RESOURCES

• https://www.genome.gov/10005107/the-encode-project-encyclopedia-of-dna-elements/
• https://genome.ucsc.edu/encode/
• https://www.ncbi.nlm.nih.gov/geo/
• https://www.ncbi.nlm.nih.gov/gap
• https://www.kaggle.com/datasets?sortBy=relevance&group=featured&search=cancerhttps://www.kaggle.com/xiaotawkaggle/inhibitors
• https://www.insilicodb.com/
• ClinVar - a database of relationships asserted between genetic variation and observed health status
• SURVIVOR - Toolset for SV simulation, comparison and filtering
• article - dbVar structural variant cluster set for data analysis and variant comparison
• Cancer Epitopes CSHL - given an SRA ID, prioritize and quantify variants with respect to immunogenicity (single score) + variant annotation
• Master_gff3_parser - Convert sequence IDs between ucsc/refseq/genbank
• here - Human Genome Resources at NCBI
• RefSeqGene - but this is probably the least ambiguous for grabbing enhancer regions!
• markdown-here - Google Chrome, Firefox, and Thunderbird extension that lets you write email in Markdown and render it before sending.
• getfasta - extracts sequences from a FASTA file for each of the intervals defined in a BED/GFF/VCF file.
• Master_gff3_parser - Convert sequence IDs between ucsc/refseq/genbank
• giab_data_indexes - This repository contains data indexes from NIST's Genome in a Bottle project.
• awsgiab - Genome in a Bottle on AWS
• tutorial on using SRA and dbGaP
• biotools - A list of useful bioinformatics resources
• Application for Participation in August (14-16) 2017 NCBI-Style Bioinformatics Hackathon at NLM (National Library of Medicine in Bethesda)
• articles - describes some of Ben Busby's experiences in running hackathons.
• hackathons - lists NCBI code results

COLLABORATIVE RESEARCH EVENTS (aka “Hackathons”)

SVAI uses “collaborative research events” (CREs) to apply the power of the computational biology community to the needs of targeted patient communities.  Like a movie production company, CREs pool the talents of a diverse set of participants for a short period of time to create a well defined deliverable.  CRE deliverables increase knowledge about the disease of interest and can be leveraged in future CREs.  For example the July 2017 CRE lasted 3 days and centered on NF2.  During that time, several groups made especially significant progress.

• The “AutoNF2 Team” extracted interaction networks from complex genomic data using “deep learning” by feeding an Autoencoder a vector of gene expressions (FPKM) to doing dimensionality reduction.  Large network weights indicated which feature was important.  Using this approach, the team postulated “miR-200a as a potential circulating marker for NF2”
• The “C3 Team” created 7 mutational signature clusters of brain cancer using 290 glioblastomas and 286 gliomas from TCGA.  Cluster analysis suggested that drugs targeting homologous repair, e.g. PARP inhibitors, could be used to treat the NF2 patient.

The p1RCC CRE will apply these NF2 CRE methods to the p1RCC dataset.