HIRIS: HIV-1 Reservoirs Integration Sites “high-rise”

Data sources for an ISDB

An ISDB is loaded with data from multiple sources. Sources are independent of each other and may represent a single publication’s data, unpublished data, data from multiple publications, or any other collection of integration data. How you use sources is largely unconstrained, but they work well for loosely organizing the data which goes into your ISDB.

Each source is contained within its own directory holding all the necessary files for that source. By convention, the name of the directory containing all the source files matches the name of the source itself.

Generally a source directory contains a copy of the primary or analyzed data (or a way to automatically download that data) along with code to transform the data into integration site records suitable for loading into the ISDB. We encourage you to use version control, such as Git, to manage your sources. We generally make each source its own git repository, although there are exceptions such as the sources bundled with the ISDB software.

Here’s an example of a source directory, which you may find in the sources/ directory of the ISDB git repository:

├── 1256304WagnerTableS3-Revised.csv
├── 1256304WagnerTableS3-Revised.xlsx
├── Makefile
├── metadata.json
├── orphaned.bed
├── tocsv
├── transform
├── transform.recs
├── integrations-GRCh37.csv
└── integrations.csv

The two required files for each source are metadata.json and integrations.csv. The former is details about the source and the latter is the data transformed into records suitable for loading into an ISDB. Each is discussed in more detail below.

The other files are the published data files and code to transform those data files into integrations.csv.


Each source must have some basic metadata associated with it. The metadata is provided by a metadata.json file which gets loaded into the source table’s document field.

Every metadata.json must have the keys name and uri. The name is used as the source’s primary key and must be unique within your ISDB. The uri should point to the upstream source of the data or provide a mailto: link for starting correspondence.

If the source is published work, the metadata should also have a pubmed_id key containing the PubMed Identifier (PMID) and a citation key with a BibTeX-style citation.

A complete example is below:

Integration site data

The ISDB tools load integration site observations from a CSV file called integrations.csv. The CSV file should contain the column headers as the first line. Each subsequent line is inserted into the database as a row in the integration table.

The integration table is the primary fact table in an ISDB. Each row records a single occurrence of a provirus detected at the given integration site in the given sample by the given source data. Sites which are detected multiple independent times (i.e. not from PCR) in a given sample are represented as multiple rows. Technical replicates are excluded if they’re from single genome sequencing rather than bulk consensus sequencing. These constraints allow the multiplicity of an IS to be calculated on the fly rather than stored in the database. One upshot of this is that it allows data loads to be independent of one another.

The following fields are supported:

  • source_name — refers to the name in your source’s metadata.json
  • environment
  • sample
  • landmark and location — both must either have values or be empty
  • orientation_in_landmarkF (forward) or R (reverse), relative to landmark
  • ltr — value of 5p or 3p
  • sequence
  • sequence_junction, if sequence is provided
  • note
  • info

For more information on how these fields are used and what values they should contain (paying particular attention to coordinate systems), please read the “Data usage guidelines” in the Manual.

Two fields, sample and info, are JSON documents which can have arbitrary structure. These structured documents are reconstructed from the flat CSV records through special field naming using / and /# to nest into hashes and arrays. For example, to store the following JSON document in the sample field:

you’d represent it in the CSV as:


See the transform.recs files in the stock data sources for examples of our sample and info document conventions.

Reference version and coordinate liftover

As described in the manual, ISDB tools handle genome coordinates using the GRCh38/hg38 human genome reference. This means that when loading your own integrations, if they were mapped to an earlier reference release (such as GRCh37/hg19), chromosomal coordinates must be translated to GRCh38 as a step in preparing your integrations.csv file. The best way of doing this is to download the UCSC [liftOver][] tool and use it to translate each coordinate in your data set.