stegle, O. 2017. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biology, 18(1). https://doi.org/10.1186/s13059-017-1189-z Ansorge, W. J. 2009. Next-generation DNA sequencing techniques. New Biotechnology, 25(4), 195–203. https://doi.org/10.1016/j.nbt.2008.12.009 Ansorge, W., Zimmermann, J., Erfze, H., Hewitt, N., Rupp, T., Schwager, C., ... & Voss, H. 1993. Sequencing reactions for ALF (EMBL) automated DNA sequencer. DNA sequencing protocols, 317-356. https://doi.org/10.1385/0-89603-248-5:317 Ardui, S., Ameur, A., Vermeesch, J. R., & Hestand, M. S. 2018. Single molecule real-time (SMRT) sequencing comes of age: applications and utilities for medical diagnostics. Nucleic Acids Research, 46(5), 2159–2168. https://doi.org/10.1093/nar/gky066 Assenov, Y., Müller, F., Lutsik, P., Walter, J., Lengauer, T., & Bock, C. 2014. Comprehensive analysis of DNA methylation data with RnBeads. Nature Methods, 11(11), 1138-1140. https://doi.org/10.1038/nmeth.3115 Auer, H., Newsom, D. L., & Kornacker, K. 2009. Expression profiling using affymetrix genechip microarrays. microchip methods in diagnostics, 35–46. https://doi.org/10.1007/978-1-59745-372-1_3 Avery, O. T., Macleod, C. M., & McCarty, M. 1944. Studies on the chemical nature of the substance inducing transformation of pneumococcal types: induction of transformation by a desoxyribonucleic acid fraction isolated from Pneumococcus type III. The Journal of Experimental Medicine, 79(2), 137–158. https://doi.org/10.1084/jem.79.2.137 Baccarelli, A., & Bollati, V. 2009. Epigenetics and environmental chemicals. Current Opinion in Pediatrics, 21(2), 243–251. https://doi.org/10.1097/mop.0b013e32832925cc Badouin, H., Gouzy, J., Grassa, C. J., Murat, F., Staton, S. E., Cottret, L., ... & Langlade, N. B. 2017. The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution. Nature, 546(7656), 148-152. https://doi.org/10.1038/nature22380 Bailey, T., Krajewski, P., Ladunga, I., Lefebvre, C., Li, Q., Liu, T., Madrigal, P., Taslim, C., & Zhang, J. 2013. Practical guidelines for the comprehensive analysis of ChIP-seq data. PLoS Computational Biology, 9(11), e1003326. https://doi.org/10.1371/journal.pcbi.1003326 Bao, R., Huang, L., Andrade, J., Tan, W., Kibbe, W. A., Jiang, H., & Feng, G. 2014. Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing. Cancer Informatics, 13s2, CIN.S13779. https://doi.org/10.4137/cin.s13779 Ballester, L. Y., Luthra, R., Kanagal-Shamanna, R., & Singh, R. R. 2016. Advances in clinical next-generation sequencing: target enrichment and sequencing technologies. Expert Review of Molecular Diagnostics, 16(3), 357–372. https://doi.org/10.1586/14737159.2016.1133298 Bamshad, M. J., Ng, S. B., Bigham, A. W., Tabor, H. K., Emond, M. J., Nickerson, D. A., & Shendure, J. 2011. Exome sequencing as a tool for mendelian disease gene discovery. Nature Reviews Genetics, 12(11), 745–755. https://doi.org/10.1038/nrg3031 Bankevich, A., Nurk, S., Antipov, D., Gurevich, A. A., Dvorkin, M., Kulikov, A. S., Lesin, V. M., Nikolenko, S. I., Pham, S., Prjibelski, A. D., Pyshkin, A. V., Sirotkin, A. V., Vyahhi, N., Tesler, G., Alekseyev, M. A., & Pevzner, P. A. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. Journal of Computational Biology, 19(5), 455–477. https://doi.org/10.1089/cmb.2012.0021 Bartel, D. P. 2018. Metazoan microRNAs. Cell, 173(1), 20–51. https://doi.org/10.1016/j.cell.2018.03.006 Baylin, S. B., & Jones, P. A. 2016. Epigenetic determinants of cancer. Cold Spring Harbor Perspectives in Biology, 8(9), a019505. https://doi.org/10.1101/cshperspect.a019505 Becker, C., Hammerle-Fickinger, A., Riedmaier, I., & Pfaffl, M. W. 2010. mRNA and microRNA quality control for RT-qPCR analysis. Methods, 50(4), 237–243. https://doi.org/10.1016/j.ymeth.2010.01.010 Beghini, F., McIver, L. J., Blanco-Míguez, A., Dubois, L., Asnicar, F., Maharjan, S., Mailyan, A., Manghi, P., Scholz, M., Thomas, A. M., Valles-Colomer, M., Weingart, G., Zhang, Y., Zolfo, M., Huttenhower, C., Franzosa, E. A., & Segata, N. 2021. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. ELife, 10. CLOCKSS. https://doi.org/10.7554/elife.65088 Behjati, S., & Tarpey, P. S. 2013. What is next generation sequencing? Archives of Disease in Childhood - Education & Practice Edition, 98(6), 236–238. https://doi.org/10.1136/archdischild-2013-304340 Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., Boutell, J. M., Bryant, J., Carter, R. J., Keira Cheetham, R., Cox, A. J., Ellis, D. J., Flatbush, M. R., Gormley, N. A., Humphray, S. J., Irving, L. J., … Smith, A. J. 2008. Accurate whole human genome sequencing using reversible terminator chemistry. Nature, 456(7218), 53–59. https://doi.org/10.1038/nature07517 Bernstein, B. E., Stamatoyannopoulos, J. A., Costello, J. F., Ren, B., Milosavljevic, A., Meissner, A., Kellis, M., Marra, M. A., Beaudet, A. L., Ecker, J. R., Farnham, P. J., Hirst, M., Lander, E. S., Mikkelsen, T. S., & Thomson, J. A. 2010. The NIH roadmap epigenomics mapping consortium. Nature Biotechnology, 28(10), 1045–1048. https://doi.org/10.1038/nbt1010-1045 Bizouarn, F. 2014. Clinical applications using digital PCR. Quantitative Real-Time PCR, 189–214. https://doi.org/10.1007/978-1-4939-0733-5_16 Bock, C. 2012. Analysing and interpreting DNA methylation data. Nature Reviews Genetics, 13(10), 705–719. https://doi.org/10.1038/nrg3273 Bolger, A. M., Lohse, M., & Usadel, B. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114-2120. https://doi.org/10.1093/bioinformatics/btu170 Børsting, C., & Morling, N. 2015. Next generation sequencing and its applications in forensic genetics. Forensic Science International: Genetics, 18, 78–89. https://doi.org/10.1016/j.fsigen.2015.02.002 Bowers, J., Mitchell, J., Beer, E., Buzby, P. R., Causey, M., Efcavitch, J. W., Jarosz, M., Krzymanska-Olejnik, E., Kung, L., Lipson, D., Lowman, G. M., Marappan, S., McInerney, P., Platt, A., Roy, A., Siddiqi, S. M., Steinmann, K., & Thompson, J. F. 2009. Virtual terminator nucleotides for next-generation DNA sequencing. Nature Methods, 6(8), 593–595. https://doi.org/10.1038/nmeth.1354 Branton, D., Deamer, D. W., Marziali, A., Bayley, H., Benner, S. A., Butler, T., Di Ventra, M., Garaj, S., Hibbs, A., Huang, X., Jovanovich, S. B., Krstic, P. S., Lindsay, S., Ling, X. S., Mastrangelo, C. H., Meller, A., Oliver, J. S., Pershin, Y. V., Ramsey, J. M., … Schloss, J. A. 2008. The potential and challenges of nanopore sequencing. Nature Biotechnology, 26(10), 1146–1153. https://doi.org/10.1038/nbt.1495 Braslavsky, I., Hebert, B., Kartalov, E., & Quake, S. R. 2003. Sequence information can be obtained from single DNA molecules. Proceedings of the National Academy of Sciences, 100(7), 3960–3964. https://doi.org/10.1073/pnas.0230489100 Brenchley, R., Spannagl, M., Pfeifer, M., Barker, G. L. A., D’Amore, R., Allen, A. M., McKenzie, N., Kramer, M., Kerhornou, A., Bolser, D., Kay, S., Waite, D., Trick, M., Bancroft, I., Gu, Y., Huo, N., Luo, M.-C., Sehgal, S., Gill, B., … Hall, N. 2012. Analysis of the bread wheat genome using whole-genome shotgun sequencing. Nature, 491(7426), 705–710. https://doi.org/10.1038/nature11650 Brito, I. L., Yilmaz, S., Huang, K., Xu, L., Jupiter, S. D., Jenkins, A. P., Naisilisili, W., Tamminen, M., Smillie, C. S., Wortman, J. R., Birren, B. W., Xavier, R. J., Blainey, P. C., Singh, A. K., Gevers, D., & Alm, E. J. 2016. Mobile genes in the human microbiome are structured from global to individual scales. Nature, 535(7612), 435–439. https://doi.org/10.1038/nature18927 Broad Institute. n.d. Picard tools. http://broadinstitute.github.io/picard/ Bryant, D. M., Johnson, K., DiTommaso, T., Tickle, T., Couger, M. B., Payzin-Dogru, D., Lee, T. J., Leigh, N. D., Kuo, T.-H., Davis, F. G., Bateman, J., Bryant, S., Guzikowski, A. R., Tsai, S. L., Coyne, S., Ye, W. W., Freeman, R. M., Peshkin, L., Tabin, C. J., … Whited, J. L. 2017. A tissue-mapped axolotl De novo transcriptome enables identification of limb regeneration factors. Cell Reports, 18(3), 762–776. https://doi.org/10.1016/j.celrep.2016.12.063 Buenrostro, J. D., Wu, B., Chang, H. Y., & Greenleaf, W. J. 2015. ATAC‐seq: a method for assaying chromatin accessibility genome‐wide. Current Protocols in Molecular Biology, 109(1). Portico. https://doi.org/10.1002/0471142727.mb2129s109 Buermans, H. P. J., & den Dunnen, J. T. 2014. Next generation sequencing technology: Advances and applications. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 1842(10), 1932–1941. https://doi.org/10.1016/j.bbadis.2014.06.015 Burbano, H. A., Hodges, E., Green, R. E., Briggs, A. W., Krause, J., Meyer, M., Good, J. M., Maricic, T., Johnson, P. L. F., Xuan, Z., Rooks, M., Bhattacharjee, A., Brizuela, L., Albert, F. W., de la Rasilla, M., Fortea, J., Rosas, A., Lachmann, M., Hannon, G. J., & Pääbo, S. 2010. Targeted investigation of the neandertal genome by array-based sequence capture. Science, 328(5979), 723–725. https://doi.org/10.1126/science.1188046 Caldwell, C. C., & Spies, M. 2017. Helicase SPRNTing through the nanopore. Proceedings of the National Academy of Sciences, 114(45), 11809–11811. https://doi.org/10.1073/pnas.1716866114 ČALOUNOVÁ, Tereza. 2021. De novo transcriptomics and its use in non-model organisms. Bakalářská práce, vedoucí Pluskal, Tomáš. Praha: Univerzita Karlova, Přírodovědecká fakulta, Katedra buněčné biologie. http://hdl.handle.net/20.500.11956/149281 Casbon, J. A., Osborne, R. J., Brenner, S., & Lichtenstein, C. P. 2011. A method for counting PCR template molecules with application to next-generation sequencing. Nucleic Acids Research, 39(12), e81–e81. https://doi.org/10.1093/nar/gkr217 Castro-Wallace, S. L., Chiu, C. Y., John, K. K., Stahl, S. E., Rubins, K. H., McIntyre, A. B. R., Dworkin, J. P., Lupisella, M. L., Smith, D. J., Botkin, D. J., Stephenson, T. A., Juul, S., Turner, D. J., Izquierdo, F., Federman, S., Stryke, D., Somasekar, S., Alexander, N., Yu, G., … Burton, A. S. 2017. Nanopore DNA sequencing and genome assembly on the international space station. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-18364-0 Cavalli, G., & Heard, E. 2019. Advances in epigenetics link genetics to the environment and disease. Nature, 571(7766), 489–499. https://doi.org/10.1038/s41586-019-1411-0 Cech, T. R., & Steitz, J. A. 2014. The noncoding RNA revolution—trashing old rules to forge new ones. Cell, 157(1), 77-94. Chaisson, M. J. P., Huddleston, J., Dennis, M. Y., Sudmant, P. H., Malig, M., Hormozdiari, F., Antonacci, F., Surti, U., Sandstrom, R., Boitano, M., Landolin, J. M., Stamatoyannopoulos, J. A., Hunkapiller, M. W., Korlach, J., & Eichler, E. E. 2014. Resolving the complexity of the human genome using single-molecule sequencing. Nature, 517(7536), 608–611. https://doi.org/10.1038/nature13907 Chandran, A. K. N., Kim, J.