{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T10:47:52Z","timestamp":1782902872069,"version":"3.54.5"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Genetic diagnosis of endocrine disorders is often hampered by the complexity of analyzing Whole Exome Sequencing (WES) data. We developed the endocrine variant extractor (EVE), a streamlined, clinician-friendly bioinformatics pipeline designed for multi-tier genetic screening with a core panel for parathyroid disorders (26 genes) and an expanded endocrine panel for broader metabolic assessment (413 genes, fully encompassing the parathyroid panel).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Encapsulated within a Docker container and automated via a custom Python wrapper, EVE integrates core bioinformatics engines, including BWA-MEM, GATK, and SnpEff. The pipeline employs a tiered reporting strategy, filtering and annotating variants across both panels using pathogenicity scores (SIFT, PolyPhen-2) and clinical databases (ClinVar, gnomAD). This architecture ensures cross-platform compatibility without complex manual configuration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      To validate the pipeline, EVE was applied to clinical datasets. EVE successfully filtered &amp;gt;300,000 raw variants down to a handful of actionable candidates. Using this pipeline, we precisely identified the first Korean case of a\n                      <jats:italic>de novo GATA3<\/jats:italic>\n                      frameshift variant (p.Ala173fs) in an HDR syndrome patient, which was not previously reported in the ClinVar database. Analysis took ~3 h, reducing manual data review by &amp;gt;99.6%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      EVE provides a streamlined, high-efficiency workflow that automates the filtering of thousands of raw WES variants into a curated list of clinically relevant variants. This robust framework enables the creation of a comprehensive \u201cendocrine variant atlas,\u201d empowering clinicians to integrate high-throughput genetic profiling into routine diagnostics and accelerate the discovery of novel disease-causing variants. The complete source code for EVE is freely available at\n                      <jats:ext-link>https:\/\/github.com\/hanyunseo01\/EVE<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1840651","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T10:19:15Z","timestamp":1782901155000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Establishment of the endocrine variant extractor and its clinical application in identifying a novel GATA3 mutation in HDR syndrome"],"prefix":"10.3389","volume":"17","author":[{"given":"Yunseo","family":"Han","sequence":"first","affiliation":[{"name":"Department of Life Sciences, Korea University","place":["Seoul, Republic of Korea"]},{"name":"National Research Laboratory for Convergence Degradation Biology, Korea University","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danbi","family":"Song","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Gachon University Gil Medical Center","place":["Incheon, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minsoo","family":"Noh","sequence":"additional","affiliation":[{"name":"Department of Life Sciences, Korea University","place":["Seoul, Republic of Korea"]},{"name":"Laboratory of Genomics and Translational Medicine, Department of Internal Medicine, Gachon University College of Medicine","place":["Incheon, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikyung","family":"Kang","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Gachon University Gil Medical Center","place":["Incheon, Republic of Korea"]},{"name":"Laboratory of Genomics and Translational Medicine, Department of Internal Medicine, Gachon University College of Medicine","place":["Incheon, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Sil","family":"Eom","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Gachon University Gil Medical Center","place":["Incheon, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hunsang","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Life Sciences, Korea University","place":["Seoul, Republic of Korea"]},{"name":"National Research Laboratory for Convergence Degradation Biology, Korea University","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sihoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Gachon University Gil Medical Center","place":["Incheon, Republic of Korea"]},{"name":"Laboratory of Genomics and Translational 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