Sequences and FASTA/FASTQ files

Sequence

A Sequence object holds a DNA sequence. Besides the actual sequence, an object may also hold a name.

Instantiation
class HTSeq.Sequence(seq, name="unnamed")

Pass the DNA sequence and, optionally, a name or ID to the constructor:

>>> myseq = HTSeq.Sequence( "ACCGTTAC", "my_sequence" )

(If the name is omitted, the default "unnamed" is used.)

Attributes
Sequence.seq
Sequence.name
Sequence.descr

The information can be accessed via the attributes seq and name, which are strings:

>>> myseq.seq
'ACCGTTAC'
>>> myseq.name
'my_sequence'

There is a third attribute, called descr, which is by default None but may contain a “description”. See class FastaReader for more information.

Representation and string conversion

The __repr__ method gives name and length:

>>> myseq
<Sequence object 'my_sequence' (length 8)>

The __str__ method returns just the sequence:

>>> print myseq
ACCGTTAC

Note that the length of a sequence is the number of bases:

>>> len( myseq )
8
Subsetting

Subsetting works as with strings:

>>> myseq2 = myseq[3:5]
>>> myseq2.name
'my_sequence[part]'
>>> myseq2.seq
'GT'

(Note that "[part]" is appended to the name of the subsetted copy.)

Reverse complement

Sequence.get_reverse_complement()
>>> print myseq.get_reverse_complement()
GTAACGGT
>>> rc = myseq.get_reverse_complement()
>>> rc.name
'revcomp_of_my_sequence'
>>> rc.seq
'GTAACGGT'

Writing to FASTA file

To write Sequence objects into a FASTA file, open a text file for writing, then call write_to_fasta_file for each sequence, providing the open file handle as only argument, and close the file:

>>> my_fasta_file = open( "test.fa", "w" )
>>> myseq.write_to_fasta_file( my_fasta_file )
>>> my_fasta_file.close()

To read from a FASTA file, see class FastaReader.

Extended UIPAC letters
These are not (yet) supported. A sequence should only contain A, C, G, T and N.

Counting bases

For read quality assessment, it is often helpful to count the proportions of called bases, stratified by position in the read. To obtain such counts, the following idiom is helpful:

>>> import numpy
>>> reads = HTSeq.FastqReader( "yeast_RNASeq_excerpt_sequence.txt" )
>>> counts = numpy.zeros( ( 36, 5 ), numpy.int )
>>> for read in reads:
...     read.add_bases_to_count_array( counts )
>>> counts        
array([[16194,  2048,  4017,  2683,    57],
       [10716,  3321,  4933,  6029,     0],
       [ 7816,  5024,  5946,  6213,     0],
       ...
       [ 8526,  4812,  5460,  6197,     4],
       [ 8088,  4915,  5531,  6464,     1]])

Here, a two-dimensional numpy array of integer zeroes is defined and then passed to the add_bases_to_count_array method of each Sequence object obtained from the Fastq file. The method add_bases_to_count_array adds, for each base, a one to one of the array elements such that, in the end, the 36 rows of the array correspond to the positions in the reads (all of length 36 bp in this example), and the 5 columns correspond to the base letters ‘A’, ‘C’, ‘G’, ‘T’, and ‘N’, as given by the constant base_to_columns

base_to_column = { 'A': 0, 'C': 1, 'G': 2, 'T': 3, 'N': 4 }

Hence, we can get the proportion of ‘C’s in each position as follows:

>>> counts = numpy.array( counts, numpy.float ) 
>>> #counts[ : , HTSeq.base_to_column['C'] ] / counts.sum(1)
array([ 0.08192328,  0.13284531,  0.20096804,  0.16872675,  0.21200848,
        ...
        0.18560742,  0.19236769,  0.19088764,  0.17872715,  0.1924877 ,
        0.19660786])

(Here, we first convert the count array to type float to allow to proper division, and then divide the second column (HTSeq.base_to_column['C']) by the row-wise sums (counts.sum(1); the 1 requests summing along rows).)

Trimming reads

Sequence.trim_left_end(pattern, mismatch_prop = 0.)
Sequence.trim_right_end(pattern, mismatch_prop = 0.)

In high-throughput sequencing, reads are sometimes contaminated with adapters or sequencing primers. These function take a pattern and attempt to match either the right end of the pattern to the left end of the sequence (trim_left_end) or the left end of the pattern to the right end of the sequence (trim_right_end). The match is the trimmed off.

Here is an example:

>>> seq2 = HTSeq.Sequence( "ACGTAAAGCGGTACGGGGGG" )
>>> left_seq = HTSeq.Sequence( "CCCACG" )
>>> print seq2.trim_left_end( left_seq )
TAAAGCGGTACGGGGGG

The right end of the pattern (“ACG”) matched the left end of the sequence, and has hence been trimmed off.

The optional argument mismatch_prop is the number of allowed mismatches as proportion of the length of the match:

>>> right_seq = HTSeq.Sequence( "GGGTGGG" )
>>> print seq2.trim_right_end( right_seq )
ACGTAAAGCGGTACGGG
>>> print seq2.trim_right_end( right_seq, 1/6. )
ACGTAAAGCGGTAC
>>> print seq2.trim_right_end( right_seq, 1/7. )
ACGTAAAGCGGTACGGG

Here, if we allow at least one mismatch per six bases, the whole pattern gets cut off.

If you have quality information, you can use this, too, to specify the allowed amount of mismatch. See SequenceWithQualities.trim_left_end_with_quals() and SequenceWithQualities.trim_left_end_with_quals().

SequenceWithQualities

The sequences obtained from high-throughput sequencing devices (in the following also referred to as “reads”) typically come with base-call quality scores, which indicate how sure the software was that the right base was called. The class SequenceWithQualities represents such reads.