-W., Yoo, Y.-H., Park, H. L., Kim, Y.-J., Cho, M.-H., & Jung, K.-H. 2019. Transcriptome analysis of rice-seedling roots under soil–salt stress using RNA-Seq method. Plant Biotechnology Reports, 13(6), 567–578. https://doi.org/10.1007/s11816-019-00550-3 Chen, K., Wallis, J. W., McLellan, M. D., Larson, D. E., Kalicki, J. M., Pohl, C. S., McGrath, S. D., Wendl, M. C., Zhang, Q., Locke, D. P., Shi, X., Fulton, R. S., Ley, T. J., Wilson, R. K., Ding, L., & Mardis, E. R. 2009. BreakDancer: an algorithm for high-resolution mapping of genomic structural variation. Nature Methods, 6(9), 677–681. https://doi.org/10.1038/nmeth.1363 Chen, S., Zhou, Y., Chen, Y., & Gu, J. 2018. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560 Chen, Y., Li, Y., Narayan, R., Subramanian, A., & Xie, X. 2016. Gene expression inference with deep learning. Bioinformatics, 32(12), 1832–1839. https://doi.org/10.1093/bioinformatics/btw074 Chiara, M., Horner, D. S., Gissi, C., & Pesole, G. 2021. Comparative genomics reveals early emergence and biased spatiotemporal distribution of SARS-CoV-2. Molecular Biology and Evolution, 38(6), 2547–2565. https://doi.org/10.1093/molbev/msab049 Choi, M., Scholl, U. I., Ji, W., Liu, T., Tikhonova, I. R., Zumbo, P., Nayir, A., Bakkaloğlu, A., Özen, S., Sanjad, S., Nelson-Williams, C., Farhi, A., Mane, S., & Lifton, R. P. 2009. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proceedings of the National Academy of Sciences, 106(45), 19096–19101. https://doi.org/10.1073/pnas.0910672106 Chomczynski, P., & Sacchi, N. 1987. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Analytical Biochemistry, 162(1), 156-159. https://doi.org/10.1016/0003-2697(87)90021-2 Church, G. M., & Gilbert, W. 1984. Genomic sequencing. Proceedings of the National Academy of Sciences of the United States of America, 81(7), 1991–1995. https://doi.org/10.1073/pnas.81.7.1991 Cingolani, P., Platts, A., Wang, L. L., Coon, M., Nguyen, T., Wang, L., ... & Ruden, D. M. 2012. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly, 6(2), 80-92. https://doi.org/10.4161/fly.19695 Clark, T. A., Murray, I. A., Morgan, R. D., Kislyuk, A. O., Spittle, K. E., Boitano, M., ... & Korlach, J. 2012. Characterization of DNA methyltransferase specificities using single-molecule, real-time DNA sequencing. Nucleic Acids Research, 40(4), e29-e29. https://doi.org/10.1093/nar/gkr1146 Cock, P. J., Fields, C. J., Goto, N., Heuer, M. L., & Rice, P. M. 2010. The sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Research, 38(6), 1767-1771. https://doi.org/10.1093/nar/gkp1137 Conesa, A., & Götz, S. 2008. Blast2GO: a comprehensive suite for functional analysis in plant genomics. International Journal of Plant Genomics, 2008, 619832. https://doi.org/10.1155/2008/619832 Conesa, A., Götz, S., García-Gómez, J. M., Terol, J., Talón, M., & Robles, M. 2005. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics, 21(18), 3674-3676. https://doi.org/10.1093/bioinformatics/bti610 Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., ... & Mortazavi, A. 2016. A survey of best practices for RNA-seq data analysis. Genome Biology, 17(1), 13. https://doi.org/10.1186/s13059-016-0881-8 Crick, F. 1970. Central dogma of molecular biology. Nature, 227(5258), 561-563. https://doi.org/10.1038/227561a0 De Filippis, F., Parente, E., & Ercolini, D. 2017. Metagenomics insights into food fermentations. Microbial Biotechnology, 10(1), 91-102. https://doi.org/10.1111/1751-7915.12421 Deamer, D., Akeson, M., & Branton, D. 2016. Three decades of nanopore sequencing. Nature Biotechnology, 34(5), 518-524. https://doi.org/10.1038/nbt.3423 Dellagostin, O. A., Grassmann, A. A., Rizzi, C., Schuch, R. A., Jorge, S., Oliveira, T. L., ... & Hartwig, D. D. 2017. Reverse vaccinology: an approach for identifying leptospiral vaccine candidates. International Journal of Molecular Sciences, 18(1), 158. https://doi.org/10.3390/ijms18010158 DePristo, M. A., Banks, E., Poplin, R., Garimella, K. V., Maguire, J. R., Hartl, C., ... & Daly, M. J. 2011. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics, 43(5), 491-498. https://doi.org/10.1038/ng.806 Desjardins, P., & Conklin, D. 2010. NanoDrop microvolume quantitation of nucleic acids. JoVE (Journal of Visualized Experiments), (45), e2565. https://doi.org/10.3791/2565 Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. 2017. Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. https://doi.org/10.1038/nbt.3820 Dirks, R. A. M., Stunnenberg, H. G., & Marks, H. 2016. Genome-wide epigenomic profiling for biomarker discovery. Clinical Epigenetics, 8(1). https://doi.org/10.1186/s13148-016-0284-4 Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., ... & Gingeras, T. R. 2013. STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15-21. https://doi.org/10.1093/bioinformatics/bts635 Dressman, D., Yan, H., Traverso, G., Kinzler, K. W., & Vogelstein, B. 2003. Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations. Proceedings of the National Academy of Sciences, 100(15), 8817–8822. https://doi.org/10.1073/pnas.1133470100 Eid, J., Fehr, A., Gray, J., Luong, K., Lyle, J., Otto, G., ... & Turner, S. 2009. Real-time DNA sequencing from single polymerase molecules. Science, 323(5910), 133-138. https://doi.org/10.1126/science.1162986 Eisenhofer, R., Minich, J. J., Marotz, C., Cooper, A., Knight, R., & Weyrich, L. S. 2019. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends in Microbiology, 27(2), 105-117. https://doi.org/10.1016/j.tim.2018.11.003 Ekblom, R., & Wolf, J. B. 2014. A field guide to whole‐genome sequencing, assembly and annotation. Evolutionary Applications, 7(9), 1026-1042. https://doi.org/10.1111/eva.12178 Eldem, V., Zararsiz, G., Taşçi, T., Duru, I. P., Bakir, Y., & Erkan, M. 2017. Transcriptome analysis for non-model organism: current status and best-practices. Applications of RNA-Seq and omics strategies - from microorganisms to human health. https://doi.org/10.5772/intechopen.68983 Escalante, A. E., Jardón Barbolla, L., Ramírez-Barahona, S., & Eguiarte, L. E. 2014. The study of biodiversity in the era of massive sequencing. Revista Mexicana de Biodiversidad, 85(4), 1249–1264. https://doi.org/10.7550/rmb.43498 Escobar-Zepeda, A., Vera-Ponce de León, A., & Sanchez-Flores, A. 2015. The road to metagenomics: from microbiology to DNA sequencing technologies and bioinformatics. Frontiers in Genetics, 6, 348. https://doi.org/10.3389/fgene.2015.00348 Esposito, S., D’Agostino, N., Taranto, F., Sonnante, G., Sestili, F., Lafiandra, D., & De Vita, P. 2022. Whole-exome sequencing of selected bread wheat recombinant inbred lines as a useful resource for allele mining and bulked segregant analysis. Frontiers in Genetics, 13, 1058471. https://doi.org/10.3389/fgene.2022.1058471 Ewels, P., Magnusson, M., Lundin, S., & Käller, M. 2016. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19), 3047-3048. https://doi.org/10.1093/bioinformatics/btw354 Fuentes‐Pardo, A. P., & Ruzzante, D. E. 2017. Whole‐genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations. Molecular Ecology, 26(20), 5369–5406. Portico. https://doi.org/10.1111/mec.14264 Fadiji, A. E., Ayangbenro, A. S., & Babalola, O. O. 2021. Shotgun metagenomics reveals the functional diversity of root-associated endophytic microbiomes in maize plant. Current Plant Biology, 25, 100195. https://doi.org/10.1016/j.cpb.2021.100195 Farlik, M., Sheffield, N. C., Nuzzo, A., Datlinger, P., Schönegger, A., Klughammer, J., & Bock, C. 2015. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Reports, 10(8), 1386-1397. https://doi.org/10.1016/j.celrep.2015.02.001 Fedurco, M. 2006. BTA, a novel reagent for DNA attachment on glass and efficient generation of solid-phase amplified DNA colonies. Nucleic Acids Research, 34(3), e22–e22. https://doi.org/10.1093/nar/gnj023 Feng, H., Conneely, K. N., & Wu, H. 2014. A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Research, 42(8), e69-e69. https://doi.org/10.1093/nar/gku154 Flusberg, B. A., Webster, D. R., Lee, J. H., Travers, K. J., Olivares, E. C., Clark, T. A., ... & Turner, S. W. 2010. Direct detection of DNA methylation during single-molecule, real-time sequencing. Nature Methods, 7(6), 461-465. https://doi.org/10.1038/nmeth.1459 Fragouli, E., Alfarawati, S., Spath, K., Babariya, D., Tarozzi, N., Borini, A., & Wells, D. 2017. Analysis of implantation and ongoing pregnancy rates following the transfer of mosaic diploid–aneuploid blastocysts. Human Genetics, 136(7), 805-819. https://doi.org/10.1007/s00439-017-1797-4 Frampton, G. M., Fichtenholtz, A., Otto, G. A., Wang, K., Downing, S. R., He, J., ... & Yelensky, R. 2013. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nature Biotechnology, 31(11), 1023-1031. https://doi.org/10.1038/nbt.2696 Franzosa, E. A., McIver, L. J., Rahnavard, G., Thompson, L. R., Schirmer, M., Weingart, G., ... & Huttenhower, C. 2018. Species-level functional profiling of metagenomes and metatranscriptomes. Nature Methods, 15(11), 962-968. https://doi.org/10.1038/s41592-018-0176-y Friedländer, M. R., Mackowiak, S. D., Li, N., Chen, W., & Rajewsky, N. 2012. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Research, 40(1), 37-52. https://doi.org/10.1093/nar/gkr688 Frommer, M., McDonald, L. E., Millar, D. S., Collis, C. M., Watt, F., Grigg, G. W., ... & Paul, C. L. 1992. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proceedings of the National Academy of Sciences, 89(5), 1827-1831. https://doi.org/10.1073/pnas.89.5.1827 Furey, T. S. 2012. ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions. Nature Reviews Genetics, 13(12), 840-852. https://doi.org/10.1038/nrg3306 Garalde, D. R., Snell, E. A., Jachimowicz, D., Sipos, B., Lloyd, J. H., Bruce, M., ... & Turner, D. J. 2018. Highly parallel direct RNA sequencing on an array of nanopores. Nature Methods, 15(3), 201-206. https://doi.org/10.1038/nmeth.4577 Gardy, J. L., & Loman, N. J. 2018. Towards a genomics-informed, real-time, global pathogen surveillance system. Nature Reviews Genetics, 19(1), 9-20. https://doi.org/10.1038/nrg.2017.88 Gargis, A. S., Kalman, L., Bick, D. P., Da Silva, C., Dimmock, D. P., Funke, B. H., ... & Lubin, I. M. 2015. Good laboratory practice for clinical next-generation sequencing informatics pipelines. Nature Biotechnology, 33(7), 689-693. https://doi.org/10.1038/nbt.3237 Garrison, E., & Marth, G. 2012. Haplotype-based variant detection from short-read sequencing. arXiv preprint arXiv:1207.3907. https://doi.org/10.48550/arXiv.1207.3907 Georges, M., Charlier, C., & Hayes, B. 2019. Harnessing genomic information for livestock improvement. Nature Reviews Genetics, 20(3), 135-156. https://doi.org/10.1038/s41576-018-0082-2 Gevaert, O. 2015. