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Binder Colab

Contents

Pf7 Data Access

This page provides information about how to access data from Plasmodium falciparum version 7 (Pf7) project via Google Cloud. This includes sample metadata and single nucleotide polymorphism (SNP) calls. This release spans multiple MalariaGEN projects including Pf community project, GenRe-Mekong and SpotMalaria, and a collaboration between 82 studies spread around the globe.

This notebook illustrates how to read data directly from the cloud, without having to first download any data locally. This notebook can be run from any computer, but will work best when run from a compute node within Google Cloud, because it will be physically closer to the data and so data transfer is faster. For example, this notebook can be run via MyBinder or Google Colab which are free interactive computing service running in the cloud.

To launch this notebook in the cloud and run it for yourself, click the launch icon (shaped like a rocket) at the top of the page and select one of the cloud computing services available.

For a quick overview on the spatial and geographical distribution of the samples available, please visit our Pf7 web-app.

Setup

Running this notebook requires some Python packages to be installed. These packages can be installed via pip or conda. E.g.:

!pip install -q --no-warn-conflicts malariagen_data

To make accessing these data more convenient, we’ve created the malariagen_data Python package, which is available from PyPI. This is experimental so please let us know if you find any bugs or have any suggestions.

Now import the packages we’ll need to use here.

import numpy as np
import dask
import dask.array as da
from dask.diagnostics.progress import ProgressBar
import allel
# silence some warnings
dask.config.set(**{'array.slicing.split_large_chunks': False})
import malariagen_data

To access the pf7 data stored on google cloud use the following code:

pf7 = malariagen_data.Pf7()

Metadata

Data on the samples that were sequenced as part of this resource are available. It includes the time and place of collection, quality metrics, and accession numbers.

To see all the information available, load sample metadata into a pandas dataframe:

pf7_metadata = pf7.sample_metadata()

pf7_metadata.head()

	Sample	Study	Country	Admin level 1	Country latitude	Country longitude	Admin level 1 latitude	Admin level 1 longitude	Year	ENA	All samples same case	Population	% callable	QC pass	Exclusion reason	Sample type	Sample was in Pf6
0	FP0008-C	1147-PF-MR-CONWAY	Mauritania	Hodh el Gharbi	20.265149	-10.337093	16.565426	-9.832345	2014.0	ERR1081237	FP0008-C	AF-W	82.16	True	Analysis_set	gDNA	True
1	FP0009-C	1147-PF-MR-CONWAY	Mauritania	Hodh el Gharbi	20.265149	-10.337093	16.565426	-9.832345	2014.0	ERR1081238	FP0009-C	AF-W	88.85	True	Analysis_set	gDNA	True
2	FP0010-CW	1147-PF-MR-CONWAY	Mauritania	Hodh el Gharbi	20.265149	-10.337093	16.565426	-9.832345	2014.0	ERR2889621	FP0010-CW	AF-W	86.46	True	Analysis_set	sWGA	False
3	FP0011-CW	1147-PF-MR-CONWAY	Mauritania	Hodh el Gharbi	20.265149	-10.337093	16.565426	-9.832345	2014.0	ERR2889624	FP0011-CW	AF-W	86.35	True	Analysis_set	sWGA	False
4	FP0012-CW	1147-PF-MR-CONWAY	Mauritania	Hodh el Gharbi	20.265149	-10.337093	16.565426	-9.832345	2014.0	ERR2889627	FP0012-CW	AF-W	89.74	True	Analysis_set	sWGA	False

print("The data set has {} samples and {} fields".format(pf7_metadata.shape[0],pf7_metadata.shape[1]))

The data set has 20864 samples and 17 fields

We can explore each of the fields:

• The Sample column gives the unique sample identifier used throughout all Pf7 analyses.

• The Study refers to the partner study which collected the sample.

• The Country & Admin level 1 describe the location where the sample was collected from.

• The Country latitude, Country longitude, Admin level 1 latitude and Admin 1 longitude contain the GADM coordinates for each country & administrative level 1.

• The Year column gives the time of sample collection.

• The ENA column gives the run accession(s) for the sequencing read data for each sample.

• The All samples same case column identifies samples set collected from the same individual.

• The Population column gives the population to which the sample has been assigned. The possible values are: Africa - West (AF-W), Africa-Central (AF-C), Africa - East (AF-E), Africa - Northeast (AF-NE), Asia - South - East (AS-S-E), Asia - South – Far East (AS-S-FE), Asia - Southeast - West (AS-SE-W), Asia - Southeast - East (AS-SE-E), Oceania - New Guinea (OC-NG), South America (SA).

• The % callable column refers to the % of the genome with coverage of at least 5 reads and less than 10% of reads with mapping quality 0.

• The QC pass column defines whether the sample passed (True) or failed (False) QC.

• The Exclusion reason describes the reason why the particular sample was excluded from the main analysis.

