Abstract: MicroRNAs (miRNAs) are essential regulators of gene expression, yet few comprehensive databases exist for miRNA expression in non-model species, limiting our ability to characterize their roles in gene regulation, development, and disease. Similarly, isomiRs - length and sequence isoforms of canonical miRNAs with potentially altered regulatory targets and functions - have received even less attention in non-model species, including the horse, leaving a critical gap in our understanding of their biological significance. To address these challenges, we developed an open-source, containerized pipeline for identifying and quantifying miRNAs and isomiRs (FARmiR: Framework for Analysis and Refinement of miRNAs), and an associated interactive browser (AIMEE: Animal IsomiR and MiRNA Expression Explorer). AIMEE was developed to make miRNA expression data more accessible and user-friendly, a feature often lacking from other expression atlases. These tools were developed using equine data but can be readily extended to other species. Using these tools, we aggregated 461 small RNA-seq datasets, spanning 61 distinct tissues, integrating data from public repositories, an American Quarter Horse cohort, and the Functional Annotation of ANimal Genome (FAANG) consortium Thoroughbred samples, predicting 5,781 miRNAs and isomiRs. This work represents the largest systematically curated atlas of equine miRNA expression to date, providing a valuable resource that will enhance our understanding of miRNA and isomiR functions in tissue-specific regulation and ultimately improve biomarker discovery, functional genomics, and precision veterinary medicine.
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.
Overview
This study developed new computational tools and a comprehensive database to identify, quantify, and visualize microRNA (miRNA) and their isoforms (isomiRs) in horses.
The research provides the largest equine miRNA expression atlas across many tissues, improving understanding of gene regulation in horses and aiding veterinary medicine.
Background
MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression post-transcriptionally, playing critical roles in development, tissue function, and disease.
Most miRNA research has focused on model organisms; non-model species like horses have limited miRNA databases and expression data.
IsomiRs, which are variants of canonical miRNAs differing in length and sequence, can have different regulatory targets and biological functions but are even less studied in horses.
The lack of data on miRNAs and isomiRs hampers understanding of their roles in equine biology and limits biomarker discovery and precision veterinary medicine.
Research Aims and Tools Developed
The authors aimed to fill the gap by creating a unified computational pipeline to identify and quantify both miRNAs and isomiRs from small RNA sequencing data.
They developed FARmiR (Framework for Analysis and Refinement of miRNAs), an open-source, containerized pipeline designed to process miRNA expression data efficiently and reproducibly.
An interactive web-based browser named AIMEE (Animal IsomiR and MiRNA Expression Explorer) was created for intuitive visualization and exploration of miRNA and isomiR expression data.
Both tools are species-agnostic and can be adapted for other animals beyond horses.
Data Aggregation and Analysis
The study compiled 461 small RNA-sequencing datasets representing 61 distinct equine tissue types.
Data was sourced from public repositories, a cohort of American Quarter Horses, and samples from the FAANG consortium’s Thoroughbred horse collection.
Applying FARmiR to this extensive dataset allowed identification and quantification of 5,781 miRNAs and isomiRs.
This large-scale, systematic approach represents the most comprehensive equine miRNA/isomiR expression atlas constructed to date.
Significance and Applications
The atlas provides detailed, tissue-specific expression profiles of miRNAs and their isoforms in horses.
It facilitates improved understanding of miRNA-mediated gene regulation mechanisms in equine biology and disease.
The AIMEE browser makes the data accessible and user-friendly for researchers, enhancing opportunities for discovery.
This resource supports biomarker identification efforts, enabling better diagnostics and precision treatment strategies in veterinary medicine.
The pipeline and browser can be extended to other species, benefiting broader non-model organism functional genomics.
Conclusions
This work addresses an important gap by integrating computational tools and large datasets to produce a high-resolution map of equine miRNA and isomiR expression.
It empowers researchers with resources to study post-transcriptional regulation in horses, aiding functional genomics and translational applications.
The open-source, modular design ensures adaptability and ongoing improvements for studying miRNAs in diverse animal species.
Cite This Article
APA
Cullen JN, Cieslak J, Petersen JL, Bellone RR, Finno CJ, Kalbfleisch TS, Calloe K, Capomaccio S, Cappelli K, Coleman SJ, Distl O, Durward-Akhurst SA, Giulotto E, Hamilton NA, Hill EW, Katz LM, Klaerke DA, Lindgren G, MacHugh DE, Mackowski M, MacLeod JN, Metzger J, Murphy BA, Orlando L, Raudsepp T, Silvestrelli M, Strand E, Tozaki T, Trachsel DS, Valderrama Figueroa LS, Velie BD, Wade CM, Waud B, Mickelson JR, McCue ME.
