This python3 package provides basic tools to work with genomic data. It is under creeping development. Meanwhile, see examples below. Note: some examples may require packages that are not automatically installed with this package (e.g. ʕ◕ᴥ◕ʔ).
Install or upgrade to newer version with:
pip3 install --upgrade tcgaMiscellaneous utilities.
import io
from pathlib import Path
from tcga.utils import mkdir
# A wrapper for Path.mkdir:
# path = mkdir(Path("/path/to/folder"))
from tcga.utils import unlist1
# Returns the object from a list
# iff the list is a singleton
assert 43 == unlist1([43])
# An iterable will be consumed:
assert 36 == unlist1((x ** 2) for x in [6])
# These fail with a ValueError:
# unlist1([])
# unlist1([1, 2])
from tcga.utils import relpath
# Returns the path relative to the script
from tcga.utils import first
# Returns the first element of an iterable
assert 'A' == first("ABCD")
from tcga.utils import at_most_n
# Lazy cut-off for iterables
print(list(at_most_n("ABCD", n=2)))
# ['A', 'B']
from tcga.utils import whatsmyname
def rose():
print(whatsmyname())
# Prints the name of
# the function: rose
from tcga.utils import assert_exists
# If `file` is a filename or path then
# assert_exists(file)
# either raises FileNotFoundError
# or returns back `file`
from tcga.utils import md5
# Computes the md5 hash of a text stream chunkwise.
# Attempts to rewind the stream back using .tell()
print(md5(io.StringIO("I know that I shall meet my fate")))
# 06a118b2f090ed1b39a1d07efdaa5d78
from tcga.utils import from_iterable
# Wraps chain.from_iterable, i.e.
print(set(from_iterable([[1, 2, 5], [4, 5]])))
print(from_iterable([[1, 2, 5], [4, 5]], type=set))
# {1, 2, 4, 5}
from tcga.utils import minidict
# A minimalistic read-only dictionary
minidict({1: 'A', 2: 'B'})
from tcga.utils import seek_then_rewind
# Context manager for rewinding file descriptors
with open(__file__, mode='r') as fd:
with seek_then_rewind(fd, seek=2):
print(fd.readline().strip())
# port io
print(fd.readline().strip())
# import ioDNA/RNA codons.
from tcga.codons import standard_rna
print("RNA codons (standard):")
print(standard_rna)
# {'UUU': 'F', 'UUC': 'F', 'UUA': 'L', ...}
from tcga.codons import tables
print("Other codon tables:")
print(tables.name)
# 1: Standard, 2: Vertebrate Mitochondrial, 3: Yeast Mitochondrial ...
print("For example:")
print(tables.loc[1])
# name Standard
# short_name SGC0
# dna_codons {'TTT': 'F', 'TTC': 'F', 'TTA': 'L', 'TTG': 'L...
# rna_codons {'UUU': 'F', 'UUC': 'F', 'UUA': 'L', 'UUG': 'L...
# Those are the same
assert tables.rna_codons[1] == standard_rna
import json
from tcga.refs import annotations
print(json.dumps(annotations[tables], indent=2))
# {
# "comments": [
# "Compiled from the NCBI list of genetic codes.",
# "https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi"
# ],
# "datetime": "2020-06-26 16:07:16.284170+00:00",
# "source": {
# "source": "ftp://ftp.ncbi.nih.gov/entrez/misc/data/gc.prt",
# "datetime": "2020-06-26 14:38:28.600903+00:00"
# }
# }Function composition.
from tcga.utils import First
from tcga.codons import standard_rna as rna_codons
from tcga.complements import dna_to_rna, dna_to_dna
from tcga.strings import triplets, reverse
X = reverse("CACGAACTTGTCGAGACCATTGCC")
f = First(dna_to_dna.reverse).then(dna_to_rna).then(triplets).each(rna_codons).join(str)
print(X, "=>", f(X))CCGTTACCAGAGCTGTTCAAGCAC => HELVETIA
Circular strings.
