{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T20:16:09Z","timestamp":1783455369888,"version":"3.55.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Genomics"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>In this comparative study we evaluate the performance of four software tools: DNAstar-D (DESeq2), DNAstar-E (edgeR), CLC Genomics and Partek Flow for identification of differentially expressed genes (DEGs) using a transcriptome of <jats:italic>E. coli.<\/jats:italic> The RNA-seq data are from the effect of below-background radiation 5.5 nGy total dose (0.2nGy\/hr) on <jats:italic>E. coli<\/jats:italic> grown shielded from natural radiation 655\u00a0m below ground in a pre-World War II steel vault. The gene expression response to three supplemented sources of radiation designed to mimic natural background, 1952 \u2013 5720 nGy in total dose (71\u2013208 nGy\/hr), are compared to this \u201cradiation-deprived\u201d treatment. In addition, RNA-seq data of <jats:italic>Caenorhabditis elegans<\/jats:italic> nematode from similar radiation treatments was analyzed by three of the software packages.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In <jats:italic>E. coli<\/jats:italic>, the four software programs identified one of the supplementary sources of radiation (KCl) to evoke about 5 times more transcribed genes than the minus-radiation treatment (69\u2013114 differentially expressed genes, DEGs), and so the rest of the analyses used this KCl vs \u201cMinus\u201d comparison. After imposing a 30-read minimum cutoff, one of the DNAStar options shared two of the three steps\u00a0(mapping, normalization, and statistic) with Partek Flow (they both used median of ratios to normalize and the DESeq2 statistical package), and these two programs identified the highest number of DEGs in common with each other (53). In contrast, when the programs used different approaches in each of the three steps, between 31 and 40 DEGs were found in common. Regarding the extent of expression differences, three of the four programs gave high fold-change results (15\u2013178 fold), but one (DNAstar\u2019s DESeq2) resulted in more conservative fold-changes (1.5\u20133.5). In a parallel study comparing three qPCR commercial validation software programs, these programs also gave variable results as to which genes were significantly regulated. Similarly, the <jats:italic>C. elegans<\/jats:italic> analysis showed exaggerated fold-changes in CLC and DNAstar\u2019s edgeR while DNAstar-D was more conservative.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Regarding the extent of expression (fold-change), and considering the subtlety of the very low level radiation treatments, in <jats:italic>E. coli<\/jats:italic> three of the four programs gave what we consider exaggerated fold-change results (15 \u2013 178 fold), but one (DNAstar\u2019s DESeq2) gave more realistic fold-changes (1.5\u20133.5). When RT-qPCR validation comparisons to transcriptome results were carried out, they supported the more conservative DNAstar-D\u2019s expression results. When another model organism\u2019s (nematode) response to these radiation differences was similarly analyzed, DNAstar-D also resulted in the most conservative expression patterns. Therefore, we would propose DESeq2 (\u201cDNAstar-D\u201d) as an appropriate software tool for differential gene expression studies for treatments expected to give subtle transcriptome responses.