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It quantifies expression variation between the two haplotypes of a diploid individual distinguished by heterozygous sites, and is a powerful tool to estimate\n                      <jats:italic>cis-<\/jats:italic>\n                      regulatory diversity of alleles. Clustering algorithms can be used to identify patterns or groups of genes\/samples based on their expression profiles. Depending on the structure of the data, different existing clustering algorithm can be adapted to allele specific expression data. However, no\n                      <jats:italic>ad-hoc<\/jats:italic>\n                      procedure has been developed.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this work, we begin defining an expression matrix capturing allele expressions from an RNA-sequencing experiment. On this matrix, we develop a novel two-phase unsupervised clustering procedure, built on top of a spectral clustering algorithm, whose aim is to partition the population into groups of similar individuals, according to their allelic expression. As case-studies, the approach is used to cluster 98 cultivars representative of the variability observed in Vitis vinifera, starting from read counts of genes of chromosome 1 of leaves, and to analyze allele-specific count data from a CASTxMRL F1 hybrid mice dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Using the above mentioned real case-studies as well as generated synthetic data, we see that our algorithm shows significant robustness and outperforms other standard clustering techniques.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-026-06398-z","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T06:54:15Z","timestamp":1775026455000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A two-phase clustering procedure based on allele specific expression"],"prefix":"10.1186","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9672-4326","authenticated-orcid":false,"given":"Roberto","family":"Pagliarini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Nascimben","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto","family":"Policriti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"issue":"1","key":"6398_CR1","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1007\/s12561-012-9068-3","volume":"5","author":"W Sun","year":"2013","unstructured":"Sun W, Hu Y. eQTL mapping using RNA-seq data. 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