Reading files
Source code in polars_bio/io.py
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read_bcf(path, info_fields=None, format_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, samples=None, genotype_output='string')
staticmethod
Read a BCF file into a DataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BCF file. The path must end in |
required |
info_fields
|
Union[list[str], None]
|
INFO fields to include. If None, all header-defined INFO fields are included. |
None
|
format_fields
|
Union[list[str], None]
|
FORMAT fields to include. Single-sample fields are top-level columns; multisample fields are nested in |
None
|
samples
|
Union[list[str], None]
|
Optional sample names to include, in requested order. |
None
|
chunk_size
|
int
|
Object-store chunk size in MB. |
8
|
concurrent_fetches
|
int
|
Number of concurrent object-store fetches. |
1
|
allow_anonymous
|
bool
|
Allow anonymous object-store access. |
True
|
enable_request_payer
|
bool
|
Enable AWS request-payer access. |
False
|
max_retries
|
int
|
Maximum number of object-store retries. |
5
|
timeout
|
int
|
Object-store timeout in seconds. |
300
|
compression_type
|
str
|
Compression override. The default detects BCF automatically. |
'auto'
|
projection_pushdown
|
bool
|
Push column projection into the BCF reader. |
True
|
predicate_pushdown
|
bool
|
Use a neighboring |
True
|
use_zero_based
|
Optional[bool]
|
Select 0-based half-open ( |
None
|
genotype_output
|
str
|
GT representation. |
'string'
|
Note
BCF is input-only. Use write_vcf or sink_vcf to write text VCF.
Source code in polars_bio/io.py
read_bgen(path, genotype_output='probability', probability_layout='nested', samples=None, genotype_fields=None, sample_path=None, bgi_path=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Read a BGEN file into a DataFrame.
One row is one BGEN variant. Encoded alleles stay ordered in alleles
and are not given reference/alternate semantics.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BGEN file. The path must end in |
required |
genotype_output
|
str
|
Genotype representation. |
'probability'
|
probability_layout
|
str
|
How probability states are stored. |
'nested'
|
samples
|
Union[list[str], None]
|
Sample identifiers to emit, in requested order. If None, all samples are emitted in file order. |
None
|
genotype_fields
|
Union[list[str], None]
|
Children of the |
None
|
sample_path
|
Union[str, None]
|
An explicit Oxford |
None
|
bgi_path
|
Union[str, None]
|
An explicit |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. |
8
|
concurrent_fetches
|
int
|
The number of concurrent fetches when reading from an object store. |
1
|
allow_anonymous
|
bool
|
Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
Whether to enable request payer for object storage. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression override. BGEN block compression is read from the file header. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown. Metadata-only scans do not read or decompress probability blocks. |
True
|
predicate_pushdown
|
bool
|
Use a |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration. |
None
|
Note
BGEN is input-only.
Source code in polars_bio/io.py
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read_bgen_matrix(path, samples=None, missing=None, sample_path=None, bgi_path=None, threads=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', use_zero_based=None)
staticmethod
Read a BGEN's ALT dosages into a dense NumPy matrix.
The counterpart of read_pgen_matrix. scan_bgen returns Arrow batches
that a caller wanting one array must then consolidate, and on a whole
chromosome that consolidation is a serial pass over 10 GB — it does not
parallelise, so it becomes the ceiling as partitions are added. This
decodes each variant at its final address instead, which on chromosome
22 scales 6.0x from one thread to eight against the Arrow path's 4.2x.
Dosage only: BGEN probabilities are variable width and have no single
dense shape. Use scan_bgen(genotype_output="probability") for those.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BGEN file. The path must end in |
required |
samples
|
Union[list[str], None]
|
Sample identifiers to emit, in requested order. If None, all samples are emitted in file order. |
None
|
missing
|
Union[float, None]
|
Written where a sample has no called genotype. Defaults to |
None
|
sample_path
|
Union[str, None]
|
An explicit Oxford |
None
|
bgi_path
|
Union[str, None]
|
An explicit |
None
|
threads
|
Union[int, None]
|
Decoder threads. Defaults to the configured |
None
|
use_zero_based
|
Optional[bool]
|
Output coordinate convention for the returned positions. |
None
|
Example
Source code in polars_bio/io.py
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read_pgen(path, genotype_fields=('GT',), samples=None, missing_sample_policy='error', psam_id_mode='iid', pvar_path=None, psam_path=None, pgi_path=None, max_range_gap=None, max_range_bytes=None, batch_soft_byte_limit=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Read a PLINK 2 PGEN fileset into a DataFrame.
