\chapter{Cookbook -- Cool things to do with it}

\label{chapter:cookbook}

Biopython now has two collections of ``cookbook'' examples -- this chapter

(which has been included in this tutorial for many years and has gradually

grown), and \url{http://biopython.org/wiki/Category:Cookbook} which is a

user contributed collection on our wiki.

We're trying to encourage Biopython users to contribute their own examples

to the wiki. In addition to helping the community, one direct benefit of

sharing an example like this is that you could also get some feedback on

the code from other Biopython users and developers - which could help you

improve all your Python code.

In the long term, we may end up moving all of the examples in this chapter

to the wiki, or elsewhere within the tutorial.

\section{Working with sequence files}

\label{sec:cookbook-sequences}

This section shows some more examples of sequence input/output, using the

\verb|Bio.SeqIO| module described in Chapter~\ref{chapter:seqio}.

\subsection{Filtering a sequence file}

Often you'll have a large file with many sequences in it (e.g. FASTA file

or genes, or a FASTQ or SFF file of reads), a separate shorter list of

the IDs for a subset of sequences of interest, and want to make a new

sequence file for this subset.

Let's say the list of IDs is in a simple text file, as the first word on

each line. This could be a tabular file where the first column is the ID.

Try something like this:

%not a doctest to avoid temp files being left behind, also no >>>

%makes it easier to copy and paste the example to a script file.

\begin{minted}{python}

from Bio import SeqIO

input_file = "big_file.sff"

id_file = "short_list.txt"

output_file = "short_list.sff"

with open(id_file) as id_handle:

wanted = set(line.rstrip("\n").split(None, 1)[0] for line in id_handle)

print("Found %i unique identifiers in %s" % (len(wanted), id_file))

records = (r for r in SeqIO.parse(input_file, "sff") if r.id in wanted)

count = SeqIO.write(records, output_file, "sff")

print("Saved %i records from %s to %s" % (count, input_file, output_file))

if count < len(wanted):

print("Warning %i IDs not found in %s" % (len(wanted) - count, input_file))

\end{minted}

Note that we use a Python \verb|set| rather than a \verb|list|, this makes

testing membership faster.

As discussed in Section~\ref{sec:low-level-fasta-fastq}, for a large FASTA

or FASTQ file for speed you would be better off not using the high-level

\verb|SeqIO| interface, but working directly with strings. This next

example shows how to do this with FASTQ files -- it is more complicated:

%not a doctest to avoid temp files being left behind, also no >>>

%makes it easier to copy and paste the example to a script file.

\begin{minted}{python}

from Bio.SeqIO.QualityIO import FastqGeneralIterator

input_file = "big_file.fastq"

id_file = "short_list.txt"

output_file = "short_list.fastq"

with open(id_file) as id_handle:

# Taking first word on each line as an identifer

wanted = set(line.rstrip("\n").split(None, 1)[0] for line in id_handle)

print("Found %i unique identifiers in %s" % (len(wanted), id_file))

with open(input_file) as in_handle:

with open(output_file, "w") as out_handle:

for title, seq, qual in FastqGeneralIterator(in_handle):

# The ID is the first word in the title line (after the @ sign):

if title.split(None, 1)[0] in wanted:

# This produces a standard 4-line FASTQ entry:

out_handle.write("@%s\n%s\n+\n%s\n" % (title, seq, qual))

count += 1

print("Saved %i records from %s to %s" % (count, input_file, output_file))

if count < len(wanted):

print("Warning %i IDs not found in %s" % (len(wanted) - count, input_file))

\end{minted}

\subsection{Producing randomised genomes}

Let's suppose you are looking at genome sequence, hunting for some sequence

feature -- maybe extreme local GC\% bias, or possible restriction digest sites.

Once you've got your Python code working on the real genome it may be sensible

to try running the same search on randomised versions of the same genome for

statistical analysis (after all, any ``features'' you've found could just be

there just by chance).

For this discussion, we'll use the GenBank file for the pPCP1 plasmid from

\textit{Yersinia pestis biovar Microtus}. The file is included with the

Biopython unit tests under the GenBank folder, or you can get it from our

website, \href{https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.gb}

{\texttt{NC\_005816.gb}}.

This file contains one and only one record, so we can read it in as a

\verb|SeqRecord| using the \verb|Bio.SeqIO.read()| function:

%doctest ../Tests/GenBank

\begin{minted}{pycon}

>>> from Bio import SeqIO

>>> original_rec = SeqIO.read("NC_005816.gb", "genbank")

\end{minted}

So, how can we generate a shuffled versions of the original sequence? I would

use the built in Python \verb|random| module for this, in particular the function

\verb|random.shuffle| -- but this works on a Python list. Our sequence is a

\verb|Seq| object, so in order to shuffle it we need to turn it into a list:

%cont-doctest

\begin{minted}{pycon}

>>> import random

>>> nuc_list = list(original_rec.seq)

>>> random.shuffle(nuc_list) # acts in situ!

