Package 'MouseFM'

Annotate with consequences

Description

Request variant consequences from Variant Effect Predictor (VEP) via Ensembl Rest Service. Not recommended for large queries.

Usage

annotate_consequences(geno, species)

Arguments

geno	Data frame or GenomicRanges::GRanges object including columns rsid, ref, alt.
species	Species name, e.g. mouse (GRCm38) or human (GRCh38).

Value

Data frame.

Examples

geno = finemap("chr1",
  start = 5000000, end = 6000000,
  strain1 = c("C57BL_6J"), strain2 = c("AKR_J", "A_J", "BALB_cJ")
)

df = annotate_consequences(geno[seq_len(10), ], "mouse")

geno.granges = finemap("chr1",
    start = 5000000, end = 6000000,
    strain1 = c("C57BL_6J"), strain2 = c("AKR_J", "A_J", "BALB_cJ"),
    return_obj = "granges"
)

df2 = annotate_consequences(geno.granges[seq_len(10), ], "mouse")

---

Annotate with genes

Description

Request mouse genes from Ensembl Biomart.

Usage

annotate_mouse_genes(geno, flanking = NULL)

Arguments

geno	Data frame or GenomicRanges::GRanges object including columns chr, pos.
flanking	Size of flanking sequence to be included.

Value

Data frame.

Examples

geno = finemap("chr1",
  start = 5000000, end = 6000000,
  strain1 = c("C57BL_6J"), strain2 = c("AKR_J", "A_J", "BALB_cJ")
)

genes = annotate_mouse_genes(geno, 50000)

---

Available chromosomes

Description

Available mouse chromosomes.

Usage

avail_chromosomes()

Value

Data frame

Examples

avail_chromosomes()

---

Available consequences

Description

Available consequence and impact types.

Usage

avail_consequences()

Value

Data frame.

Examples

avail_consequences()$consequence

unique(avail_consequences()$impact)

---

Available strains

Description

There are 37 strains available.

Usage

avail_strains()

Value

Data frame.

Examples

avail_strains()

---

Data frame to GenomicRanges::GRanges object

Description

Wrapper for GenomicRanges::makeGRangesFromDataFrame().

Usage

df2GRanges(
  geno,
  chr_name = "chr",
  start_name = "pos",
  end_name = "pos",
  strand_name = NULL,
  ref_version = ref_genome(),
  seq_lengths = NULL,
  is_circular = FALSE
)

Arguments

geno	Data frame.
chr_name	Name of chromosome column. Default is 'chr'.
start_name	Name of start position column. Default is 'pos.'
end_name	Name of end position column. Default is 'pos'
strand_name	Name of end position column. Default is NULL.
ref_version	Reference genome version. Default is 'ref_genome()'.
seq_lengths	List of sequence lengths with sequence name as key. Default is NULL.
is_circular	Whether genome is circular. Default is FALSE.

Value

GenomicRanges::GRanges object.

Examples

geno = finemap("chr1",
  start = 5000000, end = 6000000,
  strain1 = c("C57BL_6J"), strain2 = c("AKR_J", "A_J", "BALB_cJ")
)

geno$strand = "+"
seq_lengths = stats::setNames(
    as.list(avail_chromosomes()$length),
    avail_chromosomes()$chr
)
geno.granges = df2GRanges(geno,
    strand_name = "strand",
    seq_lengths = seq_lengths
)

---

Fetch

Description

Fetch homozygous genotypes for a specified chromosomal region in 37 inbred mouse strains.

Usage

fetch(
  chr,
  start = NULL,
  end = NULL,
  consequence = NULL,
  impact = NULL,
  return_obj = "dataframe"
)

Arguments

chr	Vector of chromosome names.
start	Optional vector of chromosomal start positions of target regions (GRCm38).
end	Optional vector of chromosomal end positions of target regions (GRCm38).
consequence	Optional vector of consequence types.
impact	Optional vector of impact types.
return_obj	The user can choose to get the result to be returned as data frame ("dataframe") or as a GenomicRanges::GRanges ("granges") object. Default value is "dataframe".

Value

Data frame or GenomicRanges::GRanges object containing result data.

Examples

geno = fetch("chr7", start = 5000000, end = 6000000)

comment(geno)

---

Finemapping of genetic regions

Description

Finemapping of genetic regions in 37 inbred mice by taking advantage of their very high homozygosity rate (>95 chromosomal regions (GRCm38), this method extracts homozygous SNVs for which the allele differs between two sets of strains (e.g. case vs controls) and outputs respective causal SNV/gene candidates.