from tcga.utils import Circular
# This creates a circular view onto the string
c = Circular("ABCDEFG")
print(F"c = {c}", c[0:20], c[-3:20:2], type(c[0:20]), sep=", ")
from tcga.utils import laola
# This this looks at a str/list/tuple in a circular way
v = laola[-3:20:2]
print(v("ABCDEFG"))c = Circular('ABCDEFG'), ABCDEFGABCDEFGABCDEF, EGBDFACEGBDF, <class 'str'>
EGBDFACEGBDF
Tracking of information sources.
import json
from tcga.refs import annotations
from tcga.data.sa_aa_ref_chart import properties as aa_properties
print(aa_properties)
print(json.dumps(annotations[aa_properties], indent=2)) Name Abbr3 Abbr1 ... At pH 2 At pH 7 Comment
0 Alanine Ala A ... 47 41 NaN
1 Arginine Arg R ... -26 -14 NaN
2 Asparagine Asn N ... -41 -28 NaN
...
18 Tyrosine Tyr Y ... 49 63 NaN
19 Valine Val V ... 79 76 NaN
{
"date": "2020-06-18",
"source": [
"https://www.sigmaaldrich.com/life-science/metabolomics/learning-center/amino-acid-reference-chart.html",
"http://archive.ph/qL8ek"
],
"comments": [
"Columns 'At pH 2' and 'At pH 7' contain a measure of hydrophobicity. [...]"
]
}
AAindex amino acid property repository.
import json
from tcga.data import aaindex
from tcga.refs import annotations
pretty = (lambda x: json.dumps(dict(x), indent=2, default=(lambda x: '...')))
print("AAindex objects (excerpts):")
print("indices", pretty(aaindex.indices.head(3)), sep=" = ")
print("matrices", pretty(aaindex.matrices.head(3)), sep=" = ")
print("potentials", pretty(aaindex.potentials.head(3)), sep=" = ")
print("-" * 42)
key = 'ALTS910101'
print(F"Record for '{key}'", pretty(aaindex.data[key]), sep=" = ")
print("where the matrix M is:")
print(aaindex.data[key]['M'])
print("-" * 42)
print("Source:")
print(annotations[aaindex.data]['source'])AAindex objects (excerpts):
indices = {
"ANDN920101": "alpha-CH chemical shifts (Andersen et al., 1992)",
"ARGP820102": "Signal sequence helical potential (Argos et al., 1982)",
"BEGF750101": "Conformational parameter of inner helix (Beghin-Dirkx, 1975)"
}
matrices = {
"ALTS910101": "The PAM-120 matrix (Altschul, 1991)",
"BENS940102": "Log-odds scoring matrix collected in 22-29 PAM (Benner et al., 1994)",
"BENS940104": "Genetic code matrix (Benner et al., 1994)"
}
potentials = {
"TANS760101": "Statistical contact potential derived from 25 x-ray protein structures",
"ROBB790102": "Interaction energies derived from side chain contacts in the interiors of known protein structures",
"THOP960101": "Mixed quasichemical and optimization-based protein contact potential"
}
------------------------------------------
Record for 'ALTS910101' = {
"*": NaN,
"A": "Altschul, S.F.",
"C": NaN,
"D": "The PAM-120 matrix (Altschul, 1991)",
"H": "ALTS910101",
"I": NaN,
"J": "J. Mol. Biol. 219, 555-565 (1991)",
"M": "...",
"R": "PMID:2051488",
"T": "Amino acid substitution matrices from an information theoretic perspective"
}
where the matrix M is:
A R N D C Q E ... F P S T W Y V
A 3.0 -3.0 0.0 0.0 -3.0 -1.0 0.0 ... -4.0 1.0 1.0 1.0 -7.0 -4.0 0.0
R -3.0 6.0 -1.0 -3.0 -4.0 1.0 -3.0 ... -4.0 -1.0 -1.0 -2.0 1.0 -6.0 -3.0
N 0.0 -1.0 4.0 2.0 -5.0 0.0 1.0 ... -4.0 -2.0 1.0 0.0 -5.0 -2.0 -3.0
D 0.0 -3.0 2.0 5.0 -7.0 1.0 3.0 ... -7.0 -2.0 0.0 -1.0 -8.0 -5.0 -3.0
...