• The Sample type column gives details on the DNA preparation method used

• The Sample was in Pf6 column defines whether the sample was included in the previous version of the data release (Pf6) or if it is new to Pf7.

The python package Pandas can be used to explore and query the sample metadata in different ways. For example, here is a summary of the numbers of samples grouped by the country they were collected in:

pf7_metadata.groupby("Country").size()

Country
Bangladesh                          1658
Benin                                334
Burkina Faso                          58
Cambodia                            1723
Cameroon                             294
Colombia                             159
Côte d'Ivoire                         71
Democratic Republic of the Congo     573
Ethiopia                              34
Gabon                                 59
Gambia                              1247
Ghana                               4090
Guinea                               199
India                                316
Indonesia                            133
Kenya                                726
Laos                                1052
Madagascar                            25
Malawi                               371
Mali                                1804
Mauritania                           104
Mozambique                            91
Myanmar                             1260
Nigeria                              140
Papua New Guinea                     251
Peru                                  21
Senegal                              155
Sudan                                203
Tanzania                             697
Thailand                            1106
Uganda                                15
Venezuela                              2
Vietnam                             1733
dtype: int64

Variant Calls

These files contain details of 10,145,661 discovered variant genome positions. These variants were discovered amongst all samples from the release.

4,397,801 of these variant positions are SNPs, with the remainder being either short insertion/deletions (indels), or a combination of SNPs and indels.

Data on variant calls, including the genomic positions, alleles, and genotypes, can be accessed as an xarray Dataset:

variant_dataset = pf7.variant_calls()
variant_dataset

<xarray.Dataset>
Dimensions:              (variants: 10145661, alleles: 7, samples: 20864,
                          ploidy: 2)
Coordinates:
    variant_position     (variants) int32 dask.array<chunksize=(8388608,), meta=np.ndarray>
    variant_chrom        (variants) object dask.array<chunksize=(4194304,), meta=np.ndarray>
    sample_id            (samples) object dask.array<chunksize=(20864,), meta=np.ndarray>
Dimensions without coordinates: variants, alleles, samples, ploidy
Data variables:
    variant_allele       (variants, alleles) object dask.array<chunksize=(699051, 1), meta=np.ndarray>
    variant_filter_pass  (variants) bool dask.array<chunksize=(10145661,), meta=np.ndarray>
    variant_is_snp       (variants) bool dask.array<chunksize=(10145661,), meta=np.ndarray>
    variant_numalt       (variants) int32 dask.array<chunksize=(8388608,), meta=np.ndarray>
    variant_CDS          (variants) bool dask.array<chunksize=(10145661,), meta=np.ndarray>
    call_genotype        (variants, samples, ploidy) int8 dask.array<chunksize=(167773, 100, 2), meta=np.ndarray>
    call_AD              (variants, samples, alleles) int16 dask.array<chunksize=(23968, 100, 7), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • variants: 10145661
  • alleles: 7
  • samples: 20864
  • ploidy: 2
• Coordinates: (3)
  • variant_position
    (variants)
    int32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_chrom
    (variants)
    object
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    Array Chunk Bytes 77.41 MiB 32.00 MiB Shape (10145661,) (4194304,) Dask graph 3 chunks in 1 graph layer Data type object numpy.ndarray

  • sample_id
    (samples)
    object
    dask.array<chunksize=(20864,), meta=np.ndarray>

    Array Chunk Bytes 163.00 kiB 163.00 kiB Shape (20864,) (20864,) Dask graph 1 chunks in 1 graph layer Data type object numpy.ndarray

• Data variables: (7)
  • variant_allele
    (variants, alleles)
    object
    dask.array<chunksize=(699051, 1), meta=np.ndarray>

    Array Chunk Bytes 541.84 MiB 32.00 MiB Shape (10145661, 7) (699051, 6) Dask graph 34 chunks in 6 graph layers Data type object numpy.ndarray

  • variant_filter_pass
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_is_snp
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_numalt
    (variants)
    int32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_CDS
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • call_genotype
    (variants, samples, ploidy)
    int8
    dask.array<chunksize=(167773, 100, 2), meta=np.ndarray>

    Array Chunk Bytes 394.28 GiB 32.00 MiB Shape (10145661, 20864, 2) (167773, 100, 2) Dask graph 12749 chunks in 1 graph layer Data type int8 numpy.ndarray

  • call_AD
    (variants, samples, alleles)
    int16
    dask.array<chunksize=(23968, 100, 7), meta=np.ndarray>

    Array Chunk Bytes 2.70 TiB 32.00 MiB Shape (10145661, 20864, 7) (23968, 100, 7) Dask graph 88616 chunks in 1 graph layer Data type int16 numpy.ndarray

• Indexes: (0)
• Attributes: (0)

The default returns a basic set of data most commonly used for data analysis. However, for more complex analysis the full range of variables available in the zarr can be accessed by setting the extended flag to True, as shown below:

extended_variant_dataset = pf7.variant_calls(extended=True)
extended_variant_dataset