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics, 31(11), 1839-1841. https://doi.org/10.1093/bioinformatics/btv020 Gifford, C. A., Ziller, M. J., Gu, H., Trapnell, C., Donaghey, J., Tsankov, A., ... & Meissner, A. 2013. Transcriptional and epigenetic dynamics during specification of human embryonic stem cells. Cell, 153(5), 1149-1163. https://doi.org/10.1016/j.cell.2013.04.037 Gilbert, W., & Maxam, A. 1973. The nucleotide sequence of the lac operator. Proceedings of the National Academy of Sciences, 70(12), 3581-3584. https://doi.org/10.1073/pnas.70.12.3581 Gnirke, A., Melnikov, A., Maguire, J., Rogov, P., LeProust, E. M., Brockman, W., ... & Nusbaum, C. 2009. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nature Biotechnology, 27(2), 182-189. https://doi.org/10.1038/nbt.1523 Goldberg, B., Sichtig, H., Geyer, C., Ledeboer, N., & Weinstock, G. M. 2015. Making the leap from research laboratory to clinic: challenges and opportunities for next-generation sequencing in infectious disease diagnostics. MBio, 6(6), e01888-15. https://doi.org/10.1128/mBio.01888-15 Goodwin, S., McPherson, J. D., & McCombie, W. R. 2016. Coming of age: ten years of next-generation sequencing technologies. Nature Reviews Genetics, 17(6), 333-351. https://doi.org/10.1038/nrg.2016.49 Goyal, P., Krasteva, P. V., Van Gerven, N., Gubellini, F., Van den Broeck, I., Troupiotis-Tsaïlaki, A., ... & Remaut, H. 2014. Structural and mechanistic insights into the bacterial amyloid secretion channel CsgG. Nature, 516(7530), 250-253. https://doi.org/10.1038/nature13768 Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., ... & Regev, A. 2011. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology, 29(7), 644-652. https://doi.org/10.1038/nbt.1883 Gu, W., Crawford, E. D., O’Donovan, B. D., Wilson, M. R., Chow, E. D., Retallack, H., & DeRisi, J. L. 2016. Depletion of abundant sequences by hybridization (DASH): using Cas9 to remove unwanted high-abundance species in sequencing libraries and molecular counting applications. Genome Biology, 17, 41. https://doi.org/10.1186/s13059-016-0904-5 Gupta, P. K. 2008. Single-molecule DNA sequencing technologies for future genomics research. Trends in Biotechnology, 26(11), 602-611. https://doi.org/10.1016/j.tibtech.2008.07.003 Gurevich, A., Saveliev, V., Vyahhi, N., & Tesler, G. 2013. QUAST: quality assessment tool for genome assemblies. Bioinformatics, 29(8), 1072-1075. https://doi.org/10.1093/bioinformatics/btt086 Haas, B. J., Papanicolaou, A., Yassour, M., Grabherr, M., Blood, P. D., Bowden, J., ... & Regev, A. 2013. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nature Protocols, 8(8), 1494-1512. https://doi.org/10.1038/nprot.2013.084 Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. 2017. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Medicine, 9, 75. https://doi.org/10.1186/s13073-017-0467-4 Harris, T. D., Buzby, P. R., Babcock, H., Beer, E., Bowers, J., Braslavsky, I., ... & Xie, Z. 2008. Single-molecule DNA sequencing of a viral genome. Science, 320(5872), 106-109. https://doi.org/10.1126/science.1150427 Hasin, Y., Seldin, M., & Lusis, A. 2017. Multi-omics approaches to disease. Genome Biology, 18, 83. https://doi.org/10.1186/s13059-017-1215-1 Hanssen, F., Garcia, M. U., Folkersen, L., Pedersen, A. S., Lescai, F., Jodoin, S., Miller, E., Seybold, M., Wacker, O., Smith, N., Gabernet, G., & Nahnsen, S. 2024. Scalable and efficient DNA sequencing analysis on different compute infrastructures aiding variant discovery. NAR Genomics and Bioinformatics, 6(2). https://doi.org/10.1093/nargab/lqae031 Head, S. R., Komori, H. K., LaMere, S. A., Whisenant, T., Van Nieuwerburgh, F., Salomon, D. R., & Ordoukhanian, P. 2014. Library construction for next-generation sequencing: overviews and challenges. Biotechniques, 56(2), 61-77. https://doi.org/10.2144/000114133 Heller, D., & Vingron, M. 2019. SVIM: structural variant identification using mapped long reads. Bioinformatics, 35(17), 2907-2915. https://doi.org/10.1093/bioinformatics/btz041 Hendriksen, R. S., Munk, P., Njage, P., Van Bunnik, B., McNally, L., Lukjancenko, O., ... & Aarestrup, F. M. 2019. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nature Communications, 10(1), 1124. https://doi.org/10.1038/s41467-019-08853-3 Hill, C. B., Cassin, A., Keeble-Gagnère, G., Doblin, M. S., Bacic, A., & Roessner, U. 2016. De novo transcriptome assembly and analysis of differentially expressed genes of two barley genotypes reveal root-zone-specific responses to salt exposure. Scientific Reports, 6, 31558. https://doi.org/10.1038/srep31558 Holder, L. B., Haque, M. M., & Skinner, M. K. 2017. Machine learning for epigenetics and future medical applications. Epigenetics, 12(7), 505-514. https://doi.org/10.1080/15592294.2017.1329068 Horvath, S., & Raj, K. 2018. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics, 19(6), 371-384. https://doi.org/10.1038/s41576-018-0004-3 Huang, X., & Han, B. 2014. Natural variations and genome-wide association studies in crop plants. Annual Review of Plant Biology, 65(1), 531-551. https://doi.org/10.1146/annurev-arplant-050213-035715 Huddleston, J., & Eichler, E. E. 2016. An incomplete understanding of human genetic variation. Genetics, 202(4), 1251–1254. https://doi.org/10.1534/genetics.115.180539 Huerta-Cepas, J., Forslund, K., Coelho, L. P., Szklarczyk, D., Jensen, L. J., Von Mering, C., & Bork, P. 2017. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Molecular Biology and Evolution, 34(8), 2115-2122. https://doi.org/10.1093/molbev/msx148 Hunter, J. D. 2007. Matplotlib: a 2D graphics environment. Computing in Science & Engineering, 9(3), 90-95. https://doi.org/10.1109/MCSE.2007.55 Huse, S. M., Huber, J. A., Morrison, H. G., Sogin, M. L., & Welch, D. M. 2007. Accuracy and quality of massively parallel DNA pyrosequencing. Genome Biology, 8, R143. https://doi.org/10.1186/gb-2007-8-7-r143 Hwang, B., Lee, J. H., & Bang, D. 2018. Single-cell RNA sequencing technologies and bioinformatics pipelines. Experimental & Molecular Medicine, 50(8), 1-14. https://doi.org/10.1038/s12276-018-0071-8 Hwang, S., Kim, E., Lee, I., & Marcotte, E. M. 2015. Systematic comparison of variant calling pipelines using gold standard personal exome variants. Scientific Reports, 5, 17875. https://doi.org/10.1038/srep17875 Ibeagha-Awemu, E. M., & Zhao, X. 2015. Epigenetic marks: regulators of livestock phenotypes and conceivable sources of missing variation in livestock improvement programs. Frontiers in Genetics, 6, 302. https://doi.org/10.3389/fgene.2015.00302 Ichikawa, M., Kato, N., Toda, E., Kashihara, M., Ishida, Y., Hiei, Y., Isobe, S. N., Shirasawa, K., Hirakawa, H., Okamoto, T., & Komari, T. 2023. Whole-genome sequence analysis of mutations in rice plants regenerated from zygotes, mature embryos, and immature embryos. Breeding Science, 73(3), 349–353. https://doi.org/10.1270/jsbbs.22100 Ignatiadis, M., Sledge, G. W., & Jeffrey, S. S. 2021. Liquid biopsy enters the clinic—implementation issues and future challenges. Nature Reviews Clinical Oncology, 18(5), 297-312. https://doi.org/10.1038/s41571-020-00457-x Illumina. 2021. NovaSeq 6000 System Specifications. Retrieved from https://www.illumina.com/systems/sequencing-platforms/novaseq/specifications.html Jagadeesan, B., Gerner-Smidt, P., Allard, M. W., Leuillet, S., Winkler, A., Xiao, Y., ... & Grant, K. 2019. The use of next generation sequencing for improving food safety: Translation into practice. Food Microbiology, 79, 96-115. https://doi.org/10.1016/j.fm.2018.11.005 Jain, M., Olsen, H. E., Paten, B., & Akeson, M. 2016. The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biology, 17, 239. https://doi.org/10.1186/s13059-016-1103-0 Japrung, D., Bahrami, A., Nadzeyka, A., Peto, L., Bauerdick, S., Edel, J. B., & Albrecht, T. 2014. SSB binding to single-stranded DNA probed using solid-state nanopore sensors. The journal of physical chemistry. B, 118(40), 11605–11612. https://doi.org/10.1021/jp506832u Jarvis, E. D. 2016. Perspectives from the avian phylogenomics project: questions that can be answered with sequencing all genomes of a vertebrate class. Annual Review of Animal Biosciences, 4(1), 45-59. https://doi.org/10.1146/annurev-animal-021815-111216 John, E., Singh, K. B., Oliver, R. P., Soyer, J. L., Muria-Gonzalez, J., Soo, D., Jacques, S., & Tan, K.-C. 2022. Chromatin-immunoprecipitation reveals the PnPf2 transcriptional network controlling effector-mediated virulence in a fungal pathogen of wheat. https://doi.org/10.1101/2022.06.16.496517 Jones, P. A. 2012. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nature Reviews Genetics, 13(7), 484-492. https://doi.org/10.1038/nrg3230 Jou, W. M., Haegeman, G., Ysebaert, M., & Fiers, W. 1972. Nucleotide sequence of the gene coding for the bacteriophage MS2 coat protein. Nature, 237(5350), 82-88. https://doi.org/10.1038/237082a0 Jiang, G., Zheng, J.-Y., Ren, S.-N., Yin, W., Xia, X., Li, Y., & Wang, H.-L. 2024. A comprehensive workflow for optimizing RNA-seq data analysis. BMC Genomics, 25(1). https://doi.org/10.1186/s12864-024-10414-y
Kanagal-Shamanna, R. 2016. Digital PCR: Principles and Applications. Clinical Applications of PCR, 43–50. https://doi.org/10.1007/978-1-4939-3360-0_5 Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., ... & MacArthur, D. G. 2020. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature, 581(7809), 434-443. https://doi.org/10.1038/s41586-020-2308-7 Katsanis, S. H., & Katsanis, N. 2013. Molecular genetic testing and the future of clinical genomics. Nature Reviews Genetics, 14(6), 415-426. https://doi.org/10.1038/nrg3493 Kaya-Okur, H. S., Wu, S. J., Codomo, C. A., Pledger, E. S., Bryson, T. D., Henikoff, J. G., ... & Henikoff, S. 2019. