The International Mouse Phenotyping Consortium (IMPC) is an international effort by 19 research institutions to identify the function of every protein-coding gene in the mouse genome. To achieve this, the IMPC is systematically switching off or ‘knocking out’ each of the roughly 20,000 genes that make up the mouse genome. Subsequently, the knock out mice undergo standardised physiological tests (phenotyping tests) across a range of biological systems in order to infer gene function, before the data is made freely available to the research community on their website https://www.mousephenotype.org/about-impc.
The IMPC phenotyping centres adhere to standardised allele production to knock out genes and pre-defined phenotyping tests to characterise mouse phenotypes. Each phenotyping tests forms a phenotyping pipeline. The phenotyping pipelines provide an exemplar of the potential of high-throughput pipelines for the acquisition of broad-based phenotype data at both embryonic and adult time points. The range of phenotyping platforms ensures the recovery of phenotype data across multiple systems and disease states. Wild-type mice are continuously run through the phenotyping pipelines, giving constantly growing baselines for the statistical analysis. More information about the IMPC phenotyping pipelines and procedures is available from International Mouse Phenotyping Resource of Standardised Screens (IMPReSS https://www.mousephenotype.org/impress).
All data collected by the IMPC is freely available from https://www.mousephenotype.org. Besides viewing in the web portal, it can also be downloaded for an independent analysis. Several channels are available, each tailored for accessing data for individual items, small sets, or in bulk.
The full range of up-to-date IMPC data can be accessed through an application programming interface or API. The IMPC infrastructure is powered by Apache SOLR (https://lucene.apache.org/solr/) and the full SOLR query syntax and SOLR query parameters are accessible, see https://lucene.apache.org/solr/resources.html for SOLR documentation.
Data are stored across several compartments, or cores and each can be queried independently. The cores provide many fields that can be searched on. For example, it is possible to search and filter by phenotyping centre, mouse colony, phenotype, and many other settings. Many of these filtering criteria are shared across the cores, so there are common data access patterns. There are, however, fields that are specific to each core.
The ‘experiment’ data core contains raw measurements collected on individual specimens. It contains all details of the animals as well as the pipeline, WT/KO specification, procedure, parameter (as explained in https://www.mousephenotype.org/impress), measurement etc. See https://www.mousephenotype.org/help/programmatic-data-access/data-fields/ for a complete list of available parameters. Some notable data fields available in the experiment core are in the table below.
| Field name | Description |
|---|---|
| gene_symbol | Mouse gene identifier in symbol format, e.g. Car4 |
| gene_accession_id | Mouse gene identifier in MGI id format, e.g. MGI:1096574 |
| pipeline_stable_id | Identifier of IMPRESS pipeline |
| procedure_stable_id | Identifier of phenotyping procedure |
| allele_accession_id | Allele stable identifier |
| zygosity | Zygosity of the mutant specimens |
| sex | Sex of the mutant specimens |
| weight | Weight of specimen |
| parameter_stable_id | Identifier for phenotyping procedure |
| data_point or category | Measured value |
Programmatic access to IMPC data relies on the [SOLR query syntax https://lucene.apache.org/solr/guide/7_5/searching.html]. This approach is flexible and hence powerful, but this means there are complex features and behaviours that may not always be simple to understand. Using common data access patterns can be a helpful way to limit the complexity and obtain answers to specific queries.
The examples below show URLs that query a data core called ‘genotype-phenotype’. These examples provide a guide for constructing a query URL. All the example URLs can be pasted into a browser address bar or read directly from R using the function read.csv().
Size of output
One of the most important settings to control when using the API is the size of the output or the number of records returned from the server. This can be achieved by appending a settings ‘rows’ at the end of each query.
Note. – If rows is not specified, the server returns 10 records.