<xarray.Dataset>
Dimensions:                                        (variants: 10145661,
                                                    alleles: 7, samples: 20864,
                                                    ploidy: 2, genotypes: 3,
                                                    sb_statistics: 4,
                                                    alt_alleles: 6)
Coordinates:
    variant_position                               (variants) int32 dask.array<chunksize=(8388608,), meta=np.ndarray>
    variant_chrom                                  (variants) object dask.array<chunksize=(4194304,), meta=np.ndarray>
    sample_id                                      (samples) object dask.array<chunksize=(20864,), meta=np.ndarray>
Dimensions without coordinates: variants, alleles, samples, ploidy, genotypes,
                                sb_statistics, alt_alleles
Data variables: (12/92)
    variant_allele                                 (variants, alleles) object dask.array<chunksize=(699051, 1), meta=np.ndarray>
    variant_filter_pass                            (variants) bool dask.array<chunksize=(10145661,), meta=np.ndarray>
    variant_is_snp                                 (variants) bool dask.array<chunksize=(10145661,), meta=np.ndarray>
    variant_numalt                                 (variants) int32 dask.array<chunksize=(8388608,), meta=np.ndarray>
    variant_CDS                                    (variants) bool dask.array<chunksize=(10145661,), meta=np.ndarray>
    call_genotype                                  (variants, samples, ploidy) int8 dask.array<chunksize=(167773, 100, 2), meta=np.ndarray>
    ...                                             ...
    variant_RegionType                             (variants) object dask.array<chunksize=(4194304,), meta=np.ndarray>
    variant_SOR                                    (variants) float32 dask.array<chunksize=(8388608,), meta=np.ndarray>
    variant_VQSLOD                                 (variants) float32 dask.array<chunksize=(8388608,), meta=np.ndarray>
    variant_altlen                                 (variants, alt_alleles) int32 dask.array<chunksize=(1398102, 6), meta=np.ndarray>
    variant_culprit                                (variants) object dask.array<chunksize=(4194304,), meta=np.ndarray>
    variant_set                                    (variants) object dask.array<chunksize=(4194304,), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • variants: 10145661
  • alleles: 7
  • samples: 20864
  • ploidy: 2
  • genotypes: 3
  • sb_statistics: 4
  • alt_alleles: 6
• Coordinates: (3)
  • variant_position
    (variants)
    int32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_chrom
    (variants)
    object
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    Array Chunk Bytes 77.41 MiB 32.00 MiB Shape (10145661,) (4194304,) Dask graph 3 chunks in 1 graph layer Data type object numpy.ndarray

  • sample_id
    (samples)
    object
    dask.array<chunksize=(20864,), meta=np.ndarray>

    Array Chunk Bytes 163.00 kiB 163.00 kiB Shape (20864,) (20864,) Dask graph 1 chunks in 1 graph layer Data type object numpy.ndarray

• Data variables: (92)
  • variant_allele
    (variants, alleles)
    object
    dask.array<chunksize=(699051, 1), meta=np.ndarray>

    Array Chunk Bytes 541.84 MiB 32.00 MiB Shape (10145661, 7) (699051, 6) Dask graph 34 chunks in 6 graph layers Data type object numpy.ndarray

  • variant_filter_pass
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_is_snp
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_numalt
    (variants)
    int32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_CDS
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • call_genotype
    (variants, samples, ploidy)
    int8
    dask.array<chunksize=(167773, 100, 2), meta=np.ndarray>

    Array Chunk Bytes 394.28 GiB 32.00 MiB Shape (10145661, 20864, 2) (167773, 100, 2) Dask graph 12749 chunks in 1 graph layer Data type int8 numpy.ndarray

  • call_AD
    (variants, samples, alleles)
    int16
    dask.array<chunksize=(23968, 100, 7), meta=np.ndarray>

    Array Chunk Bytes 2.70 TiB 32.00 MiB Shape (10145661, 20864, 7) (23968, 100, 7) Dask graph 88616 chunks in 1 graph layer Data type int16 numpy.ndarray

  • call_DP
    (variants, samples)
    int16
    dask.array<chunksize=(167773, 100), meta=np.ndarray>

    Array Chunk Bytes 394.28 GiB 32.00 MiB Shape (10145661, 20864) (167773, 100) Dask graph 12749 chunks in 1 graph layer Data type int16 numpy.ndarray

  • call_GQ
    (variants, samples)
    int8
    dask.array<chunksize=(335545, 100), meta=np.ndarray>

    Array Chunk Bytes 197.14 GiB 32.00 MiB Shape (10145661, 20864) (335545, 100) Dask graph 6479 chunks in 1 graph layer Data type int8 numpy.ndarray

  • call_MIN_DP
    (variants, samples)
    int32
    dask.array<chunksize=(83887, 100), meta=np.ndarray>

    Array Chunk Bytes 788.57 GiB 32.00 MiB Shape (10145661, 20864) (83887, 100) Dask graph 25289 chunks in 1 graph layer Data type int32 numpy.ndarray

