{"id":6,"date":"2023-05-31T11:23:40","date_gmt":"2023-05-31T03:23:40","guid":{"rendered":"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/?page_id=6"},"modified":"2025-12-02T11:45:45","modified_gmt":"2025-12-02T03:45:45","slug":"introduction","status":"publish","type":"page","link":"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/","title":{"rendered":"Introduction"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>FeatureHunter<\/em> was developed as the ultimate solution for untargeted nucleic acid adductomics, empowering researchers to effortlessly process data from a wide range of cutting-edge high-resolution mass spectrometry (HRMS) and leading MS vendors (e.g., Agilent, Thermo, and Waters). It was designed to process massive LC-HRMS datasets in the common data format mzML. <em>FeatureHunter<\/em> revolutionizes the process of nucleic acid modification analysis by offering automated extraction, annotation, and classification. Researchers can harness the power of a user-defined feature list to effortlessly identify and categorize multiple nucleic acid modifications with precision and efficiency. In its current version, FeatureHunter can identify ten types of nucleic acid modifications. Moreover, the program also enables peak-pair detection in LC-HRMS projects. Our main goal is to deliver a flexible and user-friendly software solution that streamlines the analysis process, facilitates comprehensive data interpretation, and enables seamless data integration for nucleic acid adductomics.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Please include the following publication citation when using FeatureHunter for your data analysis:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Chiung-Wen Hu, Yuan-Jhe Chang, Wei-Hung Chang, Marcus S. Cooke, Yet-Ran Chen, and Mu-Rong Chao. A Novel Adductomics Workflow Incorporating FeatureHunter Software: Rapid Detection of Nucleic Acid Modifications for Studying the Exposome. <em>Environmental Science &amp; Technology<\/em> (2024). <a href=\"https:\/\/doi.org\/10.1021\/acs.est.3c04674\">https:\/\/doi.org\/10.1021\/acs.est.3c04674<\/a><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"wp-block-image\">\n<figure class=\"alignright size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"559\" height=\"447\" src=\"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/wp-content\/uploads\/2023\/06\/ABRC-logo.png\" alt=\"\" class=\"wp-image-149\" style=\"width:185px;height:auto\" srcset=\"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/wp-content\/uploads\/2023\/06\/ABRC-logo.png 559w, https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/wp-content\/uploads\/2023\/06\/ABRC-logo-300x240.png 300w\" sizes=\"auto, (max-width: 559px) 100vw, 559px\" \/><\/figure>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"337\" height=\"351\" data-id=\"477\" src=\"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/wp-content\/uploads\/2023\/12\/\u5716\u72472.png\" alt=\"\" class=\"wp-image-477\" style=\"width:146px;height:auto\"\/><\/figure>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>FeatureHunter was developed as the ultimate solution for untargeted nucleic acid adductomics, empowering researchers to effortlessly process data from a wide range of cutting-edge high-resolution mass spectrometry (HRMS) and leading MS vendors (e.g., Agilent, Thermo, and Waters). It was designed to process massive LC-HRMS datasets in the common data format mzML. FeatureHunter revolutionizes the processContinue reading &rarr;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-6","page","type-page","status-publish","hentry","no-thumb"],"_links":{"self":[{"href":"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/index.php?rest_route=\/wp\/v2\/pages\/6","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6"}],"version-history":[{"count":18,"href":"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/index.php?rest_route=\/wp\/v2\/pages\/6\/revisions"}],"predecessor-version":[{"id":586,"href":"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/index.php?rest_route=\/wp\/v2\/pages\/6\/revisions\/586"}],"wp:attachment":[{"href":"https:\/\/msomics.abrc.sinica.edu.tw\/FeatureHunter\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}