Skip to content
Using PubMed to find out how a gene contributes to addiction.
HTML Python Other
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
static
templates
.gitignore
Readme.md
gwas_addiction.tab
process_gwas.py
ratspub.py
ratspub_keywords.py
server.py
stop_words_addiction_gene_search.txt
topGene_step0_extract_gene_alias_from_gene_info.sh
topGene_step1_cnt_abstracts.py
topGene_step2_cnt_sentences.py
topGene_step3_generate_html.py
topGene_step4_get_pmids_for_all_top_genes.py
topGene_symb_alias.txt
top_150_genes_symb_alias.txt

Readme.md

RatsPub: Relationship with Addiction Through Searches of PubMed

This app searches PubMed to find sentences that contain the query terms (e.g., gene symbols) and a drug addiction related keyword. These keywords belong to the following categories:

  • names of abused drugs, e.g., opioids
  • terms describing addiction, e.g., relapse
  • key brain regions implicated in addiction, e.g., ventral striatum
  • neurotrasmission, e.g., dopaminergic
  • synaptic plasticity, e.g., long term potentiation
  • intracellular signaling, e.g., phosphorylation

Live searches are conducted through PubMed to get relevant PMIDs, which are then used to retrieve the abstracts from a local archive. The relationships are presented as an interactive cytoscape graph. The nodes can be moved around to better reveal the connections. Clicking on the links will bring up the corresponding sentences in a new browser window.

top addiction related genes

  1. extract gene symbol, alias and name from NCBI gene_info for taxid 9606.
grep ^9606 ~/Downloads/gene_info |cut -f 3,5,12|sed "s/\t-//"|sed "s/\t/|/2"|sed "s/\t-//"|grep -v ^LOC|grep -v -i pseudogene|sed "s/(|)\// /g" |sort >ncbi_gene_symb_syno_name_txid9606.txt 
  1. search PubMed to get a count of these names/alias, with addiction keywords and drug name
  2. sort the genes with top counts, retrieve the abstracts and extract sentences with the 1) symbols and alias and 2) one of the keywords. manually check if there are stop words need to be removed.
  3. sort the genes based on the number of abstracts with useful sentences.
  4. generate the final list, include symbol, alias, and name

dependencies

planned

  • NLP analysis of the senences (topic modeling, ranking, etc.)
You can’t perform that action at this time.