  • call_PGT
    (variants, samples)
    object
    dask.array<chunksize=(41944, 100), meta=np.ndarray>

    Array Chunk Bytes 1.54 TiB 32.00 MiB Shape (10145661, 20864) (41944, 100) Dask graph 50578 chunks in 1 graph layer Data type object numpy.ndarray

  • call_PID
    (variants, samples)
    object
    dask.array<chunksize=(41944, 100), meta=np.ndarray>

    Array Chunk Bytes 1.54 TiB 32.00 MiB Shape (10145661, 20864) (41944, 100) Dask graph 50578 chunks in 1 graph layer Data type object numpy.ndarray

  • call_PS
    (variants, samples)
    int32
    dask.array<chunksize=(83887, 100), meta=np.ndarray>

    Array Chunk Bytes 788.57 GiB 32.00 MiB Shape (10145661, 20864) (83887, 100) Dask graph 25289 chunks in 1 graph layer Data type int32 numpy.ndarray

  • call_RGQ
    (variants, samples)
    int32
    dask.array<chunksize=(83887, 100), meta=np.ndarray>

    Array Chunk Bytes 788.57 GiB 32.00 MiB Shape (10145661, 20864) (83887, 100) Dask graph 25289 chunks in 1 graph layer Data type int32 numpy.ndarray

  • call_PL
    (variants, samples, genotypes)
    int32
    dask.array<chunksize=(27963, 100, 3), meta=np.ndarray>

    Array Chunk Bytes 2.31 TiB 32.00 MiB Shape (10145661, 20864, 3) (27963, 100, 3) Dask graph 75867 chunks in 1 graph layer Data type int32 numpy.ndarray

  • call_SB
    (variants, samples, sb_statistics)
    int32
    dask.array<chunksize=(20972, 100, 4), meta=np.ndarray>

    Array Chunk Bytes 3.08 TiB 32.00 MiB Shape (10145661, 20864, 4) (20972, 100, 4) Dask graph 101156 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_AC
    (variants, alt_alleles)
    int32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_AF
    (variants, alt_alleles)
    float32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_AN
    (variants)
    int32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_ANN_AA_length
    (variants, alt_alleles)
    int32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_ANN_AA_pos
    (variants, alt_alleles)
    int32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_ANN_Allele
    (variants, alt_alleles)
    object
    dask.array<chunksize=(699051, 6), meta=np.ndarray>

    Array Chunk Bytes 464.43 MiB 32.00 MiB Shape (10145661, 6) (699051, 6) Dask graph 15 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_ANN_Annotation
    (variants, alt_alleles)
    object
    dask.array<chunksize=(699051, 6), meta=np.ndarray>

    Array Chunk Bytes 464.43 MiB 32.00 MiB Shape (10145661, 6) (699051, 6) Dask graph 15 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_ANN_Annotation_Impact
    (variants, alt_alleles)
    object
    dask.array<chunksize=(699051, 6), meta=np.ndarray>

    Array Chunk Bytes 464.43 MiB 32.00 MiB Shape (10145661, 6) (699051, 6) Dask graph 15 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_ANN_CDS_length
    (variants, alt_alleles)
    int32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_ANN_CDS_pos
    (variants, alt_alleles)
    int32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_ANN_Distance
    (variants, alt_alleles)
    int32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_ANN_Feature_ID
    (variants, alt_alleles)
    object
    dask.array<chunksize=(699051, 6), meta=np.ndarray>

    Array Chunk Bytes 464.43 MiB 32.00 MiB Shape (10145661, 6) (699051, 6) Dask graph 15 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_ANN_Feature_Type
    (variants, alt_alleles)
    object
    dask.array<chunksize=(699051, 6), meta=np.ndarray>

    Array Chunk Bytes 464.43 MiB 32.00 MiB Shape (10145661, 6) (699051, 6) Dask graph 15 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_ANN_Gene_ID
    (variants, alt_alleles)
    object
    dask.array<chunksize=(699051, 6), meta=np.ndarray>

    Array Chunk Bytes 464.43 MiB 32.00 MiB Shape (10145661, 6) (699051, 6) Dask graph 15 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_ANN_Gene_Name
    (variants, alt_alleles)
    object
    dask.array<chunksize=(699051, 6), meta=np.ndarray>

    Array Chunk Bytes 464.43 MiB 32.00 MiB Shape (10145661, 6) (699051, 6) Dask graph 15 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_ANN_HGVS_c
    (variants, alt_alleles)
    object
    dask.array<chunksize=(699051, 6), meta=np.ndarray>

    Array Chunk Bytes 464.43 MiB 32.00 MiB Shape (10145661, 6) (699051, 6) Dask graph 15 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_ANN_HGVS_p
    (variants, alt_alleles)
    object
    dask.array<chunksize=(699051, 6), meta=np.ndarray>

