{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:02:51Z","timestamp":1777489371136,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Impact of Genomic Variation on Function","award":["1UM1HG011966"],"award-info":[{"award-number":["1UM1HG011966"]}]},{"name":"Impact of Genomic Variation on Function","award":["1UM1HG012003"],"award-info":[{"award-number":["1UM1HG012003"]}]},{"name":"Impact of Genomic Variation on Function","award":["1UM1HG011966"],"award-info":[{"award-number":["1UM1HG011966"]}]},{"DOI":"10.13039\/501100004168","name":"Universit\u00e4t zu L\u00fcbeck","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004168","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Berlin Institute of Health at Charit\u00e9"},{"DOI":"10.13039\/501100004168","name":"Universit\u00e4t zu L\u00fcbeck","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004168","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Massively parallel reporter assays (MPRAs) are an experimental technology for measuring the activity of thousands of candidate regulatory sequences or their variants in parallel, where the activity of individual sequences is measured from pools of sequence-tagged reporter genes. Activity is derived from the ratio of transcribed RNA to input DNA counts of associated tag sequences in each reporter construct, so-called barcodes. Recently, tools specifically designed to analyze MPRA data were developed that attempt to model the count data, accounting for its inherent variation. Of these tools, MPRAnalyze and mpralm are most widely used. MPRAnalyze models barcode counts to estimate the transcription rate of each sequence. While it has increased statistical power and robustness against outliers compared to mpralm, it is slow and has a high false discovery rate. Mpralm, a tool built on the R package Limma, estimates log fold-changes between different sequences. As opposed to MPRAnalyze, it is fast and has a low false discovery rate but is susceptible to outliers and has less statistical power.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We propose BCalm, an MPRA analysis framework aimed at addressing the limitations of the existing tools. BCalm is an adaptation of mpralm, but models individual barcode counts instead of aggregating counts per sequence. Leaving out the aggregation step increases statistical power and improves robustness to outliers, while being fast and precise. We show the improved performance over existing methods on both simulated MPRA data and a lentiviral MPRA library of 166,508 target sequences, including 82,258 allelic variants. Further, BCalm adds functionality beyond the existing mpralm package, such as preparing count input files from MPRAsnakeflow, as well as an option to test for sequences with enhancing or repressing activity. Its built-in plotting functionalities allow for easy interpretation of the results.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>With BCalm, we provide a new tool for analyzing MPRA data which is robust and accurate on real MPRA datasets. The package is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/kircherlab\/BCalm\" ext-link-type=\"uri\">https:\/\/github.com\/kircherlab\/BCalm<\/jats:ext-link>.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12859-025-06065-9","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T12:44:14Z","timestamp":1739450654000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Using individual barcodes to increase quantification power of massively parallel reporter assays"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8828-636X","authenticated-orcid":false,"given":"Pia","family":"Keukeleire","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6396-4219","authenticated-orcid":false,"given":"Jonathan D.","family":"Rosen","sequence":"additional","affiliation":[]},{"given":"Angelina","family":"G\u00f6bel-Knapp","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3182-7987","authenticated-orcid":false,"given":"Kilian","family":"Salomon","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2032-6679","authenticated-orcid":false,"given":"Max","family":"Schubach","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9278-5471","authenticated-orcid":false,"given":"Martin","family":"Kircher","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"issue":"8","key":"6065_CR1","doi-asserted-by":"publisher","first-page":"2387","DOI":"10.1038\/s41596-020-0333-5","volume":"15","author":"MG Gordon","year":"2020","unstructured":"Gordon MG, Inoue F, Martin B, Schubach M, Agarwal V, Whalen S, et al. lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements. Nat Protoc. 2020;15(8):2387\u2013412.","journal-title":"Nat Protoc"},{"issue":"4","key":"6065_CR2","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.cell.2013.07.018","volume":"154","author":"W Akhtar","year":"2013","unstructured":"Akhtar W, de Jong J, Pindyurin AV, Pagie L, Meuleman W, de Ridder J, et al. Chromatin position effects assayed by thousands of reporters integrated in parallel. Cell. 2013;154(4):914\u201327.","journal-title":"Cell"},{"issue":"1","key":"6065_CR3","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/nbt.4285","volume":"37","author":"BB Maricque","year":"2019","unstructured":"Maricque BB, Chaudhari HG, Cohen BA. A massively parallel reporter assay dissects the influence of chromatin structure on cis-regulatory activity. Nat Biotechnol. 