vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/VJGB-23-86vavilov-3973Research ArticleКОМПЬЮТЕРНАЯ ГЕНОМИКАCOMPUTATIONAL GENOMICSGBS-DP: биоинформатический конвейер для обработки данных, полученных генотипированием путем секвенированияGBS-DP: a bioinformatics pipeline for processing data coming from genotyping by sequencinghttps://orcid.org/0000-0002-3011-6288ПронозинА. Ю.PronozinA. Y.

Новосибирск

pronozinartem95@gmail.comhttps://orcid.org/0000-0001-8590-847XСалинаЕ. А.SalinaE. A.

Новосибирск

https://orcid.org/0000-0001-9738-1409АфонниковД. А.AfonnikovD. A.

Новосибирск

Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Курчатовский геномный центр ИЦиГ СО РАНРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RASRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Курчатовский геномный центр ИЦиГ СО РАН; Новосибирский государственный аграрный университетРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RAS; Novosibirsk State Agrarian UniversityRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Курчатовский геномный центр ИЦиГ СО РАН; Новосибирский государственный аграрный университет; Новосибирский национальный исследовательский государственный университетРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RAS; Novosibirsk State Agrarian University; Novosibirsk State UniversityRussian Federation202311122023277737745Copyright © Пронозин А.Ю., Салина Е.А., Афонников Д.А., 20232023Пронозин А.Ю., Салина Е.А., Афонников Д.А.Pronozin A.Y., Salina E.A., Afonnikov D.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/3973

Развитие технологий секвенирования нового поколения открыло новые возможности для генотипирования различных организмов, включая растения. Метод генотипирования путем секвенирования (GBS) применяется для идентификации генетической изменчивости и более быстрого генотипирования образцов, а также является более экономически эффективным методом в сравнении с полногеномным секвенированием.  GBS продемонстрировал свою надежность и гибкость для ряда видов и популяций растений. Этот метод был применен для генетического картирования, выявления молекулярных маркеров, геномной селекции, в исследовании генетического разнообразия, идентификации сортов, а также в исследованиях в области биологии  охраны природы и эволюционной экологии. Однако сокращение времени и стоимости секвенирования привело к необходимости разработки качественного биоинформатического анализа для постоянно расширяющегося количества секвенированных данных. Для этих целей были разработаны биоинформатические конвейеры анализа данных, полученных методом GBS. Вследствие схожести этапов обработки существующие конвейеры в основном различаются комбинацией программных пакетов, специфически подобранных для обработки данных как для определенных, так и для любых организмов. Несмотря на качественно подобранные пакеты программ, конвейеры имеют некоторые недостатки, например отсутствие возможности автоматизации процесса расчета (каждый этап нужно запускать вручную), что значительно снижает скорость исследования. В большинстве конвейеров отсутствует возможность автоматической установки всех необходимых программных пакетов, а также нет возможности отключения ненужного или пройденного этапа. В настоящей работе нами был разработан биоинформатический конвейер GBS-DP для анализа данных, полученных методом GBS. Конвейер применим для любых видов организмов. Реализация конвейера на платформе Snakemake позволила полностью автоматизировать процесс расчета и установки необходимых программных пакетов. Конвейер позволяет обрабатывать большие объемы данных (более 400 образцов).

The development of next-generation sequencing technologies has provided new opportunities for genotyping various organisms, including plants. Genotyping by sequencing (GBS) is used to identify genetic variability more rapidly, and is more cost-effective than whole-genome sequencing. GBS has demonstrated its reliability and flexibility for a number of plant species and populations. It has been applied to genetic mapping, molecular marker discovery, genomic selection, genetic diversity studies, variety identification, conservation biology and evolutio nary studies. However, reduction in sequencing time and cost has led to the need to develop efficient bioinformatics analyses for an ever-expanding amount of sequenced data. Bioinformatics pipelines for GBS data analysis serve the purpose. Due to the similarity of data processing steps, existing pipelines are mainly characterised by a combination of software packages specifically selected either to process data for certain organisms or to process data from any organisms.  However, despite the usage of efficient software packages, these pipelines have some disadvantages. For example, there is a lack of process automation (in some pipelines, each step must be started manually), which significantly reduces the performance of the analysis. In the majority of pipelines, there is no possibility of automatic installation of all necessary software packages; for most of them, it is also impossible to switch off unnecessary or completed steps. In the present work, we have developed a GBS-DP bioinformatics pipeline for GBS data analysis. The pipeline can be applied for various species. The pipeline is implemented using the Snakemake workflow engine. This implementation allows fully automating the process of calculation and installation of the necessary software packages. Our pipeline is able to perform analysis of large datasets (more than 400 samples).

