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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 FASTA
This notebook briefly explores the FASTA format, a very common format for storing DNA sequences.  FASTA is the preferred format for storing reference genomes.
FASTA and FASTQ are rather similar, but FASTQ is almost always used for storing sequencing reads (with associated quality values), whereas FASTA is used for storing all kinds of DNA, RNA or protein sequencines (without associated quality values).
Before delving into the format, I should mention that the BioPython project makes parsing and using many file formats, including FASTA, quite easy.  See the BioPython SeqIO module in particular.  As far as I know, though, SeqIO does not use FASTA indexes, discussed toward the bottom, which is a disadvantage.
Basic format
Here is the basic format
Step1:  This FASTA file shown above has just one sequence in it.  As we saw in the first example above, it's also possible for one FASTA file to contain multiple sequences.  These are sometimes called multi-FASTA files.  When you write code to interpret FASTA files, it's a good idea to always allow for the possibility that the FASTA file might contain multiple sequences.
FASTA files are often stored with the .fa file name extension, but this is not a rule.  .fasta is another popular extenson.  You may also see .fas, .fna, .mfa (for multi-FASTA), and others.
Parsing FASTA
Here is a simple function for parsing a FASTA file into a Python dictionary.  The dictionary maps short names to corresponding nucleotide strings (with whitespace removed).
Step2:  The first part accumulates a list of strings (one per line) for each sequence.  The second part joins those lines together so that we end up with one long string per sequence.  Why divide it up this way?  Mainly to avoid the poor performance of repeatedly concatenating (immutable) Python strings.
I'll test it by running it on the simple multi-FASTA file we saw before
Step3:  Note that only the short names survive.  This is usually fine, but it's not hard to modify the function so that information relating short names to long names is also retained.
Indexed FASTA
Say you have one or more big FASTA files (e.g. the entire human reference genome) and you'd like to access those files "randomly," peeking at substrings here and there without any regular access pattern.  Maybe you're mimicking a sequencing machine, reading snippets of DNA here and there.
You could start by using the parse_fasta function defined above to parse the FASTA files.  Then, to access a substring, do as follows
Step4:  Accessing a substring in this way is very fast and simple.  The downside is that you've stored all of the sequences in memory.  If the FASTA files are really big, this takes lots of valuable memory.  This may or may not be a good trade.
An alternative is to load only the portions of the FASTA files that you need, when you need them.  For this to be practical, we have to have a way of "jumping" to the specific part of the specific FASTA file that you're intersted in.
Fortunately, there is a standard way of indexing a FASTA file, popularized by the faidx tool in SAMtools.  When you have such an index, it's easy to calculate exactly where to jump to when you want to extract a specific substring.  Here is some Python to create such an index
Step5:  Here we use it to index a small multi-FASTA file.  We print out the index at the end.
Step8:  What do the fields in those two records mean?  Take the first record
Step9:  Here's an example of how to use the class defined above.
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Python Code:
import gzip
import urllib.request
url = 'ftp://ftp.ncbi.nlm.nih.gov/genomes/archive/old_genbank/Eukaryotes/vertebrates_mammals/Homo_sapiens/GRCh38/non-nuclear/assembled_chromosomes/FASTA/chrMT.fa.gz'
response = urllib.request.urlopen(url)
print(gzip.decompress(response.read()).decode('UTF8'))
Explanation: FASTA
This notebook briefly explores the FASTA format, a very common format for storing DNA sequences.  FASTA is the preferred format for storing reference genomes.
FASTA and FASTQ are rather similar, but FASTQ is almost always used for storing sequencing reads (with associated quality values), whereas FASTA is used for storing all kinds of DNA, RNA or protein sequencines (without associated quality values).
Before delving into the format, I should mention that the BioPython project makes parsing and using many file formats, including FASTA, quite easy.  See the BioPython SeqIO module in particular.  As far as I know, though, SeqIO does not use FASTA indexes, discussed toward the bottom, which is a disadvantage.
Basic format
Here is the basic format:
&gt;sequence1_short_name with optional additional info after whitespace
ACATCACCCCATAAACAAATAGGTTTGGTCCTAGCCTTTCTATTAGCTCTTAGTAAGATTACACATGCAA
GCATCCCCGTTCCAGTGAGTTCACCCTCTAAATCACCACGATCAAAAGGAACAAGCATCAAGCACGCAGC
AATGCAGCTCAAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAGCAGTGAT
&gt;sequence2_short_name with optional additional info after whitespace
GCCCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTACCCAAATAAAGTATAGGCG
ATAGAAATTGAAACCTGGCGCAATAGATATAGTACCGCAAGGGAAAGATGAAAAATTATAACCAAGCATA
ATATAG
A line starting with a &gt; (greater-than) sign indicates the beginning of a new sequence and specifies its name.  Take the first line above.  Everything after the &gt; up to and excluding the first whitespace character (sequence1_short_name), is the "short name."  Everything after the &gt; up to the end of the line (sequence1_short_name with optional additional info after whitespace) is the "long name."  We usually use the short name when referring to FASTA sequences.
The next three lines consists of several nucleotides.  There is a maximum number of nucleotides permitted per line; in this case, it is 70.  If the sequence is longer then 70 nucleotides, it "wraps" down to the next line.  Not every FASTA file uses the same maximum, but a given FASTA file must use the same maximum throughout the file.
