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Bio.SeqIO support for the FASTQ and QUAL file formats.
Note that you are expected to use this code via the Bio.SeqIO interface, as shown below.
The FASTQ file format is used frequently at the Wellcome Trust Sanger Institute to bundle a FASTA sequence and its PHRED quality data (integers between 0 and 90). Rather than using a single FASTQ file, often paired FASTA and QUAL files are used containing the sequence and the quality information separately.
The PHRED software reads DNA sequencing trace files, calls bases, and assigns a non-negative quality value to each called base using a logged transformation of the error probability, Q = -10 log10( Pe ), for example:
Pe = 1.0, Q = 0 Pe = 0.1, Q = 10 Pe = 0.01, Q = 20 ... Pe = 0.00000001, Q = 80 Pe = 0.000000001, Q = 90
In typical raw sequence reads, the PHRED quality valuea will be from 0 to 40. In the QUAL format these quality values are held as space separated text in a FASTA like file format. In the FASTQ format, each quality values is encoded with a single ASCI character using chr(Q+33), meaning zero maps to the character "!" and for example 80 maps to "q". For the Sanger FASTQ standard the allowed range of PHRED scores is 0 to 93 inclusive. The sequences and quality are then stored in pairs in a FASTA like format.
Unfortunately there is no official document describing the FASTQ file format, and worse, several related but different variants exist. Reasonable documentation exists at: http://maq.sourceforge.net/fastq.shtml
The good news is that Roche 454 sequencers can output files in the QUAL format, and sensibly they use PHREP style scores like Sanger. Converting a pair of FASTA and QUAL files into a Sanger style FASTQ file is easy. To extract QUAL files from a Roche 454 SFF binary file, use the Roche off instrument command line tool "sffinfo" with the -q or -qual argument. You can extract a matching FASTA file using the -s or -seq argument instead.
The bad news is that Solexa/Illumina did things differently - they have their own scoring system AND their own incompatible versions of the FASTQ format. Solexa/Illumina quality scores use Q = - 10 log10 ( Pe / (1-Pe) ), which can be negative. PHRED scores and Solexa scores are NOT interchangeable (but a reasonable mapping can be achieved between them, and they are approximately equal for high quality reads).
Confusingly early Solexa pipelines produced a FASTQ like file but using their own score mapping and an ASCII offset of 64. To make things worse, for the Solexa/Illumina pipeline 1.3 onwards, they introduced a third variant of the FASTQ file format, this time using PHRED scores (which is more consistent) but with an ASCII offset of 64.
i.e. There are at least THREE different and INCOMPATIBLE variants of the FASTQ file format: The original Sanger PHRED standard, and two from Solexa/Illumina.
You are expected to use this module via the Bio.SeqIO functions, with the following format names:
We could potentially add support for "qual-solexa" meaning QUAL files which contain Solexa scores, but thus far there isn't any reason to use such files.
For example, consider the following short FASTQ file:
@EAS54_6_R1_2_1_413_324 CCCTTCTTGTCTTCAGCGTTTCTCC + ;;3;;;;;;;;;;;;7;;;;;;;88 @EAS54_6_R1_2_1_540_792 TTGGCAGGCCAAGGCCGATGGATCA + ;;;;;;;;;;;7;;;;;-;;;3;83 @EAS54_6_R1_2_1_443_348 GTTGCTTCTGGCGTGGGTGGGGGGG + ;;;;;;;;;;;9;7;;.7;393333
This contains three reads of length 25. From the read length these were probably originally from an early Solexa/Illumina sequencer but this file follows the Sanger FASTQ convention (PHRED style qualities with an ASCII offet of 33). This means we can parse this file using Bio.SeqIO using "fastq" as the format name:
>>> from Bio import SeqIO >>> for record in SeqIO.parse(open("Quality/example.fastq"), "fastq") : ... print record.id, record.seq EAS54_6_R1_2_1_413_324 CCCTTCTTGTCTTCAGCGTTTCTCC EAS54_6_R1_2_1_540_792 TTGGCAGGCCAAGGCCGATGGATCA EAS54_6_R1_2_1_443_348 GTTGCTTCTGGCGTGGGTGGGGGGG
The qualities are held as a list of integers in each record's annotation:
>>> print record ID: EAS54_6_R1_2_1_443_348 Name: EAS54_6_R1_2_1_443_348 Description: EAS54_6_R1_2_1_443_348 Number of features: 0 Per letter annotation for: phred_quality Seq('GTTGCTTCTGGCGTGGGTGGGGGGG', SingleLetterAlphabet()) >>> print record.letter_annotations["phred_quality"] [26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 24, 26, 22, 26, 26, 13, 22, 26, 18, 24, 18, 18, 18, 18]
You can use the SeqRecord format method to show this in the QUAL format:
>>> print record.format("qual") >EAS54_6_R1_2_1_443_348 26 26 26 26 26 26 26 26 26 26 26 24 26 22 26 26 13 22 26 18 24 18 18 18 18 <BLANKLINE>
Or go back to the FASTQ format, use "fastq" (or "fastq-sanger"):
>>> print record.format("fastq") @EAS54_6_R1_2_1_443_348 GTTGCTTCTGGCGTGGGTGGGGGGG + ;;;;;;;;;;;9;7;;.7;393333 <BLANKLINE>
Or, using the Illumina 1.3+ FASTQ encoding (PHRED values with an ASCII offset of 64):
>>> print record.format("fastq-illumina") @EAS54_6_R1_2_1_443_348 GTTGCTTCTGGCGTGGGTGGGGGGG + ZZZZZZZZZZZXZVZZMVZRXRRRR <BLANKLINE>
You can also get Biopython to convert the scores and show a Solexa style FASTQ file:
>>> print record.format("fastq-solexa") @EAS54_6_R1_2_1_443_348 GTTGCTTCTGGCGTGGGTGGGGGGG + ZZZZZZZZZZZXZVZZMVZRXRRRR <BLANKLINE>
Notice that this is actually the same output as above using "fastq-illumina" as the format! The reason for this is all these scores are high enough that the PHRED and Solexa scores are almost equal. The differences become apparent for poor quality reads. See the functions solexa_quality_from_phred and phred_quality_from_solexa for more details.