генотипирование путем секвенированиябиоинформатический конвейерячменьgenotyping by sequencing (GBS)bioinformatic pipelinehordeumThe work was supported by the budget project FWNR-2022-0020.ReferencesAulchenko Yu.S., Aksenovich T.I. Methodological approaches and strategies for mapping genes controlling complex human traits. Infor matsionnyy Vestnik VOGiS = The Herald of Vavilov Society for Geneticists and Breeders. 2006;10(1):189-202 (in Russian) Bimber B.N., Raboin M.J., Letaw J., Nevonen K.A., Spindel J.E., McCouch S.R., Cervera-Juanes R., Spindel E., Carbone L., Ferguson B., Vinson A. Whole-genome characterization in pedigreed non-human primates using genotyping-by-sequencing (GBS) and imputation. BMC Genomics. 2016;17(1):676. DOI 10.1186/s12864016-2966-xAulchenko Yu.S., Aksenovich T.I. Methodological approaches and strategies for mapping genes controlling complex human traits. Infor matsionnyy Vestnik VOGiS = The Herald of Vavilov Society for Geneticists and Breeders. 2006;10(1):189-202 (in Russian) Bimber B.N., Raboin M.J., Letaw J., Nevonen K.A., Spindel J.E., McCouch S.R., Cervera-Juanes R., Spindel E., Carbone L., Ferguson B., Vinson A. Whole-genome characterization in pedigreed non-human primates using genotyping-by-sequencing (GBS) and imputation. BMC Genomics. 2016;17(1):676. DOI 10.1186/s12864016-2966-xBolser D., Staines D.M., Pritchard E., Kersey P. Ensembl plants: integrating tools for visualizing, mining, and analyzing plant genomics data. In: Edwards D. (Ed.) Plant Bioinformatics. Methods in Molecular Biology. Vol. 1374. New York: Humana Press, 2016;115-140. DOI 10.1007/978-1-4939-3167-5_6Bolser D., Staines D.M., Pritchard E., Kersey P. Ensembl plants: integrating tools for visualizing, mining, and analyzing plant genomics data. In: Edwards D. (Ed.) Plant Bioinformatics. Methods in Molecular Biology. Vol. 1374. New York: Humana Press, 2016;115-140. DOI 10.1007/978-1-4939-3167-5_6Danecek P., Bonfield J.K., Liddle J., Marshall J., Ohan V., Pollard M.O., Whitwham A., Keane T., McCarthy S.A., Davies R.M., Li H. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10(2): giab008. DOI 10.1093/gigascience/giab008Danecek P., Bonfield J.K., Liddle J., Marshall J., Ohan V., Pollard M.O., Whitwham A., Keane T., McCarthy S.A., Davies R.M., Li H. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10(2): giab008. DOI 10.1093/gigascience/giab008Elshire R.J., Glaubitz J.C., Sun Q., Poland J.A., Kawamoto K., Buckler E.S., Mitchell S.E. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011;6(5): e19379. DOI 10.1371/journal.pone.0019379Elshire R.J., Glaubitz J.C., Sun Q., Poland J.A., Kawamoto K., Buckler E.S., Mitchell S.E. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011;6(5): e19379. DOI 10.1371/journal.pone.0019379Gabriel S.B., Schaffner S.F., Nguyen H., Moore J.M., Roy J., Blumenstiel B., Higgins J., DeFelice M., Lochner A., Faggart M., LiuCordero S.N., Rotimi C., Adeyemo A., Cooper R., Ward R., Lander E.S., Daly M.J., Altshuler D. The structure of haplotype blocks in the human genome. Science. 