{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T08:56:11Z","timestamp":1782982571140,"version":"3.54.5"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,3,14]],"date-time":"2020-03-14T00:00:00Z","timestamp":1584144000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,3,14]],"date-time":"2020-03-14T00:00:00Z","timestamp":1584144000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science and Technology","doi-asserted-by":"publisher","award":["JP17K20032, JP16H05879, JP16H01318, JP16H02484"],"award-info":[{"award-number":["JP17K20032, JP16H05879, JP16H01318, JP16H02484"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) is a popular sequencing method for studying RNA modifications and, in particular, for N6-methyladenosine (m6A), the most abundant RNA methylation modification found in various species. The detection of enriched regions is a main challenge of MeRIP-Seq analysis, however current tools either require a long time or do not fully utilize features of RNA sequencing such as strand information which could cause ambiguous calling. On the other hand, with more attention on the treatment experiments of MeRIP-Seq, biologists need intuitive evaluation on the treatment effect from comparison. Therefore, efficient and user-friendly software that can solve these tasks must be developed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We developed a software named \u201cmodel-based analysis and inference of MeRIP-Seq (MoAIMS)\u201d to detect enriched regions of MeRIP-Seq and infer signal proportion based on a mixture negative-binomial model. MoAIMS is designed for transcriptome immunoprecipitation sequencing experiments; therefore, it is compatible with different RNA sequencing protocols. MoAIMS offers excellent processing speed and competitive performance when compared with other tools. When MoAIMS is applied to studies of m6A, the detected enriched regions contain known biological features of m6A. Furthermore, signal proportion inferred from MoAIMS for m6A treatment datasets (perturbation of m6A methyltransferases) showed a decreasing trend that is consistent with experimental observations, suggesting that the signal proportion can be used as an intuitive indicator of treatment effect.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>MoAIMS is efficient and easy-to-use software implemented in R. MoAIMS can not only detect enriched regions of MeRIP-Seq efficiently but also provide intuitive evaluation on treatment effect for MeRIP-Seq treatment datasets.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-020-3430-0","type":"journal-article","created":{"date-parts":[[2020,3,14]],"date-time":"2020-03-14T10:02:52Z","timestamp":1584180172000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["MoAIMS: efficient software for detection of enriched regions of MeRIP-Seq"],"prefix":"10.1186","volume":"21","author":[{"given":"Yiqian","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9466-1034","authenticated-orcid":false,"given":"Michiaki","family":"Hamada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,3,14]]},"reference":[{"key":"3430_CR1","doi-asserted-by":"crossref","unstructured":"Morena F., Argentati C., Bazzucchi M., Emiliani C., Martino S.Above the Epitranscriptome: RNA Modifications and Stem Cell Identity. Genes (Basel). 2018; 9(7).","DOI":"10.3390\/genes9070329"},{"issue":"7","key":"3430_CR2","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1016\/j.cell.2017.05.045","volume":"169","author":"I. A. Roundtree","year":"2017","unstructured":"Roundtree I. A., Evans M. E., Pan T., He C.Dynamic RNA Modifications in Gene Expression Regulation. Cell. 2017; 169(7):1187\u2013200.","journal-title":"Cell"},{"issue":"4","key":"3430_CR3","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.tibs.2012.12.006","volume":"38","author":"T. Pan","year":"2013","unstructured":"Pan T.N6-methyl-adenosine modification in messenger and long non-coding RNA. Trends Biochem Sci. 2013; 38(4):204\u20139.","journal-title":"Trends Biochem Sci"},{"issue":"7591","key":"3430_CR4","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1038\/nature16998","volume":"530","author":"D Dominissini","year":"2016","unstructured":"Dominissini D, Nachtergaele S, Moshitch-Moshkovitz S, Peer E, Kol N, Ben-Haim MS, Dai Q, Di Segni A, Salmon-Divon M, Clark WC, Zheng G, Pan T, Solomon O, Eyal E, Hershkovitz V, Han D, Dore LC, Amariglio N, Rechavi G, He C. The dynamic N(1)-methyladenosine methylome in eukaryotic messenger RNA. Nature. 