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12864-022-08673-8","type":"journal-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T08:03:12Z","timestamp":1655712192000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A transcriptome software comparison for the analyses of treatments expected to give subtle gene expression responses"],"prefix":"10.1186","volume":"23","author":[{"given":"Cung Nawl","family":"Thawng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geoffrey Battle","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"issue":"1","key":"8673_CR1","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1038\/nrg2484","volume":"10","author":"Z Wang","year":"2009","unstructured":"Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57\u201363. https:\/\/doi.org\/10.1038\/nrg2484.","journal-title":"Nat Rev Genet"},{"key":"8673_CR2","doi-asserted-by":"publisher","unstructured":"U Nagalakshmi K Waern M Snyder 2010 RNA-Seq: a method for comprehensive transcriptome analysis Current protocols in molecular biology, Chapter 4 https:\/\/doi.org\/10.1002\/0471142727.mb0411s89","DOI":"10.1002\/0471142727.mb0411s89"},{"issue":"1","key":"8673_CR3","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1093\/bib\/bbx115","volume":"20","author":"D Spies","year":"2019","unstructured":"Spies D, Renz PF, Beyer TA, Ciaudo C. Comparative analysis of differential gene expression tools for RNA sequencing time course data. Brief Bioinform. 2019;20(1):288\u201398. https:\/\/doi.org\/10.1093\/bib\/bbx115.","journal-title":"Brief Bioinform"},{"issue":"12","key":"8673_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0190152","volume":"12","author":"J Costa-Silva","year":"2017","unstructured":"Costa-Silva J, Domingues D, Lopes FM. RNA-Seq differential expression analysis: An extended review and a software tool. PLoS ONE. 2017;12(12): e0190152. https:\/\/doi.org\/10.1371\/journal.pone.0190152.","journal-title":"PLoS ONE"},{"key":"8673_CR5","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1186\/s12859-015-0778-7","volume":"16","author":"P Li","year":"2015","unstructured":"Li P, Piao Y, Shon HS, Ryu KH. Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data. BMC Bioinformatics. 2015;16:347. https:\/\/doi.org\/10.1186\/s12859-015-0778-7.","journal-title":"BMC Bioinformatics"},{"key":"8673_CR6","doi-asserted-by":"publisher","first-page":"3470","DOI":"10.1016\/j.csbj.2021.05.040","volume":"19","author":"A Stupnikov","year":"2021","unstructured":"Stupnikov A, McInerney CE, Savage KI, McIntosh SA, Emmert-Streib F, Kennedy R, Salto-Tellez M, Prise KM, McArt DG. Robustness of differential gene expression analysis of RNA-seq. Comput Struct Biotechnol J. 2021;19:3470\u201381. https:\/\/doi.org\/10.1016\/j.csbj.2021.05.040.","journal-title":"Comput Struct Biotechnol J"},{"issue":"12","key":"8673_CR7","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1186\/gb-2010-11-12-220","volume":"11","author":"A Oshlack","year":"2010","unstructured":"Oshlack A, Robinson MD, Young MD. From RNA-seq reads to differential expression results. Genome Biol. 2010;11(12):220. https:\/\/doi.org\/10.1186\/gb-2010-11-12-220.","journal-title":"Genome Biol"},{"key":"8673_CR8","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s13059-016-0881-8","volume":"17","author":"A Conesa","year":"2016","unstructured":"Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szcze\u015bniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13. https:\/\/doi.org\/10.1186\/s13059-016-0881-8.","journal-title":"Genome Biol"},{"key":"8673_CR9","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s12864-015-2353-z","volume":"17","author":"Y Lin","year":"2016","unstructured":"Lin Y, Golovnina K, Chen ZX, Lee HN, Negron YL, Sultana H, Oliver B, Harbison ST. Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster. BMC Genomics. 2016;17:28. https:\/\/doi.org\/10.1186\/s12864-015-2353-z.","journal-title":"BMC Genomics"},{"issue":"3","key":"8673_CR10","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1093\/bioinformatics\/btt688","volume":"30","author":"Y Liu","year":"2014","unstructured":"Liu Y, Zhou J, White KP. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics (Oxford, England). 2014;30(3):301\u20134. https:\/\/doi.org\/10.1093\/bioinformatics\/btt688.","journal-title":"Bioinformatics (Oxford, England)"},{"key":"8673_CR11","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1186\/1471-2164-13-484","volume":"13","author":"JA Robles","year":"2012","unstructured":"Robles JA, Qureshi SE, Stephen SJ, Wilson SR, Burden CJ, Taylor JM. Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics. 2012;13:484. https:\/\/doi.org\/10.1186\/1471-2164-13-484.","journal-title":"BMC Genomics"},{"issue":"1","key":"8673_CR12","doi-asserted-by":"publisher","first-page":"19737","DOI":"10.1038\/s41598-020-76881-x","volume":"10","author":"LA Corchete","year":"2020","unstructured":"Corchete LA, Rojas EA, Alonso-L\u00f3pez D, De Las Rivas J, Guti\u00e9rrez NC, Burguillo FJ. Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis. Sci Rep. 2020;10(1):19737. https:\/\/doi.org\/10.1038\/s41598-020-76881-x.","journal-title":"Sci Rep"},{"issue":"7","key":"8673_CR13","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1038\/nbt.1883","volume":"29","author":"MG Grabherr","year":"2011","unstructured":"Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29(7):644\u201352. https:\/\/doi.org\/10.1038\/nbt.1883.","journal-title":"Nat Biotechnol"},{"issue":"8","key":"8673_CR14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0103207","volume":"9","author":"ZH Zhang","year":"2014","unstructured":"Zhang ZH, Jhaveri DJ, Marshall VM, Bauer DC, Edson J, Narayanan RK, Robinson GJ, Lundberg AE, Bartlett PF, Wray NR, Zhao QY. A comparative study of techniques for differential expression analysis on RNA-Seq data. PLoS ONE. 2014;9(8): e103207. https:\/\/doi.org\/10.1371\/journal.pone.0103207.","journal-title":"PLoS ONE"},{"key":"8673_CR15","doi-asserted-by":"publisher","unstructured":"Liu, C. H., & Di, Y. P. (2020). Analysis of RNA Sequencing Data Using CLC Genomics Workbench.\u00a0Methods Mol Biol (Clifton, N.J.),\u00a02102, 61\u2013113. https:\/\/doi.org\/10.1007\/978-1-0716-0223-2_4","DOI":"10.1007\/978-1-0716-0223-2_4"},{"issue":"12","key":"8673_CR16","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","volume":"15","author":"MI Love","year":"2014","unstructured":"Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. https:\/\/doi.org\/10.1186\/s13059-014-0550-8.","journal-title":"Genome Biol"},{"issue":"6","key":"8673_CR17","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1093\/bib\/bby067","volume":"20","author":"A McDermaid","year":"2019","unstructured":"McDermaid A, Monier B, Zhao J, Liu B, Ma Q. Interpretation of differential gene expression results of RNA-seq data: review and integration. Brief Bioinform. 2019;20(6):2044\u201354. https:\/\/doi.org\/10.1093\/bib\/bby067.","journal-title":"Brief Bioinform"},{"issue":"1","key":"8673_CR18","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1093\/bioinformatics\/btp616","volume":"26","author":"MD Robinson","year":"2010","unstructured":"Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics (Oxford, England). 2010;26(1):139\u201340. https:\/\/doi.org\/10.1093\/bioinformatics\/btp616.","journal-title":"Bioinformatics (Oxford, England)"},{"issue":"1","key":"8673_CR19","doi-asserted-by":"publisher","first-page":"25","DOI":"10.4236\/aim.2014.41006","volume":"4","author":"A Bhagwat","year":"2014","unstructured":"Bhagwat A, Ying A, Smith A. Evaluation of Ribosomal RNA Removal Protocols for Salmonella RNA-Seq Projects. Adv Microbiol. 2014;4(1):25\u201332. https:\/\/doi.org\/10.4236\/aim.2014.41006.","journal-title":"Adv Microbiol"},{"key":"8673_CR20","doi-asserted-by":"publisher","unstructured":"Lippolis, J. D., Brunelle, B. W., Reinhardt, T. A., Sacco, R. E., Thacker, T. C., Looft, T. P., & Casey, T. A. (2016). Differential Gene Expression of Three Mastitis-Causing\u00a0Escherichia coli\u00a0Strains Grown under Planktonic, Swimming, and Swarming Culture Conditions.