\end{minted}

Now, in order to use \verb|Bio.SeqIO| to output the shuffled sequence, we need

to construct a new \verb|SeqRecord| with a new \verb|Seq| object using this

shuffled list. In order to do this, we need to turn the list of nucleotides

(single letter strings) into a long string -- the standard Python way to do

this is with the string object's join method.

%cont-doctest

\begin{minted}{pycon}

>>> from Bio.Seq import Seq

>>> from Bio.SeqRecord import SeqRecord

>>> shuffled_rec = SeqRecord(Seq("".join(nuc_list)),

... id="Shuffled", description="Based on %s" % original_rec.id)

...

\end{minted}

Let's put all these pieces together to make a complete Python script which

generates a single FASTA file containing 30 randomly shuffled versions of

the original sequence.

This first version just uses a big for loop and writes out the records one by one

(using the \verb|SeqRecord|'s format method described in

Section~\ref{sec:Bio.SeqIO-and-StringIO}):

\begin{minted}{python}

import random

from Bio.Seq import Seq

from Bio.SeqRecord import SeqRecord

from Bio import SeqIO

original_rec = SeqIO.read("NC_005816.gb", "genbank")

with open("shuffled.fasta", "w") as output_handle:

for i in range(30):

nuc_list = list(original_rec.seq)

random.shuffle(nuc_list)

shuffled_rec = SeqRecord(

Seq("".join(nuc_list)),

id="Shuffled%i" % (i + 1),

description="Based on %s" % original_rec.id,

)

output_handle.write(shuffled_rec.format("fasta"))

\end{minted}

Personally I prefer the following version using a function to shuffle the record

and a generator expression instead of the for loop:

\begin{minted}{python}

import random

from Bio.Seq import Seq

from Bio.SeqRecord import SeqRecord

from Bio import SeqIO

def make_shuffle_record(record, new_id):

nuc_list = list(record.seq)

random.shuffle(nuc_list)

return SeqRecord(

Seq("".join(nuc_list)), id=new_id, description="Based on %s" % original_rec.id,

)

original_rec = SeqIO.read("NC_005816.gb", "genbank")

shuffled_recs = (

make_shuffle_record(original_rec, "Shuffled%i" % (i + 1)) for i in range(30)

)

SeqIO.write(shuffled_recs, "shuffled.fasta", "fasta")

\end{minted}

\subsection{Translating a FASTA file of CDS entries}

\label{sec:SeqIO-translate}

Suppose you've got an input file of CDS entries for some organism, and you

want to generate a new FASTA file containing their protein sequences. i.e.

Take each nucleotide sequence from the original file, and translate it.

Back in Section~\ref{sec:translation} we saw how to use the \verb|Seq|

object's \verb|translate method|, and the optional \verb|cds| argument

which enables correct translation of alternative start codons.

We can combine this with \verb|Bio.SeqIO| as

shown in the reverse complement example in Section~\ref{sec:SeqIO-reverse-complement}.

The key point is that for each nucleotide \verb|SeqRecord|, we need to create

a protein \verb|SeqRecord| - and take care of naming it.

You can write you own function to do this, choosing suitable protein identifiers

for your sequences, and the appropriate genetic code. In this example we just

use the default table and add a prefix to the identifier:

\begin{minted}{python}

from Bio.SeqRecord import SeqRecord

def make_protein_record(nuc_record):

"""Returns a new SeqRecord with the translated sequence (default table)."""

return SeqRecord(

seq=nuc_record.seq.translate(cds=True),

id="trans_" + nuc_record.id,

description="translation of CDS, using default table",

)

\end{minted}

We can then use this function to turn the input nucleotide records into protein

records ready for output. An elegant way and memory efficient way to do this

is with a generator expression:

\begin{minted}{python}

from Bio import SeqIO

proteins = (

make_protein_record(nuc_rec)

for nuc_rec in SeqIO.parse("coding_sequences.fasta", "fasta")

)

SeqIO.write(proteins, "translations.fasta", "fasta")

\end{minted}

This should work on any FASTA file of complete coding sequences.

If you are working on partial coding sequences, you may prefer to use

\verb|nuc_record.seq.translate(to_stop=True)| in the example above, as

this wouldn't check for a valid start codon etc.

\subsection{Making the sequences in a FASTA file upper case}

Often you'll get data from collaborators as FASTA files, and sometimes the

sequences can be in a mixture of upper and lower case. In some cases this is

deliberate (e.g. lower case for poor quality regions), but usually it is not

important. You may want to edit the file to make everything consistent (e.g.

all upper case), and you can do this easily using the \verb|upper()| method

of the \verb|SeqRecord| object (added in Biopython 1.55):

\begin{minted}{python}

from Bio import SeqIO

records = (rec.upper() for rec in SeqIO.parse("mixed.fas", "fasta"))

count = SeqIO.write(records, "upper.fas", "fasta")

print("Converted %i records to upper case" % count)

\end{minted}

How does this work? The first line is just importing the \verb|Bio.SeqIO|

module. The second line is the interesting bit -- this is a Python

generator expression which gives an upper case version of each record

parsed from the input file (\texttt{mixed.fas}). In the third line we give

this generator expression to the \verb|Bio.SeqIO.write()| function and it

saves the new upper cases records to our output file (\texttt{upper.fas}).