If you wanted to trim your sequences (perhaps to remove low quality regions, or to remove a primer sequence), try slicing the SeqRecord objects. e.g.
>>> sub_rec = record[5:15] >>> print sub_rec ID: EAS54_6_R1_2_1_443_348 Name: EAS54_6_R1_2_1_443_348 Description: EAS54_6_R1_2_1_443_348 Number of features: 0 Per letter annotation for: phred_quality Seq('TTCTGGCGTG', SingleLetterAlphabet()) >>> print sub_rec.letter_annotations["phred_quality"] [26, 26, 26, 26, 26, 26, 24, 26, 22, 26] >>> print sub_rec.format("fastq") @EAS54_6_R1_2_1_443_348 TTCTGGCGTG + ;;;;;;9;7; <BLANKLINE>
If you wanted to, you could read in this FASTQ file, and save it as a QUAL file:
>>> from Bio import SeqIO >>> record_iterator = SeqIO.parse(open("Quality/example.fastq"), "fastq") >>> out_handle = open("Quality/temp.qual", "w") >>> SeqIO.write(record_iterator, out_handle, "qual") 3 >>> out_handle.close()
You can of course read in a QUAL file, such as the one we just created:
>>> from Bio import SeqIO >>> for record in SeqIO.parse(open("Quality/temp.qual"), "qual") : ... print record.id, record.seq EAS54_6_R1_2_1_413_324 ????????????????????????? EAS54_6_R1_2_1_540_792 ????????????????????????? EAS54_6_R1_2_1_443_348 ?????????????????????????
Notice that QUAL files don't have a proper sequence present! But the quality information is there:
>>> print record ID: EAS54_6_R1_2_1_443_348 Name: EAS54_6_R1_2_1_443_348 Description: EAS54_6_R1_2_1_443_348 Number of features: 0 Per letter annotation for: phred_quality UnknownSeq(25, alphabet = SingleLetterAlphabet(), character = '?') >>> print record.letter_annotations["phred_quality"] [26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 24, 26, 22, 26, 26, 13, 22, 26, 18, 24, 18, 18, 18, 18]
Just to keep things tidy, if you are following this example yourself, you can delete this temporary file now:
>>> import os >>> os.remove("Quality/temp.qual")
Sometimes you won't have a FASTQ file, but rather just a pair of FASTA and QUAL files. Because the Bio.SeqIO system is designed for reading single files, you would have to read the two in separately and then combine the data. However, since this is such a common thing to want to do, there is a helper iterator defined in this module that does this for you - PairedFastaQualIterator.
Alternatively, if you have enough RAM to hold all the records in memory at once, then a simple dictionary approach would work:
>>> from Bio import SeqIO >>> reads = SeqIO.to_dict(SeqIO.parse(open("Quality/example.fasta"), "fasta")) >>> for rec in SeqIO.parse(open("Quality/example.qual"), "qual") : ... reads[rec.id].letter_annotations["phred_quality"]=rec.letter_annotations["phred_quality"]
You can then access any record by its key, and get both the sequence and the quality scores.
>>> print reads["EAS54_6_R1_2_1_540_792"].format("fastq") @EAS54_6_R1_2_1_540_792 TTGGCAGGCCAAGGCCGATGGATCA + ;;;;;;;;;;;7;;;;;-;;;3;83 <BLANKLINE>
It is important that you explicitly tell Bio.SeqIO which FASTQ variant you are using ("fastq" or "fastq-sanger" for the Sanger standard using PHRED values, "fastq-solexa" for the original Solexa/Illumina variant, or "fastq-illumina" for the more recent variant), as this cannot be detected reliably automatically.
To illustrate this problem, let's consider an artifical example:
>>> from Bio.Seq import Seq >>> from Bio.Alphabet import generic_dna >>> from Bio.SeqRecord import SeqRecord >>> test = SeqRecord(Seq("NACGTACGTA", generic_dna), id="Test", ... description="Made up!") >>> print test.format("fasta") >Test Made up! NACGTACGTA <BLANKLINE> >>> print test.format("fastq") Traceback (most recent call last): ... ValueError: No suitable quality scores found in letter_annotations of SeqRecord (id=Test).