    Array Chunk Bytes 464.43 MiB 32.00 MiB Shape (10145661, 6) (699051, 6) Dask graph 15 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_ANN_Rank
    (variants, alt_alleles)
    int8
    dask.array<chunksize=(5592406, 6), meta=np.ndarray>

    Array Chunk Bytes 58.05 MiB 32.00 MiB Shape (10145661, 6) (5592406, 6) Dask graph 2 chunks in 1 graph layer Data type int8 numpy.ndarray

  • variant_ANN_Transcript_BioType
    (variants, alt_alleles)
    object
    dask.array<chunksize=(699051, 6), meta=np.ndarray>

    Array Chunk Bytes 464.43 MiB 32.00 MiB Shape (10145661, 6) (699051, 6) Dask graph 15 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_ANN_cDNA_length
    (variants, alt_alleles)
    int32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_ANN_cDNA_pos
    (variants, alt_alleles)
    int32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_AS_BaseQRankSum
    (variants, alt_alleles)
    float32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_AS_FS
    (variants, alt_alleles)
    float32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_AS_InbreedingCoeff
    (variants, alt_alleles)
    float32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_AS_MQ
    (variants, alt_alleles)
    float32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_AS_MQRankSum
    (variants, alt_alleles)
    float32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_AS_QD
    (variants, alt_alleles)
    float32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_AS_ReadPosRankSum
    (variants, alt_alleles)
    float32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_AS_SOR
    (variants, alt_alleles)
    float32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_BaseQRankSum
    (variants)
    float32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_DP
    (variants)
    int32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_DS
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_END
    (variants)
    int32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_ExcessHet
    (variants)
    float32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_FILTER_Apicoplast
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_Centromere
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_InternalHypervariable
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_LowQual
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_Low_VQSLOD
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_MissingVQSLOD
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_Mitochondrion
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_SubtelomericHypervariable
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_SubtelomericRepeat
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheINDEL99.50to99.60
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheINDEL99.60to99.80
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheINDEL99.80to99.90
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheINDEL99.90to99.95
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheINDEL99.95to100.00+
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheINDEL99.95to100.00
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheSNP99.50to99.60
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheSNP99.60to99.80
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheSNP99.80to99.90
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheSNP99.90to99.95
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheSNP99.95to100.00+
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FILTER_VQSRTrancheSNP99.95to100.00
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_FS
    (variants)
    float32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_ID
    (variants)
    object
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    Array Chunk Bytes 77.41 MiB 32.00 MiB Shape (10145661,) (4194304,) Dask graph 3 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_InbreedingCoeff
    (variants)
    float32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_LOF
    (variants)
    object
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    Array Chunk Bytes 77.41 MiB 32.00 MiB Shape (10145661,) (4194304,) Dask graph 3 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_MLEAC
    (variants, alt_alleles)
    int32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_MLEAF
    (variants, alt_alleles)
    float32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_MQ
    (variants)
    float32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_MQRankSum
    (variants)
    float32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_NEGATIVE_TRAIN_SITE
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_NMD
    (variants)
    object
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    Array Chunk Bytes 77.41 MiB 32.00 MiB Shape (10145661,) (4194304,) Dask graph 3 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_POSITIVE_TRAIN_SITE
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_QD
    (variants)
    float32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_QUAL
    (variants)
    float32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_RAW_MQandDP
    (variants, ploidy)
    int32
    dask.array<chunksize=(4194304, 2), meta=np.ndarray>

    Array Chunk Bytes 77.41 MiB 32.00 MiB Shape (10145661, 2) (4194304, 2) Dask graph 3 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_ReadPosRankSum
    (variants)
    float32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_RegionType
    (variants)
    object
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    Array Chunk Bytes 77.41 MiB 32.00 MiB Shape (10145661,) (4194304,) Dask graph 3 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_SOR
    (variants)
    float32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_VQSLOD
    (variants)
    float32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type float32 numpy.ndarray

  • variant_altlen
    (variants, alt_alleles)
    int32
    dask.array<chunksize=(1398102, 6), meta=np.ndarray>

    Array Chunk Bytes 232.22 MiB 32.00 MiB Shape (10145661, 6) (1398102, 6) Dask graph 8 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_culprit
    (variants)
    object
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    Array Chunk Bytes 77.41 MiB 32.00 MiB Shape (10145661,) (4194304,) Dask graph 3 chunks in 1 graph layer Data type object numpy.ndarray

  • variant_set
    (variants)
    object
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    Array Chunk Bytes 77.41 MiB 32.00 MiB Shape (10145661,) (4194304,) Dask graph 3 chunks in 1 graph layer Data type object numpy.ndarray

• Indexes: (0)
• Attributes: (0)

Each of the elements in this xarray dataset is a dask array. The individual dask arrays can be accessed as follows, replacing the string with the variable you are looking for:

pos = variant_dataset["variant_position"].data
pos

Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type int32 numpy.ndarray

Genotypes

Genotypes for individual samples are available.