The reason we use a generator expression (rather than a list or list

comprehension) is this means only one record is kept in memory at a time.

This can be really important if you are dealing with large files with

millions of entries.

\subsection{Sorting a sequence file}

\label{sec:SeqIO-sort}

Suppose you wanted to sort a sequence file by length (e.g. a set of

contigs from an assembly), and you are working with a file format like

FASTA or FASTQ which \verb|Bio.SeqIO| can read, write (and index).

If the file is small enough, you can load it all into memory at once

as a list of \verb|SeqRecord| objects, sort the list, and save it:

\begin{minted}{python}

from Bio import SeqIO

records = list(SeqIO.parse("ls_orchid.fasta", "fasta"))

records.sort(key=lambda r: len(r))

SeqIO.write(records, "sorted_orchids.fasta", "fasta")

\end{minted}

The only clever bit is specifying a comparison method for how to

sort the records (here we sort them by length). If you wanted the

longest records first, you could flip the comparison or use the

reverse argument:

\begin{minted}{python}

from Bio import SeqIO

records = list(SeqIO.parse("ls_orchid.fasta", "fasta"))

records.sort(key=lambda r: -len(r))

SeqIO.write(records, "sorted_orchids.fasta", "fasta")

\end{minted}

Now that's pretty straight forward - but what happens if you have a

very large file and you can't load it all into memory like this?

For example, you might have some next-generation sequencing reads

to sort by length. This can be solved using the

\verb|Bio.SeqIO.index()| function.

\begin{minted}{python}

from Bio import SeqIO

# Get the lengths and ids, and sort on length

len_and_ids = sorted(

(len(rec), rec.id) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")

)

ids = reversed([id for (length, id) in len_and_ids])

del len_and_ids # free this memory

record_index = SeqIO.index("ls_orchid.fasta", "fasta")

records = (record_index[id] for id in ids)

SeqIO.write(records, "sorted.fasta", "fasta")

\end{minted}

First we scan through the file once using \verb|Bio.SeqIO.parse()|,

recording the record identifiers and their lengths in a list of tuples.

We then sort this list to get them in length order, and discard the lengths.

Using this sorted list of identifiers \verb|Bio.SeqIO.index()| allows us to

retrieve the records one by one, and we pass them to \verb|Bio.SeqIO.write()|

for output.

These examples all use \verb|Bio.SeqIO| to parse the records into

\verb|SeqRecord| objects which are output using \verb|Bio.SeqIO.write()|.

What if you want to sort a file format which \verb|Bio.SeqIO.write()| doesn't

support, like the plain text SwissProt format? Here is an alternative

solution using the \verb|get_raw()| method added to \verb|Bio.SeqIO.index()|

in Biopython 1.54 (see Section~\ref{sec:seqio-index-getraw}).

\begin{minted}{python}

from Bio import SeqIO

# Get the lengths and ids, and sort on length

len_and_ids = sorted(

(len(rec), rec.id) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")

)

ids = reversed([id for (length, id) in len_and_ids])

del len_and_ids # free this memory

record_index = SeqIO.index("ls_orchid.fasta", "fasta")

with open("sorted.fasta", "wb") as out_handle:

for id in ids:

out_handle.write(record_index.get_raw(id))

\end{minted}

Note with Python 3 onwards, we have to open the file for writing in

binary mode because the \verb|get_raw()| method returns \verb|bytes| objects.

As a bonus, because it doesn't parse the data into \verb|SeqRecord| objects

a second time it should be faster. If you only want to use this with FASTA

format, we can speed this up one step further by using the low-level FASTA

parser to get the record identifiers and lengths:

\begin{minted}{python}

from Bio.SeqIO.FastaIO import SimpleFastaParser

from Bio import SeqIO

# Get the lengths and ids, and sort on length

with open("ls_orchid.fasta") as in_handle:

len_and_ids = sorted(

(len(seq), title.split(None, 1)[0])

for title, seq in SimpleFastaParser(in_handle)

)

ids = reversed([id for (length, id) in len_and_ids])

del len_and_ids # free this memory

record_index = SeqIO.index("ls_orchid.fasta", "fasta")

with open("sorted.fasta", "wb") as out_handle:

for id in ids:

out_handle.write(record_index.get_raw(id))

\end{minted}

\subsection{Simple quality filtering for FASTQ files}

\label{sec:FASTQ-filtering-example}

The FASTQ file format was introduced at Sanger and is now widely used for

holding nucleotide sequencing reads together with their quality scores.

