IMPC Release Notes

IMPC
  • Release: 4.0
  • Published: 20 October 2015
Statistical Package
  • PhenStat
  • Version: 2.2.0
Genome Assembly
  • Mus musculus
  • Version: GRCm38
Summary
  • Number of phenotyped genes: 2,218
  • Number of phenotyped mutant lines: 2,340
  • Number of phenotype calls: 12,159

Data access

Highlights

Data release 4.0

Data release 4.0 is a major release.

Data Reports

Lines and Specimens

Phenotyping Center Mutant Lines Baseline Mice Mutant Mice
JAX 390 2,495 9,309
NING 65 734 943
TCP 228 3,669 9,239
HMGU 102 1,134 1,791
MRC Harwell 286 2,905 6,757
RBRC 26 859 578
ICS 99 1,001 1,521
WTSI 588 2,632 13,519
BCM 129 815 3,170
UC Davis 427 1,249 9,427

Experimental Data and Quality Checks

Data Type QC passed QC failed issues
categorical 5,827,451 756 * 4,544 *
unidimensional 4,399,848 7,342 * 105,213 *
time series 8,721,563 116 * 81,063 *
text 81,962 13,970 * 15,690 *
image record 186,464 0 0

* Excluded from statistical analysis.

Procedures

Allele Types

Mutation Name Mutant Lines
Targeted Mutation a 433
Targeted Mutation 1 648
Targeted Mutation b 1196
Targeted Mutation 2 5
Targeted Mutation c 2
Targeted Mutation d 1
Targeted Mutation e 40

Mouse knockout programs: KOMP,EUCOMM,NCOM,mirKO,IST12471H5

Distribution of Phenotype Annotations

Status

Overall

By Center

More charts and status information are available from iMits.

Phenotype Associations Overview

We provide a 'phenome' overview of statistically significant calls. By following the links below, you'll access the details of the phenotype calls for each center.

Phenotyping Center Significant MP Calls Pipeline
JAXBrowseIMPC_001
JAXBrowseJAX_001
NINGBrowseIMPC_001
TCPBrowseTCP_001
HMGUBrowseIMPC_001
HMGUBrowseHMGU_001
MRC HarwellBrowseHRWL_001
RBRCBrowseIMPC_001
ICSBrowseIMPC_001
ICSBrowseICS_001
WTSIBrowseMGP_001
BCMBrowseIMPC_001
BCMBrowseBCM_001
UC DavisBrowseUCD_001

Statistical Analysis

Statistical Methods

Data Statistical Method
categorical Fisher's exact test
unidimensional Wilcoxon rank sum test with continuity correction
unidimensional Mixed Model framework, generalized least squares, equation withoutWeight
unidimensional Mixed Model framework, linear mixed-effects model, equation withoutWeight

P-value distributions

Trends










Previous Releases