• Formats|Formats

 劳拉的欧派有香气?纵观TPS游戏21年兴衰！

The Multiple Alignment Format, described by UCSC, stores a series of multiple alignments in a single file. Suitable for whole-genome to whole-genome alignments, metadata such as source chromosome, start position, size, and strand can be stored.

Biopython 1.69 includes a MAF reader and writer accessible via Bio.AlignIO, and an indexer accessible via Bio.AlignIO.MafIO.

All examples below make use of the Multiz 30-way alignment to mouse chromosome 10 available from UCSC.

Getting the AlignIO code from GitHub

If you can’t wait for Biopython 1.69 to be released, get the latest Biopython from GitHub:

First, clone the repository with git from the command line, like so:

git?clone git@github.com:biopython/biopython.git

This will give you the default master branch. Then install from source.

Reading in a MAF file

Parsing a MAF file is similar to any other alignment file in AlignIO. Additional data, however, is stored as a dict in the .annotations property of SeqRecords belonging to returned MultipleSeqAlignment objects.

Annotations available in SeqRecords

Example

from Bio import AlignIO

for multiple_alignment in AlignIO.parse("chr10.maf", "maf"):
    print("printing a new multiple alignment")

    for seqrec in multiple_alignment:
        print(
            "starts at %s on the %s strand of a sequence %s in length, and runs for %s bp"
            % (
                seqrec.annotations["start"],
                seqrec.annotations["strand"],
                seqrec.annotations["srcSize"],
                seqrec.annotations["size"],
            )
        )

MafIndex

Biopython may soon provide an interface for fast access to the multiple alignment of several sequences across an arbitrary interval: for example, chr10:25,079,604-25,243,324 in mm9. As MAF files are available for entire chromosomes, they can be indexed by chromosome position and accessed at random. This functionality would be available in the class Bio.AlignIO.MafIO.MafIndex.

Creating or loading a MAF index

Indexes are created by determining the chromosome start and end position for a specific sequence name (generally a species), which must appear in every alignment block in the file. An index can be generated for only one species at a time. In whole-genome alignments generated by Multiz, the chromosome of one species is generally used as the reference to which other species are aligned. This reference species will appear in every block, and should be used as the target_seqname parameter. For UCSC multiz files, the form of species.chromosome is used.

To index a MAF file, or load an existing index, create a new MafIO.MafIndex object. If the index database file sqlite_file does not exist, it will be created, otherwise it will be loaded.

# index mouse chr10 from UCSC and store it in a file for later use

from Bio.AlignIO import MafIO

# idx = MafIO.MafIndex(sqlite_file, maf_file, target_seqname)
idx = MafIO.MafIndex("chr10.mafindex", "chr10.maf", "mm9.chr10")

Retrieving alignments overlapping a given interval

The MafIO.MafIndex.search() generator function accepts a list of start and end positions, and yields MultipleSeqAlignment objects that overlap the given intervals. This is particularly useful for obtaining alignments over the multiple exons of a single transcript, eliminating the need to retrieve an entire locus.

# count the number of bases in danRer5 (Zebrafish) that align to the
# Pcmt1 locus in mouse

from Bio.AlignIO.MafIO import MafIndex

idx = MafIndex("chr10.mafindex", "chr10.maf", "mm9.chr10")
results = idx.search([7350034], [7383048])

total_bases = 0

for multiple_alignment in results:
    for seqrec in multiple_alignment:
        if seqrec.id.startswith("danRer5"):
            # don't count gaps as bases
            total_bases += len(str(seqrec.seq).replace("-", ""))

print("a total of %s bases align" % total_bases)

Retrieving a pre-spliced alignment over a given set of exons

The MafIO.MafIndex.get_spliced() function accepts a list of start and end positions representing exons, and returns a single MultipleSeqAlignment object of the in silico spliced transcript from the reference and all aligned sequences. If part of the sequence range is not found in a particular species in the alignment, dashes (“-“) are used to fill the gaps, or “N”s if the sequence is not present in the reference (target_seqname) sequence. If strand is opposite that in the reference sequence, all sequences in the returned alignment will be reverse complemented.

# convert the alignment for mouse Foxo3 (NM_019740) from MAF to FASTA

from Bio import AlignIO

idx = AlignIO.MafIO.MafIndex("chr10.mafindex", "chr10.maf", "mm9.chr10")

multiple_alignment = idx.get_spliced(
    [41905591, 41916271, 41994621, 41996331],
    [41906101, 41917707, 41995347, 41996548],
    strand="+",
)

AlignIO.write(multiple_alignment, "mm9_foxo3.fa", "fasta")

# find every gene on chr10 in the current UCSC refGene database,
# retrieve its spliced multiple alignment, and write it to
# a FASTA file in the current directory
#
# depends: MySQLdb

import MySQLdb
from Bio import AlignIO

# connect to UCSC's live MySQL database
mysql_conn = MySQLdb.connect(
    host="genome-mysql.cse.ucsc.edu", user="genome", passwd="", db="mm9"
)

db_conn = mysql_conn.cursor(MySQLdb.cursors.DictCursor)

# load MAF index
idx = AlignIO.MafIO.MafIndex("chr10.mafindex", "chr10.maf", "mm9.chr10")

# fetch all records on chr10
db_conn.execute("SELECT * FROM refGene WHERE chrom = 'chr10'")

for record in db_conn.fetchall():
    multiple_alignment = idx.get_spliced(
        map(int, record["exonStarts"].split(",")[:-1]),
        map(int, record["exonEnds"].split(",")[:-1]),
        strand=record["strand"],
    )

    print("writing %s.fa" % record["name"])

    AlignIO.write(multiple_alignment, "%s.fa" % record["name"], "fasta")

Format

track name=euArc visibility=pack
##maf version=1 scoring=tba.v8
# tba.v8 (((human chimp) baboon) (mouse rat))

a score=23262.0
s hg18.chr7    27578828 38 + 158545518 AAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG
s panTro1.chr6 28741140 38 + 161576975 AAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG
s baboon         116834 38 +   4622798 AAA-GGGAATGTTAACCAAATGA---GTTGTCTCTTATGGTG
s mm4.chr6     53215344 38 + 151104725 -AATGGGAATGTTAAGCAAACGA---ATTGTCTCTCAGTGTG
s rn3.chr4     81344243 40 + 187371129 -AA-GGGGATGCTAAGCCAATGAGTTGTTGTCTCTCAATGTG

a score=5062.0
s hg18.chr7    27699739 6 + 158545518 TAAAGA
s panTro1.chr6 28862317 6 + 161576975 TAAAGA
s baboon         241163 6 +   4622798 TAAAGA
s mm4.chr6     53303881 6 + 151104725 TAAAGA
s rn3.chr4     81444246 6 + 187371129 taagga

a score=6636.0
s hg18.chr7    27707221 13 + 158545518 gcagctgaaaaca
s panTro1.chr6 28869787 13 + 161576975 gcagctgaaaaca
s baboon         249182 13 +   4622798 gcagctgaaaaca
s mm4.chr6     53310102 13 + 151104725 ACAGCTGAAAATA