2019;37(1):90\u20135.","journal-title":"Nat Biotechnol"},{"issue":"6","key":"6065_CR4","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1016\/j.cell.2016.04.027","volume":"165","author":"R Tewhey","year":"2016","unstructured":"Tewhey R, Kotliar D, Park DS, Liu B, Winnicki S, Reilly SK, et al. Direct identification of hundreds of expression-modulating variants using a multiplexed reporter assay. Cell. 2016;165(6):1519\u201329.","journal-title":"Cell"},{"issue":"1","key":"6065_CR5","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s40488-014-0019-z","volume":"1","author":"E Xekalaki","year":"2014","unstructured":"Xekalaki E. On the distribution theory of over-dispersion. J Stat Distrib Appl. 2014;1(1):19.","journal-title":"J Stat Distrib Appl"},{"issue":"5","key":"6065_CR6","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1093\/bioinformatics\/btx598","volume":"34","author":"CA Kalita","year":"2018","unstructured":"Kalita CA, Moyerbrailean GA, Brown C, Wen X, Luca F, Pique-Regi R. QuASAR-MPRA: accurate allele-specific analysis for massively parallel reporter assays. Bioinformatics. 2018;34(5):787\u201394.","journal-title":"Bioinformatics"},{"issue":"9","key":"6065_CR7","doi-asserted-by":"publisher","first-page":"1638","DOI":"10.1101\/gr.268599.120","volume":"31","author":"D Lee","year":"2021","unstructured":"Lee D, Kapoor A, Lee C, Mudgett M, Beer MA, Chakravarti A. Sequence-based correction of barcode bias in massively parallel reporter assays. Genome Res. 2021;31(9):1638\u201345.","journal-title":"Genome Res"},{"issue":"1","key":"6065_CR8","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1186\/s11689-022-09461-x","volume":"14","author":"JC McAfee","year":"2022","unstructured":"McAfee JC, Bell JL, Krupa O, Matoba N, Stein JL, Won H. Focus on your locus with a massively parallel reporter assay. J Neurodev Disord. 2022;14(1):50.","journal-title":"J Neurodev Disord"},{"issue":"5","key":"6065_CR9","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1038\/nmeth.2885","volume":"11","author":"M Murtha","year":"2014","unstructured":"Murtha M, Tokcaer-Keskin Z, Tang Z, Strino F, Chen X, Wang Y, et al. FIREWACh: high-throughput functional detection of transcriptional regulatory modules in mammalian cells. Nat Methods. 2014;11(5):559\u201365.","journal-title":"Nat Methods"},{"issue":"6","key":"6065_CR10","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1038\/nprot.2014.072","volume":"9","author":"W Akhtar","year":"2014","unstructured":"Akhtar W, Pindyurin AV, de Jong J, Pagie L, Ten Hoeve J, Berns A, et al. Using TRIP for genome-wide position effect analysis in cultured cells. Nat Protoc. 2014;9(6):1255\u201381.","journal-title":"Nat Protoc"},{"issue":"7","key":"6065_CR11","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1002\/gepi.22337","volume":"44","author":"D Qiao","year":"2020","unstructured":"Qiao D, Zigler C, Cho MH, Silverman EK, Zhou X, Castaldi PJ, et al. Statistical considerations for the analysis of massively parallel reporter assays data. Genet Epidemiol. 2020;44(7):785\u201394.","journal-title":"Genet Epidemiol"},{"issue":"1","key":"6065_CR12","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. 2010;26(1):139\u201340.","journal-title":"Bioinformatics"},{"issue":"12","key":"6065_CR13","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.","journal-title":"Genome Biol"},{"issue":"2","key":"6065_CR14","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1093\/bioinformatics\/btz545","volume":"36","author":"WH Majoros","year":"2020","unstructured":"Majoros WH, Kim YS, Barrera A, Li F, Wang X, Cunningham SJ, et al. Bayesian estimation of genetic regulatory effects in high-throughput reporter assays. Bioinformatics. 2020;36(2):331\u20138.","journal-title":"Bioinformatics"},{"issue":"7","key":"6065_CR15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007504","volume":"16","author":"AR Ghazi","year":"2020","unstructured":"Ghazi AR, Kong X, Chen ES, Edelstein LC, Shaw CA. Bayesian modelling of high-throughput sequencing assays with malacoda. PLoS Comput Biol. 2020;16(7): e1007504.","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"6065_CR16","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1186\/s13059-019-1787-z","volume":"20","author":"T Ashuach","year":"2019","unstructured":"Ashuach T, Fischer DS, Kreimer A, Ahituv N, Theis FJ, Yosef N. MPRAnalyze: statistical framework for massively parallel reporter assays. Genome Biol. 2019;20(1):183.","journal-title":"Genome Biol"},{"issue":"1","key":"6065_CR17","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1038\/s41467-021-21038-1","volume":"12","author":"KD Zimmerman","year":"2021","unstructured":"Zimmerman KD, Espeland MA, Langefeld CD. A practical solution to pseudoreplication bias in single-cell studies. Nat Commun. 2021;12(1):738.","journal-title":"Nat Commun"},{"issue":"4","key":"6065_CR18","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1038\/s42003-021-02146-6","volume":"26","author":"L He","year":"2021","unstructured":"He L, Davila-Velderrain J, Sumida TS, Hafler DA, Kellis M, Kulminski AM. