<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>2021</volume><submitter>Lee YW</submitter><pubmed_abstract>Over the past few years, with the rapid growth of deep-sequencing technology and the development of computational prediction algorithms, a large number of long non-coding RNAs (lncRNAs) have been identified in various types of human cancers. Therefore, it has become critical to determine how to properly annotate the potential function of lncRNAs from RNA-sequencing (RNA-seq) data and arrange the robust information and analysis into a useful system readily accessible by biological and clinical researchers. In order to produce a collective interpretation of lncRNA functions, it is necessary to integrate different types of data regarding the important functional diversity and regulatory role of these lncRNAs. In this study, we utilized transcriptomic sequencing data to systematically observe </pubmed_abstract><journal>Database : the journal of biological databases and curation</journal><pagination>baab053</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8407485</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>lncExplore: a database of pan-cancer analysis and systematic functional annotation for lncRNAs from RNA-sequencing data.</pubmed_title><pmcid>PMC8407485</pmcid><pubmed_authors>Chen M</pubmed_authors><pubmed_authors>Chang TY</pubmed_authors><pubmed_authors>Lee YW</pubmed_authors><pubmed_authors>Chung IF</pubmed_authors></additional><is_claimable>false</is_claimable><name>lncExplore: a database of pan-cancer analysis and systematic functional annotation for lncRNAs from RNA-sequencing data.</name><description>Over the past few years, with the rapid growth of deep-sequencing technology and the development of computational prediction algorithms, a large number of long non-coding RNAs (lncRNAs) have been identified in various types of human cancers. Therefore, it has become critical to determine how to properly annotate the potential function of lncRNAs from RNA-sequencing (RNA-seq) data and arrange the robust information and analysis into a useful system readily accessible by biological and clinical researchers. In order to produce a collective interpretation of lncRNA functions, it is necessary to integrate different types of data regarding the important functional diversity and regulatory role of these lncRNAs. In this study, we utilized transcriptomic sequencing data to systematically observe </description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Aug</publication><modification>2025-04-04T13:48:58.99Z</modification><creation>2022-02-11T12:04:31.104Z</creation></dates><accession>S-EPMC8407485</accession><cross_references><pubmed>34464437</pubmed><doi>10.1093/database/baab053</doi></cross_references></HashMap>