SequenceWithQualities is a daughter class of Sequence and inherits all its features.

Instantiation

class HTSeq.SequenceWithQualities(seq, name qualstr, qualscale="phred")

A SequenceWithQualities can be instantiated as a Sequence, but now with a third argument, the quality string:

>>> myread = HTSeq.SequenceWithQualities( "ACGACTGACC", "my_read", "IICGAB##(!" )

The quality string is interpreted as Sanger-encoded string of Phred values, as defined in the FASTQ format specification, i.e., each letter in the quality string corresponds to one base in the sequence and if the value 33 is subtracted from the quality characters ASCII value, the Phred score is obtained.

The Phred scores can then be found in the slot qual:

>>> myread.qualstr
'IICGAB##(!'
>>> myread.qual
array([40, 40, 34, 38, 32, 33,  2,  2,  7,  0])

If the quality string follows the Solexa FASTQ specification, the value to be subtracted is not 33 but 64. If you pass a quality string in this format, set qualscale="solexa".

Prior to version 1.3, the SolexaPipeline software used a yet another style of encoding quality string. If you want to use this one, specify qualscale="solexa-old"

Attributes

As for Sequence objects, there are attributes name, seq, and descr.

Furthermore, we now have the attributes qual and qualstr, already mentioned above.

SequenceWithQuality.qual

qual is a numpy array of data type integer, with as many elements as there are bases. Each element is a Phred score. A Phred score S is defined to mean that the base caller estimates the probability p of the base call being wrong as p = -log10 ( S/10 ).

Note that qual is always the probability, even if the solexa-old quality string format has been used, which encodes the odds p ( 1 - p ), i.e., in that case, the odds are converted to probabilities.

SequenceWithQuality.qualstr

The quality string according to Sanger Phred encoding. In case the quality was originally given in solexa or solexa-old format, it is converted:

>>> read2 = HTSeq.SequenceWithQualities( "ACGACTGACC", "my_read", "hhgddaZVFF", "solexa" )
>>> read2.qual
array([40, 40, 39, 36, 36, 33, 26, 22,  6,  6])
>>> read2.qualstr
"IIHEEB;7''"

Writing to FASTQ file

SequenceWithQuality.write_to_fastq_file(fasta_file)

To write SequenceWithQualities objects into a FASTQ file, open a text file for writing, then call write_to_fastq_file for each sequence, providing the open file handle as only argument, and close the file:

>>> my_fastq_file = open( "test.fq", "w" )
>>> myread.write_to_fastq_file( my_fastq_file )
>>> my_fastq_file.close()

Note that the reads will always be written with quality strings in Sanger encoding.

To read from a FASTQ file, see class FastqReader.

Counting quality values

Similar to Sequence.add_bases_to_count_array(), this method counts the occuring quality values stratified by position. This then allows to calculate average qualities as well as histograms.

Here is a usage example:

>>> import numpy       
>>> reads = HTSeq.FastqReader( "yeast_RNASeq_excerpt_sequence.txt", "solexa" )
>>> counts = numpy.zeros( ( 36, 41 ), numpy.int )
>>> for read in reads:
...    read.add_qual_to_count_array( counts )
>>> #counts
array([[   0,    0,   64, ...,    0,    0,    0],
       [   0,    0,   93, ...,    0,    0,    0],
       ...,
       [   0,    0, 2445, ...,    0,    0,    0],
       [   0,    0, 2920, ...,    0,    0,    0]])

The value counts[i,j] is then the number of reads for which the base at position i hat the quality scores j. According to the Fastq standard, quality scores range from 0 to 40; hence, the array is initialized to have 41 columns.

Trimming reads

SequenceWithQualities.trim_left_end_with_quals(pattern, max_mm_qual = 5)
SequenceWithQualities.trim_right_end_with_quals(pattern, max_mm_qual = 5)

These methods work as Sequence.trim_left_end() and Sequence.trim_right_end() (which are, of course, avilable for SequenceWithQualities objects, too). The difference is, that for the _with_quals trimming methods, the maximum amount of allowed mismatch is specified as the maximum value that the sum of the quality scores of the mismatched bases may take.

TODO: Add example

FastaReader

The FastaReader class allows to read and parse a FASTA file. It can generates an iterator of Sequence objects.

class HTSeq.FastaReader(filename_or_sequence)

As daughter class of FileOrSequence, FastaReader can be instantiated with either a filename, or with a sequence. See FileOrSequence for details.

Example 1

The typical use for FastaReader is to go through a FASTA file and do something with each sequence, e.g.:

>>> for s in HTSeq.FastaReader( "fastaEx.fa" ):
...     print "Sequence '%s' has length %d." % ( s.name, len(s) )
Sequence 'sequence1' has length 72.
Sequence 'sequence2' has length 70.
Example 2

Often, one might to read a whole Fasta file into memory to access it as a dict. This is a good idiom for this purpose:

>>> sequences = dict( (s.name, s) for s in HTSeq.FastaReader("fastaEx.fa") )
>>> sequences["sequence1"].seq
'AGTACGTAGTCGCTGCTGCTACGGGCGCTAGCTAGTACGTCACGACGTAGATGCTAGCTGACTAAACGATGC'

FastqReader

The FastqReader class works similar to FastaReader. It reads a Fastq file and generates an iterator over SequenceWithQualities objects.

class HTSeq.FastqReader(filename_or_sequence, qual_scale="phred")

As daughter class of FileOrSequence, FastaReader can be instantiated with either a filename, or with a sequence. See FileOrSequence for details.

By default, the quality strings are assumed to be encoded according to the Sanger/Phred standard. You may alternatively specify "solexa" or "solexa-old" (see SequenceWithQuality).

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