FASTQ files (and the related QUAL files) are an excellent example of

per-letter-annotation, because for each nucleotide in the sequence there is

an associated quality score. Any per-letter-annotation is held in a

\verb|SeqRecord| in the \verb|letter_annotations| dictionary as a list,

tuple or string (with the same number of elements as the sequence length).

One common task is taking a large set of sequencing reads and filtering them

(or cropping them) based on their quality scores.

The following example is very simplistic, but should illustrate the basics of

working with quality data in a \verb|SeqRecord| object. All we are going to

do here is read in a file of FASTQ data, and filter it to pick out only those

records whose PHRED quality scores are all above some threshold (here 20).

For this example we'll use some real data downloaded from the ENA sequence

read archive,

\url{ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz}

(2MB) which unzips to a 19MB file \texttt{SRR020192.fastq}. This is some

Roche 454 GS FLX single end data from virus infected California sea lions

(see \url{https://www.ebi.ac.uk/ena/data/view/SRS004476} for details).

First, let's count the reads:

\begin{minted}{python}

from Bio import SeqIO

count = 0

for rec in SeqIO.parse("SRR020192.fastq", "fastq"):

count += 1

print("%i reads" % count)

\end{minted}

\noindent Now let's do a simple filtering for a minimum PHRED quality of 20:

\begin{minted}{python}

from Bio import SeqIO

good_reads = (

rec

for rec in SeqIO.parse("SRR020192.fastq", "fastq")

if min(rec.letter_annotations["phred_quality"]) >= 20

)

count = SeqIO.write(good_reads, "good_quality.fastq", "fastq")

print("Saved %i reads" % count)

\end{minted}

\noindent This pulled out only $14580$ reads out of the $41892$ present.

A more sensible thing to do would be to quality trim the reads, but this

is intended as an example only.

FASTQ files can contain millions of entries, so it is best to avoid loading

them all into memory at once. This example uses a generator expression, which

means only one \verb|SeqRecord| is created at a time - avoiding any memory

limitations.

Note that it would be faster to use the low-level \verb|FastqGeneralIterator|

parser here (see Section~\ref{sec:low-level-fasta-fastq}), but that does not

turn the quality string into integer scores.

\subsection{Trimming off primer sequences}

\label{sec:FASTQ-slicing-off-primer}

For this example we're going to pretend that \texttt{GATGACGGTGT} is a 5' primer

sequence we want to look for in some FASTQ formatted read data. As in the example

above, we'll use the \texttt{SRR020192.fastq} file downloaded from the ENA

(\url{ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz}).

By using the main \verb|Bio.SeqIO| interface, the same approach would work with

any other supported file format (e.g. FASTA files). However, for large FASTQ

files it would be faster the low-level \verb|FastqGeneralIterator| parser here

(see the earlier example, and Section~\ref{sec:low-level-fasta-fastq}).

This code uses \verb|Bio.SeqIO| with a generator expression (to avoid loading

all the sequences into memory at once), and the \verb|Seq| object's

\verb|startswith| method to see if the read starts with the primer sequence:

\begin{minted}{python}

from Bio import SeqIO

primer_reads = (

rec

for rec in SeqIO.parse("SRR020192.fastq", "fastq")

if rec.seq.startswith("GATGACGGTGT")

)

count = SeqIO.write(primer_reads, "with_primer.fastq", "fastq")

print("Saved %i reads" % count)

\end{minted}

\noindent That should find $13819$ reads from \texttt{SRR014849.fastq} and save them to

a new FASTQ file, \texttt{with\_primer.fastq}.

Now suppose that instead you wanted to make a FASTQ file containing these reads

but with the primer sequence removed? That's just a small change as we can slice the

\verb|SeqRecord| (see Section~\ref{sec:SeqRecord-slicing}) to remove the first eleven

letters (the length of our primer):

\begin{minted}{python}

from Bio import SeqIO

trimmed_primer_reads = (

rec[11:]

for rec in SeqIO.parse("SRR020192.fastq", "fastq")

if rec.seq.startswith("GATGACGGTGT")

)

count = SeqIO.write(trimmed_primer_reads, "with_primer_trimmed.fastq", "fastq")

print("Saved %i reads" % count)

\end{minted}

\noindent Again, that should pull out the $13819$ reads from \texttt{SRR020192.fastq},

but this time strip off the first ten characters, and save them to another new

FASTQ file, \texttt{with\_primer\_trimmed.fastq}.

Now, suppose you want to create a new FASTQ file where these reads have

their primer removed, but all the other reads are kept as they were?

