<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">medgen</journal-id><journal-title-group><journal-title xml:lang="ru">Медицинская генетика</journal-title><trans-title-group xml:lang="en"><trans-title>Medical Genetics</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2073-7998</issn><publisher><publisher-name>Publishing House «Genius Media» LLC</publisher-name></publisher></journal-meta><article-meta><article-id custom-type="elpub" pub-id-type="custom">medgen-1818</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>КРАТКИЕ СООБЩЕНИЯ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>BRIEF REPORT</subject></subj-group></article-categories><title-group><article-title>Деконволюция клеточного состава тканей на основе данных секвенирования микроРНК</article-title><trans-title-group xml:lang="en"><trans-title>Tissue-specific deconvolution of cell composition using miRNA sequencing data</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Зарубин</surname><given-names>А. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Zarubin</surname><given-names>A. A.</given-names></name></name-alternatives><email xlink:type="simple">aleksei.zarubin@medgenetics.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Марков</surname><given-names>А. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Markov</surname><given-names>A. V.</given-names></name></name-alternatives><email xlink:type="simple">noemail@neicon.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Слепцов</surname><given-names>А. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Sleptcov</surname><given-names>A. A.</given-names></name></name-alternatives><email xlink:type="simple">noemail@neicon.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Назаренко</surname><given-names>М. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Nazarenko</surname><given-names>M. S.</given-names></name></name-alternatives><email xlink:type="simple">noemail@neicon.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Научно-исследовательский институт медицинской генетики, Томский национальный исследовательский медицинский центр Российской академии наук</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Research Institute of Medical Genetics, Tomsk National Research Medical Center, Russian Academy of Sciences</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2020</year></pub-date><pub-date pub-type="epub"><day>02</day><month>02</month><year>2021</year></pub-date><volume>19</volume><issue>12</issue><fpage>64</fpage><lpage>65</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Зарубин А.А., Марков А.В., Слепцов А.А., Назаренко М.С., 2021</copyright-statement><copyright-year>2021</copyright-year><copyright-holder xml:lang="ru">Зарубин А.А., Марков А.В., Слепцов А.А., Назаренко М.С.</copyright-holder><copyright-holder xml:lang="en">Zarubin A.A., Markov A.V., Sleptcov A.A., Nazarenko M.S.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.medgen-journal.ru/jour/article/view/1818">https://www.medgen-journal.ru/jour/article/view/1818</self-uri><abstract><p>Экспрессия микроРНК (miRNA) изменяется под действием различных факторов, что создаёт проблему интерпретации результатов исследований, поскольку патологические процессы могут изменять не только функциональную активность клеток, но и соотношения клеточных популяций. Возможным решением является применение подходов клеточной деконволюции. Нами был разработан алгоритм создания референсной панели экспрессии miRNA, специфичной для определенных типов клеток, что позволяет оценить представленность различных клеточных популяций в стенке артерий. Показано, что существующие алгоритмы деконволюции подходят для данных miRNA.</p></abstract><trans-abstract xml:lang="en"><p>The expression of miRNAs is affected by different factors. This poses challenges when interpreting the results of the study. Pathological processes can influence both the functional activity of cells and the ratio of cell populations. A possible solution is to utilize the approaches of deconvolution. We have developed an algorithm to assess reference panel from the microRNA dataset on the example of artery walls. In this study, we have shown that existing deconvolution algorithms are suitable for miRNA data.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>микроРНК</kwd><kwd>деконволюция</kwd><kwd>секвенирование</kwd></kwd-group><kwd-group xml:lang="en"><kwd>miRNA</kwd><kwd>deconvolution</kwd><kwd>sequencing</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">De Rie D. et al. An integrated expression atlas of miRNAs and their promoters in human and mouse. Nature biotechnology. 2017;35(9):872-878.</mixed-citation><mixed-citation xml:lang="en">De Rie D. et al. An integrated expression atlas of miRNAs and their promoters in human and mouse. Nature biotechnology. 2017;35(9):872-878.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Gong T., Szustakowski J. D. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics. 2013;29(8):1083-1085.</mixed-citation><mixed-citation xml:lang="en">Gong T., Szustakowski J. D. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics. 2013;29(8):1083-1085.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Newman A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nature methods. 2015;12(5):453-457.</mixed-citation><mixed-citation xml:lang="en">Newman A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nature methods. 2015;12(5):453-457.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