(2025).
Charting the equine miRNA landscape: An integrated pipeline and browser for annotating, quantifying, and visualizing expression.
PLoS Genet, 21(9), e1011835.
https://doi.org/10.1371/journal.pgen.1011835
Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, United States of America.
Cieslak, Jakub
Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Poznan, Poland.
Petersen, Jessica L
Department of Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America.
Bellone, Rebecca R
Department of Population Health and Reproduction, School of Veterinary Medicine, University of California - Davis, Davis, California, United States of America.
Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California - Davis, Davis, California, United States of America.
Finno, Carrie J
Department of Population Health and Reproduction, School of Veterinary Medicine, University of California - Davis, Davis, California, United States of America.
Kalbfleisch, Ted S
Department of Veterinary Science, Martin-Gatton College of Agriculture, Food, and Environment, University of Kentucky, Lexington, Kentucky, United States of America.
Calloe, Kirstine
Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark.
Capomaccio, Stefano
Sport Horse Research Centre, Department of Veterinary Medicine, University of Perugia, Perugia, Italy.
Cappelli, Katia
Sport Horse Research Centre, Department of Veterinary Medicine, University of Perugia, Perugia, Italy.
Coleman, Stephen J
Department of Animal Sciences, Colorado State University, Fort Collins, Colorado, United States of America.
Distl, Ottmar
Institute of Animal Genomics, University of Veterinary Medicine Hannover, Hannover, Germany.
Durward-Akhurst, Sian A
Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, United States of America.
Giulotto, Elena
Department of Biology and Biotechnology, University of Pavia, Pavia, Italy.
Hamilton, Natasha A
Equine Genetics Research Centre, Racing Australia, Scone, New South Wales, Australia.
Hill, Emmeline W
UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland.
Katz, Lisa M
UCD School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland.
Klaerke, Dan A
Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark.
Lindgren, Gabriella
Department of Animal Biosciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Department of Biosystems, Center for Animal Breeding and Genetics, KU Leuven, Leuven, Belgium.
MacHugh, David E
UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland.
UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland.
Mackowski, Mariusz
Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Poznan, Poland.
MacLeod, James N
Department of Veterinary Science, Gluck Equine Research Center, University of Kentucky, Lexington, Kentucky, United States of America.
Metzger, Julia
Institute of Animal Genomics, University of Veterinary Medicine Hannover, Hannover, Germany.
Murphy, Barbara A
UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland.
Orlando, Ludovic
Centre for Anthropobiology and Genomics of Toulouse, CNRS, Université de Toulouse, Toulouse, France.
Raudsepp, Terje
College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, United States of America.
Silvestrelli, Maurizio
Sport Horse Research Centre, Department of Veterinary Medicine, University of Perugia, Perugia, Italy.
Strand, Eric
Faculty of Veterinary Medicine, Norwegian University of Life Sciences, Aas, Norway.
Tozaki, Teruaki
Genetic Analysis Department, Laboratory of Racing Chemistry, Utsunomiya, Tochigi, Japan.
Trachsel, Dagmar S
Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark.
Valderrama Figueroa, Laura S
Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, United States of America.
Velie, Brandon D
Equine Genetics & Genomics Group, School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia.
Wade, Claire M
School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia.
Waud, Bianca
Sydney School of Veterinary Science, University of Sydney, Sydney, New South Wales, Australia.
Mickelson, James R
Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, St Paul, Minnesota, United States of America.
McCue, Molly E
Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, United States of America.
MeSH Terms
Horses / genetics
MicroRNAs / genetics
Animals
Gene Expression Regulation
Software
Molecular Sequence Annotation
Databases, Genetic
Computational Biology / methods
Gene Expression Profiling
Web Browser
Grant Funding
K12 TR004373 / NCATS NIH HHS
KL2 TR002492 / NCATS NIH HHS
UL1 TR002494 / NCATS NIH HHS
UM1 TR004405 / NCATS NIH HHS
Conflict of Interest Statement
The authors have declared that no competing interests exist.
References
This article includes 128 references
Huntzinger E, Izaurralde E. Gene silencing by microRNAs: contributions of translational repression and mRNA decay. Nat Rev Genet 2011;12(2):99–110.
de Rie D, Abugessaisa I, Alam T, Arner E, Arner P, Ashoor H. An integrated expression atlas of miRNAs and their promoters in human and mouse. Nat Biotechnol 2017;35(9):872–8.