Genotypes are stored as a three-dimensional array, where:

• the first dimension corresponds to genomic positions,

• the second dimension is samples,

• the third dimension is ploidy (2).

Values coded as integers, where -1 represents a missing value, 0 represents the reference allele, and 1, 2, and 3 represent alternate alleles.

Variant genotypes can be accessed as dask arrays as shown below.

gt = variant_dataset["call_genotype"].data
gt

Array Chunk Bytes 394.28 GiB 32.00 MiB Shape (10145661, 20864, 2) (167773, 100, 2) Dask graph 12749 chunks in 1 graph layer Data type int8 numpy.ndarray

Note that the columns of this array (second dimension) match the rows in the sample metadata. You can use this correspondance to apply further subsetting operations to the genotypes by querying the sample metadata. E.g.:

loc_colombia = pf7_metadata.eval("Country == 'Colombia'").values
print(f"found {np.count_nonzero(loc_colombia)} samples from Colombia")
variant_dataset_colombia = variant_dataset.isel(samples=loc_colombia)
variant_dataset_colombia

found 159 samples from Colombia

<xarray.Dataset>
Dimensions:              (variants: 10145661, alleles: 7, samples: 159,
                          ploidy: 2)
Coordinates:
    variant_position     (variants) int32 dask.array<chunksize=(8388608,), meta=np.ndarray>
    variant_chrom        (variants) object dask.array<chunksize=(4194304,), meta=np.ndarray>
    sample_id            (samples) object dask.array<chunksize=(159,), meta=np.ndarray>
Dimensions without coordinates: variants, alleles, samples, ploidy
Data variables:
    variant_allele       (variants, alleles) object dask.array<chunksize=(699051, 1), meta=np.ndarray>
    variant_filter_pass  (variants) bool dask.array<chunksize=(10145661,), meta=np.ndarray>
    variant_is_snp       (variants) bool dask.array<chunksize=(10145661,), meta=np.ndarray>
    variant_numalt       (variants) int32 dask.array<chunksize=(8388608,), meta=np.ndarray>
    variant_CDS          (variants) bool dask.array<chunksize=(10145661,), meta=np.ndarray>
    call_genotype        (variants, samples, ploidy) int8 dask.array<chunksize=(167773, 32, 2), meta=np.ndarray>
    call_AD              (variants, samples, alleles) int16 dask.array<chunksize=(23968, 32, 7), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • variants: 10145661
  • alleles: 7
  • samples: 159
  • ploidy: 2
• Coordinates: (3)
  • variant_position
    (variants)
    int32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_chrom
    (variants)
    object
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    Array Chunk Bytes 77.41 MiB 32.00 MiB Shape (10145661,) (4194304,) Dask graph 3 chunks in 1 graph layer Data type object numpy.ndarray

  • sample_id
    (samples)
    object
    dask.array<chunksize=(159,), meta=np.ndarray>

    Array Chunk Bytes 1.24 kiB 1.24 kiB Shape (159,) (159,) Dask graph 1 chunks in 2 graph layers Data type object numpy.ndarray

• Data variables: (7)
  • variant_allele
    (variants, alleles)
    object
    dask.array<chunksize=(699051, 1), meta=np.ndarray>

    Array Chunk Bytes 541.84 MiB 32.00 MiB Shape (10145661, 7) (699051, 6) Dask graph 34 chunks in 6 graph layers Data type object numpy.ndarray

  • variant_filter_pass
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_is_snp
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • variant_numalt
    (variants)
    int32
    dask.array<chunksize=(8388608,), meta=np.ndarray>

    Array Chunk Bytes 38.70 MiB 32.00 MiB Shape (10145661,) (8388608,) Dask graph 2 chunks in 1 graph layer Data type int32 numpy.ndarray

  • variant_CDS
    (variants)
    bool
    dask.array<chunksize=(10145661,), meta=np.ndarray>

    Array Chunk Bytes 9.68 MiB 9.68 MiB Shape (10145661,) (10145661,) Dask graph 1 chunks in 1 graph layer Data type bool numpy.ndarray

  • call_genotype
    (variants, samples, ploidy)
    int8
    dask.array<chunksize=(167773, 32, 2), meta=np.ndarray>

    Array Chunk Bytes 3.00 GiB 20.16 MiB Shape (10145661, 159, 2) (167773, 63, 2) Dask graph 244 chunks in 2 graph layers Data type int8 numpy.ndarray

  • call_AD
    (variants, samples, alleles)
    int16
    dask.array<chunksize=(23968, 32, 7), meta=np.ndarray>

    Array Chunk Bytes 21.03 GiB 20.16 MiB Shape (10145661, 159, 7) (23968, 63, 7) Dask graph 1696 chunks in 2 graph layers Data type int16 numpy.ndarray

• Indexes: (0)
• Attributes: (0)

The data on genomic variants can be loaded into memory as numpy arrays as shown in the following example, where we read genotypes for the first 5 SNPs and the first 3 samples:

g = gt[:5, :3, :].compute()
g

array([[[-1, -1],
        [ 0,  0],
        [-1, -1]],

       [[-1, -1],
        [ 0,  0],
        [-1, -1]],

       [[-1, -1],
        [ 0,  0],
        [-1, -1]],

       [[-1, -1],
        [ 0,  0],
        [-1, -1]],

       [[-1, -1],
        [ 0,  0],
        [-1, -1]]], dtype=int8)

If you want to work with the genotype calls, you may find it convenient to use scikit-allel. E.g., the code below sets up a genotype array using the Colombian samples subset we created above.