\u00a0mSystems,\u00a01(4), e00064\u201316. https:\/\/doi.org\/10.1128\/mSystems.00064-16","DOI":"10.1128\/mSystems.00064-16"},{"key":"8673_CR21","doi-asserted-by":"publisher","first-page":"204173142199084","DOI":"10.1177\/2041731421990842","volume":"12","author":"AR Szojka","year":"2021","unstructured":"Szojka AR, Marqueti RC, Li DX, Molter CW, Liang Y, Kunze M, Mulet-Sierra A, Jomha NM, Adesida AB. Human engineered meniscus transcriptome after short-term combined hypoxia and dynamic compression. J Tissue Eng. 2021;12:2041731421990842. https:\/\/doi.org\/10.1177\/2041731421990842.","journal-title":"J Tissue Eng"},{"issue":"5","key":"8673_CR22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0196472","volume":"13","author":"H Castillo","year":"2018","unstructured":"Castillo H, Li X, Schilkey F, Smith GB. Transcriptome analysis reveals a stress response of Shewanella oneidensis deprived of background levels of ionizing radiation. PLoS ONE. 2018;13(5): e0196472. https:\/\/doi.org\/10.1371\/journal.pone.0196472.","journal-title":"PLoS ONE"},{"key":"8673_CR23","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2020.581796","volume":"8","author":"WA Van Voorhies","year":"2020","unstructured":"Van Voorhies WA, Castillo HA, Thawng CN, Smith GB. The Phenotypic and Transcriptomic Response of the Caenorhabditis elegans Nematode to Background and Below-Background Radiation Levels. Front Public Health. 2020;8: 581796. https:\/\/doi.org\/10.3389\/fpubh.2020.581796.","journal-title":"Front Public Health"},{"key":"8673_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s00411-021-00951-5","author":"H Castillo","year":"2021","unstructured":"Castillo H, Winder J, Smith G. Chinese hamster V79 cells\u2019 dependence on background ionizing radiation for optimal growth. Radiat Environ Biophys. 2021. https:\/\/doi.org\/10.1007\/s00411-021-00951-5.Advanceonlinepublication.doi:10.1007\/s00411-021-00951-5.","journal-title":"Radiat Environ Biophys"},{"issue":"9","key":"8673_CR25","doi-asserted-by":"publisher","first-page":"749","DOI":"10.3109\/09553002.2015.1062571","volume":"91","author":"H Castillo","year":"2015","unstructured":"Castillo H, Schoderbek D, Dulal S, Escobar G, Wood J, Nelson R, Smith G. Stress induction in the bacteria Shewanella oneidensis and Deinococcus radiodurans in response to below-background ionizing radiation. Int J Radiat Biol. 2015;91(9):749\u201356. https:\/\/doi.org\/10.3109\/09553002.2015.1062571.","journal-title":"Int J Radiat Biol"},{"issue":"10","key":"8673_CR26","doi-asserted-by":"publisher","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","volume":"11","author":"S Anders","year":"2010","unstructured":"Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11(10):R106. https:\/\/doi.org\/10.1186\/gb-2010-11-10-r106.","journal-title":"Genome Biol"},{"issue":"1","key":"8673_CR27","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1186\/s12859-018-2261-8","volume":"19","author":"T. P. Quinn","year":"2018","unstructured":"Quinn T. P., Crowley T. M., Richardson M. F. Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods. BMC Bioinformatics. 2018;19(1):274. https:\/\/doi.org\/10.1186\/s12859-018-2261-8.","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"8673_CR28","doi-asserted-by":"publisher","first-page":"R25","DOI":"10.1186\/gb-2010-11-3-r25","volume":"11","author":"MD Robinson","year":"2010","unstructured":"Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25. https:\/\/doi.org\/10.1186\/gb-2010-11-3-r25.","journal-title":"Genome Biol"},{"issue":"8","key":"8673_CR29","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1261\/rna.074922.120","volume":"26","author":"Zhao","year":"2020","unstructured":"Zhao, Ye Z, Stanton R. Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols. RNA. 2020;26(8):903\u20139. https:\/\/doi.org\/10.1261\/rna.074922.120.","journal-title":"RNA"},{"issue":"1","key":"8673_CR30","doi-asserted-by":"publisher","first-page":"46","DOI":"10.32398\/cjhp_20181601","volume":"16","author":"J Chacon","year":"2018","unstructured":"Chacon J, Cuajungco M. P. Comparative De Novo Transcriptome Assembly of\u00a0Notophthalmus viridescens\u00a0RNA-seq Data using Two Commercial Software Programs. \u00a0Californ J Health Promot. 