The following assumes basic Python programming experience (and that you have installed Goldilocks and familiarised yourself with the basics), skip to the end if you think you know what you’re doing.

## Filtering Regions¶

### Group¶

By default when returning region data the “total” group is used, in our running example of counting missing nucleotides, this would represent the total number of ‘N’ bases seen in sequence data across each sample in the same genomic region on the same chromosome. But if you are more interested in a particular sample:

g.query("max", group="my_sample")


### Track¶

When using tracks (for strategies that calculate multiple distinct values for each genomic region - such as different nucleotide bases or k-mers), you may wish to extract regions based on scores for a certain track:

g.query("max", track="AAA")


### Absolute distance¶

You may be interested in regions within some distance of the mean:

g.query("mean", acutal_distance=10)


### Percentile distance¶

Or perhaps the “top 10%”, or the “middle 25%” around the mean:

g.query("max", percentile_distance=10)
g.query("mean", percentile_distance=25)


When not using max or min, by default both actual and percentile differences calculate ‘around’ the mean or median value instead. If you’d like to control this behaviour you can specify a direction: Let’s fetch regions that have values falling within 25% above or below the mean respectively:

g.query("mean", percentile_distance=25, direction=1)
g.query("mean", percentile_distance=25, direction=-1)


### Multiple criteria¶

You can of course use these at the same time (though actual and percentile distances are mutually exclusive), let’s fetch the top 10% of regions that contain the most “AAA” k-mers for all chromosomes in a hypothetical sample called “my_sample”:

g.query("max", group="my_sample", track="N", percentile_distance=10)


## Excluding Regions¶

The filter function also allows users to specify a dictionary of exclusion criteria.

### Starting position¶

To filter regions based on the 1-indexed starting position greater than or equal to 3:

g.query("min", exclusions={
"start_gte": 3,
})


### Ending position¶

To filter regions based on the 1-indexed ending position less than or equal to 9:

g.query("min", exclusions={
"end_lte": 9,
})


### Chromosome¶

You can filter regions that appear on particular chromosomes completely by providing a list:

g.query("min", exclusions={
"chr": ["X", 6],
})


### Value of another count group¶

When using groups, one may wish to exclude results where the value of another group is less than the one selected by the query. For example, for each region the following would result in regions where the count for my-other-sample is greater than my-sample:

g.query("min", group="my-sample", exclusions={
"region_group_lte": "my-other-sample",
})


### Multiple Criteria¶

You may want to use such exclusion criteria at the same time. Let’s say we have a bunch of sequence data from a species whose chromosomes all feature centromeres between bases 500-1000. Let’s ignore regions from that area. Let’s also exclude anything from chromosome ‘G’. If a single one of these criteria are true, a region will be excluded:

g.query("mean", exclusions={
"start_gte": 500,
"end_lte": 1000,
"chr": ['G'],
})


What if you want to exclude based on multiple criteria that should all be true? Let’s exclude regions that start before or on base 100 on chromosome X or Y [1]. Note the use of use_and=True! [2]

g.query("mean", exclusions={
"start_lte": 100,
"chr": ['X', 'Y'],
}, use_and=True)


### Chromosome specific criteria¶

Finally applying exclusions across all chromosomes might seem quite naive, what if we want to ignore centromeres on a real species? Introducing chromosome dependent exclusions; the syntax is the same as previously, just the exclusions dictionary is a dictionary of dictionaries with keys representing each chromosome. Note the use of use_chrom=True:

g.query("median", exclusions={
"one": {
"start_lte": 3,
"end_gte": 4
},
2: {
"start_gte": 9
},
"X": {
"chr": True
}}, use_chrom=True)


It is important to note that currently Goldilocks does not sanity check the contents of the exclusions dictionary including the spelling of exclusion names or whether you have correctly set use_chrom if you are providing chromosome specific filtering. However, on this latter point, if Goldilocks detects a key in the exclusions dictionary matches the name of a chromosome, it will print a warning (but continue regardless).

 [1] Support for chromosome matching is still ‘or’ based even when using use_and=True, a region can’t appear on more than one chromosome and so this seemed a more natural and useful behaviour.
 [2] Apart from the above caveat on chromosome matching always being or-based, currently there is no support for more complicated queries such as exclude if (statement1 and statement2) or statement3. It’s or, or and on all criteria!

## Limiting Regions¶

One may also limit the number of results returned by Goldilocks:

g.query("mean", limit=10)


## Full Example¶

Almost all of these options can be used together! Let’s finish off our examples by finding the top 5 regions that are within an absolute distance of 1.0 from the maximum number of ‘N’ bases seen across all subsequences over the ‘my_sample’ sample. We’ll exclude any region that appears on chromosome “one” and any regions on chromosome 2 that start on a base position greater than or equal to 5 and end on a base position less than or equal to 10. Although when filtering the default track is indeed ‘default’, we’ve explicity set that here too.:

g.query("max",
group="my_sample",
track="default",
actual_distance=1,
exclusions={
2: {
"start_gte": 5,
"end_lte": 10
},
"one": {
"chr":True
}
},
use_chrom=True,
use_and=True,
limit=5
).export_meta(sep="\t")

[NOTE] Filtering values between 1.00 and 2.00 (inclusive)
[NOTE] 28 processed, 12 match search criteria, 7 excluded, 5 limit
chr     pos_start       pos_end my_other_sample_default my_sample_default
2       1       3       1.0     2.0
2       3       5       1.0     2.0
2       2       4       1.0     1.0
2       4       6       1.0     1.0
X       13      15      1.0     1.0