We created a sample SeqRecord, and can show it in FASTA format - but for QUAL or FASTQ format we need to provide some quality scores. These are held as a list of integers (one for each base) in the letter_annotations dictionary:
>>> test.letter_annotations["phred_quality"] = [0,1,2,3,4,5,10,20,30,40] >>> print test.format("qual") >Test Made up! 0 1 2 3 4 5 10 20 30 40 <BLANKLINE> >>> print test.format("fastq") @Test Made up! NACGTACGTA + !"#$%&+5?I <BLANKLINE>
We can check this FASTQ encoding - the first PHRED quality was zero, and this mapped to a exclamation mark, while the final score was 40 and this mapped to the letter "I":
>>> ord('!') - 33 0 >>> ord('I') - 33 40 >>> [ord(letter)-33 for letter in '!"#$%&+5?I'] [0, 1, 2, 3, 4, 5, 10, 20, 30, 40]
Similarly, we could produce an Illumina 1.3+ style FASTQ file using PHRED scores with an offset of 64:
>>> print test.format("fastq-illumina") @Test Made up! NACGTACGTA + @ABCDEJT^h <BLANKLINE>
And we can check this too - the first PHRED score was zero, and this mapped to "@", while the final score was 40 and this mapped to "h":
>>> ord("@") - 64 0 >>> ord("h") - 64 40 >>> [ord(letter)-64 for letter in "@ABCDEJT^h"] [0, 1, 2, 3, 4, 5, 10, 20, 30, 40]
Notice how different the standard Sanger FASTQ and the Illumina 1.3+ style FASTQ files look for the same data! Then we have the older Solexa/Illumina format to consider which encodes Solexa scores instead of PHRED scores.
First let's see what Biopython says if we convert the PHRED scores into Solexa scores (rounding to one decimal place):
>>> for q in [0,1,2,3,4,5,10,20,30,40] : ... print "PHRED %i maps to Solexa %0.1f" % (q, solexa_quality_from_phred(q)) PHRED 0 maps to Solexa -5.0 PHRED 1 maps to Solexa -5.0 PHRED 2 maps to Solexa -2.3 PHRED 3 maps to Solexa -0.0 PHRED 4 maps to Solexa 1.8 PHRED 5 maps to Solexa 3.3 PHRED 10 maps to Solexa 9.5 PHRED 20 maps to Solexa 20.0 PHRED 30 maps to Solexa 30.0 PHRED 40 maps to Solexa 40.0
Now here is the record using the old Solexa style FASTQ file:
>>> print test.format("fastq-solexa") @Test Made up! NACGTACGTA + ;;>@BCJT^h <BLANKLINE>
Again, this is using an ASCII offset of 64, so we can check the Solexa scores:
>>> [ord(letter)-64 for letter in ";;>@BCJT^h"] [-5, -5, -2, 0, 2, 3, 10, 20, 30, 40]
This explains why the last few letters of this FASTQ output matched that using the Illumina 1.3+ format - high quality PHRED scores and Solexa scores are approximately equal.
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FastqPhredWriter Class to write standard FASTQ format files (using PHRED quality scores). |
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QualPhredWriter Class to write QUAL format files (using PHRED quality scores). |
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FastqSolexaWriter Write old style Solexa/Illumina FASTQ format files (with Solexa qualities). |
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FastqIlluminaWriter Write Illumina 1.3+ FASTQ format files (with PHRED quality scores). |
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SANGER_SCORE_OFFSET = 33
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SOLEXA_SCORE_OFFSET = 64
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_phred_to_sanger_quality_str =
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_solexa_to_sanger_quality_str =
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_phred_to_illumina_quality_str =
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_solexa_to_illumina_quality_str =
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_solexa_to_solexa_quality_str =
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_phred_to_solexa_quality_str =
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__package__ =
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Covert a PHRED quality (range 0 to about 90) to a Solexa quality. PHRED and Solexa quality scores are both log transformations of a probality of error (high score = low probability of error). This function takes a PHRED score, transforms it back to a probability of error, and then re-expresses it as a Solexa score. This assumes the error estimates are equivalent. How does this work exactly? Well the PHRED quality is minus ten times the base ten logarithm of the probability of error: phred_quality = -10*log(error,10) Therefore, turning this round: error = 10 ** (- phred_quality / 10) Now, Solexa qualities use a different log transformation: solexa_quality = -10*log(error/(1-error),10) After substitution and a little manipulation we get: solexa_quality = 10*log(10**(phred_quality/10.0) - 1, 10) However, real Solexa files use a minimum quality of -5. This does have a good reason - a random a random base call would be correct 25% of the time, and thus have a probability of error of 0.75, which gives 1.25 as the PHRED quality, or -4.77 as the Solexa quality. Thus (after rounding), a random nucleotide read would have a PHRED quality of 1, or a Solexa quality of -5. Taken