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nature Communications, 10(1), 1930. https://doi.org/10.1038/s41467-019-09982-5 Kelsey, G., Stegle, O., & Reik, W. 2017. Single-cell epigenomics: Recording the past and predicting the future. Science, 358(6359), 69-75. https://doi.org/10.1126/science.aan6826 Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, D. 2002. The human genome browser at UCSC. Genome Research, 12(6), 996-1006. https://doi.org/10.1101/gr.229102 Kidder, B. L., Hu, G., & Zhao, K. 2011. ChIP-Seq: technical considerations for obtaining high-quality data. Nature Immunology, 12(10), 918-922. https://doi.org/10.1038/ni.2117 Kimes, N. E., Callaghan, A. V., Aktas, D. F., Smith, W. L., Sunner, J., Golding, B., ... & Morris, P. J. 2013. Metagenomic analysis and metabolite profiling of deep–sea sediments from the Gulf of Mexico following the Deepwater Horizon oil spill. Frontiers in Microbiology, 4, 50. https://doi.org/10.3389/fmicb.2013.00050 Kiselev, V. Y., Andrews, T. S., & Hemberg, M. 2019. Challenges in unsupervised clustering of single-cell RNA-seq data. Nature Reviews Genetics, 20(5), 273-282. https://doi.org/10.1038/s41576-018-0088-9 Kivioja, T., Vähärautio, A., Karlsson, K., Bonke, M., Enge, M., Linnarsson, S., & Taipale, J. 2012. Counting absolute numbers of molecules using unique molecular identifiers. Nature Methods, 9(1), 72-74. https://doi.org/10.1038/nmeth.1778 Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., ... & Dorrestein, P. C. 2018. Best practices for analysing microbiomes. Nature Reviews Microbiology, 16(7), 410-422. https://doi.org/10.1038/s41579-018-0029-9 Knudsen, B. E., Bergmark, L., Munk, P., Lukjancenko, O., Priemé, A., Aarestrup, F. M., & Pamp, S. J. 2016. Impact of sample type and DNA isolation procedure on genomic inference of microbiome composition. mSystems, 1(5), e00095-16. https://doi.org/10.1128/mSystems.00095-16 Koboldt, D. C. 2020. Best practices for variant calling in clinical sequencing. Genome Medicine, 12(1). https://doi.org/10.1186/s13073-020-00791-w Koboldt, D. C., Steinberg, K. M., Larson, D. E., Wilson, R. K., & Mardis, E. R. 2013. The next-generation sequencing revolution and its impact on genomics. Cell, 155(1), 27-38. https://doi.org/10.1016/j.cell.2013.09.006 Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. 2015. The Technology and Biology of Single-Cell RNA Sequencing. Molecular Cell, 58(4), 610–620. https://doi.org/10.1016/j.molcel.2015.04.005 Korlach, J., Bibillo, A., Wegener, J., Peluso, P., Pham, T. T., Park, I., Clark, S., Otto, G. A., & Turner, S. W. 2008. Long, processive enzymatic dna synthesis using 100% dye-labeled terminal phosphate-linked nucleotides. Nucleosides, Nucleotides and Nucleic Acids, 27(9), 1072–1082. https://doi.org/10.1080/15257770802260741 Korlach, J., Marks, P. J., Cicero, R. L., Gray, J. J., Murphy, D. L., Roitman, D. B., ... & Turner, S. W. 2008. Selective aluminum passivation for targeted immobilization of single DNA polymerase molecules in zero-mode waveguide nanostructures. Proceedings of the National Academy of Sciences, 105(4), 1176-1181. https://doi.org/10.1073/pnas.0710982105 Köster, J., & Rahmann, S. 2012. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics, 28(19), 2520-2522. https://doi.org/10.1093/bioinformatics/bts480 Kosugi, S., Momozawa, Y., Liu, X., Terao, C., Kubo, M., & Kamatani, Y. 2019. Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing. Genome Biology, 20, 117. https://doi.org/10.1186/s13059-019-1720-5 Kozarewa, I., Armisen, J., Gardner, A. F., Slatko, B. E., & Hendrickson, C. L. 2015. Overview of target enrichment strategies. Current Protocols in Molecular Biology, 112(1). Portico. https://doi.org/10.1002/0471142727.mb0721s112 Krueger, F. 2015. Trim Galore! available online at: https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ Krueger, F., & Andrews, S. R. 2011. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics, 27(11), 1571-1572. https://doi.org/10.1093/bioinformatics/btr167 Krzywinski, M., Schein, J., Birol, I., Connors, J., Gascoyne, R., Horsman, D., ... & Marra, M. A. 2009. Circos: an information aesthetic for comparative genomics. Genome Research, 19(9), 1639-1645. https://doi.org/10.1101/gr.092759.109 Kukurba, K. R., & Montgomery, S. B. 2015. RNA sequencing and analysis. Cold Spring Harbor Protocols, 2015(11), pdb-top084970. https://doi.org/10.1101/pdb.top084970 Kurdyukov, S., & Bullock, M. 2016. DNA methylation analysis: choosing the right method. Biology, 5(1), 3. https://doi.org/10.3390/biology5010003 Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. 2018. Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies. Nature Protocols, 13(12), 2742–2757. https://doi.org/10.1038/s41596-018-0073-y Laird, P. W. 2010. Principles and challenges of genome-wide DNA methylation analysis. Nature Reviews Genetics, 11(3), 191-203. https://doi.org/10.1038/nrg2732 Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., ... & Snyder, M. 2012. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research, 22(9), 1813-1831. https://doi.org/10.1101/gr.136184.111 Langmead, B., & Salzberg, S. L. 2012. Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4), 357-359. https://doi.org/10.1038/nmeth.1923 Laszlo, A. H., Derrington, I. M., Ross, B. C., Brinkerhoff, H., Adey, A., Nova, I. C., ... & Gundlach, J. H. 2014. Decoding long nanopore sequencing reads of natural DNA. Nature Biotechnology, 32(8), 829-833. https://doi.org/10.1038/nbt.2950 Leggett, R. M., & Clark, M. D. 2017. A world of opportunities with nanopore sequencing. Journal of Experimental Botany, 68(20), 5419-5429. https://doi.org/10.1093/jxb/erx289 Levene, M. J., Korlach, J., Turner, S. W., Foquet, M., Craighead, H. G., & Webb, W. W. 2003. Zero-mode waveguides for single-molecule analysis at high concentrations. Science, 299(5607), 682-686. https://doi.org/10.1126/science.1079700 Levy-Sakin, M., Pastor, S., Mostovoy, Y., Li, L., Leung, A. K., McCaffrey, J., ... & Kwok, P. Y. 2019. Genome maps across 26 human populations reveal population-specific patterns of structural variation. Nature Communications, 10(1), 1025. https://doi.org/10.1038/s41467-019-08992-7 Li, D., Liu, C. M., Luo, R., Sadakane, K., & Lam, T. W. 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics, 31(10), 1674-1676. https://doi.org/10.1093/bioinformatics/btv033 Li, H. 2011. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics, 27(21), 2987-2993. https://doi.org/10.1093/bioinformatics/btr509 Li, H. 2013. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:1303.3997. https://doi.org/10.48550/arXiv.1303.3997 Li, H. 2014. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics, 30(20), 2843-2851. https://doi.org/10.1093/bioinformatics/btu356 Li, H., & Durbin, R. 2009. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25(14), 1754-1760. https://doi.org/10.1093/bioinformatics/btp324 Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., ... & 1000 Genome Project Data Processing Subgroup. 2009. The sequence alignment/map format and SAMtools. Bioinformatics, 25(16), 2078-2079. https://doi.org/10.1093/bioinformatics/btp352 Li, Y., & Tollefsbol, T. O. 2011. DNA methylation detection: bisulfite genomic sequencing analysis. Epigenetics Protocols, 11-21. https://doi.org/10.1007/978-1-61779-316-5_2 Liang, Y., Ridzon, D., Wong, L., & Chen, C. 2007. Characterization of microRNA expression profiles in normal human tissues. BMC Genomics, 8, 166. https://doi.org/10.1186/1471-2164-8-166 Libbrecht, M. W., & Noble, W. S. 2015. Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321-332. https://doi.org/10.1038/nrg3920 Liu, H., Able, A. J., & Able, J. A. 2021. Small RNAs and their targets are associated with the transgenerational effects of water-deficit stress in durum wheat. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-83074-7 Liu, L., Li, Y., Li, S., Hu, N., He, Y., Pong, R., ... & Law, M. 2012. Comparison of next‐generation sequencing systems. BioMed Research International, 2012, 251364. https://doi.org/10.1155/2012/251364 Liu, L., Li, Y., Li, S., Hu, N., He, Y., Pong, R., Lin, D., Lu, L., & Law, M. 2012. Comparison of next-generation sequencing systems. Journal of Biomedicine & Biotechnology, 2012, 251364. https://doi.org/10.1155/2012/251364 Liu, X. S., Wu, H., Ji, X., Stelzer, Y., Wu, X., Czauderna, S., ... & Jaenisch, R. 2016. Editing DNA methylation in the mammalian genome. Cell, 167(1), 233-247. https://doi.org/10.1016/j.cell.2016.08.056 Liu, Y., Wang, X., Yuan, L., Liu, Y., Shen, T., & Zhang, Y. 2021. Comparative small RNA profiling and functional exploration on wheat with high-and low-cadmium accumulation. Frontiers in Genetics, 12, 635599. https://doi.org/10.3389/fgene.2021.635599 Lloyd-Price, J., Arze, C., Ananthakrishnan, A. N., Schirmer, M., Avila-Pacheco, J., Poon, T. W., ... & Huttenhower, C. 2019. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature, 569(7758), 655-662. https://doi.org/10.1038/s41586-019-1237-9 Lloyd-Price, J., Mahurkar, A., Rahnavard, G., Crabtree, J., Orvis, J., Hall, A. B., ... & Huttenhower, C. 2017. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature, 550(7674), 61-66. https://doi.org/10.1038/nature23889 Logsdon, G. A., Vollger, M. R., & Eichler, E. E. 2020. Long-read human genome sequencing and its applications. Nature Reviews Genetics, 21(10), 597-614. https://doi.org/10.1038/s41576-020-0236-x Loose, M., Malla, S., & Stout, M. 2016. Real-time selective sequencing using nanopore technology. Nature Methods, 13(9), 751-754. https://doi.org/10.1038/nmeth.3930 Love, M. I., Huber, W., & Anders, S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8 Luecken, M. D., & Theis, F. J. 2019. Current best practices in single‐cell RNA‐seq analysis: a tutorial. Molecular Systems Biology, 15(6), e8746. https://doi.org/10.15252/msb.20188746 Luo, C., Hajkova, P., & Ecker, J. R. 2018. Dynamic DNA methylation: In the right place at the right time. Science, 361(6409), 1336-1340. https://doi.org/10.1126/science.aat6806 Ma, P., Wu, L., Xu, Y., Xu, H., Zhang, X., Wang, W., ... & Wang, B. 2021. Bulked segregant RNA-Seq provides distinctive expression profile against powdery mildew in the wheat genotype YD588. Frontiers in Plant Science, 12, 764978. https://doi.org/10.3389/fpls.2021.764978 Machanick, P., & Bailey, T. L. 2011. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics, 27(12), 1696-1697. https://doi.org/10.1093/bioinformatics/btr189 Maes, R. K., Langohr, I. M., Wise, A. G., Smedley, R. C., Thaiwong, T., & Kiupel, M. 2014. Beyond H&E: Integration of nucleic acid–based analyses into diagnostic pathology. Veterinary Pathology, 51(1), 238-256. https://doi.org/10.1177/0300985813505878 Magli, M. C., Pomante, A., Cafueri, G., Valerio, M., Crippa, A., Ferraretti, A. P., & Gianaroli, L. 2016. Preimplantation genetic testing: polar bodies, blastomeres, trophectoderm cells, or blastocoelic fluid? Fertility and Sterility, 105(3), 676-683.e5. https://doi.org/10.1016/j.fertnstert.2015.11.018 Mallick, S., Li, H., Lipson, M., Mathieson, I., Gymrek, M., Racimo, F., ... & Reich, D. 2016. The Simons genome diversity project: 300 genomes from 142 diverse populations. Nature, 538(7624), 201-206. https://doi.org/10.1038/nature18964 Mamanova, L., Coffey, A. J., Scott, C. E., Kozarewa, I., Turner, E. H., Kumar, A., ... & Turner, D. J. 2010. Target-enrichment strategies for next-generation sequencing. Nature Methods, 7(2), 111-118. https://doi.org/10.1038/nmeth.1419 Manrao, E. A., Derrington, I. M., Laszlo, A. H., Langford, K. W., Hopper, M. K., Gillgren, N., ... & Gundlach, J. H. 2012. Reading DNA at single-nucleotide resolution with a mutant MspA nanopore and phi29 DNA polymerase. Nature Biotechnology, 30(4), 349-353. https://doi.org/10.1038/nbt.2171 Mardis, E. R. 2008. Next-generation DNA sequencing methods. Annual Review of Genomics and Human Genetics, 9(1), 387-402. https://doi.org/10.1146/annurev.genom.9.081307.164359 Mardis, E. R. 2008. The impact of next-generation sequencing technology on genetics. Trends in Genetics, 24(3), 133-141. https://doi.org/10.1016/j.tig.2007.12.007 Mardis, E. R. 2011. A decade’s perspective on DNA sequencing technology. Nature, 470(7333), 198-203. https://doi.org/10.1038/nature09796 Mardis, E. R. 2013. Next-generation sequencing platforms. Annual Review of Analytical Chemistry, 6(1), 287-303. https://doi.org/10.1146/annurev-anchem-062012-092628 Mardis, E. R. 2017. DNA sequencing technologies: 2006–2016. Nature Protocols, 12(2), 213-218. https://doi.org/10.1038/nprot.2016.182 Maree, H. J., Fox, A., Al Rwahnih, M., Boonham, N., & Candresse, T. 2018. Application of HTS for routine plant virus diagnostics: state of the art and challenges. Frontiers in Plant Science, 9, 1082. https://doi.org/10.3389/fpls.2018.01082 Margulies, M., Egholm, M., Altman, W. E., Attiya, S., Bader, J. S., Bemben, L. A., ... & Rothberg, J. M. 2005. Genome sequencing in microfabricated high-density picolitre reactors. Nature, 437(7057), 376-380. https://doi.org/10.1038/nature03959 Martin, M. 