The sequences above are made up.  Here's a real-world reference sequence (the human mitochondrial genome) in FASTA format:
End of explanation
def parse_fasta(fh):
    fa = {}
    current_short_name = None
    # Part 1: compile list of lines per sequence
    for ln in fh:
        if ln[0] == '>':
            # new name line; remember current sequence's short name
            long_name = ln[1:].rstrip()
            current_short_name = long_name.split()[0]
            fa[current_short_name] = []
        else:
            # append nucleotides to current sequence
            fa[current_short_name].append(ln.rstrip())
    # Part 2: join lists into strings
    for short_name, nuc_list in fa.items():
        # join this sequence's lines into one long string
        fa[short_name] = ''.join(nuc_list)
    return fa
Explanation: This FASTA file shown above has just one sequence in it.  As we saw in the first example above, it's also possible for one FASTA file to contain multiple sequences.  These are sometimes called multi-FASTA files.  When you write code to interpret FASTA files, it's a good idea to always allow for the possibility that the FASTA file might contain multiple sequences.
FASTA files are often stored with the .fa file name extension, but this is not a rule.  .fasta is another popular extenson.  You may also see .fas, .fna, .mfa (for multi-FASTA), and others.
Parsing FASTA
Here is a simple function for parsing a FASTA file into a Python dictionary.  The dictionary maps short names to corresponding nucleotide strings (with whitespace removed).
End of explanation
from io import StringIO
fasta_example = StringIO(
'''>sequence1_short_name with optional additional info after whitespace
ACATCACCCCATAAACAAATAGGTTTGGTCCTAGCCTTTCTATTAGCTCTTAGTAAGATTACACATGCAA
GCATCCCCGTTCCAGTGAGTTCACCCTCTAAATCACCACGATCAAAAGGAACAAGCATCAAGCACGCAGC
AATGCAGCTCAAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAGCAGTGAT
>sequence2_short_name with optional additional info after whitespace
GCCCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTACCCAAATAAAGTATAGGCG
ATAGAAATTGAAACCTGGCGCAATAGATATAGTACCGCAAGGGAAAGATGAAAAATTATAACCAAGCATA
ATATAG''')
parsed_fa = parse_fasta(fasta_example)
parsed_fa
Explanation: The first part accumulates a list of strings (one per line) for each sequence.  The second part joins those lines together so that we end up with one long string per sequence.  Why divide it up this way?  Mainly to avoid the poor performance of repeatedly concatenating (immutable) Python strings.
I'll test it by running it on the simple multi-FASTA file we saw before:
End of explanation
parsed_fa['sequence2_short_name'][100:130]
Explanation: Note that only the short names survive.  This is usually fine, but it's not hard to modify the function so that information relating short names to long names is also retained.
Indexed FASTA
Say you have one or more big FASTA files (e.g. the entire human reference genome) and you'd like to access those files "randomly," peeking at substrings here and there without any regular access pattern.  Maybe you're mimicking a sequencing machine, reading snippets of DNA here and there.
You could start by using the parse_fasta function defined above to parse the FASTA files.  Then, to access a substring, do as follows:
End of explanation
def index_fasta(fh):
    index = []
    current_short_name = None
    current_byte_offset, running_seq_length, running_byte_offset = 0, 0, 0
    line_length_including_ws, line_length_excluding_ws = 0, 0
    for ln in fh:
        ln_stripped = ln.rstrip()
        running_byte_offset += len(ln)
        if ln[0] == '>':
            if current_short_name is not None:
                index.append((current_short_name, running_seq_length,
                              current_byte_offset, line_length_excluding_ws,
                              line_length_including_ws))
            long_name = ln_stripped[1:]
            current_short_name = long_name.split()[0]
            current_byte_offset = running_byte_offset
            running_seq_length = 0
        else:
            line_length_including_ws = max(line_length_including_ws, len(ln))
            line_length_excluding_ws = max(line_length_excluding_ws, len(ln_stripped))
            running_seq_length += len(ln_stripped)
    if current_short_name is not None:
        index.append((current_short_name, running_seq_length,
                      current_byte_offset, line_length_excluding_ws,
                      line_length_including_ws))
    return index
Explanation: Accessing a substring in this way is very fast and simple.  The downside is that you've stored all of the sequences in memory.  If the FASTA files are really big, this takes lots of valuable memory.  This may or may not be a good trade.
An alternative is to load only the portions of the FASTA files that you need, when you need them.  For this to be practical, we have to have a way of "jumping" to the specific part of the specific FASTA file that you're intersted in.
Fortunately, there is a standard way of indexing a FASTA file, popularized by the faidx tool in SAMtools.  When you have such an index, it's easy to calculate exactly where to jump to when you want to extract a specific substring.  Here is some Python to create such an index:
End of explanation
fasta_example = StringIO(
'''>sequence1_short_name with optional additional info after whitespace
ACATCACCCCATAAACAAATAGGTTTGGTCCTAGCCTTTCTATTAGCTCTTAGTAAGATTACACATGCAA
GCATCCCCGTTCCAGTGAGTTCACCCTCTAAATCACCACGATCAAAAGGAACAAGCATCAAGCACGCAGC
AATGCAGCTCAAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAGCAGTGAT
>sequence2_short_name with optional additional info after whitespace
GCCCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTACCCAAATAAAGTATAGGCG
ATAGAAATTGAAACCTGGCGCAATAGATATAGTACCGCAAGGGAAAGATGAAAAATTATAACCAAGCATA
ATATAG''')
idx = index_fasta(fasta_example)
idx
Explanation: Here we use it to index a small multi-FASTA file.  We print out the index at the end.
End of explanation
import re
class FastaOOB(Exception):
     Out-of-bounds exception for FASTA sequences 
    