# use the scikit-allel wrapper class for genotype calls
gt = allel.GenotypeDaskArray(variant_dataset_colombia["call_genotype"].data)
gt

<GenotypeDaskArray shape=(10145661, 159, 2) dtype=int8>

	0	1	2	3	4	...	154	155	156	157	158
0	0/0	0/0	./.	0/0	0/0	...	0/0	0/0	0/0	./.	0/0
1	0/0	0/0	./.	0/0	0/0	...	0/0	0/0	0/0	./.	0/0
2	0/0	0/0	0/0	0/0	0/0	...	0/0	0/0	0/0	./.	0/0
...	...
10145658	./.	./.	./.	./.	./.	...	./.	./.	./.	./.	./.
10145659	./.	./.	./.	./.	./.	...	./.	./.	./.	./.	./.
10145660	./.	./.	./.	./.	./.	...	./.	./.	./.	./.	./.

Genome Annotations

Gene annotations provide information on which regions of the genome contain DNA sequences that encode genes, which are transcribed and spliced into messenger RNA (mRNA) and then translated to make proteins.

For convenience, we’ve added some functionality to the malariagen_data package for loading these gene annotations into a pandas data frame as shown below:

genome_features = pf7.genome_features()
genome_features

	contig	source	type	start	end	score	strand	phase	ID	Parent	Name	alias
0	Pf3D7_01_v3	chado	repeat_region	1	360	NaN	+	NaN	Pfalciparum_REP_20	NaN	NaN	NaN
1	Pf3D7_01_v3	chado	repeat_region	361	1418	NaN	+	NaN	Pfalciparum_REP_15	NaN	NaN	NaN
2	Pf3D7_01_v3	chado	repeat_region	2160	3858	NaN	+	NaN	Pfalciparum_REP_35	NaN	NaN	NaN
3	Pf3D7_01_v3	chado	repeat_region	8856	9021	NaN	+	NaN	Pfalciparum_REP_5	NaN	NaN	NaN
4	Pf3D7_01_v3	chado	repeat_region	9313	9529	NaN	+	NaN	Pfalciparum_REP_25	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
40708	Pf_M76611	chado	rRNA	5772	5854	NaN	-	NaN	PF3D7_MIT04100.1	PF3D7_MIT04100	NaN	NaN
40709	Pf_M76611	chado	CDS	5772	5854	NaN	-	0.0	PF3D7_MIT04100.1:exon:1	PF3D7_MIT04100.1	NaN	NaN
40710	Pf_M76611	chado	gene	5861	5954	NaN	-	NaN	PF3D7_MIT04200	NaN	RNA8	mal_rna_19
40711	Pf_M76611	chado	rRNA	5861	5954	NaN	-	NaN	PF3D7_MIT04200.1	PF3D7_MIT04200	NaN	NaN
40712	Pf_M76611	chado	CDS	5861	5954	NaN	-	0.0	PF3D7_MIT04200.1:exon:1	PF3D7_MIT04200.1	NaN	NaN

40713 rows × 12 columns

The above loads a default set of attributes "ID", "Parent", "Name", "alias". To access all features set attributes to "*".

pf7.genome_features(attributes="*")

	contig	source	type	start	end	score	strand	phase	Dbxref	Derives_from	...	paralogous_to	polypeptide_domain	previous_systematic_id	product	product_synonym	signal_peptide	stop_codon_redefined_as_selenocysteine	synonym	translation	transmembrane_polypeptide_region
0	Pf3D7_01_v3	chado	repeat_region	1	360	NaN	+	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	Pf3D7_01_v3	chado	repeat_region	361	1418	NaN	+	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	Pf3D7_01_v3	chado	repeat_region	2160	3858	NaN	+	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	Pf3D7_01_v3	chado	repeat_region	8856	9021	NaN	+	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	Pf3D7_01_v3	chado	repeat_region	9313	9529	NaN	+	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
40708	Pf_M76611	chado	rRNA	5772	5854	NaN	-	NaN	NaN	NaN	...	NaN	NaN	NaN	term=large subunit ribosomal RNA fragment D;db...	NaN	NaN	NaN	NaN	NaN	NaN
40709	Pf_M76611	chado	CDS	5772	5854	NaN	-	0.0	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
40710	Pf_M76611	chado	gene	5861	5954	NaN	-	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
40711	Pf_M76611	chado	rRNA	5861	5954	NaN	-	NaN	NaN	NaN	...	NaN	NaN	NaN	term=ribosomal RNA fragment RNA8;db_xref=PMID:...	NaN	NaN	NaN	NaN	NaN	NaN
40712	Pf_M76611	chado	CDS	5861	5954	NaN	-	0.0	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