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal, 17(1), 10-12. https://doi.org/10.14806/ej.17.1.200 Massel, K., Campbell, B. C., Mace, E. S., Tai, S., Tao, Y., Worland, B. G., Jordan, D. R., Botella, J. R., & Godwin, I. D. 2016. Whole genome sequencing reveals potential new targets for improving nitrogen uptake and utilization in Sorghum bicolor. Frontiers in Plant Science, 7, 1544. https://doi.org/10.3389/fpls.2016.01544 Mathur, M., Prajapat, R. K., Upadhyay, T. K., Lal, D., Khatik, N., & Sharma, D. 2021. Advances in Genomics and Proteomics in Agriculture. Crop Improvement, 23–35. https://doi.org/10.1201/9781003099079-2 Maxam, A. M., & Gilbert, W. 1977. A new method for sequencing DNA. Proceedings of the National Academy of Sciences, 74(2), 560-564. https://doi.org/10.1073/pnas.74.2.560 McGuire, A. L., Caulfield, T., & Cho, M. K. 2008. Research ethics and the challenge of whole-genome sequencing. Nature Reviews Genetics, 9(2), 152-156. https://doi.org/10.1038/nrg2302 McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. 2010. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297-1303. https://doi.org/10.1101/gr.107524.110 McKernan, K. J., Peckham, H. E., Costa, G. L., McLaughlin, S. F., Fu, Y., Tsung, E. F., ... & Blanchard, A. P. 2009. Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding. Genome Research, 19(9), 1527-1541. https://doi.org/10.1101/gr.091868.109 McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R., Thormann, A., ... & Cunningham, F. 2016. The ensembl variant effect predictor. Genome Biology, 17, 122. https://doi.org/10.1186/s13059-016-0974-4 McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., ... & Bejerano, G. 2010. GREAT improves functional interpretation of cis-regulatory regions. Nature Biotechnology, 28(5), 495-501. https://doi.org/10.1038/nbt.1630 McMurdie, P. J., & Holmes, S. 2013. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PloS One, 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217 Meienberg, J., Bruggmann, R., Oexle, K., & Matyas, G. 2016. Clinical sequencing: is WGS the better WES? Human Genetics, 135(3), 359–362. https://doi.org/10.1007/s00439-015-1631-9 Meissner, A. 2010. Epigenetic modifications in pluripotent and differentiated cells. Nature Biotechnology, 28(10), 1079-1088. https://doi.org/10.1038/nbt.1684 Meissner, A., Gnirke, A., Bell, G. W., Ramsahoye, B., Lander, E. S., & Jaenisch, R. 2005. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Research, 33(18), 5868-5877. https://doi.org/10.1093/nar/gki901 Mendizabal, I., Keller, T. E., Zeng, J., & Yi, S. V. 2014. Epigenetics and evolution. Integrative and Comparative Biology, 54(1), 31-42. https://doi.org/10.1093/icb/icu040 Merriman, B., R&D Team, I. T., & Rothberg, J. M. 2012. Progress in ion torrent semiconductor chip based sequencing. Electrophoresis, 33(23), 3397-3417. https://doi.org/10.1002/elps.201200424 Mertes, F., ElSharawy, A., Sauer, S., van Helvoort, J. M., Van Der Zaag, P. J., Franke, A., ... & Brookes, A. J. 2011. Targeted enrichment of genomic DNA regions for next-generation sequencing. Briefings in Functional Genomics, 10(6), 374-386. https://doi.org/10.1093/bfgp/elr033 Metzker, M. L. 2010. Sequencing technologies—the next generation. Nature Reviews Genetics, 11(1), 31-46. https://doi.org/10.1038/nrg2626 Meyer, C. A., & Liu, X. S. 2014. Identifying and mitigating bias in next-generation sequencing methods for chromatin biology. Nature Reviews Genetics, 15(11), 709-721. https://doi.org/10.1038/nrg3788 Michael, T. P., & VanBuren, R. 2020. Building near-complete plant genomes. Current Opinion in Plant Biology, 54, 26–33. https://doi.org/10.1016/j.pbi.2019.12.009 Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. 2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods, 5(7), 621-628. https://doi.org/10.1038/nmeth.1226 Mwangi, W., de Figueiredo, P., & Criscitiello, M. F. 2016. One health: addressing global challenges at the nexus of human, animal, and environmental health. PLOS Pathogens, 12(9), e1005731. https://doi.org/10.1371/journal.ppat.1005731 Nagarajan, N., & Pop, M. 2013. Sequence assembly demystified. Nature Reviews Genetics, 14(3), 157-167. https://doi.org/10.1038/nrg3367 Nakamura, K., Oshima, T., Morimoto, T., Ikeda, S., Yoshikawa, H., Shiwa, Y., ... & Kanaya, S. 2011. Sequence-specific error profile of Illumina sequencers. Nucleic Acids Research, 39(13), e90. https://doi.org/10.1093/nar/gkr344 Nakato, R., & Shirahige, K. 2017. Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation. Briefings in Bioinformatics, 18(2), 279-290. https://doi.org/10.1093/bib/bbw023 Nefissi Ouertani, R., Arasappan, D., Abid, G., Ben Chikha, M., Jardak, R., Mahmoudi, H., ... & Jansen, R. K. 2021. Transcriptomic analysis of salt-stress-responsive genes in barley roots and leaves. International Journal of Molecular Sciences, 22(15), 8155. https://doi.org/10.3390/ijms22158155 Nekrutenko, A., & Taylor, J. 2012. Next-generation sequencing data interpretation: enhancing reproducibility and accessibility. Nature Reviews Genetics, 13(9), 667-672. https://doi.org/10.1038/nrg3305 Nesme, J., Achouak, W., Agathos, S. N., Bailey, M., Baldrian, P., Brunel, D., ... & Simonet, P. 2016. Back to the future of soil metagenomics. Frontiers in Microbiology, 7, 73. https://doi.org/10.3389/fmicb.2016.00073 Ng, S. B., Turner, E. H., Robertson, P. D., Flygare, S. D., Bigham, A. W., Lee, C., ... & Shendure, J. 2009. Targeted capture and massively parallel sequencing of 12 human exomes. Nature, 461(7261), 272-276. https://doi.org/10.1038/nature08250 Nielsen, R., Paul, J. S., Albrechtsen, A., & Song, Y. S. 2011. Genotype and SNP calling from next-generation sequencing data. Nature Reviews Genetics, 12(6), 443-451. https://doi.org/10.1038/nrg2986 Niu, J., Ma, S., Zheng, S., Zhang, C., Lu, Y., Si, Y., Tian, S., Shi, X., Liu, X., Naeem, M. K., Sun, H., Hu, Y., Wu, H., Cui, Y., Chen, C., Long, W., Zhang, Y., Gu, M., Cui, M., … Ling, H.-Q. 2023. Whole-genome sequencing of diverse wheat accessions uncovers genetic changes during modern breeding in China and the United States. The Plant Cell, 35(12), 4199–4216. https://doi.org/10.1093/plcell/koad229 Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. 2017. metaSPAdes: a new versatile metagenomic assembler. Genome Research, 27(5), 824-834. https://doi.org/10.1101/gr.213959.116 Okonechnikov, K., Conesa, A., & García-Alcalde, F. 2016. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics, 32(2), 292-294. https://doi.org/10.1093/bioinformatics/btv566 Oksanen, J. 2010. Vegan: community ecology package. Retrieved from http://vegan.r-forge.r-project.org/ Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., ... & Wagner, H. 2019. vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan Olsvik, Ø., Wahlberg, J., Petterson, B., Uhlen, M., Popovic, T., Wachsmuth, I. K., & Fields, P. I. 1993. Use of automated sequencing of polymerase chain reaction-generated amplicons to identify three types of cholera toxin subunit B in Vibrio cholerae O1 strains. Journal of Clinical Microbiology, 31(1), 22-25. https://doi.org/10.1128/jcm.31.1.22-25.1993 Ozsolak, F., & Milos, P. M. 2011. RNA sequencing: advances, challenges and opportunities. Nature Reviews Genetics, 12(2), 87-98. https://doi.org/10.1038/nrg2934 Ozsolak, F., Goren, A., Gymrek, M., Guttman, M., Regev, A., Bernstein, B. E., & Milos, P. M. 2010. Digital transcriptome profiling from attomole-level RNA samples. Genome Research, 20(4), 519–525. https://doi.org/10.1101/gr.102129.109 Ozsolak, F., Platt, A. R., Jones, D. R., Reifenberger, J. G., Sass, L. E., McInerney, P., ... & Milos, P. M. 2009. Direct RNA sequencing. Nature, 461(7265), 814-818. https://doi.org/10.1038/nature08390 Pandey, V., Nutter, R. C., & Prediger, E. 2008. Applied Biosystems SOLiD™ System: Ligation‐Based Sequencing. Next Generation Genome Sequencing, 29–42. Portico. https://doi.org/10.1002/9783527625130.ch3 Pareek, C. S., Smoczynski, R., & Tretyn, A. 2011. Sequencing technologies and genome sequencing. Journal of Applied Genetics, 52(4), 413-435. https://doi.org/10.1007/s13353-011-0057-x Pereira, R., Oliveira, J., & Sousa, M. 2020. Bioinformatics and computational tools for next-generation sequencing analysis in clinical genetics. Journal of Clinical Medicine, 9(1), 132. https://doi.org/10.3390/jcm9010132 Park, P. J. 2009. ChIP–seq: advantages and challenges of a maturing technology. Nature Reviews Genetics, 10(10), 669-680. https://doi.org/10.1038/nrg2641 Payne, A., Holmes, N., Rakyan, V., & Loose, M. 2018. BulkVis: a graphical viewer for Oxford nanopore bulk FAST5 files. Bioinformatics, 35(13), 2193–2198. https://doi.org/10.1093/bioinformatics/bty841 Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., & Salzberg, S. L. 2016. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nature Protocols, 11(9), 1650-1667. https://doi.org/10.1038/nprot.2016.095 Pettersson, E., Lundeberg, J., & Ahmadian, A. 2009. Generations of sequencing technologies. Genomics, 93(2), 105-111. https://doi.org/10.1016/j.ygeno.2008.10.003 Pinxten, W., & Howard, H. C. 2014. Ethical issues raised by whole genome sequencing. Best Practice & Research Clinical Gastroenterology, 28(2), 269–279. https://doi.org/10.1016/j.bpg.2014.02.004 Pollard, M. O., Gurdasani, D., Mentzer, A. J., Porter, T., & Sandhu, M. S. 2018. Long reads: their purpose and place. Human Molecular Genetics, 27(R2), R234-R241. https://doi.org/10.1093/hmg/ddy177 Poplin, R., Chang, P. C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., ... & DePristo, M. A. 2018. A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology, 36(10), 983-987. https://doi.org/10.1038/nbt.4235 Portela, A., & Esteller, M. 2010. Epigenetic modifications and human disease. Nature Biotechnology, 28(10), 1057-1068. https://doi.org/10.1038/nbt.1685 Price, V., Ngwira, L. G., Lewis, J. M., Baker, K. S., Peacock, S. J., Jauneikaite, E., & Feasey, N. 2023. A systematic review of economic evaluations of whole-genome sequencing for the surveillance of bacterial pathogens. Microbial Genomics, 9(2), 000947. https://doi.org/10.1099/mgen.0.000947 Priestley, P., Baber, J., Lolkema, M. P., Steeghs, N., de Bruijn, E., Shale, C., ... & Cuppen, E. 2019. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature, 575(7781), 210-216. https://doi.org/10.1038/s41586-019-1689-y Pritchard, C. C., Cheng, H. H., & Tewari, M. 2012. MicroRNA profiling: approaches and considerations. Nature Reviews Genetics, 13(5), 358-369. https://doi.org/10.1038/nrg3198 Prober, J. M., Trainor, G. L., Dam, R. J., Hobbs, F. W., Robertson, C. W., Zagursky, R. J., ... & Baumeister, K. 1987. A system for rapid DNA sequencing with fluorescent chain-terminating dideoxynucleotides. Science, 238(4825), 336-341. https://doi.org/10.1126/science.2443975 Psifidi, A., Dovas, C. I., Bramis, G., Lazou, T., Russel, C. L., Arsenos, G., & Banos, G. 2015. Comparison of eleven methods for genomic DNA extraction suitable for large-scale whole-genome genotyping and long-term DNA banking using blood samples. PLOS ONE, 10(1), e0115960. https://doi.org/10.1371/journal.pone.0115960 Purugganan, M. D., & Jackson, S. A. 2021. Advancing crop genomics from lab to field. Nature Genetics, 53(5), 595–601. https://doi.org/10.1038/s41588-021-00866-3 Pushkarev, D., Neff, N. F., & Quake, S. R. 2009. Single-molecule sequencing of an individual human genome. Nature Biotechnology, 27(9), 847-850. https://doi.org/10.1038/nbt.1561 Quail, M. A., Smith, M., Coupland, P., Otto, T. D., Harris, S. R., Connor, T. R., ... & Gu, Y. 2012. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics, 13, 341. https://doi.org/10.1186/1471-2164-13-341 Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. 2017. Shotgun metagenomics, from sampling to analysis. Nature Biotechnology, 35(9), 833-844. https://doi.org/10.1038/nbt.3935 Rabbani, B., Nakaoka, H., Akhondzadeh, S., Tekin, M., & Mahdieh, N. 2016. Next generation sequencing: implications in personalized medicine and pharmacogenomics. Molecular BioSystems, 12(6), 1818–1830. https://doi.org/10.1039/c6mb00115g Rabbani, B., Tekin, M., & Mahdieh, N. 2014. The promise of whole-exome sequencing in medical genetics. Journal of Human Genetics, 59(1), 5-15. https://doi.org/10.1038/jhg.2013.114 Rajkumar, M. S., Shankar, R., Garg, R., & Jain, M. 2020. Bisulphite sequencing reveals dynamic DNA methylation under desiccation and salinity stresses in rice cultivars. Genomics, 112(5), 3537–3548. https://doi.org/10.1016/j.ygeno.2020.04.005 Rambaut, A., Holmes, E. C., O’Toole, Á., Hill, V., McCrone, J. T., Ruis, C., ... & Pybus, O. G. 2020. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nature Microbiology, 5(11), 1403-1407. https://doi.org/10.1038/s41564-020-0770-5 Rang, F. J., Kloosterman, W. P., & de Ridder, J. 2018. From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy. Genome Biology, 19(1), 90. https://doi.org/10.1186/s13059-018-1462-9 Ranjan, R., Rani, A., Metwally, A., McGee, H. S., & Perkins, D. L. 2016. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochemical and Biophysical Research Communications, 469(4), 967-977. https://doi.org/10.1016/j.bbrc.2015.12.083 Rasheed, A., Hao, Y., Xia, X., Khan, A., Xu, Y., Varshney, R. K., & He, Z. 2017. Crop breeding chips and genotyping platforms: progress, challenges, and perspectives. Molecular Plant, 10(8), 1047-1064. https://doi.org/10.1016/j.molp.2017.06.008 Rausch, P., Rühlemann, M., Hermes, B. M., Doms, S., Dagan, T., Dierking, K., Domin, H., Fraune, S., von Frieling, J., Hentschel, U., Heinsen, F.-A., Höppner, M., Jahn, M. T., Jaspers, C., Kissoyan, K. A. B., Langfeldt, D., Rehman, A., Reusch, T. B. H., Roeder, T., … Baines, J. F. 2019. Comparative analysis of amplicon and metagenomic sequencing methods reveals key features in the evolution of animal metaorganisms. Microbiome, 7(1). https://doi.org/10.1186/s40168-019-0743-1 Rausch, T., Zichner, T., Schlattl, A., Stütz, A. M., Benes, V., & Korbel, J. O. 2012. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics, 28(18), i333-i339. https://doi.org/10.1093/bioinformatics/bts378 Rauschert, S., Raubenheimer, K., Melton, P. E., & Huang, R. C. 2020. Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification. Clinical Epigenetics, 12(1). https://doi.org/10.1186/s13148-020-00842-4 Regier, A. A., Farjoun, Y., Larson, D. E., Krasheninina, O., Kang, H. M., Howrigan, D. P., ... & Hall, I. M. 2018. Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. Nature Communications, 9(1), 4038. https://doi.org/10.1038/s41467-018-06159-4 Rehm, H. L. 2013. Disease-targeted sequencing: a cornerstone in the clinic. Nature Reviews Genetics, 14(4), 295-300. https://doi.org/10.1038/nrg3463 Reinert, K., Langmead, B., Weese, D., & Evers, D. J. 2015. Alignment of next-generation sequencing reads. Annual Review of Genomics and Human Genetics, 16(1), 133-151. https://doi.org/10.1146/annurev-genom-090413-025358 Reuter, J. A., Spacek, D. V., & Snyder, M. P. 2015. High-throughput sequencing technologies. Molecular Cell, 58(4), 586-597. https://doi.org/10.1016/j.molcel.2015.05.004 Rhoads, A., & Au, K. F. 2015. PacBio sequencing and its applications. Genomics, Proteomics & Bioinformatics, 13(5), 278-289. https://doi.org/10.1016/j.gpb.2015.08.002 Rio, D. C., Ares, M., Hannon, G. J., & Nilsen, T. W. 2010. Purification of RNA using TRIzol (TRI reagent). Cold Spring Harbor Protocols, 2010(6), pdb. prot5439. https://doi.org/10.1101/pdb.prot5439 Robin, J. D., Ludlow, A. T., LaRanger, R., Wright, W. E., & Shay, J. W. 2016. Comparison of DNA quantification methods for next generation sequencing. Scientific Reports, 6(1), 24067. https://doi.org/10.1038/srep24067 Robinson, J. T., Thorvaldsdóttir, H., Winckler, W., Guttman, M., Lander, E. S., Getz, G., & Mesirov, J. P. 2011. Integrative genomics viewer. Nature Biotechnology, 29(1), 24-26. https://doi.org/10.1038/nbt.1754 Robinson, M. D., McCarthy, D. J., & Smyth, G. K. 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140. https://doi.org/10.1093/bioinformatics/btp616 Roden, D. M., Wilke, R. A., Kroemer, H. K., & Stein, C. M. 2011. Pharmacogenomics: the genetics of variable drug responses. Circulation, 123(15), 1661-1670. https://doi.org/10.1161/CIRCULATIONAHA.109.914820 Ronaghi, M. 2001. Pyrosequencing sheds light on DNA sequencing. Genome Research, 11(1), 3-11. https://doi.org/10.1101/gr.11.1.3 Ronaghi, M., Karamohamed, S., Pettersson, B., Uhlén, M., & Nyrén, P. 1996. Real-time DNA sequencing using detection of pyrophosphate release. Analytical Biochemistry, 242(1), 84-89. https://doi.org/10.1006/abio.1996.0432 Rothberg, J. M., & Leamon, J. H. 2008. The development and impact of 454 sequencing. Nature Biotechnology, 26(10), 1117-1124. https://doi.org/10.1038/nbt1485 Rothberg, J. M., Hinz, W., Rearick, T. M., Schultz, J., Mileski, W., Davey, M., ... & Bustillo, J. 2011. An integrated semiconductor device enabling non-optical genome sequencing. Nature, 475(7356), 348-352. https://doi.org/10.1038/nature10242 Royer-Bertrand, B., Cisarova, K., Niel-Butschi, F., Mittaz-Crettol, L., Fodstad, H., & Superti-Furga, A. 2021. CNV detection from exome sequencing data in routine diagnostics of rare genetic disorders: opportunities and limitations. Genes, 12(9), 1427. https://doi.org/10.3390/genes12091427 Ruppert, K. M., Kline, R. J., & Rahman, M. S. 2019. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Global Ecology and Conservation, 17, e00547. https://doi.org/10.1016/j.gecco.2019.e00547 Salk, J. J., Schmitt, M. W., & Loeb, L. A. 2018. Enhancing the accuracy of next-generation sequencing for detecting rare and subclonal mutations. Nature Reviews Genetics, 19(5), 269-285. https://doi.org/10.1038/nrg.2017.117 Sanger, F., & Coulson, A. R. 1975. A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. Journal of Molecular Biology, 94(3), 441-448. https://doi.org/10.1016/0022-2836(75)90213-2 Sanger, F., Nicklen, S., & Coulson, A. R. 1977. DNA sequencing with chain-terminating inhibitors. Proceedings of the National Academy of Sciences, 74(12), 5463-5467. https://doi.org/10.1073/pnas.74.12.5463 Sanger, F., Nicklen, S., & Coulson, A. R. 1977. DNA sequencing with chain-terminating inhibitors. Proceedings of the National Academy of Sciences, 74(12), 5463–5467. https://doi.org/10.1073/pnas.74.12.5463 Savic, D., Gertz, J., Jain, P., Cooper, G. M., & Myers, R. M. 2013. Mapping genome-wide transcription factor binding sites in frozen tissues. Epigenetics & Chromatin, 6, 1-10. https://doi.org/10.1186/1756-8935-6-30 Schadt, E. E., Turner, S., & Kasarskis, A. 2010. A window into third-generation sequencing. Human Molecular Genetics, 19(R2), R227-R240. https://doi.org/10.1093/hmg/ddq416 Schmidt, D., Wilson, M. D., Ballester, B., Schwalie, P. C., Brown, G. D., Marshall, A., ... & Odom, D. T. 2010. Five-vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science, 328(5981), 1036-1040. https://doi.org/10.1126/science.1186176 Schneider, G. F., & Dekker, C. 2012. DNA sequencing with nanopores. Nature Biotechnology, 30(4), 326-328. https://doi.org/10.1038/nbt.2181 Schroeder, A., Mueller, O., Stocker, S., Salowsky, R., Leiber, M., Gassmann, M., ... & Ragg, T. 2006. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Molecular Biology, 7, 3. https://doi.org/10.1186/1471-2199-7-3 Schuster, S. C. 2008. Next-generation sequencing transforms today's biology. Nature Methods, 5(1), 16-18. https://doi.org/10.1038/nmeth1156 Schwarze, K., Buchanan, J., Taylor, J. C., & Wordsworth, S. 2018. Are whole-exome and whole-genome sequencing approaches cost-effective? A systematic review of the literature. Genetics in Medicine, 20(10), 1122-1130. https://doi.org/10.1038/gim.2017.247 Sedlazeck, F. J., Lee, H., Darby, C. A., & Schatz, M. C. 2018. Piercing the dark matter: bioinformatics of long-range sequencing and mapping. Nature Reviews Genetics, 19(6), 329-346. https://doi.org/10.1038/s41576-018-0003-4 Sedlazeck, F. J., Rescheneder, P., Smolka, M., Fang, H., Nattestad, M., von Haeseler, A., & Schatz, M. C. 2018. Accurate detection of complex structural variations using single-molecule sequencing. Nature Methods, 15(6), 461–468. https://doi.org/10.1038/s41592-018-0001-7 Segata, N., Waldron, L., Ballarini, A., Narasimhan, V., Jousson, O., & Huttenhower, C. 2012. Metagenomic microbial community profiling using unique clade-specific marker genes. Nature Methods, 9(8), 811-814. https://doi.org/10.1038/nmeth.2066 Shakya, M., Lo, C. C., & Chain, P. S. 2019. Advances and challenges in metatranscriptomic analysis. Frontiers in Genetics, 10, 904. https://doi.org/10.3389/fgene.2019.00904 Shendure, J., & Ji, H. 2008. Next-generation DNA sequencing. Nature Biotechnology, 26(10), 1135-1145. https://doi.org/10.1038/nbt1486 Shendure, J., Balasubramanian, S., Church, G. M., Gilbert, W., Rogers, J., Schloss, J. A., & Waterston, R. H. 2017. DNA sequencing at 40: past, present and future. Nature, 550(7676), 345-353. https://doi.org/10.1038/nature24286 Shendure, J., Porreca, G. J., Reppas, N. B., Lin, X., McCutcheon, J. P., Rosenbaum, A. M., ... & Church, G. M. 2005. Accurate multiplex polony sequencing of an evolved bacterial genome. Science, 309(5741), 1728-1732. https://doi.org/10.1126/science.1117389 Shimizu, K. K., Copetti, D., Okada, M., Wicker, T., Tameshige, T., Hatakeyama, M., Shimizu-Inatsugi, R., Aquino, C., Nishimura, K., Kobayashi, F., Murata, K., Kuo, T., Delorean, E., Poland, J., Haberer, G., Spannagl, M., Mayer, K. F. X., Gutierrez-Gonzalez, J., Muehlbauer, G. J., … Handa, H. 2020. De Novo genome assembly of the japanese wheat cultivar norin 61 highlights functional variation in flowering time and fusarium-resistant genes in east asian genotypes. Plant and Cell Physiology, 62(1), 8–27. https://doi.org/10.1093/pcp/pcaa152 Shokralla, S., Spall, J. L., Gibson, J. F., & Hajibabaei, M. 2012. Next‐generation sequencing technologies for environmental DNA research. Molecular Ecology, 21(8), 1794-1805. https://doi.org/10.1111/j.1365-294X.2012.05538.x Simpson, J. T., Workman, R. E., Zuzarte, P. C., David, M., Dursi, L. J., & Timp, W. 2017. Detecting DNA cytosine methylation using nanopore sequencing. Nature Methods, 14(4), 407-410. https://doi.org/10.1038/nmeth.4184 Sims, D., Sudbery, I., Ilott, N. E., Heger, A., & Ponting, C. P. 2014. Sequencing depth and coverage: key considerations in genomic analyses. Nature Reviews Genetics, 15(2), 121-132. https://doi.org/10.1038/nrg3642 Skene, P. J., & Henikoff, S. 2015. A simple method for generating high-resolution maps of genome-wide protein binding. Elife, 4, e09225. https://doi.org/10.7554/eLife.09225 Skvortsova, K., Iovino, N., & Bogdanović, O. 2018. Functions and mechanisms of epigenetic inheritance in animals. Nature Reviews Molecular Cell Biology, 19(12), 774-790. https://doi.org/10.1038/s41580-018-0074-2 Slatko, B. E., Gardner, A. F., & Ausubel, F. M. 2018. Overview of next‐generation sequencing technologies. Current Protocols in Molecular Biology, 122(1), e59. https://doi.org/10.1002/cpmb.59 Smallwood, S. A., Lee, H. J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., ... & Kelsey, G. 2014. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nature Methods, 11(8), 817-820. https://doi.org/10.1038/nmeth.3035 Smith, L. M., Sanders, J. Z., Kaiser, R. J., Hughes, P., Dodd, C., Connell, C. R., ... & Hood, L. E. 1986. Fluorescence detection in automated DNA sequence analysis. Nature, 321(6071), 674-679. https://doi.org/10.1038/321674a0 Sogin, M. L., Morrison, H. G., Huber, J. A., Welch, D. M., Huse, S. M., Neal, P. R., ... & Herndl, G. J. 2006. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proceedings of the National Academy of Sciences, 103(32), 12115-12120. https://doi.org/10.1073/pnas.0605127103 Sohn, J. I., & Nam, J. W. 2018. The present and future of de novo whole-genome assembly. Briefings in Bioinformatics, 19(1), 23-40. https://doi.org/10.1093/bib/bbw096 Soni, V., Akhade, A. S., Bhattacharya, C., Chawla, Y., Bukhari, Z., Gupta, S. L., Basu, S., Jain, S., & Soni, Y. 2023. Genomic surveillance of bacterial pathogens. Genomic Surveillance and Pandemic Preparedness, 71–117. https://doi.org/10.1016/b978-0-443-18769-8.00011-8 Spielmann, M., Lupiáñez, D. G., & Mundlos, S. 2018. Structural variation in the 3D genome. Nature Reviews Genetics, 19(7), 453-467. https://doi.org/10.1038/s41576-018-0007-0 Staats, M., Arulandhu, A. J., Gravendeel, B., Holst-Jensen, A., Scholtens, I., Peelen, T., ... & Kok, E. 2016. Advances in DNA metabarcoding for food and wildlife forensic species identification. Analytical and Bioanalytical Chemistry, 408, 4615-4630. https://doi.org/10.1007/s00216-016-9595-8 Ståhlberg, A., & Kubista, M. 2014. The workflow of single-cell expression profiling using quantitative real-time PCR. Expert Review of Molecular Diagnostics, 14(3), 323–331. https://doi.org/10.1586/14737159.2014.901154 Stankiewicz, P., & Lupski, J. R. 2010. Structural variation in the human genome and its role in disease. Annual Review of Medicine, 61(1), 437-455. https://doi.org/10.1146/annurev-med-100708-204735 Stapleton, J. A., Kim, J., Hamilton, J. P., Wu, M., Irber, L. C., Maddamsetti, R., ... & Whitehead, T. A. 2016. Haplotype-phased synthetic long reads from short-read sequencing. PLOS ONE, 11(1), e0147229. https://doi.org/10.1371/journal.pone.0147229 Stark, R., Grzelak, M., & Hadfield, J. 2019a. RNA sequencing: the teenage years. Nature Reviews Genetics, 20(11), 631-656. https://doi.org/10.1038/s41576-019-0150-2 Stark, Z., Dolman, L., Manolio, T. A., Ozenberger, B., Hill, S. L., Caulfied, M. J., ... & North, K. N. 2019b. Integrating genomics into healthcare: a global responsibility. The American Journal of Human Genetics, 104(1), 13-20. https://doi.org/10.1016/j.ajhg.2018.11.014 Steinbock, L. J., & Radenovic, A. 2015. The emergence of nanopores in next-generation sequencing. Nanotechnology, 26(7), 074003. https://doi.org/10.1088/0957-4484/26/7/074003 Stellrecht, C. M., & Chen, L. S. 2011. Transcription inhibition as a therapeutic target for cancer. Cancers, 3(4), 4170-4190. https://doi.org/10.3390/cancers3044170 Stricker, S. H., Köferle, A., & Beck, S. 2017. From profiles to function in epigenomics. Nature Reviews Genetics, 18(1), 51-66. https://doi.org/10.1038/nrg.2016.138 Structure, function and diversity of the healthy human microbiome. 2012. Nature, 486(7402), 207-214. https://doi.org/10.1038/nature11234 Stuart, T., & Satija, R. 2019. Integrative single-cell analysis. Nature Reviews Genetics, 20(5), 257-272. https://doi.org/10.1038/s41576-019-0093-7 Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck III, W. M., ... & Satija, R. 2019. Comprehensive integration of single-cell data. Cell, 177(7), 1888-1902. https://doi.org/10.1016/j.cell.2019.05.031 Sudmant, P. H., Rausch, T., Gardner, E. J., Handsaker, R. E., Abyzov, A., Huddleston, J., ... & Korbel, J. O. 2015. An integrated map of structural variation in 2,504 human genomes. Nature, 526(7571), 75-81. https://doi.org/10.1038/nature15394 Sugden, P. H., & Clerk, A. 1998. “Stress-responsive” mitogen-activated protein kinases (c-Jun N-terminal kinases and p38 mitogen-activated protein kinases) in the myocardium. Circulation Research, 83(4), 345-352. https://doi.org/10.1161/01.RES.83.4.345 Sun, K., Li, D., Xia, A., Zhao, H., Wen, Q., Jia, S., Wang, J., Yang, G., Zhou, D., Huang, C., Wang, H., Chen, Z., & Guo, T. 2022. Targeted Identification of Rice Grain-Associated Gene Allelic Variation Through Mutation Induction, Targeted Sequencing, and Whole Genome Sequencing Combined with a Mixed-Samples Strategy. Rice, 15(1). https://doi.org/10.1186/s12284-022-00603-2 Sunagawa, S., Coelho, L. P., Chaffron, S., Kultima, J. R., Labadie, K., Salazar, G., ... & Velayoudon, D. 2015. Structure and function of the global ocean microbiome. Science, 348(6237), 1261359. https://doi.org/10.1126/science.1261359 Suvakov, M., Panda, A., Diesh, C., Holmes, I., & Abyzov, A. 2021. CNVpytor: a tool for copy number variation detection and analysis from read depth and allele imbalance in whole-genome sequencing. GigaScience, 10(11), giab074. https://doi.org/10.1093/gigascience/giab074 Taliun, D., Harris, D. N., Kessler, M. D., Carlson, J., Szpiech, Z. A., Torres, R., ... & Stilp, A. M. 2021. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature, 590(7845), 290-299. https://doi.org/10.1038/s41586-021-03205-y Tam, S., Tsao, M. S., & McPherson, J. D. 2015. Optimization of miRNA-seq data preprocessing. Briefings in Bioinformatics, 16(6), 950-963. https://doi.org/10.1093/bib/bbv019 Tasic, B., Yao, Z., Graybuck, L. T., Smith, K. A., Nguyen, T. N., Bertagnolli, D., ... & Zeng, H. 2018. Shared and distinct transcriptomic cell types across neocortical areas. Nature, 563(7729), 72-78. https://doi.org/10.1038/s41586-018-0654-5 Thermo Fisher Scientific. 2021. 5500 Series Genetic Analysis Systems. Retrieved from https://www.thermofisher.com/order/catalog/product/4474867 Thompson, J. F., & Milos, P. M. 2011. The properties and applications of single-molecule DNA sequencing. Genome Biology, 12, 217. https://doi.org/10.1186/gb-2011-12-2-217 Thompson, J. F., & Steinmann, K. E. 2010. Single molecule sequencing with a HeliScope genetic analysis system. Current Protocols in Molecular Biology, 92(1), 7-10. https://doi.org/10.1002/0471142727.mb0710s92 Toolkit, P. 2019. GitHub repository. Broad Institute. Available online at: http://broadinstitute.github.io/picard. Toumazou, C., Shepherd, L. M., Reed, S. C., Chen, G. I., Patel, A., Garner, D. M., ... & Zhang, L. 2013. Simultaneous DNA amplification and detection using a pH-sensing semiconductor system. Nature Methods, 10(7), 641-646. https://doi.org/10.1038/nmeth.2520 Travers, K. J., Chin, C. S., Rank, D. R., Eid, J. S., & Turner, S. W. 2010. A flexible and efficient template format for circular consensus sequencing and SNP detection. Nucleic acids research, 38(15), e159-e159. https://doi.org/10.1093/nar/gkq543 Treangen, T. J., & Salzberg, S. L. 2012. Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nature Reviews Genetics, 13(1), 36-46. https://doi.org/10.1038/nrg3117 Turcatti, G., Romieu, A., Fedurco, M., & Tairi, A.