2002;296(5576):2225-2229. DOI 10.1126/science.1069424Gabriel S.B., Schaffner S.F., Nguyen H., Moore J.M., Roy J., Blumenstiel B., Higgins J., DeFelice M., Lochner A., Faggart M., LiuCordero S.N., Rotimi C., Adeyemo A., Cooper R., Ward R., Lander E.S., Daly M.J., Altshuler D. The structure of haplotype blocks in the human genome. Science. 2002;296(5576):2225-2229. DOI 10.1126/science.1069424Glaubitz J.C., Casstevens T.M., Lu F., Harriman J., Elshire R.J., Sun Q., Buckler E.S. TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS One. 2014;9(2):e90346. DOI 10.1371/journal.pone.0090346Glaubitz J.C., Casstevens T.M., Lu F., Harriman J., Elshire R.J., Sun Q., Buckler E.S. TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS One. 2014;9(2):e90346. DOI 10.1371/journal.pone.0090346Jayakodi M., Padmarasu S., Haberer G., Bonthala V.S., Gundlach H., Monat C., Lux T., Kamal N., Lang D., Himmelbach A., Ens J., Zhang X.Q., Angessa T.T., Zhou G., Tan C., Hill C., Wang P., Schreiber M., Boston L.B., Plott C., Jenkins J., Guo Y., Fiebig A., Budak H., Xu D., Zhang J., Wang C., Grimwood J., Schmutz J., Guo G., Zhang G., Mochida K., Hirayama T., Sato K., Chal mers K.J., Langridge P., Waugh R., Pozniak C.J., Scholz U., Mayer K.F.X., Spannagl M., Li C., Mascher M., Stein N. The barley pan-genome reveals the hidden legacy of mutation breeding. Nature. 2020;588(7837): 284-289. DOI 10.1038/s41586-020-2947-8Jayakodi M., Padmarasu S., Haberer G., Bonthala V.S., Gundlach H., Monat C., Lux T., Kamal N., Lang D., Himmelbach A., Ens J., Zhang X.Q., Angessa T.T., Zhou G., Tan C., Hill C., Wang P., Schreiber M., Boston L.B., Plott C., Jenkins J., Guo Y., Fiebig A., Budak H., Xu D., Zhang J., Wang C., Grimwood J., Schmutz J., Guo G., Zhang G., Mochida K., Hirayama T., Sato K., Chal mers K.J., Langridge P., Waugh R., Pozniak C.J., Scholz U., Mayer K.F.X., Spannagl M., Li C., Mascher M., Stein N. The barley pan-genome reveals the hidden legacy of mutation breeding. Nature. 2020;588(7837): 284-289. DOI 10.1038/s41586-020-2947-8Kanukova K.R., Gazaev I.Kh., Sabanchieva L.K., Bogotova Z.I., Appaev S.P. DNA markers in crop production. Izvestiya KabardinoBalkarskogo Nauchnogo Tsentra RAN = News of the KabardinBalkar Scientific Center of RAS. 2019;6(92):220-232. DOI 10.35330/ 1991-6639-2019-6-92-220-232 (in Russian)Kanukova K.R., Gazaev I.Kh., Sabanchieva L.K., Bogotova Z.I., Appaev S.P. DNA markers in crop production. Izvestiya KabardinoBalkarskogo Nauchnogo Tsentra RAN = News of the KabardinBalkar Scientific Center of RAS. 2019;6(92):220-232. DOI 10.35330/ 1991-6639-2019-6-92-220-232 (in Russian)Khlestkina E.K. Molecular markers in genetic studies and breeding. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2013;17(4/2):1044-1054 (in Russian) Köster J., Rahmann S. Snakemake – a scalable bioinformatics workflow engine. Bioinformatics. 2012;28(19):2520-2522. DOI 10.1093/bioinformatics/bts480Khlestkina E.K. Molecular markers in genetic studies and breeding. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2013;17(4/2):1044-1054 (in Russian) Köster J., Rahmann S. Snakemake – a scalable bioinformatics workflow engine. Bioinformatics. 