This R package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%).
Method fetch allows to download homozygous genotypes of 37 inbred mouse strains for a given genetic region.
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MouseFM")library(MouseFM)
#>
#> ---------
#> For example usage please run: vignette('MouseFM')
#>
#> Support me: https://matthiasmunz.de/en/thanks/
#>
#> Citation appreciated:
#> Munz M, Khodaygani M, Aherrahrou Z, Busch H, Wohlers I (2021) In silico candidate variant and gene identification using inbred mouse strains. PeerJ. doi:10.7717/peerj.11017
#>
#> Github Repo: https://github.com/matmu/MouseFM
#> MouseFM Backend: https://github.com/matmu/MouseFM-Backend
#> ---------Fetch genotypes for region chr1:5000000-6000000.
df = fetch("chr1", start=5000000, end=6000000)
#> Query chr1:5,000,000-6,000,000
df[1:10,]
#> chr pos rsid ref alt most_severe_consequence
#> 1 1 5000016 rs47088541 A T intron_variant
#> 2 1 5000029 rs48827827 G A intron_variant
#> 3 1 5000057 rs48099867 C T intron_variant
#> 4 1 5000062 rs246021564 G C intron_variant
#> 5 1 5000067 rs265132353 C T intron_variant
#> 6 1 5000068 rs51419610 A G intron_variant
#> 7 1 5000101 rs253320650 C G intron_variant
#> 8 1 5000156 <NA> C T intron_variant
#> 9 1 5000157 rs216747169 G A intron_variant
#> 10 1 5000240 <NA> T G intron_variant
#> consequences C57BL_6J
#> 1 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 2 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 3 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 4 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 5 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 6 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 7 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 8 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 9 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 10 non_coding_transcript_variant,intron_variant,NMD_transcript_variant 0
#> 129P2_OlaHsd 129S1_SvImJ 129S5SvEvBrd AKR_J A_J BALB_cJ BTBR BUB_BnJ C3H_HeH
#> 1 0 0 0 0 0 0 0 0 1
#> 2 0 0 0 0 0 0 0 0 1
#> 3 0 0 0 0 0 0 0 0 1
#> 4 0 0 0 0 0 0 0 0 1
#> 5 0 0 0 0 0 0 0 0 1
#> 6 0 0 0 0 0 0 0 0 1
#> 7 0 0 0 0 0 0 0 0 1
#> 8 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 1
#> 10 0 0 0 0 0 0 0 0 0
#> C3H_HeJ C57BL_10J C57BL_6NJ C57BR_cdJ C57L_J C58_J CAST_EiJ CBA_J DBA_1J
#> 1 1 0 0 0 0 0 1 1 1
#> 2 1 0 0 0 0 0 0 1 1
#> 3 1 0 0 0 0 0 0 1 1
#> 4 1 0 0 0 0 0 0 1 1
#> 5 1 0 0 0 0 0 0 1 1
#> 6 1 0 0 0 0 0 0 1 1
#> 7 1 0 0 0 0 0 0 1 1
#> 8 0 0 0 0 0 0 0 0 0
#> 9 1 0 0 0 0 0 0 1 0
#> 10 0 0 0 0 0 0 0 0 0
#> DBA_2J FVB_NJ I_LnJ KK_HiJ LEWES_EiJ LP_J MOLF_EiJ NOD_ShiLtJ NZB_B1NJ
#> 1 1 0 0 0 1 0 0 0 1
#> 2 1 0 0 0 1 0 0 0 0
#> 3 1 0 0 0 1 0 0 0 0
#> 4 1 0 0 0 1 0 0 0 0
#> 5 1 0 0 0 1 0 0 0 0
#> 6 1 0 0 0 1 0 0 0 0
#> 7 1 0 0 0 1 0 0 0 0
#> 8 0 0 0 0 0 0 0 0 1
#> 9 0 0 0 0 1 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 1
#> NZO_HlLtJ NZW_LacJ PWK_PhJ RF_J SEA_GnJ SPRET_EiJ ST_bJ WSB_EiJ ZALENDE_EiJ
#> 1 0 0 1 1 0 1 0 1 1
#> 2 0 0 1 1 0 1 0 1 1
#> 3 0 0 1 1 0 1 0 1 1
#> 4 0 0 1 1 0 1 0 1 1
#> 5 0 0 1 1 0 0 0 1 1
#> 6 0 0 1 1 0 1 0 1 1
#> 7 0 0 1 1 0 1 0 1 1
#> 8 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 1 0 0 0 1 1
#> 10 0 0 0 0 0 0 0 0 0View meta information
comment(df)
#> [1] "#Alleles of strain C57BL_6J represent the reference (ref) alleles"
#> [2] "#reference_version=GRCm38"The output of sessionInfo() on the system
on which this document was compiled:
sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] MouseFM_1.20.1 BiocStyle_2.38.0
#>
#> loaded via a namespace (and not attached):
#> [1] KEGGREST_1.50.0 gtable_0.3.6 xfun_0.57
#> [4] bslib_0.10.0 ggplot2_4.0.2 httr2_1.2.2
#> [7] rlist_0.4.6.2 Biobase_2.70.0 vctrs_0.7.2
#> [10] tools_4.5.2 generics_0.1.4 stats4_4.5.2
#> [13] curl_7.0.0 tibble_3.3.1 AnnotationDbi_1.72.0
#> [16] RSQLite_2.4.6 blob_1.3.0 pkgconfig_2.0.3
#> [19] data.table_1.18.2.1 RColorBrewer_1.1-3 dbplyr_2.5.2
#> [22] S7_0.2.1 S4Vectors_0.48.0 lifecycle_1.0.5
#> [25] farver_2.1.2 compiler_4.5.2 stringr_1.6.0
#> [28] Biostrings_2.78.0 progress_1.2.3 Seqinfo_1.0.0
#> [31] htmltools_0.5.9 sass_0.4.10 yaml_2.3.12
#> [34] tidyr_1.3.2 pillar_1.11.1 crayon_1.5.3
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#> [40] tidyselect_1.2.1 digest_0.6.39 stringi_1.8.7
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#> [46] bookdown_0.46 biomaRt_2.66.2 fastmap_1.2.0
#> [49] grid_4.5.2 cli_3.6.5 magrittr_2.0.4
#> [52] dichromat_2.0-0.1 scales_1.4.0 prettyunits_1.2.0
#> [55] filelock_1.0.3 rappdirs_0.3.4 bit64_4.6.0-1
#> [58] rmarkdown_2.30 XVector_0.50.0 httr_1.4.8
#> [61] bit_4.6.0 otel_0.2.0 png_0.1-9
#> [64] hms_1.1.4 memoise_2.0.1 evaluate_1.0.5
#> [67] knitr_1.51 GenomicRanges_1.62.1 IRanges_2.44.0
#> [70] BiocFileCache_3.0.0 rlang_1.1.7 Rcpp_1.1.1
#> [73] glue_1.8.0 DBI_1.3.0 BiocManager_1.30.27
#> [76] BiocGenerics_0.56.0 jsonlite_2.0.0 plyr_1.8.9
#> [79] R6_2.6.1