literally, this logarithic formula would map a PHRED quality of zero to a Solexa quality of minus infinity. Of course, taken literally, a PHRED score of zero means a probability of error of one (i.e. the base call is definitely wrong), which is worse than random! In practice, a PHRED quality of zero usually means a default value, or perhaps random - and therefore mapping it to the minimum Solexa score of -5 is reasonable. In conclusion, we follow EMBOSS, and take this logarithmic formula but also apply a minimum value of -5.0 for the Solexa quality, and also map a PHRED quality of zero to -5.0 as well. Note this function will return a floating point number, it is up to you to round this to the nearest integer if appropriate. e.g. >>> print "%0.2f" % round(solexa_quality_from_phred(80),2) 80.00 >>> print "%0.2f" % round(solexa_quality_from_phred(50),2) 50.00 >>> print "%0.2f" % round(solexa_quality_from_phred(20),2) 19.96 >>> print "%0.2f" % round(solexa_quality_from_phred(10),2) 9.54 >>> print "%0.2f" % round(solexa_quality_from_phred(5),2) 3.35 >>> print "%0.2f" % round(solexa_quality_from_phred(4),2) 1.80 >>> print "%0.2f" % round(solexa_quality_from_phred(3),2) -0.02 >>> print "%0.2f" % round(solexa_quality_from_phred(2),2) -2.33 >>> print "%0.2f" % round(solexa_quality_from_phred(1),2) -5.00 >>> print "%0.2f" % round(solexa_quality_from_phred(0),2) -5.00 Notice that for high quality reads PHRED and Solexa scores are numerically equal. The differences are important for poor quality reads, where PHRED has a minimum of zero but Solexa scores can be negative. Finally, as a special case where None is used for a "missing value", None is returned: >>> print solexa_quality_from_phred(None) None |
Convert a Solexa quality (which can be negative) to a PHRED quality. PHRED and Solexa quality scores are both log transformations of a probality of error (high score = low probability of error). This function takes a Solexa score, transforms it back to a probability of error, and then re-expresses it as a PHRED score. This assumes the error estimates are equivalent. The underlying formulas are given in the documentation for the sister function solexa_quality_from_phred, in this case the operation is: phred_quality = 10*log(10**(solexa_quality/10.0) + 1, 10) This will return a floating point number, it is up to you to round this to the nearest integer if appropriate. e.g. >>> print "%0.2f" % round(phred_quality_from_solexa(80),2) 80.00 >>> print "%0.2f" % round(phred_quality_from_solexa(20),2) 20.04 >>> print "%0.2f" % round(phred_quality_from_solexa(10),2) 10.41 >>> print "%0.2f" % round(phred_quality_from_solexa(0),2) 3.01 >>> print "%0.2f" % round(phred_quality_from_solexa(-5),2) 1.19 Note that a solexa_quality less then -5 is not expected, will trigger a warning, but will still be converted as per the logarithmic mapping (giving a number between 0 and 1.19 back). As a special case where None is used for a "missing value", None is returned: >>> print phred_quality_from_solexa(None) None |
Extract PHRED qualities from a SeqRecord's letter_annotations (PRIVATE). If there are no PHRED qualities, but there are Solexa qualities, those are used instead after conversion. |
Returns a Sanger FASTQ encoded quality string (PRIVATE). >>> from Bio.Seq import Seq >>> from Bio.SeqRecord import SeqRecord >>> r = SeqRecord(Seq("ACGTAN"), id="Test", ... letter_annotations = {"phred_quality":[50,40,30,20,10,0]}) >>> _get_sanger_quality_str(r) 'SI?5+!' If as in the above example (or indeed a SeqRecord parser with Bio.SeqIO), the PHRED qualities are integers, this function is able to use a very fast pre-cached mapping. However, if they are floats which differ slightly, then it has to do the appropriate rounding - which is slower: >>> r2 = SeqRecord(Seq("ACGTAN"), id="Test2", ... letter_annotations = {"phred_quality":[50.0,40.05,29.99,20,9.55,0.01]}) >>> _get_sanger_quality_str(r2) 'SI?5+!' If your scores include a None value, this raises an exception: >>> r3 = SeqRecord(Seq("ACGTAN"), id="Test3", ... letter_annotations = {"phred_quality":[50,40,30,20,10,None]}) >>> _get_sanger_quality_str(r3) Traceback (most recent call last): ... TypeError: A quality value of None was found If (strangely) your record has both PHRED and Solexa scores, then the PHRED scores are used in preference: >>> r4 = SeqRecord(Seq("ACGTAN"), id="Test4", ... letter_annotations = {"phred_quality":[50,40,30,20,10,0], ... "solexa_quality":[-5,-4,0,None,0,40]}) >>> _get_sanger_quality_str(r4) 'SI?5+!' If there are no PHRED scores, but there are Solexa scores, these are used instead (after the approriate conversion): >>> r5 = SeqRecord(Seq("ACGTAN"), id="Test5", ... letter_annotations = {"solexa_quality":[40,30,20,10,0,-5]}) >>> _get_sanger_quality_str(r5) 'I?5+$"' Again, integer Solexa scores can be looked up in a pre-cached mapping making this very fast. You can still use approximate floating point scores: >>> r6 = SeqRecord(Seq("ACGTAN"), id="Test6", ... letter_annotations = {"solexa_quality":[40.1,29.7,20.01,10,0.0,-4.9]}) >>> _get_sanger_quality_str(r6) 'I?5+$"' Notice that due to the limited range of printable ASCII characters, a PHRED quality of 93 is the maximum that can be held in an Illumina FASTQ file (using ASCII 126, the tilde). This function will issue a warning in this situation. |
