variant 0.0.39

No project description provided

Python pakcage for genomic variant analysis

How to use?

pip install variant

• run variant-effect in the command line
• more functions will be supported in the future

variant-effect command can infer the effect of a mutation

Usage: variant-effect

  Variant (genomic variant analysis in python)

Options:
  -i, --input TEXT       Input position file.  [required]
                         The input file has 5 columns: `chromosome`, `position`, `strand`, `reference allele`, `alternative allele`.

                         The 3rd column (strand) is not used by default, just for compatibility with RNA mode.
                         By default, the base of reference and alternative allele are based on DNA information
                         For RNA mode (through `-t RNA` argument), the base of reference and alternative allele is reverse complement if the strand is negative(-).

  -o, --output TEXT      Output annotation file
  -r, --reference TEXT   reference species
                         specify reference name, can be human / mouse / dog / cat / chicken ...

  -e, --release INTEGER  ensembl release
                         NOTE: Change the release version to choice different version of genome. eg. set release as 75 for GRCh37.
  -t, --type [DNA|RNA]
  -n, --npad INTEGER     Number of padding base to call motif.
  -a, --all-effects      Output all effects.
  -H, --with-header      With header line in input file.
  -c, --columns INTEGER  Sets columns for site info.
                         (Chrom,Pos,Strand,Ref,Alt)  [default: 1, 2, 3, 4, 5]
  --help                 Show this message and exit.

demo:

Store the following table in file (sites.tsv).

chr1	230703034	-	C	T
chr2	215361150	-	A	T
chr2	84906537	+	C	T
chr3	10301112	-	G	T
chr3	20301112	-	G	T
chr7	45893389	+	G	T
chr7	94669540	+	G	N
chr12	69353439	+	A	T
chr14	23645352	+	G	T

Run command variant-effect -i sites.tsv -r human -e 106 -t RNA to get the following output.

#chrom	pos	strand	ref	alt	mut_type	gene_name	gene_pos	transcript_name	transcript_pos	transcript_motif	coding_pos	codon_ref	aa_pos	aa_ref	distance2splice
chr1	230703034	-	G	A	ThreePrimeUTR	ENSG00000135744(AGT)	42543	ENST00000680041(AGT-208)	1753	TGTGTCACCCCCAGTCTCCCA	None	None	None	None	295
chr2	215361150	-	T	A	ThreePrimeUTR	ENSG00000115414(FN1)	74924	ENST00000323926(FN1-201)	8012	GGCCCGCAATACTGTAGGAAC	None	None	None	None	476
chr2	84906537	+	C	T	ThreePrimeUTR	ENSG00000034510(TMSB10)	882	ENST00000233143(TMSB10-201)	327	CCTGGGCACTCCGCGCCGATG	None	None	None	None	148
chr3	10301112	-	C	A	Silent	ENSG00000157020(SEC13)	20001	ENST00000397117(SEC13-209)	1441	TTGATCATCTGCCTTAACGTG	849	CTG	283	L	35
chr3	20301112	-	C	A	Intergenic	None	None	None	None	None	None	None	None	None	None
chr7	45893389	+	G	T	ThreePrimeUTR	ENSG00000146678(IGFBP1)	5030	ENST00000275525(IGFBP1-201)	1243	CAAAGCTCCTGCGTCTGTTTT	None	None	None	None	429
chr7	94669540	+	G	N	ThreePrimeUTR	ENSG00000242265(PEG10)	13216	ENST00000612941(PEG10-206)	6240	TTTTACCCCTGTCAGTAGCCC	None	None	None	None	5030
chr12	69353439	+	A	T	ThreePrimeUTR	ENSG00000090382(LYZ)	5059	ENST00000261267(LYZ-201)	695	TAGAACTAATACTGGTGAAAA	None	None	None	None	286
chr14	23645352	+	G	T	ThreePrimeUTR	ENSG00000100867(DHRS2)	15238	ENST00000344777(DHRS2-202)	1391	CTGCCATTCTGCCAGACTAGC	None	None	None	None	210

TODO:

• imporve speed. Base on cgranges, pyranges?, or BioCantor?