If we want to still use a generator expression, it is probably clearest to

define our own trim function:

\begin{minted}{python}

from Bio import SeqIO

def trim_primer(record, primer):

if record.seq.startswith(primer):

return record[len(primer) :]

else:

return record

trimmed_reads = (

trim_primer(record, "GATGACGGTGT")

for record in SeqIO.parse("SRR020192.fastq", "fastq")

)

count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")

print("Saved %i reads" % count)

\end{minted}

This takes longer, as this time the output file contains all $41892$ reads.

Again, we're used a generator expression to avoid any memory problems.

You could alternatively use a generator function rather than a generator

expression.

\begin{minted}{python}

from Bio import SeqIO

def trim_primers(records, primer):

"""Removes perfect primer sequences at start of reads.

This is a generator function, the records argument should

be a list or iterator returning SeqRecord objects.

"""

len_primer = len(primer) # cache this for later

for record in records:

if record.seq.startswith(primer):

yield record[len_primer:]

else:

yield record

original_reads = SeqIO.parse("SRR020192.fastq", "fastq")

trimmed_reads = trim_primers(original_reads, "GATGACGGTGT")

count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")

print("Saved %i reads" % count)

\end{minted}

This form is more flexible if you want to do something more complicated

where only some of the records are retained -- as shown in the next example.

\subsection{Trimming off adaptor sequences}

\label{sec:FASTQ-slicing-off-adaptor}

This is essentially a simple extension to the previous example. We are going

to going to pretend \texttt{GATGACGGTGT} is an adaptor sequence in some FASTQ

formatted read data, again the \texttt{SRR020192.fastq} file from the NCBI

(\url{ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz}).

This time however, we will look for the sequence \emph{anywhere} in the reads,

not just at the very beginning:

\begin{minted}{python}

from Bio import SeqIO

def trim_adaptors(records, adaptor):

"""Trims perfect adaptor sequences.

This is a generator function, the records argument should

be a list or iterator returning SeqRecord objects.

"""

len_adaptor = len(adaptor) # cache this for later

for record in records:

index = record.seq.find(adaptor)

if index == -1:

# adaptor not found, so won't trim

yield record

else:

# trim off the adaptor

yield record[index + len_adaptor :]

original_reads = SeqIO.parse("SRR020192.fastq", "fastq")

trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT")

count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")

print("Saved %i reads" % count)

\end{minted}

Because we are using a FASTQ input file in this example, the \verb|SeqRecord|

objects have per-letter-annotation for the quality scores. By slicing the

\verb|SeqRecord| object the appropriate scores are used on the trimmed

records, so we can output them as a FASTQ file too.

Compared to the output of the previous example where we only looked for

a primer/adaptor at the start of each read, you may find some of the

trimmed reads are quite short after trimming (e.g. if the adaptor was

found in the middle rather than near the start). So, let's add a minimum

length requirement as well:

\begin{minted}{python}

from Bio import SeqIO

def trim_adaptors(records, adaptor, min_len):

"""Trims perfect adaptor sequences, checks read length.

This is a generator function, the records argument should

be a list or iterator returning SeqRecord objects.

"""

len_adaptor = len(adaptor) # cache this for later

for record in records:

len_record = len(record) # cache this for later

if len(record) < min_len:

# Too short to keep

continue

index = record.seq.find(adaptor)

if index == -1:

# adaptor not found, so won't trim

yield record

elif len_record - index - len_adaptor >= min_len:

# after trimming this will still be long enough

yield record[index + len_adaptor :]

original_reads = SeqIO.parse("SRR020192.fastq", "fastq")

trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT", 100)

count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")

print("Saved %i reads" % count)

\end{minted}

By changing the format names, you could apply this to FASTA files instead.

This code also could be extended to do a fuzzy match instead of an exact

match (maybe using a pairwise alignment, or taking into account the read

quality scores), but that will be much slower.

\subsection{Converting FASTQ files}

\label{sec:SeqIO-fastq-conversion}

Back in Section~\ref{sec:SeqIO-conversion} we showed how to use

\verb|Bio.SeqIO| to convert between two file formats. Here we'll go into a

little more detail regarding FASTQ files which are used in second generation

DNA sequencing. Please refer to Cock \textit{et al.} (2009) \cite{cock2010}

for a longer description. FASTQ files store both the DNA sequence (as a string)

and the associated read qualities.

PHRED scores (used in most FASTQ files, and also in QUAL files, ACE files

and SFF files) have become a \textit{de facto} standard for representing

the probability of a sequencing error (here denoted by $P_e$) at a given

base using a simple base ten log transformation:

\begin{equation}

Q_{\textrm{PHRED}} = - 10 \times \textrm{log}_{10} ( P_e )

\end{equation}

This means a wrong read ($P_e = 1$) gets a PHRED quality of $0$, while a very

good read like $P_e = 0.00001$ gets a PHRED quality of $50$. While for raw

sequencing data qualities higher than this are rare, with post processing

such as read mapping or assembly, qualities of up to about $90$ are possible

(indeed, the MAQ tool allows for PHRED scores in the range 0 to 93 inclusive).