Returns an Illumina 1.3+ FASTQ encoded quality string (PRIVATE). Notice that due to the limited range of printable ASCII characters, a PHRED quality of 62 is the maximum that can be held in an Illumina FASTQ file (using ASCII 126, the tilde). This function will issue a warning in this situation. |
Returns a Solexa FASTQ encoded quality string (PRIVATE). Notice that due to the limited range of printable ASCII characters, a Solexa quality of 62 is the maximum that can be held in a Solexa FASTQ file (using ASCII 126, the tilde). This function will issue a warning in this situation. |
Iterate over Fastq records as string tuples (not as SeqRecord objects). This code does not try to interpret the quality string numerically. It just returns tuples of the title, sequence and quality as strings. For the sequence and quality, any whitespace (such as new lines) is removed. Our SeqRecord based FASTQ iterators call this function internally, and then turn the strings into a SeqRecord objects, mapping the quality string into a list of numerical scores. If you want to do a custom quality mapping, then you might consider calling this function directly. For parsing FASTQ files, the title string from the "@" line at the start of each record can optionally be omitted on the "+" lines. If it is repeated, it must be identical. The sequence string and the quality string can optionally be split over multiple lines, although several sources discourage this. In comparison, for the FASTA file format line breaks between 60 and 80 characters are the norm. WARNING - Because the "@" character can appear in the quality string, this can cause problems as this is also the marker for the start of a new sequence. In fact, the "+" sign can also appear as well. Some sources recommended having no line breaks in the quality to avoid this, but even that is not enough, consider this example: @071113_EAS56_0053:1:1:998:236 TTTCTTGCCCCCATAGACTGAGACCTTCCCTAAATA +071113_EAS56_0053:1:1:998:236 IIIIIIIIIIIIIIIIIIIIIIIIIIIIICII+III @071113_EAS56_0053:1:1:182:712 ACCCAGCTAATTTTTGTATTTTTGTTAGAGACAGTG + @IIIIIIIIIIIIIIICDIIIII<%<6&-*).(*%+ @071113_EAS56_0053:1:1:153:10 TGTTCTGAAGGAAGGTGTGCGTGCGTGTGTGTGTGT + IIIIIIIIIIIICIIGIIIII>IAIIIE65I=II:6 @071113_EAS56_0053:1:3:990:501 TGGGAGGTTTTATGTGGA AAGCAGCAATGTACAAGA + IIIIIII.IIIIII1@44 @-7.%<&+/$/%4(++(% This is four PHRED encoded FASTQ entries originally from an NCBI source (given the read length of 36, these are probably Solexa Illumna reads where the quality has been mapped onto the PHRED values). This example has been edited to illustrate some of the nasty things allowed in the FASTQ format. Firstly, on the "+" lines most but not all of the (redundant) identifiers are ommited. In real files it is likely that all or none of these extra identifiers will be present. Secondly, while the first three sequences have been shown without line breaks, the last has been split over multiple lines. In real files any line breaks are likely to be consistent. Thirdly, some of the quality string lines start with an "@" character. For the second record this is unavoidable. However for the fourth sequence this only happens because its quality string is split over two lines. A naive parser could wrongly treat any line starting with an "@" as the beginning of a new sequence! This code copes with this possible ambiguity by keeping track of the length of the sequence which gives the expected length of the quality string. Using this tricky example file as input, this short bit of code demonstrates what this parsing function would return: >>> handle = open("Quality/tricky.fastq", "rU") >>> for (title, sequence, quality) in FastqGeneralIterator(handle) : ... print title ... print sequence, quality 071113_EAS56_0053:1:1:998:236 TTTCTTGCCCCCATAGACTGAGACCTTCCCTAAATA IIIIIIIIIIIIIIIIIIIIIIIIIIIIICII+III 071113_EAS56_0053:1:1:182:712 ACCCAGCTAATTTTTGTATTTTTGTTAGAGACAGTG @IIIIIIIIIIIIIIICDIIIII<%<6&-*).(*%+ 071113_EAS56_0053:1:1:153:10 TGTTCTGAAGGAAGGTGTGCGTGCGTGTGTGTGTGT IIIIIIIIIIIICIIGIIIII>IAIIIE65I=II:6 071113_EAS56_0053:1:3:990:501 TGGGAGGTTTTATGTGGAAAGCAGCAATGTACAAGA IIIIIII.IIIIII1@44@-7.%<&+/$/%4(++(% >>> handle.close() Finally we note that some sources state that the quality string should start with "!" (which using the PHRED mapping means the first letter always has a quality score of zero). This rather restrictive rule is not widely observed, so is therefore ignored here. One plus point about this "!" rule is that (provided there are no line breaks in the quality sequence) it would prevent the above problem with the "@" character. |
Generator function to iterate over FASTQ records (as SeqRecord objects).