2016; 530(7591):441\u20136.","journal-title":"Nature"},{"issue":"1","key":"3430_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-016-1139-1","volume":"18","author":"T Amort","year":"2017","unstructured":"Amort T, Rieder D, Wille A, Khokhlova-Cubberley D, Riml C, Trixl L, Jia XY, Micura R, Lusser A. Distinct 5-methylcytosine profiles in poly(A) RNA from mouse embryonic stem cells and brain. Genome Biol. 2017; 18(1):1.","journal-title":"Genome Biol"},{"issue":"7","key":"3430_CR6","doi-asserted-by":"publisher","first-page":"1635","DOI":"10.1016\/j.cell.2012.05.003","volume":"149","author":"KD Meyer","year":"2012","unstructured":"Meyer KD, Saletore Y, Zumbo P, Elemento O, Mason CE, Jaffrey SR. Comprehensive analysis of mRNA methylation reveals enrichment in 3\u2019 UTRs and near stop codons. Cell. 2012; 149(7):1635\u201346.","journal-title":"Cell"},{"issue":"7397","key":"3430_CR7","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1038\/nature11112","volume":"485","author":"D Dominissini","year":"2012","unstructured":"Dominissini D, Moshitch-Moshkovitz S, Schwartz S, Salmon-Divon M, Ungar L, Osenberg S, Cesarkas K, Jacob-Hirsch J, Amariglio N, Kupiec M, Sorek R, Rechavi G. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature. 2012; 485(7397):201\u20136.","journal-title":"Nature"},{"issue":"7671","key":"3430_CR8","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1038\/nature23883","volume":"549","author":"C Zhang","year":"2017","unstructured":"Zhang C, Chen Y, Sun B, Wang L, Yang Y, Ma D, Lv J, Heng J, Ding Y, Xue Y, Lu X, Xiao W, Yang YG, Liu F. m6A modulates haematopoietic stem and progenitor cell specification. Nature. 2017; 549(7671):273\u20136.","journal-title":"Nature"},{"issue":"5830","key":"3430_CR9","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1126\/science.1141319","volume":"316","author":"DS Johnson","year":"2007","unstructured":"Johnson DS, Mortazavi A, Myers RM, Wold B. Genome-wide mapping of in vivo protein-DNA interactions. Science. 2007; 316(5830):1497\u2013502.","journal-title":"Science"},{"issue":"3","key":"3430_CR10","doi-asserted-by":"publisher","first-page":"173","DOI":"10.2174\/1389202911314030003","volume":"14","author":"JD Mills","year":"2013","unstructured":"Mills JD, Kawahara Y, Janitz M. Strand-Specific RNA-Seq Provides Greater Resolution of Transcriptome Profiling. Curr. Genomics. 2013; 14(3):173\u201381.","journal-title":"Curr. Genomics"},{"issue":"9","key":"3430_CR11","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1186\/gb-2008-9-9-r137","volume":"9","author":"Y Zhang","year":"2008","unstructured":"Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008; 9(9):137.","journal-title":"Genome Biol"},{"issue":"12","key":"3430_CR12","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1093\/bioinformatics\/btt171","volume":"29","author":"J Meng","year":"2013","unstructured":"Meng J, Cui X, Rao MK, Chen Y, Huang Y. Exome-based analysis for RNA epigenome sequencing data. Bioinformatics. 2013; 29(12):1565\u20137.","journal-title":"Bioinformatics"},{"issue":"12","key":"3430_CR13","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1093\/bioinformatics\/btw281","volume":"32","author":"X Cui","year":"2016","unstructured":"Cui X, Meng J, Zhang S, Chen Y, Huang Y. A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data. Bioinformatics. 2016; 32(12):378\u201385.","journal-title":"Bioinformatics"},{"issue":"1","key":"3430_CR14","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1038\/nprot.2012.148","volume":"8","author":"D Dominissini","year":"2013","unstructured":"Dominissini D, Moshitch-Moshkovitz S, Salmon-Divon M, Amariglio N, Rechavi G. Transcriptome-wide mapping of N(6)-methyladenosine by m(6)A-seq based on immunocapturing and massively parallel sequencing. Nat Protoc. 2013; 8(1):176\u201389.","journal-title":"Nat Protoc"},{"issue":"1","key":"3430_CR15","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1093\/bioinformatics\/bts635","volume":"29","author":"A Dobin","year":"2013","unstructured":"Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013; 29(1):15\u201321.","journal-title":"Bioinformatics"},{"issue":"4","key":"3430_CR16","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/gb-2013-14-4-r36","volume":"14","author":"D Kim","year":"2013","unstructured":"Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013; 14(4):36.","journal-title":"Genome Biol"},{"issue":"4","key":"3430_CR17","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/nmeth.3317","volume":"12","author":"D Kim","year":"2015","unstructured":"Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015; 12(4):357\u201360.","journal-title":"Nat Methods"},{"key":"3430_CR18","unstructured":"Broad Institute. Picard Tools. http:\/\/broadinstitute.github.io\/picard\/. Accessed 21 Feb 2018."