The FASTQ format has the potential to become a \textit{de facto} standard for

storing the letters and quality scores for a sequencing read in a single plain

text file. The only fly in the ointment is that there are at least three

versions of the FASTQ format which are incompatible and difficult to

distinguish...

\begin{enumerate}

\item The original Sanger FASTQ format uses PHRED qualities encoded with an

ASCII offset of 33. The NCBI are using this format in their Short Read

Archive. We call this the \texttt{fastq} (or \texttt{fastq-sanger}) format

in \verb|Bio.SeqIO|.

\item Solexa (later bought by Illumina) introduced their own version using

Solexa qualities encoded with an ASCII offset of 64. We call this the

\texttt{fastq-solexa} format.

\item Illumina pipeline 1.3 onwards produces FASTQ files with PHRED qualities

(which is more consistent), but encoded with an ASCII offset of 64. We call

this the \texttt{fastq-illumina} format.

\end{enumerate}

The Solexa quality scores are defined using a different log transformation:

\begin{equation}

Q_{\textrm{Solexa}} = - 10 \times \textrm{log}_{10} \left( \frac{P_e}{1-P_e} \right)

\end{equation}

Given Solexa/Illumina have now moved to using PHRED scores in version 1.3 of

their pipeline, the Solexa quality scores will gradually fall out of use.

If you equate the error estimates ($P_e$) these two equations allow conversion

between the two scoring systems - and Biopython includes functions to do this

in the \verb|Bio.SeqIO.QualityIO| module, which are called if you use

\verb|Bio.SeqIO| to convert an old Solexa/Illumina file into a standard Sanger

FASTQ file:

\begin{minted}{python}

from Bio import SeqIO

SeqIO.convert("solexa.fastq", "fastq-solexa", "standard.fastq", "fastq")

\end{minted}

If you want to convert a new Illumina 1.3+ FASTQ file, all that gets changed

is the ASCII offset because although encoded differently the scores are all

PHRED qualities:

\begin{minted}{python}

from Bio import SeqIO

SeqIO.convert("illumina.fastq", "fastq-illumina", "standard.fastq", "fastq")

\end{minted}

Note that using \verb|Bio.SeqIO.convert()| like this is \emph{much} faster

than combining \verb|Bio.SeqIO.parse()| and \verb|Bio.SeqIO.write()|

because optimised code is used for converting between FASTQ variants

(and also for FASTQ to FASTA conversion).

For good quality reads, PHRED and Solexa scores are approximately equal,

which means since both the \texttt{fasta-solexa} and \texttt{fastq-illumina}

formats use an ASCII offset of 64 the files are almost the same. This was a

deliberate design choice by Illumina, meaning applications expecting the old

\texttt{fasta-solexa} style files will probably be OK using the newer

\texttt{fastq-illumina} files (on good data). Of course, both variants are

very different from the original FASTQ standard as used by Sanger,

the NCBI, and elsewhere (format name \texttt{fastq} or \texttt{fastq-sanger}).

For more details, see the built in help (also \href{http://www.biopython.org/docs/\bpversion/api/Bio.SeqIO.QualityIO.html}{online}):

\begin{minted}{pycon}

>>> from Bio.SeqIO import QualityIO

>>> help(QualityIO)

...

\end{minted}

\subsection{Converting FASTA and QUAL files into FASTQ files}

\label{sec:SeqIO-fasta-qual-conversion}

FASTQ files hold \emph{both} sequences and their quality strings.

FASTA files hold \emph{just} sequences, while QUAL files hold \emph{just}

the qualities. Therefore a single FASTQ file can be converted to or from

\emph{paired} FASTA and QUAL files.

Going from FASTQ to FASTA is easy:

\begin{minted}{python}

from Bio import SeqIO

SeqIO.convert("example.fastq", "fastq", "example.fasta", "fasta")

\end{minted}

Going from FASTQ to QUAL is also easy:

\begin{minted}{python}

from Bio import SeqIO

SeqIO.convert("example.fastq", "fastq", "example.qual", "qual")

\end{minted}

However, the reverse is a little more tricky. You can use \verb|Bio.SeqIO.parse()|

to iterate over the records in a \emph{single} file, but in this case we have

two input files. There are several strategies possible, but assuming that the

two files are really paired the most memory efficient way is to loop over both

together. The code is a little fiddly, so we provide a function called

\verb|PairedFastaQualIterator| in the \verb|Bio.SeqIO.QualityIO| module to do

this. This takes two handles (the FASTA file and the QUAL file) and returns

a \verb|SeqRecord| iterator:

\begin{minted}{python}

from Bio.SeqIO.QualityIO import PairedFastaQualIterator

for record in PairedFastaQualIterator(open("example.fasta"), open("example.qual")):

print(record)

\end{minted}

This function will check that the FASTA and QUAL files are consistent (e.g.

the records are in the same order, and have the same sequence length).

