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lncRNA Function Prediction


Gene-lncRNA correlations were computed from the 11,284 TCGA RNA-seq samples processed by Recount2 (Collado-Torres et al., 2017). We quantile normalized the gene counts and computed the Pearson’s correlation coefficient across all genes. We define genes as lncRNA genes when their gene_biotype is annotated by BioMart (Smedley et al., 2009) as lncRNA and it is not a protein coding gene based on EntrezGene. Then this gene-lncRNA correlation matrix used to predict novel lncRNA functions. A lncRNA gene symbol can be entered into the search field of the start page. The search will return a table with ranked gene sets representing a biological function. The method applied is the same as described for creating gene function prediction for the Geneshot application (Lachmann et al, 2019).

Gene-lncRNA Correlation

Gene-lncRNA correlation is computed from the 11,284 TCGA samples processed by recount2. We quantile normalize the gene counts and compute Pearson correlation. We define genes as lncRNA genes when their gene_biotype is annotated by biomaRt as lncRNA.

API Documentation

lncHUB supports GET functions that enable programmatic access to information about lncRNA function predictions. Below we demonstrate examples on how to access this information with Python.

Retrieve lncRNAs and their maximal scores
Method
GET
URL
/api/listlnc
Returns
JSON object containing list with fields: "lnc", "phenotype", "pathway", "process", "gene". These fields point to arrays with maximal association score for the lncRNA in the corresponsing domain (Phenotype, Pathways, Processes, Gene correlation). "lnc" is an array of lncRNA genes. The order of the association scores in "phenotype", "pathway", "process", and "gene" matches the order in "lnc".
Paramaters
none
Example 
import requests

LNCHUB_URL = 'http://amp.pharm.mssm.edu/lnchub/api/listlnc'
response = requests.get(LNCHUB_URL)
data = response.json()

print(data)
Example Result 
{
  "lnc": ["MAP3K14", "PSMB1", "LINC01587", ... ],
  "phenotype": [ 7.323, 4.753, 3.488, ... ],
  "pathway": [ 4.613, 6.052, 1.591, ... ],
  "process": [ 4.994, 6.444, 3.368, ... ],
  "gene": [ 0.42, 0.644, 0.341, ... ]
}

Retrieve lncRNA assiciation scores for one lncRNA and a functional domain
Method
GET
URL
/api/lnc
Returns
JSON object containing list with fields: "lncrna", "domain", "attributes", and "association_score". "lncrna" is the official gene symbol of lncRNA, "domain" is one of {"phenotype", "pathway", "process", "gene"}. "attributes" and "association_score" are arrays with domain function names and matching association scores respectively. Both arrays are sorted by decending association scores.
Paramaters
lnc
Gene Symbol of lncRNA
domain
Functional Domain {phenotype, pathway, process, gene}
Example 
import requests

LNCHUB_URL = 'http://amp.pharm.mssm.edu/lnchub/api/lnc'
parameters = {"lnc": "PSMB1", "domain": "pathway"}
response = requests.get(LNCHUB_URL, parameters)
data = response.json()

print(data)
Example Result 
{
  lncrna: "PSMB1",
  domain: "pathway",
  attributes: [ "Proteasome", "Nucleotide excision repair", "Ribosome", "Pyrimidine metabolism", ... ],
  association_score: [ 6.053, 4.407, 4.22, 4.214, ...]
}

Terms of Use

lncHUB is not published at the moment. Please acknowledge predictions made with lncHUB by citing the following reference (Geneshot):

Geneshot: search engine for ranking genes from arbitrary text queries
Alexander Lachmann Brian M Schilder Megan L Wojciechowicz Denis Torre Maxim V Kuleshov Alexandra B Keenan Avi Ma’ayan
Nucleic Acids Research, gkz393, https://doi.org/10.1093/nar/gkz393


The lncHUB source code is available from GitHub under the Apache License 2.0. Commercial users should contact Mount Sinai Innovation Partners at MSIPInfo@mssm.edu for licensing.

GitHub Repository
The Geneshot source code is available on GitHub at https://github.com/MaayanLab/lnchub

Disclaimer
lncHUB is not to be used for treating or diagnosing human subjects. lncHUB or any documents available from this server are provided as is without any warranty of any kind, either express, implied, or statutory, including, but not limited to, any implied warranties of merchantability, fitness for particular purpose and freedom from infringement, or that lncHUB or any documents available from this server will be error free. The Ma'ayan Laboratory makes no representations that the use of lncHUB or any documents available from this server will not infringe any patent or proprietary rights of third parties. In no event will the Ma'ayan Laboratory or any of its members be liable for any damages, including but not limited to direct, indirect, special or consequential damages, arising out of, resulting from, or in any way connected with the use of lncHUB or documents available from this server.

Contact

Licencing
MSIPInfo@mssm.edu

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