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File Formats and Directory Formats

File Formats and Directory Formats#

Formats in QIIME 2 are on-disk representations of data. When associated with an artifact class, they define how data are stored in or read from the data/ directory of artifacts.

QIIME 2 doesn’t have much of an opinion on how data should be represented or stored, so long as it can be represented or stored in an archive.

File Formats#

The simplest Formats are the TextFileFormat (qiime2.plugin.TextFileFormat) and the BinaryFileFormat (qiime2.plugin.BinaryFileFormat). These formats represent a single file with a fixed on-disk representation.

Validation#

Both types of FileFormat support validation. This is (typically) a small bit of code that is run when initially loading a file from an archive that allows the framework to ensure that the the data contained within the archive at least looks like its declared type. This works very well for on-the-fly loading and saving, and goes a long way to preventing corrupt or invalid data from persisting. The one “gotcha” here is that in order to keep things quick, we typically recommend that “minimal” validation occurs over a limited subset of the file (e.g. the first 10 records in a FASTQ file). Because of this, formats allow for multiple levels of sniffing to be defined. As of this writing (March 2024) there currently there are two validation levels: min and max.

Here we provide an example of a TextFileFormat definition, with a focus on the _validate_ function.

class IntSequenceFormat(TextFileFormat):
    """
    A sequence of integers stored on new lines in a file.
    To make validation more interesting, values in the list can be any integer as long
    as that integer is not equal to the previous value plus 3
    (i.e., `line[i] != (line[i-1]) + 3`).
    """
    def _validate_n_ints(self, n):
        with self.open() as fh:
            previous_val = None
            for idx, line in enumerate(fh, 1):
                if n is not None and idx >= n:
                    # we have passed the min validation level,
                    # so bail out
                    break
                try:
                    val = int(line.rstrip('\n'))
                except (TypeError, ValueError):
                    raise ValidationError(
                        f"Line {idx} contains {val}, but must be an integer.")
                if previous_val is not None and previous_val + 3 == val:
                    raise ValidationError(
                        f"Value on line {idx} is 3 more than the value on "
                        f"line {idx-1}.")
                previous_val = val

    # `_validate_` is exposed through the public method `validate`.
    def _validate_(self, level):
        record_map = {'min': 5, 'max': None}
        self._validate_n_ints(record_map[level])

format_instance = IntSequenceFormat(temp_dir.name, mode='r')
format_instance.validate()  # Shouldn't error!

In the IntSequenceFormat example, when validate is called with level='min', _validate_ will check the first 5 records. Otherwise, when level='max', it will check the entire file.

Astute observers might notice that the method defined in the IntSequenceFormat is called _validate_, but the method called on the format_instance was validate. This is because defining format validation is optional (although highly recommended!).

Warning

We consider skipping validation all together when defining formats to be a plugin development anti-pattern. For more information, see Skipping format validation.

Every format has a validate method available to interfaces (for performing ad-hoc validation). The framework will check for the presence of a _validate_ method on the format in question, and if it exists it will include that method as part of more general validations that the framework will perform. The aim here is that the framework is capable of ensuring common basic patterns, like presence of required files, while the _validate_ method is the place for the format developer to declare any special “business” logic necessary for ensuring the validity of their format.

Text File Formats#

The TextFileFormat (qiime2.plugin.TextFileFormat) is for creating text-based formats (e.g. FASTQ, TSV, etc.). An example of one of these formats is the DNAFASTAFormat, used for storing FASTA data.

Binary File Formats#

The BinaryFileFormat (qiime2.plugin.BinaryFileFormat) is for creating binary formats (e.g. BIOM, gzip, etc.). An example of one of these formats is the FastqGzFormat, a format for storing gzipped FASTQ files.

Directory Formats#

While many formats can accurately be described using a single file, many formats exist that require the presence of more than one file present together as a set. QIIME 2 allows more than one FileFormat to be combined together as a DirectoryFormat (qiime2.plugin.DirectoryFormat).

Fixed Layouts#

Some directory layouts can be accurately described with a fixed number of members. An example of this is the EMPPairedEndDirFmt. This directory format is always composed of three FastqGzFormat files: one for the forward reads (forward.fastq.gz), one for the reverse reads (reverse.fastq.gz), and one for the barcodes (barcodes.fastq.gz). The underlying FastqGzFormat is defined once — it doesn’t need to know about the sematic difference between biological reads and barcode reads, unlike the EMPPairedEndDirFmt which must be able to differentiate these.

   class EMPPairedEndDirFmt(model.DirectoryFormat):
       forward = model.File(r'forward.fastq.gz', format=FastqGzFormat)
       reverse = model.File(r'reverse.fastq.gz', format=FastqGzFormat)
       barcodes = model.File(r'barcodes.fastq.gz', format=FastqGzFormat)

The component files of this DirectoryFormat are defined using the File (qiime2.plugin.model.File) class.

Variable Layouts#

While some layouts are accurately described with a fixed set of members, others are highly variable, preventing formats from accurately knowing how many files to expect in its payload. An example of this kind of format are any of the demultiplexed file formats — when sequences are demultiplexed there is one (or two) files per sample, but how many samples are there? One study might have 5 samples, while another has 5000. For these situations the DirectoryFormat (qiime2.plugin.DirectoryFormat) can be configured to watch for set pattern of filenames present in its payload. An example of this is the CasavaOneEightSingleLanePerSampleDirFmt class, which stores demultiplexed sequence data in files named with a pattern used by Illumina’s Casava v1.8 software.

class CasavaOneEightSingleLanePerSampleDirFmt(model.DirectoryFormat):
    sequences = model.FileCollection(
        r'.+_.+_L[0-9][0-9][0-9]_R[12]_001\.fastq\.gz',
        format=FastqGzFormat)

    @sequences.set_path_maker
    def sequences_path_maker(self, sample_id, barcode_id, lane_number,
                            read_number):
        return '%s_%s_L%03d_R%d_001.fastq.gz' % (sample_id, barcode_id,
                                                lane_number, read_number)

Single File Directory Formats#

Currently QIIME 2 requires that all formats registered to an artifact class be a directory format. For those cases, there exists a factory for quickly constructing directory layouts that contain only a single file. This requirement might be removed in the future, but for now it is a necessary evil (and also isn’t too much extra work for format developers).

DNASequencesDirectoryFormat = model.SingleFileDirectoryFormat(
    'DNASequencesDirectoryFormat', 'dna-sequences.fasta', DNAFASTAFormat)

Associating Formats with a Type#

Formats on their own aren’t of much use. It is only once they are associated with a semantic type to define an artifact class that things become interesting. Artifact classes define the data that can be provided as input or generated as output by QIIME 2 Actions. An example of this can be seen in the registration of the SampleData[PairedEndSequencesWithQuality] artifact class.

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