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.
A branch of Biopython on GitHub (not yet in the main distribution for
general use) implements a MAF reader and writer accessible via
Bio.AlignIO, and an indexer accessible via
All examples below make use of the Multiz 30-way alignment to mouse chromosome 10 available from UCSC.
First, clone the repository with git from the command line, like so:
git clone -b alignio-maf git://github.com/polyatail/biopython.git alignio-maf
If you’re using an older version of git, you may need to do this:
git clone git://github.com/polyatail/biopython.git alignio-maf cd alignio-maf git checkout -b alignio-maf origin/alignio-maf
To access the MAF parser, you’ll need to manually specify the path to it in your code, as in:
import sys # replace "./alignio-maf" with the full path of the alignio-maf branch # you cloned from github, or keep if it's in the current directory sys.path.insert(1, "./alignio-maf") try: from Bio.AlignIO import MafIO except ImportError: print("oops, the import didn't work")
For help, contact the branch maintainer Andrew Sczesnak (firstname dot lastname at med dot nyu dot edu) or the Biopython Developers.
Parsing a MAF file is similar to any other alignment file in
Additional data, however, is stored as a dict in the
SeqRecords belonging to returned
|start||integer||The start position in the source sequence of this alignment|
|size||integer||The ungapped length of this sequence|
|strand||enum(“+”, “-“)||The strand this sequence originates from on the source sequence/chromosome|
|srcSize||integer||The total length of the source sequence/chromosome|
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"]))
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
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")
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(, ) 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)
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
# 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")
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