Keller A, Gröger L, Tschernig T, Solomon J, Laham O, Schaum N. miRNATissueAtlas2: an update to the human miRNA tissue atlas. Nucleic Acids Res 2022;50(D1):D211–21.
Yao X, Sun S, Zi Y, Liu Y, Yang J, Ren L. Comprehensive microRNA-seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats. Sci Data 2022;9(1):201.
Morin RD, O’Connor MD, Griffith M, Kuchenbauer F, Delaney A, Prabhu A-L. Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res 2008;18(4):610–21.
Kawahara Y, Zinshteyn B, Sethupathy P, Iizasa H, Hatzigeorgiou AG, Nishikura K. Redirection of silencing targets by adenosine-to-inosine editing of miRNAs. Science 2007;315(5815):1137–40.
Glogovitis I, Yahubyan G, Würdinger T, Koppers-Lalic D, Baev V. isomiRs-Hidden Soldiers in the miRNA Regulatory Army, and How to Find Them?. Biomolecules 2020;11(1):41.
Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 2007;129(7):1401–14.
Fromm B, Billipp T, Peck LE, Johansen M, Tarver JE, King BL. A Uniform System for the Annotation of Vertebrate microRNA Genes and the Evolution of the Human microRNAome. Annu Rev Genet 2015;49:213–42.
Fromm B, Høye E, Domanska D, Zhong X, Aparicio-Puerta E, Ovchinnikov V. MirGeneDB 2.1: toward a complete sampling of all major animal phyla. Nucleic Acids Res 2022;50(D1):D204–10.
Cloonan N, Wani S, Xu Q, Gu J, Lea K, Heater S. MicroRNAs and their isomiRs function cooperatively to target common biological pathways.. Genome Biol 2011;12(12):R126.
Tan GC, Chan E, Molnar A, Sarkar R, Alexieva D, Isa IM. 5’ isomiR variation is of functional and evolutionary importance.. Nucleic Acids Res 2014;42(14):9424–35.
Telonis AG, Loher P, Jing Y, Londin E, Rigoutsos I. Beyond the one-locus-one-miRNA paradigm: microRNA isoforms enable deeper insights into breast cancer heterogeneity.. Nucleic Acids Res 2015;43(19):9158–75.
Salem O, Erdem N, Jung J, Münstermann E, Wörner A, Wilhelm H. The highly expressed 5’isomiR of hsa-miR-140-3p contributes to the tumor-suppressive effects of miR-140 by reducing breast cancer proliferation and migration.. BMC Genomics 2016;17:566.
van der Kwast RVCT, Woudenberg T, Quax PHA, Nossent AY. MicroRNA-411 and Its 5’-IsomiR Have Distinct Targets and Functions and Are Differentially Regulated in the Vasculature under Ischemia.. Mol Ther 2020;28(1):157–70.
Manzano M, Forte E, Raja AN, Schipma MJ, Gottwein E. Divergent target recognition by coexpressed 5’-isomiRs of miR-142-3p and selective viral mimicry.. RNA 2015;21(9):1606–20.
Londin E, Loher P, Telonis AG, Quann K, Clark P, Jing Y. Analysis of 13 cell types reveals evidence for the expression of numerous novel primate- and tissue-specific microRNAs.. Proc Natl Acad Sci U S A 2015;112(10):E1106-15.
Aparicio-Puerta E, Hirsch P, Schmartz GP, Fehlmann T, Keller V, Engel A. isomiRdb: microRNA expression at isoform resolution.. Nucleic Acids Res 2023;51(D1):D179–85.
Telonis AG, Magee R, Loher P, Chervoneva I, Londin E, Rigoutsos I. Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types.. Nucleic Acids Res 2017;45(6):2973–85.
Ma M, Yin Z, Zhong H, Liang T, Guo L. Analysis of the expression, function, and evolution of miR-27 isoforms and their responses in metabolic processes.. Genomics 2019;111(6):1249–57.
Schmartz GP, Kern F, Fehlmann T, Wagner V, Fromm B, Keller A. Encyclopedia of tools for the analysis of miRNA isoforms.. Brief Bioinform 2021;22(4):bbaa346.
Schmauch E, Laitinen P, Turunen TA, Väänänen M-A, Malm T, Kellis M. isomiRs-specific differential expression is the rule, not the exception: Are we missing hundreds of species in microRNA analysis?. Cold Spring Harbor Laboratory 2021.
Bofill-De Ros X, Luke B, Guthridge R, Mudunuri U, Loss M, Gu S. Tumor IsomiR Encyclopedia (TIE): a pan-cancer database of miRNA isoforms.. Bioinformatics 2021;37(18):3023–5.