2012;28(19):2520-2522. DOI 10.1093/bioinformatics/bts480Leinonen R., Akhtar R., Birney E., Bower L., Cerdeno-Tárraga A., Cheng Y., Cleland I., Faruque N., Goodgame N., Gibson R., Hoad G., Jang M., Pakseresht N., Plaister S., Radhakrishnan R., Reddy K., Sobhany S., Ten Hoopen P., Vaughan R., Zalunin V., Cochrane G. The European nucleotide archive. Nucleic Acids Res. 2011; 39( Database issue):D28-D31. DOI 10.1093/nar/gkq967Leinonen R., Akhtar R., Birney E., Bower L., Cerdeno-Tárraga A., Cheng Y., Cleland I., Faruque N., Goodgame N., Gibson R., Hoad G., Jang M., Pakseresht N., Plaister S., Radhakrishnan R., Reddy K., Sobhany S., Ten Hoopen P., Vaughan R., Zalunin V., Cochrane G. The European nucleotide archive. Nucleic Acids Res. 2011; 39( Database issue):D28-D31. DOI 10.1093/nar/gkq967Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv. 2013. DOI 10.48550/arXiv.1303.3997Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv. 2013. DOI 10.48550/arXiv.1303.3997Li M., Guo G., Pidon H., Melzer M., Prina A.R., Börner T., Stein N. ATP-dependent Clp protease subunit C1, HvClpC1, is a strong candidate gene for barley variegation mutant luteostrians as revealed by genetic mapping and genomic re-sequencing. Front. Plant Sci. 2021;12:664085. DOI 10.3389/fpls.2021.664085Li M., Guo G., Pidon H., Melzer M., Prina A.R., Börner T., Stein N. ATP-dependent Clp protease subunit C1, HvClpC1, is a strong candidate gene for barley variegation mutant luteostrians as revealed by genetic mapping and genomic re-sequencing. Front. Plant Sci. 2021;12:664085. DOI 10.3389/fpls.2021.664085Lu F., Lipka A.E., Glaubitz J., Elshire R., Cherney J.H., Casler M.D., Buckler E.S., Costich D.E. Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol. PLoS Genet. 2013;9(1):e1003215. DOI 10.1371/journal.pgen.1003215Lu F., Lipka A.E., Glaubitz J., Elshire R., Cherney J.H., Casler M.D., Buckler E.S., Costich D.E. Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol. PLoS Genet. 2013;9(1):e1003215. DOI 10.1371/journal.pgen.1003215Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17(1):10-12. DOI 10.14806/ej.17.1.200Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17(1):10-12. DOI 10.14806/ej.17.1.200Melo A.T., Bartaula R., Hale I. GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data. BMC Bioinformatics. 2016;17(1):29. DOI 10.1186/s12859-0160879-yMelo A.T., Bartaula R., Hale I. GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data. BMC Bioinformatics. 2016;17(1):29. DOI 10.1186/s12859-0160879-yMilner S.G., Jost M., Taketa S., Mazón E.R., Himmelbach A., Oppermann M., Weise S., Knüpffer H., Basterrechea M., König P., Schüler D., Sharma R., Pasam R.K., Rutten T., Guo G., Xu D., Zhang J., Herren G., Müller T., Krattinger S.G., Keller B., Jiang Y., González M.Y., Zhao Y., Habekuß A., Färber S., Ordon F., Lange M., Börner A., Graner A., Reif J.C., Scholz U., Mascher M., Stein N. Genebank genomics highlights the diversity of a global barley collection. Nat. Genet. 2019;51(2):319-326. DOI 10.1038/s41588-0180266-xMilner S.G., Jost M., Taketa S., Mazón E.R., Himmelbach A., Oppermann M., Weise S., Knüpffer H., Basterrechea M., König P., Schüler D., Sharma R., Pasam R.K., Rutten T., Guo G., Xu D., Zhang J., Herren G., Müller T., Krattinger S.G., Keller B., Jiang Y., González M.Y., Zhao Y., Habekuß A., Färber S., Ordon F., Lange M., Börner A., Graner A., Reif J.C., Scholz U., Mascher M., Stein N. Genebank genomics highlights the diversity of a global barley collection. Nat. Genet. 