    def __init__(self, value):
        self.value = value
    
    def __str__(self):
        return repr(self.value)
class FastaIndexed(object):
     Encapsulates a set of indexed FASTA files.  Does not load the FASTA
        files into memory but still allows the user to extract arbitrary
        substrings, with the help of the index. 
    
    __removeWs = re.compile(r'\s+')
    
    def __init__(self, fafns):
        self.fafhs = {}
        self.faidxs = {}
        self.chr2fh = {}
        self.offset = {}
        self.lens = {}
        self.charsPerLine = {}
        self.bytesPerLine = {}
        
        for fafn in fafns:
            # Open FASTA file
            self.fafhs[fafn] = fh = open(fafn, 'r')
            # Parse corresponding .fai file
            with open(fafn + '.fai') as idxfh:
                for ln in idxfh:
                    toks = ln.rstrip().split()
                    if len(toks) == 0:
                        continue
                    assert len(toks) == 5
                    # Parse and save the index line
                    chr, ln, offset, charsPerLine, bytesPerLine = toks
                    self.chr2fh[chr] = fh
                    self.offset[chr] = int(offset) # 0-based
                    self.lens[chr] = int(ln)
                    self.charsPerLine[chr] = int(charsPerLine)
                    self.bytesPerLine[chr] = int(bytesPerLine)
    