Platt RN II, Vandewege MW, Kern C, Schmidt CJ, Hoffmann FG, Ray DA. Large Numbers of Novel miRNAs Originate from DNA Transposons and Are Coincident with a Large Species Radiation in Bats.. Molecular Biology and Evolution 2014;31(6):1536–45.
Loher P, Karathanasis N, Londin E, F Bray P, Pliatsika V, Telonis AG. IsoMiRmap: fast, deterministic and exhaustive mining of isomiRs from short RNA-seq datasets.. Bioinformatics 2021;37(13):1828–38.
Ibing S, Michels BE, Mosdzien M, Meyer HR, Feuerbach L, Körner C. On the impact of batch effect correction in TCGA isomiR expression data.. NAR Cancer 2021;3(1):zcab007.
Wang Q, Armenia J, Zhang C, Penson AV, Reznik E, Zhang L. Unifying cancer and normal RNA sequencing data from different sources.. Sci Data 2018;5:180061.
Ahmed K, LaPierre MP, Gasser E, Denzler R, Yang Y, Rülicke T. Loss of microRNA-7a2 induces hypogonadotropic hypogonadism and infertility.. J Clin Invest 2017;127(3):1061–74.
Wang C-J, Gao F, Huang Y-J, Han D-X, Zheng Y, Wang W-H. circAkap17b acts as a miR-7 family molecular sponge to regulate FSH secretion in rat pituitary cells.. J Mol Endocrinol 2020;65(4):135–48.
Yuan B, Sun GJ, Zhang GL, Wu J, Xu C, Dai LS. Identification of target genes for adenohypophysis-prefer miR-7 and miR-375 in cattle.. Genet Mol Res 2015;14(3):9753–63.
Zhang Y, Huang F, Xu N, Wang J, Li D, Yin L. Overexpression of serum extracellular vesicle microRNA-215-5p is associated with early tumor recurrence and poor prognosis of gastric cancer.. Clinics (Sao Paulo) 2021;76:e2081.
Vychytilova-Faltejskova P, Merhautova J, Machackova T, Gutierrez-Garcia I, Garcia-Solano J, Radova L. MiR-215-5p is a tumor suppressor in colorectal cancer targeting EGFR ligand epiregulin and its transcriptional inducer HOXB9.. Oncogenesis 2017;6(11):399.
Sun B, Xing K, Qi C, Yan K, Xu Y. Down-regulation of miR-215 attenuates lipopolysaccharide-induced inflammatory injury in CCD-18co cells by targeting GDF11 through the TLR4/NF-kB and JNK/p38 signaling pathways.. Histol Histopathol 2020;35(12):1473–81.
Deiuliis JA. MicroRNAs as regulators of metabolic disease: pathophysiologic significance and emerging role as biomarkers and therapeutics.. Int J Obes (Lond) 2016;40(1):88–101.
Han H, Chen Q, Gao Y, Li J, Li W, Dang R, et al. Comparative Transcriptomics Analysis of Testicular miRNA from Cryptorchid and Normal Horses. Animals (Basel). 2020;10(2):338. doi: 10.3390/ani10020338
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60. doi: 10.1093/bioinformatics/btp324
Li Y, Zhang Z, Liu F, Vongsangnak W, Jing Q, Shen B. Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis. Nucleic Acids Res. 2012;40(10):4298–305. doi: 10.1093/nar/gks043
Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, et al. Genome sequence, comparative analysis, and population genetics of the domestic horse. Science. 2009;326(5954):865–7. doi: 10.1126/science.1178158
Fridrich A, Hazan Y, Moran Y. Too Many False Targets for MicroRNAs: Challenges and Pitfalls in Prediction of miRNA Targets and Their Gene Ontology in Model and Non-model Organisms. Bioessays. 2019;41(4):e1800169. doi: 10.1002/bies.201800169
Smith CM, Hutvagner G. A comparative analysis of single cell small RNA sequencing data reveals heterogeneous isomiR expression and regulation. Sci Rep. 2022;12(1):2834. doi: 10.1038/s41598-022-06876-3
McCall MN, Kim M-S, Adil M, Patil AH, Lu Y, Mitchell CJ, et al. Toward the human cellular microRNAome. Genome Res. 2017;27(10):1769–81. doi: 10.1101/gr.222067.117
Hagemann-Jensen M, Abdullayev I, Sandberg R, Faridani OR. Small-seq for single-cell small-RNA sequencing. Nat Protoc. 2018;13(10):2407–24. doi: 10.1038/s41596-018-0049-y
Zarski LM, Weber PSD, Lee Y, Soboll Hussey G. Transcriptomic Profiling of Equine and Viral Genes in Peripheral Blood Mononuclear Cells in Horses during Equine Herpesvirus 1 Infection. Pathogens. 2021;10(1):43. doi: 10.3390/pathogens10010043
