FAQ

* How GenomeRunner can help me?

GenomeRunner helps to interpret potential regulatory effect of SNPs (features of interest, FOIs) by identifying functional elements (aka (epi)genomic features, GFs) most statistically significantly co-localized with them (see Enrichment analysis).

If one analyzes three or more sets of SNPs, such as SNPs from different individuals, populations, diseases, GenomeRunner visualizes their regulatory similarity (see Regulatory Similarity analysis). This information may be used, e.g., to group patients by similarity of their individual sets of genomic variants within sell type-specific regulatory landscapes.

* How should my input data look like (Step 1)?

Use tab-separated text files with genomic coordinates of the SNPs of interest in BED format, see examples. As a bare minimun, chromosome, start, and end coordinates should be provided. One can upload BED file(s), or copy-paste tab-separated coordinates.

Note: a set of SNPs should contain at least 5 SNPs to be eligible for the analysis. Genomic coordinates should be 0-bases. The end coordinate should equal start coordinate + 1.

Note: lists of rsIDs (e.g., rs2789489, rs4360154, rs630642), each rsID is on a separate line, can be submitted.

* Do you have some sets of SNPs for demo runs?

Sure. Several buttons on the front page will select pre-defined sets of SNPs for the analysis. For Homo Sapiens these include (To be updated):

* What is the "background" (Step 2)?

See the Background section. In short, the background is a “universe” of all SNPs assessed in a study, from which the SNPs of interest came from. Several pre-defined background sets are provided, for Homo Sapiens these include:

For a GWAS, the background is likely to be all SNPs (snp141 for Homo Sapiens). For a study using microarrays, the background should contain coordinates of all SNPs on the microarray - upload or copy/paste them.

Note: The SNPs of interest should be a subset of the background SNPs. If some SNPs of interest do not overlap the background, a non-critical error is issued. Use BEDtools for creating custom backgrounds and for any other manipulations with the BED files.

* What are "regulatory datasets" (Step 3)?

Regulatory datasets are sets of discrete regions potentially having functional/regulatory properties. Vast majority of these data were experimentally obtained by the ENCODE project.

* There are just too many regulatory datasets!!! What to choose?

Don't panic. The genome annotation features are organized by categories mirrored from the UCSC genome browser (see Database structure). Use search box and/or checkboxes in the TreeView control to select one or more categories of regulatory datasets. Clicking on a regulatory dataset’ name will bring up description, if available.

The ENCODE data are organized by source/data type, tiers (quality), and by cell types. Hint: Several well-known/specially processes genome annotation features sets are brought forward as “default genome annotation features”. For Homo Sapiens these include:

Examples of what to choose:

• Select the tfbsEncode category to get an answer whether the SNPs of interest are enriched in any of the 161 transcription factor binding sites identified by ChIP-seq.
• Select the H3K4me3 category to get an insight whether the SNPs of interest are enriched in H3K4me3 histone mark, and in which tissue/cell type.
• Select the genes category to answer a question whether the SNPs of interest are enriched in genes/exons.

* How GenomeRunner web is different from previously published version?

The original version of GenomeRunner, hosted on SourceForge, was designed as an "all purpose" tool. It has several advantages over the web interface, as well as disadvantages, such as learning curve, various non-obvious settings, need to download large databases, complicated database maintenance, restriction to Windows platform, lacking visualization capabilities. GenomeRunner web server addresses these issues - its key functionality includes:

• data-driven, i.e., unsupervised, as well as hypothesis-driven, analyses;
• extensive functionality for high-level interpretation analysis of SNP sets, including annotation, enrichment, and regulatory similarity analyses;
• intuitive web interface to be used with minimal need to consult documentation;
• systematically organized and expandable multi-organism database of regulatory datasets with strand- and signal strength filters;
• interactive visualization of the results as tables and heatmaps;
• flexibility to run GenomeRunner web on a personal computer, on a high-performance computing environment, and in custom pipelines via command line;
• sufficient performance, even on a single computer, to process thousands of regulatory datasets;
• reproducibility of the results achieved via a database and code version control system.

* What's the last time you updated the database? When you will update it again?

The database update time is shown in the drop-down menu for database version selection. Last time we updated and optimized the hg19 and mm9 databases on December 1, 2014. The following database updates are scheculed every 6-month intervals.

* Are you limited to the ENCODE data from the UCSC genome database?

No! We plan to expand the database, without sacrificing its categorical structure, with other datasets, such as from the Roadmap Epigenomics project. In the current release, we added the data from Nuclear Receptor Cistrome DB.

* How do I cite GenomeRunner?

If you find GenomeRunner useful, please, cite the 2012 paper. The manuscript describing the web version is submitted.

* I still have questions/suggestions/bug report. What to do?

Please, contact