You can combine this with the \verb|Bio.SeqIO.write()| function to convert a

pair of FASTA and QUAL files into a single FASTQ files:

\begin{minted}{python}

from Bio import SeqIO

from Bio.SeqIO.QualityIO import PairedFastaQualIterator

with open("example.fasta") as f_handle, open("example.qual") as q_handle:

records = PairedFastaQualIterator(f_handle, q_handle)

count = SeqIO.write(records, "temp.fastq", "fastq")

print("Converted %i records" % count)

\end{minted}

\subsection{Indexing a FASTQ file}

\label{sec:fastq-indexing}

FASTQ files are usually very large, with millions of reads in them. Due to the

sheer amount of data, you can't load all the records into memory at once.

This is why the examples above (filtering and trimming) iterate over the file

looking at just one \verb|SeqRecord| at a time.

However, sometimes you can't use a big loop or an iterator - you may need

random access to the reads. Here the \verb|Bio.SeqIO.index()| function

may prove very helpful, as it allows you to access any read in the FASTQ file

by its name (see Section~\ref{sec:SeqIO-index}).

Again we'll use the \texttt{SRR020192.fastq} file from the ENA

(\url{ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz}),

although this is actually quite a small FASTQ file with less than $50,000$ reads:

\begin{minted}{pycon}

>>> from Bio import SeqIO

>>> fq_dict = SeqIO.index("SRR020192.fastq", "fastq")

>>> len(fq_dict)

41892

>>> list(fq_dict.keys())[:4]

['SRR020192.38240', 'SRR020192.23181', 'SRR020192.40568', 'SRR020192.23186']

>>> fq_dict["SRR020192.23186"].seq

Seq('GTCCCAGTATTCGGATTTGTCTGCCAAAACAATGAAATTGACACAGTTTACAAC...CCG')

\end{minted}

When testing this on a FASTQ file with seven million reads,

indexing took about a minute, but record access was almost instant.

The sister function \verb|Bio.SeqIO.index_db()| lets you save the index

to an SQLite3 database file for near instantaneous reuse - see

Section~\ref{sec:SeqIO-index} for more details.

The example in Section~\ref{sec:SeqIO-sort} show how you can use the

\verb|Bio.SeqIO.index()| function to sort a large FASTA file -- this

could also be used on FASTQ files.

\subsection{Converting SFF files}

\label{sec:SeqIO-sff-conversion}

If you work with 454 (Roche) sequence data, you will probably have access

to the raw data as a Standard Flowgram Format (SFF) file. This contains

the sequence reads (called bases) with quality scores and the original

flow information.

A common task is to convert from SFF to a pair of FASTA and QUAL files,

or to a single FASTQ file. These operations are trivial using the

\verb|Bio.SeqIO.convert()| function (see Section~\ref{sec:SeqIO-conversion}):

\begin{minted}{pycon}

>>> from Bio import SeqIO

>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.fasta", "fasta")

10

>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.qual", "qual")

10

>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.fastq", "fastq")

10

\end{minted}

\noindent Remember the convert function returns the number of records, in

this example just ten. This will give you the \emph{untrimmed} reads, where

the leading and trailing poor quality sequence or adaptor will be in lower

case. If you want the \emph{trimmed} reads (using the clipping information

recorded within the SFF file) use this:

\begin{minted}{pycon}

>>> from Bio import SeqIO

>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.fasta", "fasta")

10

>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.qual", "qual")

10

>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.fastq", "fastq")

10

\end{minted}

If you run Linux, you could ask Roche for a copy of their ``off instrument''

tools (often referred to as the Newbler tools). This offers an alternative way to

do SFF to FASTA or QUAL conversion at the command line (but currently FASTQ output

is not supported), e.g.

\begin{minted}{console}

$ sffinfo -seq -notrim E3MFGYR02_random_10_reads.sff > reads.fasta

$ sffinfo -qual -notrim E3MFGYR02_random_10_reads.sff > reads.qual

$ sffinfo -seq -trim E3MFGYR02_random_10_reads.sff > trimmed.fasta

$ sffinfo -qual -trim E3MFGYR02_random_10_reads.sff > trimmed.qual

\end{minted}

\noindent The way Biopython uses mixed case sequence strings to represent

the trimming points deliberately mimics what the Roche tools do.

For more information on the Biopython SFF support, consult the built in help:

\begin{minted}{pycon}

>>> from Bio.SeqIO import SffIO

>>> help(SffIO)

...

\end{minted}

\subsection{Identifying open reading frames}

A very simplistic first step at identifying possible genes is to look for

open reading frames (ORFs). By this we mean look in all six frames for long

regions without stop codons -- an ORF is just a region of nucleotides with

no in frame stop codons.

Of course, to find a gene you would also need to worry about locating a start

codon, possible promoters -- and in Eukaryotes there are introns to worry about

too. However, this approach is still useful in viruses and Prokaryotes.