Note that use of title2ids matches that of Bio.SeqIO.FastaIO. For each sequence in a (Sanger style) FASTQ file there is a matching string encoding the PHRED qualities (integers between 0 and about 90) using ASCII values with an offset of 33. For example, consider a file containing three short reads: @EAS54_6_R1_2_1_413_324 CCCTTCTTGTCTTCAGCGTTTCTCC + ;;3;;;;;;;;;;;;7;;;;;;;88 @EAS54_6_R1_2_1_540_792 TTGGCAGGCCAAGGCCGATGGATCA + ;;;;;;;;;;;7;;;;;-;;;3;83 @EAS54_6_R1_2_1_443_348 GTTGCTTCTGGCGTGGGTGGGGGGG + ;;;;;;;;;;;9;7;;.7;393333 For each sequence (e.g. "CCCTTCTTGTCTTCAGCGTTTCTCC") there is a matching string encoding the PHRED qualities using a ASCI values with an offset of 33 (e.g. ";;3;;;;;;;;;;;;7;;;;;;;88"). Using this module directly you might run: >>> handle = open("Quality/example.fastq", "rU") >>> for record in FastqPhredIterator(handle) : ... print record.id, record.seq EAS54_6_R1_2_1_413_324 CCCTTCTTGTCTTCAGCGTTTCTCC EAS54_6_R1_2_1_540_792 TTGGCAGGCCAAGGCCGATGGATCA EAS54_6_R1_2_1_443_348 GTTGCTTCTGGCGTGGGTGGGGGGG >>> handle.close() Typically however, you would call this via Bio.SeqIO instead with "fastq" (or "fastq-sanger") as the format: >>> from Bio import SeqIO >>> handle = open("Quality/example.fastq", "rU") >>> for record in SeqIO.parse(handle, "fastq") : ... print record.id, record.seq EAS54_6_R1_2_1_413_324 CCCTTCTTGTCTTCAGCGTTTCTCC EAS54_6_R1_2_1_540_792 TTGGCAGGCCAAGGCCGATGGATCA EAS54_6_R1_2_1_443_348 GTTGCTTCTGGCGTGGGTGGGGGGG >>> handle.close() If you want to look at the qualities, they are record in each record's per-letter-annotation dictionary as a simple list of integers: >>> print record.letter_annotations["phred_quality"] [26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 24, 26, 22, 26, 26, 13, 22, 26, 18, 24, 18, 18, 18, 18] |
Parsing old Solexa/Illumina FASTQ like files (which differ in the quality mapping). The optional arguments are the same as those for the FastqPhredIterator. For each sequence in Solexa/Illumina FASTQ files there is a matching string encoding the Solexa integer qualities using ASCII values with an offset of 64. Solexa scores are scaled differently to PHRED scores, and Biopython will NOT perform any automatic conversion when loading. NOTE - This file format is used by the OLD versions of the Solexa/Illumina pipeline. See also the FastqIlluminaIterator function for the NEW version. For example, consider a file containing these five records: @SLXA-B3_649_FC8437_R1_1_1_610_79 GATGTGCAATACCTTTGTAGAGGAA +SLXA-B3_649_FC8437_R1_1_1_610_79 YYYYYYYYYYYYYYYYYYWYWYYSU @SLXA-B3_649_FC8437_R1_1_1_397_389 GGTTTGAGAAAGAGAAATGAGATAA +SLXA-B3_649_FC8437_R1_1_1_397_389 YYYYYYYYYWYYYYWWYYYWYWYWW @SLXA-B3_649_FC8437_R1_1_1_850_123 GAGGGTGTTGATCATGATGATGGCG +SLXA-B3_649_FC8437_R1_1_1_850_123 YYYYYYYYYYYYYWYYWYYSYYYSY @SLXA-B3_649_FC8437_R1_1_1_362_549 GGAAACAAAGTTTTTCTCAACATAG +SLXA-B3_649_FC8437_R1_1_1_362_549 YYYYYYYYYYYYYYYYYYWWWWYWY @SLXA-B3_649_FC8437_R1_1_1_183_714 GTATTATTTAATGGCATACACTCAA +SLXA-B3_649_FC8437_R1_1_1_183_714 YYYYYYYYYYWYYYYWYWWUWWWQQ Using this module directly you might run: >>> handle = open("Quality/solexa_example.fastq", "rU") >>> for record in FastqSolexaIterator(handle) : ... print record.id, record.seq SLXA-B3_649_FC8437_R1_1_1_610_79 GATGTGCAATACCTTTGTAGAGGAA SLXA-B3_649_FC8437_R1_1_1_397_389 GGTTTGAGAAAGAGAAATGAGATAA SLXA-B3_649_FC8437_R1_1_1_850_123 GAGGGTGTTGATCATGATGATGGCG SLXA-B3_649_FC8437_R1_1_1_362_549 GGAAACAAAGTTTTTCTCAACATAG SLXA-B3_649_FC8437_R1_1_1_183_714 GTATTATTTAATGGCATACACTCAA >>> handle.close() Typically however, you would call this via Bio.SeqIO instead with "fastq-solexa" as the format: >>> from Bio import SeqIO >>> handle = open("Quality/solexa_example.fastq", "rU") >>> for record in SeqIO.parse(handle, "fastq-solexa") : ... print record.id, record.seq SLXA-B3_649_FC8437_R1_1_1_610_79 GATGTGCAATACCTTTGTAGAGGAA SLXA-B3_649_FC8437_R1_1_1_397_389 GGTTTGAGAAAGAGAAATGAGATAA SLXA-B3_649_FC8437_R1_1_1_850_123 GAGGGTGTTGATCATGATGATGGCG SLXA-B3_649_FC8437_R1_1_1_362_549 GGAAACAAAGTTTTTCTCAACATAG SLXA-B3_649_FC8437_R1_1_1_183_714 GTATTATTTAATGGCATACACTCAA >>> handle.close() If you want to look at the qualities, they are recorded in each record's per-letter-annotation dictionary as a simple list of integers: >>> print record.letter_annotations["solexa_quality"] [25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 23, 25, 25, 25, 25, 23, 25, 23, 23, 21, 23, 23, 23, 17, 17] These scores aren't very good, but they are high enough that they map almost exactly onto PHRED scores: >>> print "%0.2f" % phred_quality_from_solexa(25) 25.01 Let's look at faked example read which is even worse, where there are more noticeable differences between the Solexa and PHRED scores: @slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN +slxa_0001_1_0001_01 hgfedcba`_^]\[ZYXWVUTSRQPONMLKJIHGFEDCBA@?