Pawlina-Tyszko K, Oczkowicz M, Gurgul A, Szmatoła T, Bugno-Poniewierska M. MicroRNA profiling of the pig periaqueductal grey (PAG) region reveals candidates potentially related to sex-dependent differences. Biol Sex Differ. 2020;11(1):67. doi: 10.1186/s13293-020-00343-2
Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–90. doi: 10.1093/bioinformatics/bty560
Tsuji J, Weng Z. DNApi: A De Novo Adapter Prediction Algorithm for Small RNA Sequencing Data. PLoS One. 2016;11(10):e0164228. doi: 10.1371/journal.pone.0164228
Sanchez Herrero JF, Pluvinet R, Luna de Haro A, Sumoy L. Paired-end small RNA sequencing reveals a possible overestimation in the isomiR sequence repertoire previously reported from conventional single read data analysis. BMC Bioinformatics. 2021;22(1):215. doi: 10.1186/s12859-021-04128-1
Zhang X, Ping P, Hutvagner G, Blumenstein M, Li J. Aberration-corrected ultrafine analysis of miRNA reads at single-base resolution: a k-mer lattice approach. Nucleic Acids Res. 2021;49(18):e106. doi: 10.1093/nar/gkab610
Kalvari I, Nawrocki EP, Argasinska J, Quinones-Olvera N, Finn RD, Bateman A, et al. Non-Coding RNA Analysis Using the Rfam Database. Curr Protoc Bioinformatics. 2018;62(1):e51. doi: 10.1002/cpbi.51
Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25. doi: 10.1186/gb-2009-10-3-r25
Li J, Kho AT, Chase RP, Pantano L, Farnam L, Amr SS, et al. COMPSRA: a COMprehensive Platform for Small RNA-Seq data Analysis. Sci Rep. 2020;10(1):4552. doi: 10.1038/s41598-020-61495-0
Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30. doi: 10.1093/bioinformatics/btt656
Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8. doi: 10.1093/bioinformatics/btw354
Park C, Kim H, Wang M. Investigation of finite-sample properties of robust location and scale estimators. Communications in Statistics - Simulation and Computation. 2020;51(5):2619–45. doi: 10.1080/03610918.2019.1699114
Shi J, Dong M, Li L, Liu L, Luz-Madrigal A, Tsonis PA, et al. mirPRo-a novel standalone program for differential expression and variation analysis of miRNAs. Sci Rep. 2015;5:14617. doi: 10.1038/srep14617
Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40(1):37–52. doi: 10.1093/nar/gkr688
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841–2. doi: 10.1093/bioinformatics/btq033
Bilbao-Arribas M, Guisasola-Serrano A, Varela-Martínez E, Jugo BM. The sheep miRNAome: Characterization and distribution of miRNAs in 21 tissues. Gene. 2023;851:146998. doi: 10.1016/j.gene.2022.146998
Desvignes T, Loher P, Eilbeck K, Ma J, Urgese G, Fromm B, et al. Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API. Bioinformatics. 2020;36(3):698–703. doi: 10.1093/bioinformatics/btz675
Loher P, Londin ER, Rigoutsos I. IsomiR expression profiles in human lymphoblastoid cell lines exhibit population and gender dependencies. Oncotarget. 2014;5(18):8790–802. doi: 10.18632/oncotarget.2405
Pliatsika V, Loher P, Telonis AG, Rigoutsos I. MINTbase: a framework for the interactive exploration of mitochondrial and nuclear tRNA fragments. Bioinformatics. 2016;32(16):2481–9. doi: 10.1093/bioinformatics/btw194
Fay C, Guyader V, Rochette S, Girard C. Golem: A framework for robust shiny applications. https://github.com/ThinkR-open/golem. 2023.
Krassowski M. ComplexUpset: Create Complex UpSet Plots Using “ggplot2” Components. https://cran.r-project.org/web/packages/ComplexUpset/index.html. 2021.
Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–3. doi: 10.1093/bioinformatics/bts034
Ludwig N, Leidinger P, Becker K, Backes C, Fehlmann T, Pallasch C, et al. Distribution of miRNA expression across human tissues. Nucleic Acids Res. 2016;44(8):3865–77. doi: 10.1093/nar/gkw116
Yanai I, Benjamin H, Shmoish M, Chalifa-Caspi V, Shklar M, Ophir R, et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics. 2005;21(5):650–9. doi: 10.1093/bioinformatics/bti042