In lncExplore you can browse or search:

1. Novel lncRNAs predicted from TCGA RNA-sequencing datasets

2. Basic genome annotation: location, sequence, gene ontology and in silico predicted targeted coding gene

3. Regulatory annotations: enhancer RNAs and competing endogenous RNAs (miRNA binding sites in lncRNAs)

4. Pan-cancer analysis for lncRNAs expression comparison across 24 human cancers

5. lncRNAs-disease association: disease specificity score and clinical survival analysis

The Framework of lncExplore construction

Identify novel lncRNAs from RNA-seq datasets across 24 human cancers

Comprehensive annotation of lncRNA from different aspects including expression profile, gene ontology enrichment analysis, disease specificity score, potential regulatory role (eRNAs, and ceRNAs), and survival curve analysis

The comparison of lncExplore with other lncRNAs-related database:

Abbreviation: NA, not Available
Table1. The Comparison of lncExplore with available lncRNA bioinformatic resources

MAIN FUNCTIONS

1. Search Menu

   Search results -- sequence annotations:
   Step 1: Select ID type (ENSEMBL ID, lncExploree ID, or Gene Symbol) and Input your lncRNAs IDs
   Step 2: lncRNA annotation:
   Basic information: sequence, coding potential, disease specificity score
   Cis-regulatory: We searched coding genes 10k/100k upstream and downstream of lncRNA as the cis target gene
   Gene-Ontology: biological process (from Cis-regulatory 100k population), molecular function, cellular component. The GO results included p-value, q-value (fdr), fold enrichment and significant. Significant: the number of genes annotated with the term.

2. Expression
   Search results -- lncRNAs expression profile:
   Step 1: Select ID type (ENSEMBL ID, lncExploree ID, or Gene Symbol) and Input your lncRNAs IDs
   Step 2: A bar chart and a box plot will visualize the expression profiles of lncRNAs across different cancers
   Search results: comparison between two RNA transcripts:
   Step 1: Select ID type (ENSEMBL ID, lncExplore ID, and Gene Symbol) and Input your lncRNAs IDs.
   Step 2: A bar chart and a box plot will visualize the expression profiles of lncRNAs across different cancers.
   Step 3: A Pearson correlation coefficient will measure the strength of a linear association between two transcripts.
   Search results: Compare the Expression of Pairwise Datasets:
   SStep1: Select disease name and enter gene IDs (Eensembl ID, gene symbol or lncExplore ID).
   Step2: The scatter plot will visualize the pairwise comparison results.

3. Disease specificity
   Search results -- disease specificity score:
   Step 1: Search Disease Specificity using genomic coordinate and the region of Disease Specificity Score
   
   Step 2: This score reflects expression preferentially of the lncRNA contributed to a particular tissue. The score varies from 0 to 1. A score close to 1.0 indicates that lncRNA signature is restricted to specific tissues or cell lines and provides an important clue about biomarker candidate.

4. Clinical Analysis
   Search results -- survival curve analysis:
   Step 1: Choose the specific disease, gene identifier ( Ensembl ID, gene symbol or lncExplore ID) and quartile value.
   Step 2: Clinical information and lncRNA expression values will be integrated into a survival curve analysis. This provides useful information for evaluating the potential of the lncRNA as a biomarker.

5. Enhancer RNA
   Search results -- eRNA annotation:
   Step 1: Search Enhancer RNA using genomic coordinate or adjacent known transcripts
   Step 2: A list of genome-wide identified eRNAs will be provided based on the genomic information of the enhancer regions from VISTA database and ENSEMBL.

6. ceRNA Network
   Search results -- ceRNA annotation:
   Step 1: Enter a transcript identifier or miRNA identifier, for instance, has-miR-100-30 for browsing which microRNAs have interaction with ncT0114002055.
   Step 2: A list of precompiled interactions of miRNA and lncRNAs predicted by TargetScan will be provided.