Usage

finemap(
  chr,
  start = NULL,
  end = NULL,
  strain1,
  strain2,
  consequence = NULL,
  impact = NULL,
  thr1 = 0,
  thr2 = 0,
  return_obj = "dataframe"
)

Arguments

chr	Vector of chromosome names.
start	Optional vector of chromosomal start positions of target regions (GRCm38).
end	Optional vector of chromosomal end positions of target regions (GRCm38).
strain1	First strain set with strains from avail_strains().
strain2	Second strain set with strains from avail_strains().
consequence	Optional vector of consequence types.
impact	Optional vector of impact types.
thr1	Number discordant strains in strain1. Between 0 and length(strain1)-1. 0 by default.
thr2	Number discordant strains in strain2. Between 0 and length(strain2)-1. 0 by default.
return_obj	The user can choose to get the result to be returned as data frame ("dataframe") or as a GenomicRanges::GRanges ("granges") object. Default value is "dataframe".

Value

Data frame or GenomicRanges::GRanges object containing result data.

Examples

geno = finemap("chr1",
    start = 5000000, end = 6000000,
    strain1 = c("C57BL_6J"), strain2 = c(
        "129S1_SvImJ", "129S5SvEvBrd",
        "AKR_J"
    )
)

comment(geno)

---

Best strain combinations

Description

Get best strain combinations

Usage

get_top(red, n_top)

Arguments

red	Reduction factors data frame.
n_top	Number of combinations to be returned.

Value

Data frame

Examples

l = prio("chr1",
  start = 5000000, end = 6000000,
  strain1 = "C57BL_6J", strain2 = "AKR_J"
)

get_top(l$reduction, 3)

---

Get backend service url

Description

Get backend service URL. Default: http://mousefm.genehopper.de/rest/finemap/

Usage

getURL()

Value

URL string.

Examples

getURL()

---

GenomicRanges::GRanges object to data frame

Description

Wrapper for as.data.frame().

Usage

GRanges2df(granges)

Arguments

granges

GenomicRanges::GRanges object

Value

Data frame.

Examples

geno.granges = finemap("chr1",
    start = 5000000, end = 6000000,
    strain1 = c("C57BL_6J"), strain2 = c("AKR_J", "A_J", "BALB_cJ"),
    return_obj = "granges"
)

geno = GRanges2df(geno.granges)

---

Prioritization of inbred mouse strains for refining genetic regions

Description

This method allows to select strain combinations which best refine a specified genetic region (GRCm38). E.g. if a crossing experiment with two inbred mouse strains 'strain1' and 'strain2' resulted in a QTL, the outputted strain combinations can be used to refine the respective region in further crossing experiments.

Usage

prio(
  chr,
  start = NULL,
  end = NULL,
  strain1 = NULL,
  strain2 = NULL,
  consequence = NULL,
  impact = NULL,
  min_strain_benef = 0.1,
  max_set_size = 3,
  return_obj = "dataframe"
)

Arguments

chr	Vector of chromosome names.
start	Optional vector of chromosomal start positions of target regions (GRCm38).
end	Optional vector of chromosomal end positions of target regions (GRCm38).
strain1	First strain set with strains from avail_strains().
strain2	Second strain set with strains from avail_strains().
consequence	Optional vector of consequence types.
impact	Optional vector of impact types.
min_strain_benef	Minimum reduction factor (min) of a single strain.
max_set_size	Maximum set of strains.
return_obj	The user can choose to get the result to be returned as data frame ("dataframe") or as a GenomicRanges::GRanges ("granges") object. Default value is "data frame".

Value

Data frame

Examples

res = prio("chr1",
  start = 5000000, end = 6000000, strain1 = "C57BL_6J",
  strain2 = "AKR_J"
)

comment(res$genotypes)

---

Reference genome version

Description

Returns version of reference genome used in package MouseFM.

Usage

ref_genome()

Value

Vector.

Examples

ref_genome()

---

Set backend service url

Description

Set backend service URL. Default: http://mousefm.genehopper.de/rest/finemap/

Usage

setURL(url)

Arguments

url	URL of backend service.

Value

No return value.

Examples

setURL("http://backendserver.com")

---

Visualize

Description

Visualize reduction factors

Usage

vis_reduction_factors(geno, red, n_top)

Arguments

geno	Genotype data frame or GenomicRanges::GRanges object.
red	Reduction factor data frame.
n_top	Number if combinations to be returned.

Value

Data frame

Examples

l = prio(c("chr1", "chr2"),
  start = c(5000000, 5000000),
  end = c(6000000, 6000000), strain1 = c("C3H_HeH"), strain2 = "AKR_J"
)

plots = vis_reduction_factors(l$genotypes, l$reduction, 2)

plots[[1]]
plots[[2]]

---