Using the experiment core as an example, the following URLs provide two small subsets of the available data.
https://www.ebi.ac.uk/mi/impc/solr/experiment/select?q=*:*&rows=1
https://www.ebi.ac.uk/mi/impc/solr/experiment/select?q=*:*&rows=5Output format
Two common output formats are JavaScript Object Notation (JSON) and Comma Separated Values (CSV), which can be toggled via an argument ‘wt’. The queries above become as follows.
https://www.ebi.ac.uk/mi/impc/solr/genotype-phenotype/select?q=*:*&rows=1&wt=json
https://www.ebi.ac.uk/mi/impc/solr/genotype-phenotype/select?q=*:*&rows=5&wt=csvDepending on software used, one or the other may appear more readable in certain situations. The csv format is convenient for use with spreadsheet programs such as R or Python. Both formats are compatible with programmatic processing in R, Python, or the majority of data analysis frameworks. Below shows the examples of reading the data directly from R using read.csv() functon:
url <- "https://www.ebi.ac.uk/mi/impc/solr/experiment/select?q=*:*&rows=50&wt=csv&fq=parameter_stable_id:IMPC_BWT_001_001"
df <- read.csv(url, as.is = TRUE)
df[, c(
"external_sample_id",
"biological_sample_group",
"sex",
"age_in_days"
)]## external_sample_id biological_sample_group sex age_in_days
## 1 191998 experimental female 76
## 2 191998 experimental female 48
## 3 191998 experimental female 94
## 4 191998 experimental female 52
## 5 191998 experimental female 63
## 6 191998 experimental female 68
## 7 414884 experimental female 66
## 8 414884 experimental female 73
## 9 414884 experimental female 82
## 10 74654 experimental female 79
## 11 414884 experimental female 88
## 12 74654 experimental female 92
## 13 75863 experimental male 32
## 14 75863 experimental male 100
## 15 75863 experimental male 113
## 16 410615 experimental female 36
## 17 75986 experimental female 68
## 18 410615 experimental female 44
## 19 75986 experimental female 114
## 20 418195 experimental female 30
## 21 418195 experimental female 62
## 22 75987 experimental female 88
## 23 403847 control male 49
## 24 399042 experimental female 65
## 25 403847 control male 85
## 26 403847 control male 90
## 27 399042 experimental female 93
## 28 403847 control male 95
## 29 399042 experimental female 99
## 30 403847 control male 104
## 31 403847 control male 76
## 32 399042 experimental female 106
## 33 403848 control male 29
## 34 399046 experimental male 30
## 35 403848 control male 35
## 36 403848 control male 43
## 37 403849 control male 90
## 38 399046 experimental male 63
## 39 403848 control male 61
## 40 403848 control male 85
## 41 399046 experimental male 85
## 42 403848 control male 90
## 43 403848 control male 95
## 44 403848 control male 104
## 45 399046 experimental male 55
## 46 398816 experimental female 53
## 47 403848 control male 69
## 48 399046 experimental male 77
## 49 403853 control female 61
## 50 403853 control female 69Output fields
The default behaviour for each endpoint is to return all fields available in a data store, akin to returning all columns from a large table. It is possible to limit the output by specifying the desired fields via an argument ‘fl’.
https://www.ebi.ac.uk/mi/impc/solr/experiment/select?q=*:*&rows=5&fl=gene_symbol,phenotyping_center&wt=jsonNote that some records in the output may appear to be identical – they only appear so because their distinguishing features are not provided in the immediate output.
Filter fields
The queries can be set to return data on a subset of records of interest by replacing the text ‘q=:’ in the previous queries. Any of the available fields from https://www.mousephenotype.org/help/programmatic-data-access/data-fields/ can be used in a filter. Common patterns include filter by gene symbol, procedure, or phenotype.
Combined with the other techniques, filtering provides a direct mechanism to answer very specific queries. The following fetches all significant phenotypes for a gene symbol.
https://www.ebi.ac.uk/mi/impc/solr/experiment/select?q=gene_symbol:Car4&rows=20&fl=gene_symbol,zygosity,sex&wt=json
https://www.ebi.ac.uk/mi/impc/solr/experiment/select?q=gene_symbol:Car4&rows=20&fl=gene_symbol,zygosity,sex&wt=csvNote that the query requests 20 records, but the server returns a smaller number. This is an indication that the output contains all the data that satisfy the filter, i.e. none have been left out.
For a complete description of the IMPC data see the IMPC FAQ page on https://www.mousephenotype.org/help/faqs/ or use the IMPC contact page https://www.mousephenotype.org/contact-us/