2018;16(1):46\u201353. https:\/\/doi.org\/10.32398\/cjhp_20181601.","journal-title":"Californian J Health Promot"},{"issue":"2","key":"8673_CR31","doi-asserted-by":"publisher","first-page":"248","DOI":"10.3732\/ajb.1100340","volume":"99","author":"VM Kvam","year":"2012","unstructured":"Kvam VM, Liu P, Si Y. A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Bot. 2012;99(2):248\u201356. https:\/\/doi.org\/10.3732\/ajb.1100340.","journal-title":"Am J Bot"},{"issue":"5","key":"8673_CR32","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.3390\/ijms21051720","volume":"21","author":"S Schaarschmidt","year":"2020","unstructured":"Schaarschmidt S, Fischer A, Zuther E, Hincha DK. Evaluation of Seven Different RNA-Seq Alignment Tools Based on Experimental Data from the Model Plant Arabidopsis thaliana. Int J Mol Sci. 2020;21(5):1720. https:\/\/doi.org\/10.3390\/ijms21051720.","journal-title":"Int J Mol Sci"},{"issue":"1","key":"8673_CR33","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1093\/bib\/bbt086","volume":"16","author":"F Seyednasrollah","year":"2015","unstructured":"Seyednasrollah F, Laiho A, Elo LL. Comparison of software packages for detecting differential expression in RNA-seq studies. Brief Bioinform. 2015;16(1):59\u201370. https:\/\/doi.org\/10.1093\/bib\/bbt086.","journal-title":"Brief Bioinform"},{"issue":"1","key":"8673_CR34","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1038\/leu.2012.151","volume":"27","author":"GM Kendall","year":"2014","unstructured":"Kendall GM, Little MP, Wakeford R, Bunch KJ, Miles JC, Vincent TJ, Meara JR, Murphy MF. A record-based case-control study of natural background radiation and the incidence of childhood leukaemia and other cancers in Great Britain during 1980\u20132006. Leukemia. 2014;27(1):3\u20139. https:\/\/doi.org\/10.1038\/leu.2012.151.","journal-title":"Leukemia"},{"key":"8673_CR35","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.644292","volume":"12","author":"H Castillo","year":"2021","unstructured":"Castillo H, Li X, Smith GB. Deinococcus radiodurans UWO298 Dependence on Background Radiation for Optimal Growth. Front Genet. 2021;12: 644292. https:\/\/doi.org\/10.3389\/fgene.2021.644292.","journal-title":"Front Genet"},{"issue":"2","key":"8673_CR36","doi-asserted-by":"publisher","first-page":"R19","DOI":"10.1186\/gb-2007-8-2-r19","volume":"8","author":"J Hellemans","year":"2007","unstructured":"Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol. 2007;8(2):R19. https:\/\/doi.org\/10.1186\/gb-2007-8-2-r19.","journal-title":"Genome Biol"},{"issue":"9","key":"8673_CR37","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/30.9.e36","volume":"30","author":"MW Pfaffl","year":"2002","unstructured":"Pfaffl MW, Horgan GW, Dempfle L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 2002;30(9): e36. https:\/\/doi.org\/10.1093\/nar\/30.9.e36.","journal-title":"Nucleic Acids Res"},{"key":"8673_CR38","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc: Ser B (Methodol). 1995;57:289\u2013300. https:\/\/doi.org\/10.1111\/j.2517-6161.1995.tb02031.x.","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"key":"8673_CR39","doi-asserted-by":"publisher","first-page":"801","DOI":"10.3389\/fmicb.2014.00801","volume":"5","author":"BW Brunelle","year":"2015","unstructured":"Brunelle BW, Bearson BL, Bearson SM. Chloramphenicol and tetracycline decrease motility and increase invasion and attachment gene expression in specific isolates of multidrug-resistant Salmonella enterica serovar Typhimurium. Front Microbiol. 2015;5:801. https:\/\/doi.org\/10.3389\/fmicb.2014.00801.","journal-title":"Front Microbiol"}],"container-title":["BMC Genomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12864-022-08673-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12864-022-08673-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12864-022-08673-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T08:04:22Z","timestamp":1655712262000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcgenomics.biomedcentral.com\/articles\/10.1186\/s12864-022-08673-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,20]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["8673"],"URL":"https:\/\/doi.org\/10.1186\/s12864-022-08673-8","relation":{},"ISSN":["1471-2164"],"issn-type":[{"value":"1471-2164","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,20]]},"assertion":[{"value":"11 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"452"}}