},{"issue":"14","key":"3430_CR19","doi-asserted-by":"publisher","first-page":"1754","DOI":"10.1093\/bioinformatics\/btp324","volume":"25","author":"H Li","year":"2009","unstructured":"Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25(14):1754\u201360.","journal-title":"Bioinformatics"},{"issue":"7","key":"3430_CR20","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1093\/bioinformatics\/btt656","volume":"30","author":"Y Liao","year":"2014","unstructured":"Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014; 30(7):923\u201330.","journal-title":"Bioinformatics"},{"issue":"495","key":"3430_CR21","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1198\/jasa.2011.ap09706","volume":"106","author":"PF Kuan","year":"2011","unstructured":"Kuan PF, Chung D, Pan G, Thomson JA, Stewart R, Kele\u015f S. A Statistical Framework for the Analysis of ChIP-Seq Data. J Am Stat Assoc. 2011; 106(495):891\u2013903.","journal-title":"J Am Stat Assoc"},{"key":"3430_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21706-2","volume-title":"Modern Applied Statistics with S","author":"WN Venables","year":"2002","unstructured":"Venables WN, Ripley BD. Modern Applied Statistics with S, Fourth. New York: Springer; 2002. https:\/\/www.bibsonomy.org\/bibtex\/2923b9e072a30847bc042e7035f829c06\/sveng. http:\/\/www.stats.ox.ac.uk\/pub\/MASS4."},{"key":"3430_CR23","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1214\/ss\/1177013604","volume":"1","author":"T Hastie","year":"1986","unstructured":"Hastie T, Tibshirani R. Generalized additive models. Stat Sci. 1986; 1:297\u2013310.","journal-title":"Stat Sci"},{"issue":"1","key":"3430_CR24","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1111\/j.1467-9868.2010.00749.x","volume":"73","author":"S. N. Wood","year":"2011","unstructured":"Wood S. N.Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc B. 2011; 73(1):3\u201336.","journal-title":"J R Stat Soc B"},{"key":"3430_CR25","doi-asserted-by":"publisher","unstructured":"Wahba G. A comparison of gcv and gml for choosing the smoothing parameter in the generalized spline smoothing problem. Ann Stat. 1985; 13. https:\/\/doi.org\/10.1214\/aos\/1176349743.","DOI":"10.1214\/aos\/1176349743"},{"issue":"3","key":"3430_CR26","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1111\/j.1467-9574.2012.00530.x","volume":"66","author":"E. Wit","year":"2012","unstructured":"Wit E., Heuvel E. v. d., Romeijn J. -W.\u2019All models are wrong...\u2019: an introduction to model uncertainty. Statistica Neerlandica. 2012; 66(3):217\u201336. https:\/\/doi.org\/10.1111\/j.1467-9574.2012.00530.x.","journal-title":"Statistica Neerlandica"},{"issue":"1","key":"3430_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B. 1977; 39(1):1\u201338.","journal-title":"J R Stat Soc Ser B"},{"issue":"1","key":"3430_CR28","doi-asserted-by":"publisher","first-page":"85150","DOI":"10.1371\/journal.pone.0085150","volume":"9","author":"I Zwiener","year":"2014","unstructured":"Zwiener I, Frisch B, Binder H. Transforming RNA-Seq data to improve the performance of prognostic gene signatures. PLoS ONE. 2014; 9(1):85150.","journal-title":"PLoS ONE"},{"key":"3430_CR29","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1186\/1471-2105-14-169","volume":"14","author":"Y. Bao","year":"2013","unstructured":"Bao Y., Vinciotti V., Wit E., Hoen P. A\u2019t. Accounting for immunoprecipitation efficiencies in the statistical analysis of ChIP-seq data. BMC Bioinformatics. 2013; 14:169.","journal-title":"BMC Bioinformatics"},{"issue":"8","key":"3430_CR30","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1093\/bioinformatics\/btl035","volume":"22","author":"P Broet","year":"2006","unstructured":"Broet P, Richardson S. Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model. Bioinformatics. 2006; 22(8):911\u20138.","journal-title":"Bioinformatics"},{"issue":"6","key":"3430_CR31","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1016\/j.stem.2014.09.019","volume":"15","author":"PJ Batista","year":"2014","unstructured":"Batista PJ, Molinie B, Wang J, Qu K, Zhang J, Li L, Bouley DM, Lujan E, Haddad B, Daneshvar K, Carter AC, Flynn RA, Zhou C, Lim KS, Dedon P, Wernig M, Mullen AC, Xing Y, Giallourakis CC, Chang HY. m(6)A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell. 2014; 15(6):707\u201319.","journal-title":"Cell Stem Cell"},{"issue":"1","key":"3430_CR32","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.celrep.2014.05.048","volume":"8","author":"S Schwartz","year":"2014","unstructured":"Schwartz S, Mumbach MR, Jovanovic M, Wang T, Maciag K, Bushkin GG, Mertins P, Ter-Ovanesyan D, Habib N, Cacchiarelli D, Sanjana NE, Freinkman E, Pacold ME, Satija R, Mikkelsen TS, Hacohen N, Zhang F, Carr SA, Lander ES, Regev A. Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5\u2019 sites. Cell Rep. 2014; 8(1):284\u201396.","journal-title":"Cell Rep"},{"issue":"Database issue","key":"3430_CR33","first-page":"991","volume":"41","author":"T Barrett","year":"2013","unstructured":"Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A. NCBI GEO: archive for functional genomics data sets\u2013update. Nucleic Acids Res. 2013; 41(Database issue):991\u20135.","journal-title":"Nucleic Acids Res"},{"issue":"9","key":"3430_CR34","doi-asserted-by":"publisher","first-page":"1760","DOI":"10.1101\/gr.135350.111","volume":"22","author":"J Harrow","year":"2012","unstructured":"Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S, Barnes I, Bignell A, Boychenko V, Hunt T, Kay M, Mukherjee G, Rajan J, Despacio-Reyes G, Saunders G, Steward C, Harte R, Lin M, Howald C, Tanzer A, Derrien T, Chrast J, Walters N, Balasubramanian S, Pei B, Tress M, Rodriguez JM, Ezkurdia I, van Baren J, Brent M, Haussler D, Kellis M, Valencia A, Reymond A, Gerstein M, Guigo R, Hubbard TJ. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 2012; 22(9):1760\u201374.","journal-title":"Genome Res"},{"issue":"6","key":"3430_CR35","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1093\/bioinformatics\/btq033","volume":"26","author":"AR Quinlan","year":"2010","unstructured":"Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010; 26(6):841\u20132.","journal-title":"Bioinformatics"},{"issue":"8","key":"3430_CR36","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1038\/nmeth.3453","volume":"12","author":"B Linder","year":"2015","unstructured":"Linder B, Grozhik AV, Olarerin-George AO, Meydan C, Mason CE, Jaffrey SR. Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat Methods. 2015; 12(8):767\u201372.","journal-title":"Nat Methods"},{"issue":"19","key":"3430_CR37","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1101\/gad.269415.115","volume":"29","author":"S Ke","year":"2015","unstructured":"Ke S, Alemu EA, Mertens C, Gantman EC, Fak JJ, Mele A, Haripal B, Zucker-Scharff I, Moore MJ, Park CY, V\u00e5gbo CB, Kus\u015bnierczyk A, Klungland A, Darnell JE, Darnell RB. A majority of m6A residues are in the last exons, allowing the potential for 3\u2019 UTR regulation. Genes Dev. 2015; 29(19):2037\u201353.","journal-title":"Genes Dev"},{"issue":"2","key":"3430_CR38","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1093\/bib\/bbs017","volume":"14","author":"H Thorvaldsdottir","year":"2013","unstructured":"Thorvaldsdottir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinformatics. 2013; 14(2):178\u201392.","journal-title":"Brief Bioinformatics"},{"issue":"10","key":"3430_CR39","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1093\/nar\/gkx120","volume":"45","author":"B Uyar","year":"2017","unstructured":"Uyar B, Yusuf D, Wurmus R, Rajewsky N, Ohler U, Akalin A. RCAS: an RNA centric annotation system for transcriptome-wide regions of interest. Nucleic Acids Res. 2017; 45(10):91.","journal-title":"Nucleic Acids Res"},{"key":"3430_CR40","doi-asserted-by":"publisher","unstructured":"McIntyre ABR, Gokhale NS, Cerchietti L, Jaffrey SR, Horner SM, Mason CE. Limits in the detection of m6a changes using merip\/m6a-seq. bioRxiv. 2019. https:\/\/doi.org\/10.1101\/657130.","DOI":"10.1101\/657130"},{"issue":"2","key":"3430_CR41","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s41593-017-0057-1","volume":"21","author":"Y Wang","year":"2018","unstructured":"Wang Y, Li Y, Yue M, Wang J, Kumar S, Wechsler-Reya RJ, Zhang Z, Ogawa Y, Kellis M, Duester G, Zhao JC. N6-methyladenosine RNA modification regulates embryonic neural stem cell self-renewal through histone modifications. Nat Neurosci. 2018; 21(2):195\u2013206.","journal-title":"Nat Neurosci"},{"key":"3430_CR42","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1186\/1471-2105-14-254","volume":"14","author":"M Esnaola","year":"2013","unstructured":"Esnaola M, Puig P, Gonzalez D, Castelo R, Gonzalez JR. A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments. BMC Bioinformatics. 2013; 14:254.","journal-title":"BMC Bioinformatics"},{"key":"3430_CR43","doi-asserted-by":"publisher","first-page":"588","DOI":"10.3389\/fgene.2018.00588","volume":"9","author":"Z Gao","year":"2018","unstructured":"Gao Z, Zhao Z, Tang W. DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data. Front Genet. 2018; 9:588.","journal-title":"Front Genet"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-3430-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-020-3430-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-3430-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T05:19:18Z","timestamp":1722575958000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-3430-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,14]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["3430"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-3430-0","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,14]]},"assertion":[{"value":"22 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"103"}}