-P. 2008. A new class of cleavable fluorescent nucleotides: synthesis and optimization as reversible terminators for DNA sequencing by synthesis. Nucleic Acids Research, 36(4), e25–e25. https://doi.org/10.1093/nar/gkn021 Umu, S. U., Langseth, H., Bucher-Johannessen, C., Fromm, B., Keller, A., Meese, E., Lauritzen, M., Leithaug, M., Lyle, R., & Rounge, T. B. 2017. A comprehensive profile of circulating RNAs in human serum. RNA Biology, 15(2), 242–250. https://doi.org/10.1080/15476286.2017.1403003 Ugur Sezerman, O., Ulgen, E., Seymen, N., & Melis Durasi, I. 2019. Bioinformatics workflows for genomic variant discovery, interpretation and prioritization. bioinformatics tools for detection and clinical interpretation of genomic variations. https://doi.org/10.5772/intechopen.85524 Ungaro, A., Pech, N., Martin, J. F., McCairns, R. S., Mévy, J. P., Chappaz, R., & Gilles, A. 2017. Challenges and advances for transcriptome assembly in non-model species. PloS one, 12(9), e0185020. https://doi.org/10.1371/journal.pone.0185020 Valouev, A., Ichikawa, J., Tonthat, T., Stuart, J., Ranade, S., Peckham, H., ... & Johnson, S. M. 2008. A high-resolution, nucleosome position map of C. elegans reveals a lack of universal sequence-dictated positioning. Genome research, 18(7), 1051-1063. https://doi.org/10.1101/gr.076463.108 Van der Auwera, G. A., & O'Connor, B. D. 2020. Genomics in the cloud: using Docker, GATK, and WDL in Terra. O'Reilly Media. ISBN:1491975164, 9781491975169 Van der Auwera, G. A., Carneiro, M. O., Hartl, C., Poplin, R., Del Angel, G., Levy‐Moonshine, A., ... & DePristo, M. A. 2013. From FastQ data to high‐confidence variant calls: the genome analysis toolkit best practices pipeline. Current Protocols in Bioinformatics, 43(1), 11-10. https://doi.org/10.1002/0471250953.bi1110s43 Van Dijk, E. L., Auger, H., Jaszczyszyn, Y., & Thermes, C. 2014. Ten years of next-generation sequencing technology. Trends in genetics, 30(9), 418-426. https://doi.org/10.1016/j.tig.2014.07.001 Vanwonterghem, I., Jensen, P. D., Dennis, P. G., Hugenholtz, P., Rabaey, K., & Tyson, G. W. 2014. Deterministic processes guide long-term synchronised population dynamics in replicate anaerobic digesters. The ISME Journal, 8(10), 2015–2028. https://doi.org/10.1038/ismej.2014.50 Varshney, R. K., Bohra, A., Yu, J., Graner, A., Zhang, Q., & Sorrells, M. E. 2021. Designing future crops: genomics-assisted breeding comes of age. Trends in Plant Science, 26(6), 631-649. https://doi.org/10.1016/j.tplants.2021.03.010 Varshney, R. K., Singh, V. K., Kumar, A., Powell, W., & Sorrells, M. E. 2018. Can genomics deliver climate-change ready crops? Current Opinion in Plant Biology, 45, 205–211. https://doi.org/10.1016/j.pbi.2018.03.007 Vermeesch, J. R., Voet, T., & Devriendt, K. 2016. Prenatal and pre-implantation genetic diagnosis. Nature Reviews Genetics, 17(10), 643–656. https://doi.org/10.1038/nrg.2016.97 Voelkerding, K. V., Dames, S. A., & Durtschi, J. D. 2009. Next-generation sequencing: from basic research to diagnostics. Clinical chemistry, 55(4), 641-658. https://doi.org/10.1373/clinchem.2008.112789 Wagner, G. P., Kin, K., & Lynch, V. J. 2012. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory in biosciences, 131, 281-285. https://doi.org/10.1007/s12064-012-0162-3 Wang, K., Li, M., & Hakonarson, H. 2010. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 38(16), e164-e164. https://doi.org/10.1093/nar/gkq603 Wang, Z., Gerstein, M., & Snyder, M. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57-63. https://doi.org/10.1038/nrg2484 Watson, J. D., & Crick, F. H. 1953. Molecular structure of nucleic acids: a structure for deoxyribose nucleic acid. Nature, 171(4356), 737-738. https://doi.org/10.1038/171737a0 Weischenfeldt, J., Symmons, O., Spitz, F., & Korbel, J. O. 2013. Phenotypic impact of genomic structural variation: insights from and for human disease. Nature Reviews Genetics, 14(2), 125-138. https://doi.org/10.1038/nrg3373 Wenger, A. M., Peluso, P., Rowell, W. J., Chang, P. C., Hall, R. J., Concepcion, G. T., ... & Hunkapiller, M. W. 2019. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nature Biotechnology, 37(10), 1155-1162. https://doi.org/10.1038/s41587-019-0217-9 Wheeler, D. A., Srinivasan, M., Egholm, M., Shen, Y., Chen, L., McGuire, A., ... & Rothberg, J. M. 2008. The complete genome of an individual by massively parallel DNA sequencing. Nature, 452(7189), 872-876. https://doi.org/10.1038/nature06884 Wick, R. R., Judd, L. M., & Holt, K. E. 2019. Performance of neural network basecalling tools for Oxford Nanopore sequencing. Genome Biology, 20, 1-10. https://doi.org/10.1186/s13059-019-1727-y Wickham, H., & Wickham, H. 2016. Data analysis (pp. 189-201). Springer International Publishing. https://doi.org/10.1007/978-3-319-24277-4_7 Wickham, H., & Wickham, H. 2016. Getting Started with ggplot2. ggplot2: Elegant graphics for data analysis, 11-31. https://doi.org/10.1007/978-3-319-24277-4_2 Wood, D. E., & Salzberg, S. L. 2014. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biology, 15(3). https://doi.org/10.1186/gb-2014-15-3-r46 Wood, D. E., Lu, J., & Langmead, B. 2019. Improved metagenomic analysis with Kraken 2. Genome Biology, 20, 1-13. https://doi.org/10.1186/s13059-019-1891-0 Wörheide, M. A., Krumsiek, J., Kastenmüller, G., & Arnold, M. 2021. Multi-omics integration in biomedical research – A metabolomics-centric review. Analytica Chimica Acta, 1141, 144–162. https://doi.org/10.1016/j.aca.2020.10.038 Xi, Y., & Li, W. 2009. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics, 10, 1-9. https://doi.org/10.1186/1471-2105-10-232 Xie, C., Mao, X., Huang, J., Ding, Y., Wu, J., Dong, S., ... & Wei, L. 2011. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Research, 39(suppl_2), W316-W322. https://doi.org/10.1093/nar/gkr483 Xie, K., Wu, S., Li, Z., Zhou, Y., Zhang, D., Dong, Z., An, X., Zhu, T., Zhang, S., Liu, S., Li, J., & Wan, X. 2018. Map-based cloning and characterization of Zea mays male sterility33 (ZmMs33) gene, encoding a glycerol-3-phosphate acyltransferase. Theoretical and Applied Genetics, 131(6), 1363–1378. https://doi.org/10.1007/s00122-018-3083-9 Xie, Y., Wu, G., Tang, J., Luo, R., Patterson, J., Liu, S., ... & Wang, J. 2014. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics, 30(12), 1660-1666. https://doi.org/10.1093/bioinformatics/btu077 Xu, J., Yuan, Y., Xu, Y., Zhang, G., Guo, X., Wu, F., ... & Lu, Y. 2014. Identification of candidate genes for drought tolerance by whole-genome resequencing in maize. BMC Plant Biology, 14, 1-15. https://doi.org/10.1186/1471-2229-14-83 Xu, X., Crow, M., Rice, B. R., Li, F., Harris, B., Liu, L., Demesa-Arevalo, E., Lu, Z., Wang, L., Fox, N., Wang, X., Drenkow, J., Luo, A., Char, S. N., Yang, B., Sylvester, A. W., Gingeras, T. R., Schmitz, R. J., Ware, D., … Jackson, D. 2021. Single-cell RNA sequencing of developing maize ears facilitates functional analysis and trait candidate gene discovery. Developmental Cell, 56(4), 557-568.e6. https://doi.org/10.1016/j.devcel.2020.12.015 Ye, K., Schulz, M. H., Long, Q., Apweiler, R., & Ning, Z. 2009. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics, 25(21), 2865-2871. https://doi.org/10.1093/bioinformatics/btp394 Ying, Y.-L., Hu, Z.-L., Zhang, S., Qing, Y., Fragasso, A., Maglia, G., Meller, A., Bayley, H., Dekker, C., & Long, Y.-T. 2022. Nanopore-based technologies beyond DNA sequencing. Nature Nanotechnology, 17(11), 1136–1146. https://doi.org/10.1038/s41565-022-01193-2 Yoon, S., Xuan, Z., Makarov, V., Ye, K., & Sebat, J. 2009. Sensitive and accurate detection of copy number variants using read depth of coverage. Genome Research, 19(9), 1586–1592. https://doi.org/10.1101/gr.092981.109 Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. 2010. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology, 11, 1-12. https://doi.org/10.1186/gb-2010-11-2-r14 Yu, G., Wang, L. G., & He, Q. Y. 2015. ChIPseeker: an R/bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics, 31(14), 2382-2383. https://doi.org/10.1093/bioinformatics/btv145 Zarraonaindia, I., Owens, S. M., Weisenhorn, P., West, K., Hampton-Marcell, J., Lax, S., Bokulich, N. A., Mills, D. A., Martin, G., Taghavi, S., van der Lelie, D., & Gilbert, J. A. 2015. The soil microbiome influences grapevine-associated microbiota. MBio, 6(2). https://doi.org/10.1128/mbio.02527-14 Zha, W., Li, C., Wu, Y., Chen, J., Li, S., Sun, M., ... & You, A. 2023. Single-Cell RNA sequencing of leaf sheath cells reveals the mechanism of rice resistance to brown planthopper (Nilaparvata lugens). Frontiers in Plant Science, 14, 1200014. https://doi.org/10.3389/fpls.2023.1200014 Zhang, X., Wang, Y., Chiang, H.-C., Hsieh, Y.-P., Lu, C., Park, B. H., Jatoi, I., Jin, V. X., Hu, Y., & Li, R. 2019. BRCA1 mutations attenuate super-enhancer function and chromatin looping in haploinsufficient human breast epithelial cells. Breast Cancer Research, 21(1). https://doi.org/10.1186/s13058-019-1132-1 Zhang, X., Yazaki, J., Sundaresan, A., Cokus, S., Chan, S. W. L., Chen, H., ... & Ecker, J. R. 2006. Genome-wide high-resolution mapping and functional analysis of DNA methylation in Arabidopsis. Cell, 126(6), 1189-1201. https://doi.org/10.1016/j.cell.2006.08.003 Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., ... & Liu, X. S. 2008. Model-based analysis of ChIP-Seq (MACS). Genome Biology, 9(9), R137. https://doi.org/10.1186/gb-2008-9-9-r137 Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., ... & Liu, X. S. 2008. Model-based analysis of ChIP-Seq (MACS). Genome Biology, 9, 1-9. https://doi.org/10.1186/gb-2008-9-9-r137 Zheng, G. X., Lau, B. T., Schnall-Levin, M., Jarosz, M., Bell, J. M., Hindson, C. M., ... & Ji, H. P. 2016. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nature Biotechnology, 34(3), 303-311. https://doi.org/10.1038/nbt.3432 Zhong, S., Joung, J. G., Zheng, Y., Chen, Y. R., Liu, B., Shao, Y., ... & Giovannoni, J. J. 2011. High-throughput illumina strand-specific RNA sequencing library preparation. Cold Spring Harbor Protocols, 2011(8), pdb-prot5652. https://doi.org/10.1101/pdb.prot5652 Zhu, D., Chen, Q. L., An, X. L., Yang, X. R., Christie, P., Ke, X., ... & Zhu, Y. G. 2018. Exposure of soil collembolans to microplastics perturbs their gut microbiota and alters their isotopic composition. Soil Biology and Biochemistry, 116, 302-310. https://doi.org/10.1016/j.soilbio.2017.10.027 Zhu, M., Zhang, M., Huang, K., Lu, F., Wang, H., Zhao, S., Yu, Y., Tang, S., Wu, H., Hu, P., & Wei, X. 2024. Single-cell transcriptome sequencing reveals the mechanism regulating rice plumule development. The Crop Journal, 12(3), 688–697. https://doi.org/10.1016/j.cj.2024.04.009 Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., ... & Enard, W. 2017. Comparative analysis of single-cell RNA sequencing methods. Molecular Cell, 65(4), 631-643. https://doi.org/10.1016/j.molcel.2017.01.023 Ziller, M. J., Hansen, K. D., Meissner, A., & Aryee, M. J. 2015. Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing. Nature Methods, 12(3), 230-232. https://doi.org/10.1038/nmeth.3152 Zook, J. M., McDaniel, J., Olson, N. D., Wagner, J., Parikh, H., Heaton, H., ... & Salit, M. 2019. An open resource for accurately benchmarking small variant and reference calls. Nature Biotechnology, 37(5), 561-566. https://doi.org/10.1038/s41587-019-0074-6 Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., & Telenti, A. 2019. A primer on deep learning in genomics. Nature Genetics, 51(1), 12-18. https://doi.org/10.1038/s41588-018-0295-5. | ||
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