    def __enter__(self):
        return self
    
    def __exit__(self, type, value, traceback):
        # Close all the open FASTA files
        for fafh in self.fafhs.values():
            fafh.close()
    
    def has_name(self, refid):
        return refid in self.offset
    
    def name_iter(self):
        return self.offset.iterkeys()
    
    def length_of_ref(self, refid):
        return self.lens[refid]
    
    def get(self, refid, start, ln):
        ''' Return the specified substring of the reference. '''
        assert refid in self.offset
        if start + ln > self.lens[refid]:
            raise ReferenceOOB('"%s" has length %d; tried to get [%d, %d)' % (refid, self.lens[refid], start, start + ln))
        fh, offset, charsPerLine, bytesPerLine = \
            self.chr2fh[refid], self.offset[refid], \
            self.charsPerLine[refid], self.bytesPerLine[refid]
        byteOff = offset
        byteOff += (start // charsPerLine) * bytesPerLine
        into = start % charsPerLine
        byteOff += into
        fh.seek(byteOff)
        left = charsPerLine - into
        # Count the number of line breaks interrupting the rest of the
        # string we're trying to read
        if ln < left:
            return fh.read(ln)
        else:
            nbreaks = 1 + (ln - left) // charsPerLine
            res = fh.read(ln + nbreaks * (bytesPerLine - charsPerLine))
            res = re.sub(self.__removeWs, '', res)
        return res
Explanation: What do the fields in those two records mean?  Take the first record: ('sequence1_short_name', 194, 69, 70, 71).  The fields from left to right are (1) the short name, (2) the length (in nucleotides), (3) the byte offset in the FASTA file of the first nucleotide of the sequence, (4) the maximum number of nucleotides per line, and (5) the maximum number of bytes per line, including whitespace.  It's not hard to convince yourself that, if you know all these things, it's not hard to figure out the byte offset of any position in any of the sequences.  (This is what the get member of the FastaIndexed class defined below does.)
A typical way to build a FASTA index like this is to use SAMtools, specifically the samtools faidx command.  This and all the other samtools commands are documented in its manual.
When you use a tool like this to index a FASTA file, a new file containing the index is written with an additional .fai extension.  E.g. if the FASTA file is named hg19.fa, then running samtools faidx hg19.fa will create a new file hg19.fa.fai containing the index.
The following Python class shows how you might use the FASTA file together with its index to extract arbitrary substrings without loading all of the sequences into memory:
End of explanation
# first we'll write a new FASTA file
with open('tmp.fa', 'w') as fh:
    fh.write('''>sequence1_short_name with optional additional info after whitespace
ACATCACCCCATAAACAAATAGGTTTGGTCCTAGCCTTTCTATTAGCTCTTAGTAAGATTACACATGCAA
GCATCCCCGTTCCAGTGAGTTCACCCTCTAAATCACCACGATCAAAAGGAACAAGCATCAAGCACGCAGC
AATGCAGCTCAAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAGCAGTGAT
>sequence2_short_name with optional additional info after whitespace
GCCCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTACCCAAATAAAGTATAGGCG
ATAGAAATTGAAACCTGGCGCAATAGATATAGTACCGCAAGGGAAAGATGAAAAATTATAACCAAGCATA
ATATAG''')
with open('tmp.fa') as fh:
    idx = index_fasta(fh)
with open('tmp.fa.fai', 'w') as fh:
    fh.write('\n'.join(['\t'.join(map(str, x)) for x in idx]))
with FastaIndexed(['tmp.fa']) as fa_idx:
    print(fa_idx.get('sequence2_short_name', 100, 30))
Explanation: Here's an example of how to use the class defined above.
End of explanation
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