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Commun Biol. 2021;26(4):629.","journal-title":"Commun Biol"},{"issue":"1","key":"6065_CR19","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1186\/s12864-019-5556-x","volume":"20","author":"L Myint","year":"2019","unstructured":"Myint L, Avramopoulos DG, Goff LA, Hansen KD. Linear models enable powerful differential activity analysis in massively parallel reporter assays. BMC Genom. 2019;20(1):209.","journal-title":"BMC Genom"},{"issue":"24","key":"6065_CR20","doi-asserted-by":"publisher","first-page":"5351","DOI":"10.1093\/bioinformatics\/btz591","volume":"35","author":"A Niroula","year":"2019","unstructured":"Niroula A, Ajore R, Nilsson B. MPRAscore: robust and non-parametric analysis of massively parallel reporter assays. Bioinformatics. 2019;35(24):5351\u20133.","journal-title":"Bioinformatics"},{"key":"6065_CR21","doi-asserted-by":"crossref","unstructured":"Kosicki M, Cintr\u00f3n DL, Page NF, Georgakopoulos-Soares I, Akiyama JA, Plajzer-Frick I, et al. Massively parallel reporter assays and mouse transgenic assays provide complementary information about neuronal enhancer activity. bioRxiv. 2024 Apr 23;2024.04.22.590634.","DOI":"10.1101\/2024.04.22.590634"},{"key":"6065_CR22","doi-asserted-by":"crossref","unstructured":"Agarwal V, Inoue F, Schubach M, Martin BK, Dash PM, Zhang Z, et al. Massively parallel characterization of transcriptional regulatory elements in three diverse human cell types. bioRxiv. 2023 Jan 1;2023.03.05.531189.","DOI":"10.1101\/2023.03.05.531189"},{"key":"6065_CR23","unstructured":"Deng C, Whalen S, Steyert M, Ziffra R, Przytycki PF, Inoue F, et al. Massively parallel characterization of regulatory elements in the developing human cortex. Science. 2024 May 24;384(6698):eadh0559."},{"key":"6065_CR24","unstructured":"kircherlab\/MPRAsnakeflow [Internet]. kircherlab; 2024 [cited 2024 Sep 18]. Available from: https:\/\/github.com\/kircherlab\/MPRAsnakeflow"},{"issue":"8028","key":"6065_CR25","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/s41586-024-07510-0","volume":"633","author":"JM Engreitz","year":"2024","unstructured":"Engreitz JM, Lawson HA, Singh H, Starita LM, Hon GC, Carter H, et al. Deciphering the impact of genomic variation on function. Nature. 2024;633(8028):47\u201357.","journal-title":"Nature"},{"issue":"D1","key":"6065_CR26","doi-asserted-by":"publisher","first-page":"D1143","DOI":"10.1093\/nar\/gkad989","volume":"52","author":"M Schubach","year":"2024","unstructured":"Schubach M, Maass T, Nazaretyan L, R\u00f6ner S, Kircher M. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res. 2024;52(D1):D1143\u201354.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"6065_CR27","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s13059-016-0974-4","volume":"17","author":"W McLaren","year":"2016","unstructured":"McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, et al. The ensembl variant effect predictor. Genome Biol. 2016;17(1):122.","journal-title":"Genome Biol"},{"issue":"7","key":"6065_CR28","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1890\/10-1831.1","volume":"92","author":"A Lind\u00e9n","year":"2011","unstructured":"Lind\u00e9n A, M\u00e4ntyniemi S. Using the negative binomial distribution to model overdispersion in ecological count data. Ecology. 2011;92(7):1414\u201321.","journal-title":"Ecology"},{"issue":"2","key":"6065_CR29","doi-asserted-by":"publisher","first-page":"R29","DOI":"10.1186\/gb-2014-15-2-r29","volume":"15","author":"CW Law","year":"2014","unstructured":"Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29.","journal-title":"Genome Biol"},{"issue":"7","key":"6065_CR30","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv007","volume":"43","author":"ME Ritchie","year":"2015","unstructured":"Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7): e47.","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"6065_CR31","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1093\/bioinformatics\/btp053","volume":"25","author":"DJ McCarthy","year":"2009","unstructured":"McCarthy DJ, Smyth GK. Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics. 2009;25(6):765\u201371.","journal-title":"Bioinformatics"},{"key":"6065_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-70578-7","volume-title":"Testing statistical hypotheses","author":"EL Lehmann","year":"2022","unstructured":"Lehmann EL, Romano JP. Testing statistical hypotheses. 4th ed. Cham: Springer; 2022.","edition":"4"},{"key":"6065_CR33","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2202\/1544-6115.1027","volume":"3","author":"GK Smyth","year":"2004","unstructured":"Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:3.","journal-title":"Stat Appl Genet Mol Biol"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06065-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-025-06065-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06065-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T12:44:22Z","timestamp":1739450662000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-025-06065-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,13]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6065"],"URL":"https:\/\/doi.org\/10.1186\/s12859-025-06065-9","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,13]]},"assertion":[{"value":"17 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2025","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 no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"52"}}