40713 rows × 35 columns

Or to get a specific set of attributes specify them in a list

pf7.genome_features(attributes=['alias','comment','product'])

	contig	source	type	start	end	score	strand	phase	alias	comment	product
0	Pf3D7_01_v3	chado	repeat_region	1	360	NaN	+	NaN	NaN	telomeric repeat region	NaN
1	Pf3D7_01_v3	chado	repeat_region	361	1418	NaN	+	NaN	NaN	14bp repeat	NaN
2	Pf3D7_01_v3	chado	repeat_region	2160	3858	NaN	+	NaN	NaN	65bp repeat	NaN
3	Pf3D7_01_v3	chado	repeat_region	8856	9021	NaN	+	NaN	NaN	25bp repeat	NaN
4	Pf3D7_01_v3	chado	repeat_region	9313	9529	NaN	+	NaN	NaN	26bp repeat	NaN
...	...	...	...	...	...	...	...	...	...	...	...
40708	Pf_M76611	chado	rRNA	5772	5854	NaN	-	NaN	NaN	fragment number 7 transcript contains a 3' oli...	term=large subunit ribosomal RNA fragment D;db...
40709	Pf_M76611	chado	CDS	5772	5854	NaN	-	0.0	NaN	NaN	NaN
40710	Pf_M76611	chado	gene	5861	5954	NaN	-	NaN	mal_rna_19	NaN	NaN
40711	Pf_M76611	chado	rRNA	5861	5954	NaN	-	NaN	NaN	transcript contains a 3' oligo(A) structure fr...	term=ribosomal RNA fragment RNA8;db_xref=PMID:...
40712	Pf_M76611	chado	CDS	5861	5954	NaN	-	0.0	NaN	NaN	NaN

40713 rows × 11 columns

Genome Reference

We mapped sequence reads for all samples against the P. falciparum 3D7 v3 reference genome.

For convenience, the reference genome sequence can be loaded as a dask array, e.g.:

ref = pf7.genome_sequence()
ref

Array Chunk Bytes 22.25 MiB 412.03 kiB Shape (23332839,) (421914,) Dask graph 72 chunks in 17 graph layers Data type |S1 numpy.ndarray

This can be loaded as a numpy array using the following

ref.compute()

array([b't', b'g', b'a', ..., b'a', b't', b'a'], dtype='|S1')

The reference can also be subset by contig.

The set of contigs used can be accessed as follows:

pf7.contigs

['Pf3D7_01_v3',
 'Pf3D7_02_v3',
 'Pf3D7_03_v3',
 'Pf3D7_04_v3',
 'Pf3D7_05_v3',
 'Pf3D7_06_v3',
 'Pf3D7_07_v3',
 'Pf3D7_08_v3',
 'Pf3D7_09_v3',
 'Pf3D7_10_v3',
 'Pf3D7_11_v3',
 'Pf3D7_12_v3',
 'Pf3D7_13_v3',
 'Pf3D7_14_v3',
 'Pf3D7_API_v3',
 'Pf_M76611']

To load a single contig

pf7.genome_sequence(region='Pf3D7_01_v3')

Array Chunk Bytes 625.83 kiB 312.92 kiB Shape (640851,) (320426,) Dask graph 2 chunks in 1 graph layer Data type |S1 numpy.ndarray

To load multiple contigs specify them in a list. The data will be concatenated.

pf7.genome_sequence(region=['Pf3D7_07_v3','Pf3D7_02_v3','Pf3D7_03_v3'])

Array Chunk Bytes 3.30 MiB 352.83 kiB Shape (3460280,) (361302,) Dask graph 12 chunks in 4 graph layers Data type |S1 numpy.ndarray

You can also specify a specific region of the contig.

pf7.genome_sequence(region=['Pf3D7_07_v3','Pf3D7_02_v3:15-20','Pf3D7_03_v3:40-50'])

Array Chunk Bytes 1.38 MiB 352.83 kiB Shape (1445224,) (361302,) Dask graph 6 chunks in 6 graph layers Data type |S1 numpy.ndarray

If you know the gene name you would like to access, but aren’t sure what the ID would be you can access this through the annotations. Below is an example for CRT.

gene_name = str(genome_features.loc[genome_features.Name == 'CRT'].ID.values)
print(gene_name)

['PF3D7_0709000']

You can then enter this as the region

pf7.genome_sequence(region='PF3D7_0709000')

Array Chunk Bytes 3.86 kiB 3.86 kiB Shape (3957,) (3957,) Dask graph 1 chunks in 2 graph layers Data type |S1 numpy.ndarray

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