2019;51(2):319-326. DOI 10.1038/s41588-0180266-xMonat C., Schreiber M., Stein N., Mascher M. Prospects of pan-genomics in barley. Theor. Appl. Genet. 2019;132(3):785-796. DOI 10.1007/s00122-018-3234-zMonat C., Schreiber M., Stein N., Mascher M. Prospects of pan-genomics in barley. Theor. Appl. Genet. 2019;132(3):785-796. DOI 10.1007/s00122-018-3234-zNarum S.R., Buerkle C.A., Davey J.W., Miller M.R., Hohenlohe P.A. Genotyping-by-sequencing in ecological and conservation genomics. Mol. Ecol. 2013;22(11):2841-2847. DOI 10.1111/mec.12350Narum S.R., Buerkle C.A., Davey J.W., Miller M.R., Hohenlohe P.A. Genotyping-by-sequencing in ecological and conservation genomics. Mol. Ecol. 2013;22(11):2841-2847. DOI 10.1111/mec.12350Peterson G.W., Dong Y., Horbach C., Fu Y.-B. Genotyping-by-sequencing for plant genetic diversity analysis: a lab guide for SNP genotyping. Diversity. 2014;6(4):665-680. DOI 10.3390/d6040665Peterson G.W., Dong Y., Horbach C., Fu Y.-B. Genotyping-by-sequencing for plant genetic diversity analysis: a lab guide for SNP genotyping. Diversity. 2014;6(4):665-680. DOI 10.3390/d6040665Poland J., Endelman J., Dawson J., Rutkoski J., Wu S., Manes Y., Drei sigacker S., Crossa J., Sánchez-Villeda H., Sorrells M., Jannink J.-L. Genomic selection in wheat breeding using genotypingby-sequencing. Plant Genome. 2012;5(3):103-113. DOI 10.3835/plantgenome2012.06.0006Poland J., Endelman J., Dawson J., Rutkoski J., Wu S., Manes Y., Drei sigacker S., Crossa J., Sánchez-Villeda H., Sorrells M., Jannink J.-L. Genomic selection in wheat breeding using genotypingby-sequencing. Plant Genome. 2012;5(3):103-113. DOI 10.3835/plantgenome2012.06.0006Ponomarenko I.V. Selection of polymorphic loci for association analysis in genetic-epidemiological studies. Nauchnye Rezultaty Biomeditsynskikh Issledovaniy = Research Results in Biomedicine. 2018;4(2):40-54. DOI 10.18413/2313-8955-2018-4-2-0-5 (in Russian)Ponomarenko I.V. Selection of polymorphic loci for association analysis in genetic-epidemiological studies. Nauchnye Rezultaty Biomeditsynskikh Issledovaniy = Research Results in Biomedicine. 2018;4(2):40-54. DOI 10.18413/2313-8955-2018-4-2-0-5 (in Russian)Rajendran N.R., Qureshi N., Pourkheirandish M. Genotyping by sequencing advancements in barley. Front. Plant Sci. 2022;13:931423. DOI 10.3389/fpls.2022.931423Rajendran N.R., Qureshi N., Pourkheirandish M. Genotyping by sequencing advancements in barley. Front. Plant Sci. 2022;13:931423. DOI 10.3389/fpls.2022.931423Scheben A., Batley J., Edwards D. Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application. Plant Biotechnol. J. 2017;15(2):149-161. DOI 10.1111/pbi.12645Scheben A., Batley J., Edwards D. Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application. Plant Biotechnol. J. 2017;15(2):149-161. DOI 10.1111/pbi.12645Sukhareva A.S., Kuluev B.R. DNA markers for genetic analysis of crops. Biomika = Biomics. 2018;10(1):69-84. DOI 10.31301/22216197.bmcs.2018-15 (in Russian)Sukhareva A.S., Kuluev B.R. DNA markers for genetic analysis of crops. Biomika = Biomics. 