>=<; Again, you would typically use Bio.SeqIO to read this file in (rather than calling the Bio.SeqIO.QualtityIO module directly). Most FASTQ files will contain thousands of reads, so you would normally use Bio.SeqIO.parse() as shown above. This example has only as one entry, so instead we can use the Bio.SeqIO.read() function: >>> from Bio import SeqIO >>> handle = open("Quality/solexa_faked.fastq", "rU") >>> record = SeqIO.read(handle, "fastq-solexa") >>> handle.close() >>> print record.id, record.seq slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN >>> print record.letter_annotations["solexa_quality"] [40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, -2, -3, -4, -5] These quality scores are so low that when converted from the Solexa scheme into PHRED scores they look quite different: >>> print "%0.2f" % phred_quality_from_solexa(-1) 2.54 >>> print "%0.2f" % phred_quality_from_solexa(-5) 1.19 Note you can use the Bio.SeqIO.write() function or the SeqRecord's format method to output the record(s): >>> print record.format("fastq-solexa") @slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN + hgfedcba`_^]\[ZYXWVUTSRQPONMLKJIHGFEDCBA@?>=<; <BLANKLINE> Note this output is slightly different from the input file as Biopython has left out the optional repetition of the sequence identifier on the "+" line. If you want the to use PHRED scores, use "fastq" or "qual" as the output format instead, and Biopython will do the conversion for you: >>> print record.format("fastq") @slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN + IHGFEDCBA@?>=<;:9876543210/.-,++*)('&&%%$$##"" <BLANKLINE> >>> print record.format("qual") >slxa_0001_1_0001_01 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 10 9 8 7 6 5 5 4 4 3 3 2 2 1 1 <BLANKLINE> As shown above, the poor quality Solexa reads have been mapped to the equivalent PHRED score (e.g. -5 to 1 as shown earlier). |
Parse new Illumina 1.3+ FASTQ like files (which differ in the quality mapping). The optional arguments are the same as those for the FastqPhredIterator. For each sequence in Illumina 1.3+ FASTQ files there is a matching string encoding PHRED integer qualities using ASCII values with an offset of 64. NOTE - Older versions of the Solexa/Illumina pipeline encoded Solexa scores with an ASCII offset of 64. They are approximately equal but only for high qaulity reads. If you have an old Solexa/Illumina file with negative Solexa scores, and try and read this as an Illumina 1.3+ file it will fail: >>> from Bio import SeqIO >>> record = SeqIO.read(open("Quality/solexa_faked.fastq"), "fastq-solexa") >>> print record.id, record.seq slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN >>> print record.letter_annotations["solexa_quality"] [40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, -2, -3, -4, -5] >>> record2 = SeqIO.read(open("Quality/solexa_faked.fastq"), "fastq-illumina") Traceback (most recent call last): ... ValueError: Invalid character in quality string NOTE - True Sanger style FASTQ files use PHRED scores with an offset of 33. |
For QUAL files which include PHRED quality scores, but no sequence. For example, consider this short QUAL file: >EAS54_6_R1_2_1_413_324 26 26 18 26 26 26 26 26 26 26 26 26 26 26 26 22 26 26 26 26 26 26 26 23 23 >EAS54_6_R1_2_1_540_792 26 26 26 26 26 26 26 26 26 26 26 22 26 26 26 26 26 12 26 26 26 18 26 23 18 >EAS54_6_R1_2_1_443_348 26 26 26 26 26 26 26 26 26 26 26 24 26 22 26 26 13 22 26 18 24 18 18 18 18 Using this module directly you might run: >>> handle = open("Quality/example.qual", "rU") >>> for record in QualPhredIterator(handle) : ... print record.id, record.seq EAS54_6_R1_2_1_413_324 ????????????????????????? EAS54_6_R1_2_1_540_792 ????????????????????????? EAS54_6_R1_2_1_443_348 ????????????????????????? >>> handle.close() Typically however, you would call this via Bio.SeqIO instead with "qual" as the format: >>> from Bio import SeqIO >>> handle = open("Quality/example.qual", "rU") >>> for record in SeqIO.parse(handle, "qual") : ... print record.id, record.seq EAS54_6_R1_2_1_413_324 ????????????????????????? EAS54_6_R1_2_1_540_792 ????????????????????????? EAS54_6_R1_2_1_443_348 ????????????????????????? >>> handle.close() Becase QUAL files don't contain the sequence string itself, the seq property is set to an UnknownSeq object. As no alphabet was given, this has defaulted to a generic single letter alphabet and the character "?" used. By specifying a nucleotide alphabet, "N" is used instead: >>> from Bio import SeqIO >>> from Bio.Alphabet import generic_dna >>> handle = open("Quality/example.qual", "rU") >>> for record in SeqIO.parse(handle, "qual", alphabet=generic_dna) : ... print record.id, record.seq EAS54_6_R1_2_1_413_324 NNNNNNNNNNNNNNNNNNNNNNNNN EAS54_6_R1_2_1_540_792 NNNNNNNNNNNNNNNNNNNNNNNNN EAS54_6_R1_2_1_443_348 NNNNNNNNNNNNNNNNNNNNNNNNN >>> handle.close() However, the quality scores themselves are available as a list of integers in each record's per-letter-annotation: >>> print record.letter_annotations["phred_quality"] [26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 24, 26, 22, 26, 26, 13, 22, 26, 18, 24, 18, 18, 18, 18] You can still slice one of these SeqRecord objects with an UnknownSeq: >>> sub_record = record[5:10] >>> print sub_record.id, sub_record.letter_annotations["phred_quality"] EAS54_6_R1_2_1_443_348 [26, 26, 26, 26, 26] |
Iterate over matched FASTA and QUAL files as SeqRecord objects. For example, consider this short QUAL file with PHRED quality scores: >EAS54_6_R1_2_1_413_324 26 26 18 26 26 26 26 26 26 26 26 26 26 26 26 22 26 26 26 26 26 26 26 23 23 >EAS54_6_R1_2_1_540_792 26 26 26 26 26 26 26 26 26 26 26 22 26 26 26 26 26 12 26 26 26 18 26 23 18 >EAS54_6_R1_2_1_443_348 26 26 26 26 26 26 26 26 26 26 26 24 26 22 26 26 13 22 26 18 24 18 18 18 18 And a matching FASTA file: >EAS54_6_R1_2_1_413_324 CCCTTCTTGTCTTCAGCGTTTCTCC >EAS54_6_R1_2_1_540_792 TTGGCAGGCCAAGGCCGATGGATCA >EAS54_6_R1_2_1_443_348 GTTGCTTCTGGCGTGGGTGGGGGGG You can parse these separately using Bio.SeqIO with the "qual" and "fasta" formats, but then you'll get a group of SeqRecord objects with no sequence, and a matching group with the sequence but not the qualities. Because it only deals with one input file handle, Bio.SeqIO can't be used to read the two files together - but this function can! For example, >>> rec_iter = PairedFastaQualIterator(open("Quality/example.fasta", "rU"), ... open("Quality/example.qual", "rU")) >>> for record in rec_iter : ... print record.id, record.seq EAS54_6_R1_2_1_413_324 CCCTTCTTGTCTTCAGCGTTTCTCC EAS54_6_R1_2_1_540_792 TTGGCAGGCCAAGGCCGATGGATCA EAS54_6_R1_2_1_443_348 GTTGCTTCTGGCGTGGGTGGGGGGG As with the FASTQ or QUAL parsers, if you want to look at the qualities, they are in each record's per-letter-annotation dictionary as a simple list of integers: >>> print record.letter_annotations["phred_quality"] [26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 24, 26, 22, 26, 26, 13, 22, 26, 18, 24, 18, 18, 18, 18] If you have access to data as a FASTQ format file, using that directly would be simpler and more straight forward. Note that you can easily use this function to convert paired FASTA and QUAL files into FASTQ files: >>> from Bio import SeqIO >>> rec_iter = PairedFastaQualIterator(open("Quality/example.fasta", "rU"), ... open("Quality/example.qual", "rU")) >>> out_handle = open("Quality/temp.fastq", "w") >>> SeqIO.write(rec_iter, out_handle, "fastq") 3 >>> out_handle.close() And don't forget to clean up the temp file if you don't need it anymore: >>> import os >>> os.remove("Quality/temp.fastq") |
Run the Bio.SeqIO module's doctests. This will try and locate the unit tests directory, and run the doctests from there in order that the relative paths used in the examples work. |
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_phred_to_sanger_quality_str
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_solexa_to_sanger_quality_str
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_phred_to_illumina_quality_str
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_solexa_to_illumina_quality_str
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_solexa_to_solexa_quality_str
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_phred_to_solexa_quality_str
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