------------------------------------------
Source:
Created on 2020-06-19 14:59:55.332753+00:00 using
...
list_of_matrices:
Downloaded on 2020-06-18 15:00:16.545749+00:00 from
https://www.genome.jp/aaindex/AAindex/list_of_matrices
...
Downloading files directly to a compressed file and attaching metainformation.
from tcga.utils import download
from pathlib import Path
from tempfile import gettempdir
download = download.to(abs_path=(Path(gettempdir()) / "tcga_download_cache"))
print("Will download to:", download.local_folder)
# Will download to: /tmp/tcga_download_cache
data = download("https://www.ebi.ac.uk/ena/browser/api/fasta/J02459.1").again(False).now
print(data.meta) # same as tcga.refs.annotations[data]
# {'source': 'https://www.ebi.ac.uk/ena/browser/api/fasta/J02459.1', 'datetime': '2020-06-25 07:18:52.065826+00:00'}
print(data.text[0:42], "...", data.text[330:350], "...")
# >ENA|J02459|J02459.1 Escherichia phage Lam ... CAGGGAATGCCCGTTCTGCG ...
print(data.local_file)
# /tmp/tcga_download_cache/Z9tBKiJCqrfWuYy5BlgrA3zZAWav2CUd4xrPsya93Os=.zip
try:
from Bio import SeqIO
except ImportError:
print("Need `biopython`")
else:
with data.open(mode='r') as fd:
print(SeqIO.read(fd, format='fasta'))
# ID: ENA|J02459|J02459.1
# Name: ENA|J02459|J02459.1
# Description: ENA|J02459|J02459.1 Escherichia phage Lambda, complete genome.
# Number of features: 0
# Seq('GGGCGGCGACCTCGCGGGTTTTCGCTATTTATGAAAATTTTCCGGTTTAAGGCG...ACG', SingleLetterAlphabet())BLOSUM matrices.
import json
pretty = (lambda x: json.dumps(dict(x), indent=2, default=(lambda x: '...')))
print("[BLOSUM62 -- AAindex]")
from tcga.data.aaindex import data
# https://www.genome.jp/dbget-bin/www_bget?aaindex:HENS920102
i = 'HENS920102' # ID of BLOSUM62
A = data[i].M
print(data[i].D, data[i]['*'], sep='\n')
print("Matrix:", A.astype(int).head(3), "...", sep='\n')
print("[BLOSUM62 -- FASTA]")
from tcga.data.blosum import blosum62_12 as B
from tcga.refs import annotations
print(pretty(annotations[B]), sep="\n")
print("Matrix:", B.head(3), "...", sep='\n')[BLOSUM62 -- AAindex]
BLOSUM62 substitution matrix (Henikoff-Henikoff, 1992)
matrix in 1/3 Bit Units
Matrix:
A R N D C Q E G H I L K M F P S T W Y V
A 6 -2 -2 -3 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 2 0 -4 -3 0
R -2 8 -1 -2 -5 1 0 -3 0 -4 -3 3 -2 -4 -3 -1 -2 -4 -3 -4
N -2 -1 8 2 -4 0 0 -1 1 -5 -5 0 -3 -4 -3 1 0 -6 -3 -4
...
[BLOSUM62 -- FASTA]
{
"source": "https://github.com/wrpearson/fasta36/tree/7c0dba1dfe5fc92d937f2bd5f9c90b8bfdb14743/data",
"date": "2020-06-15",
"comments": [
"BLOSUM62 is in 1/2 bit units, the others are in 1/3 bit units.",
"The BLOSUM62 matrix is the original, miscalculated one according to [1, Supplementary Fig 4]."
],
"references": [
"[1] Styczynski, M., Jensen, K., Rigoutsos, I. et al. BLOSUM62 miscalculations improve search performance. Nat Biotechnol 26, 274-275 (2008). https://doi.org/10.1038/nbt0308-274"
]
}
Matrix:
A R N D C Q E G H I L K M F P S T W Y V B Z X
A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 0
R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 -1
N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 3 0 -1
...
MIT/Expat.
Suggestions and help are most welcome.