2018;10(1):69-84. DOI 10.31301/22216197.bmcs.2018-15 (in Russian)Torkamaneh D., Laroche J., Bastien M., Abed A., Belzile F. FastGBS: a new pipeline for the efficient and highly accurate calling of SNPs from genotyping-by-sequencing data. BMC Bioinformatics. 2017;18(1):5. DOI 10.1186/s12859-016-1431-9Torkamaneh D., Laroche J., Bastien M., Abed A., Belzile F. FastGBS: a new pipeline for the efficient and highly accurate calling of SNPs from genotyping-by-sequencing data. BMC Bioinformatics. 2017;18(1):5. DOI 10.1186/s12859-016-1431-9Wang N., Yuan Y., Wang H., Yu D., Liu Y., Zhang A., Gowda M., Nair S.K., Hao Z., Lu Y., San Vicente F., Prasanna B.M., Li X., Zhang X. Applications of genotyping-by-sequencing (GBS) in maize genetics and breeding. Sci. Rep. 2020;10(1):16308. DOI 10.1038/s41598-020-73321-8Wang N., Yuan Y., Wang H., Yu D., Liu Y., Zhang A., Gowda M., Nair S.K., Hao Z., Lu Y., San Vicente F., Prasanna B.M., Li X., Zhang X. Applications of genotyping-by-sequencing (GBS) in maize genetics and breeding. Sci. Rep. 2020;10(1):16308. DOI 10.1038/s41598-020-73321-8Wendler N., Mascher M., Himmelbach A., Johnston P., Pickering R., Stein N. Bulbosum to go: a toolbox to utilize Hordeum vulgare/bulbosum introgressions for breeding and beyond. Mol. Plant. 2015; 8(10):1507-1519. DOI 10.1016/j.molp.2015.05.004Wendler N., Mascher M., Himmelbach A., Johnston P., Pickering R., Stein N. Bulbosum to go: a toolbox to utilize Hordeum vulgare/bulbosum introgressions for breeding and beyond. Mol. Plant. 2015; 8(10):1507-1519. DOI 10.1016/j.molp.2015.05.004Wickland D.P., Battu G., Hudson K.A., Diers B.W., Hudson M.E. A comparison of genotyping-by-sequencing analysis methods on low-coverage crop datasets shows advantages of a new workflow, GB-eaSy. BMC Bioinformatics. 2017;18:586. DOI 10.1186/s12859017-2000-6Wickland D.P., Battu G., Hudson K.A., Diers B.W., Hudson M.E. A comparison of genotyping-by-sequencing analysis methods on low-coverage crop datasets shows advantages of a new workflow, GB-eaSy. BMC Bioinformatics. 2017;18:586. DOI 10.1186/s12859017-2000-6Yao Z., You F.M., N’Diaye A., Knox R.E., McCartney C., Hiebert C.W., Pozniak C., Xu W. Evaluation of variant calling tools for large plant genome re-sequencing. BMC Bioinformatics. 2020;21(1):360. DOI 10.1186/s12859-020-03704-1Yao Z., You F.M., N’Diaye A., Knox R.E., McCartney C., Hiebert C.W., Pozniak C., Xu W. Evaluation of variant calling tools for large plant genome re-sequencing. BMC Bioinformatics. 2020;21(1):360. DOI 10.1186/s12859-020-03704-1Zheng X., Gogarten S.M., Lawrence M., Stilp A., Conomos M.P., Weir B.S., Laurie C., Levine D. SeqArray – a storage-efficient high-performance data format for WGS variant calls. Bioinformatics. 2017;33(15):2251-2257. DOI 10.1093/bioinformatics/btx145Zheng X., Gogarten S.M., Lawrence M., Stilp A., Conomos M.P., Weir B.S., Laurie C., Levine D. SeqArray – a storage-efficient high-performance data format for WGS variant calls. Bioinformatics. 2017;33(15):2251-2257. DOI 10.1093/bioinformatics/btx145

The authors declare that there are no conflicts of interest present.