To show how you might approach this with Biopython, we'll need a sequence to

search, and as an example we'll again use the bacterial plasmid -- although

this time we'll start with a plain FASTA file with no pre-marked genes:

\href{https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.fna}

{\texttt{NC\_005816.fna}}. This is a bacterial sequence, so we'll want to use

NCBI codon table 11 (see Section~\ref{sec:translation} about translation).

%doctest ../Tests/GenBank

\begin{minted}{pycon}

>>> from Bio import SeqIO

>>> record = SeqIO.read("NC_005816.fna", "fasta")

>>> table = 11

>>> min_pro_len = 100

\end{minted}

Here is a neat trick using the \verb|Seq| object's \verb|split| method to

get a list of all the possible ORF translations in the six reading frames:

%cont-doctest

\begin{minted}{pycon}

>>> for strand, nuc in [(+1, record.seq), (-1, record.seq.reverse_complement())]:

... for frame in range(3):

... length = 3 * ((len(record)-frame) // 3) #Multiple of three

... for pro in nuc[frame:frame+length].translate(table).split("*"):

... if len(pro) >= min_pro_len:

... print("%s...%s - length %i, strand %i, frame %i" \

... % (pro[:30], pro[-3:], len(pro), strand, frame))

GCLMKKSSIVATIITILSGSANAASSQLIP...YRF - length 315, strand 1, frame 0

KSGELRQTPPASSTLHLRLILQRSGVMMEL...NPE - length 285, strand 1, frame 1

GLNCSFFSICNWKFIDYINRLFQIIYLCKN...YYH - length 176, strand 1, frame 1

VKKILYIKALFLCTVIKLRRFIFSVNNMKF...DLP - length 165, strand 1, frame 1

NQIQGVICSPDSGEFMVTFETVMEIKILHK...GVA - length 355, strand 1, frame 2

RRKEHVSKKRRPQKRPRRRRFFHRLRPPDE...PTR - length 128, strand 1, frame 2

TGKQNSCQMSAIWQLRQNTATKTRQNRARI...AIK - length 100, strand 1, frame 2

QGSGYAFPHASILSGIAMSHFYFLVLHAVK...CSD - length 114, strand -1, frame 0

IYSTSEHTGEQVMRTLDEVIASRSPESQTR...FHV - length 111, strand -1, frame 0

WGKLQVIGLSMWMVLFSQRFDDWLNEQEDA...ESK - length 125, strand -1, frame 1

RGIFMSDTMVVNGSGGVPAFLFSGSTLSSY...LLK - length 361, strand -1, frame 1

WDVKTVTGVLHHPFHLTFSLCPEGATQSGR...VKR - length 111, strand -1, frame 1

LSHTVTDFTDQMAQVGLCQCVNVFLDEVTG...KAA - length 107, strand -1, frame 2

RALTGLSAPGIRSQTSCDRLRELRYVPVSL...PLQ - length 119, strand -1, frame 2

\end{minted}

Note that here we are counting the frames from the 5' end (start) of

\emph{each} strand. It is sometimes easier to always count from the 5' end

(start) of the \emph{forward} strand.

You could easily edit the above loop based code to build up a list of the

candidate proteins, or convert this to a list comprehension. Now, one thing

this code doesn't do is keep track of where the proteins are.

You could tackle this in several ways. For example, the following code tracks

the locations in terms of the protein counting, and converts back to the

parent sequence by multiplying by three, then adjusting for the frame and

strand:

\begin{minted}{python}

from Bio import SeqIO

record = SeqIO.read("NC_005816.gb", "genbank")

table = 11

min_pro_len = 100

def find_orfs_with_trans(seq, trans_table, min_protein_length):

answer = []

seq_len = len(seq)

for strand, nuc in [(+1, seq), (-1, seq.reverse_complement())]:

for frame in range(3):

trans = nuc[frame:].translate(trans_table)

trans_len = len(trans)

aa_start = 0

aa_end = 0

while aa_start < trans_len:

aa_end = trans.find("*", aa_start)

if aa_end == -1:

aa_end = trans_len

if aa_end - aa_start >= min_protein_length:

if strand == 1:

start = frame + aa_start * 3

end = min(seq_len, frame + aa_end * 3 + 3)

else:

start = seq_len - frame - aa_end * 3 - 3

end = seq_len - frame - aa_start * 3

answer.append((start, end, strand, trans[aa_start:aa_end]))

aa_start = aa_end + 1

answer.sort()

return answer

orf_list = find_orfs_with_trans(record.seq, table, min_pro_len)

for start, end, strand, pro in orf_list:

print(

"%s...%s - length %i, strand %i, %i:%i"

% (pro[:30], pro[-3:], len(pro), strand, start, end)

)

\end{minted}

\noindent And the output:

\begin{minted}{text}