One row is one PVAR variant. The .pvar and .psam companions are
discovered from the .pgen basename.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the PGEN file. The path must end in |
required |
genotype_fields
|
Sequence[str]
|
Genotype children to emit, from |
('GT',)
|
samples
|
Union[list[str], None]
|
Sample identifiers to emit, in requested order. If None, all samples are emitted in PSAM order. |
None
|
missing_sample_policy
|
str
|
|
'error'
|
psam_id_mode
|
str
|
How selectable sample names are built from PSAM identifiers. |
'iid'
|
pvar_path
|
Union[str, None]
|
An explicit |
None
|
psam_path
|
Union[str, None]
|
An explicit |
None
|
pgi_path
|
Union[str, None]
|
An explicit |
None
|
max_range_gap
|
Union[int, None]
|
The largest run of unselected bytes bridged when coalescing reads, in bytes. The provider default is 0, which never bridges a gap and issues one read per contiguous run of selected variants. Raising it trades wasted bytes for fewer requests, which matters most on object storage. If None, the provider default is used. |
None
|
max_range_bytes
|
Union[int, None]
|
The largest coalesced read, in bytes. If None, the provider default is used. |
None
|
batch_soft_byte_limit
|
Union[int, None]
|
A soft target for genotype bytes in one RecordBatch. If None, the provider default is used. |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. |
8
|
concurrent_fetches
|
int
|
The number of concurrent fetches when reading from an object store. |
1
|
allow_anonymous
|
bool
|
Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
Whether to enable request payer for object storage. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression override. PGEN record compression is read from the file header. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown. Metadata-only scans do not read genotype records. |
True
|
predicate_pushdown
|
bool
|
Push |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration. |
None
|
Note
PGEN is input-only.
Source code in polars_bio/io.py
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read_pgen_matrix(path, field='ALT_COUNT', samples=None, missing=None, missing_sample_policy='error', psam_id_mode='iid', pvar_path=None, psam_path=None, pgi_path=None, max_range_gap=None, max_range_bytes=None, batch_soft_byte_limit=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', use_zero_based=None, copy_threads=None)
staticmethod
Read one genotype field of a PGEN fileset into a dense NumPy matrix.
The whole-cohort matrix is what association testing, PCA, and relatedness pipelines consume, and going through a DataFrame to get one costs a second full copy of every value: the scan builds Arrow batches, and something then has to consolidate them into a contiguous array. The decoder here writes genotypes at their final address instead, so they are written once.
On chromosome 22 of 1000 Genomes (993,881 variants x 2,548 samples) the
DS matrix takes 1.29 s and 12.6 GB, against 3.2 s and 22.3 GB
through read_pgen. ALT_COUNT takes 0.70 s. Both are faster than
PLINK 2's own pgenlib at one thread, and roughly three times faster
again given eight partitions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the PGEN file. The path must end in |
required |
field
|
str
|
The genotype field to materialize: |
'ALT_COUNT'
|
samples
|
Union[list[str], None]
|
Sample identifiers to emit, in requested order. If None, all samples are emitted in PSAM order. The matrix has one column per selected sample. |
None
|
missing
|
Union[int, float, None]
|
The value written where a genotype is missing. Defaults to |
None
|
missing_sample_policy
|
str
|
|
'error'
|
psam_id_mode
|
str
|
How selectable sample names are built from PSAM identifiers. See |
'iid'
|
pvar_path
|
Union[str, None]
|
An explicit |
None
|
psam_path
|
Union[str, None]
|
An explicit |
None
|
pgi_path
|
Union[str, None]
|
An explicit |
None
|
max_range_gap
|
Union[int, None]
|
The largest run of unselected bytes bridged when coalescing reads. |
None
|
max_range_bytes
|
Union[int, None]
|
The largest coalesced read, in bytes. |
None
|
batch_soft_byte_limit
|
Union[int, None]
|
A soft target for genotype bytes in one RecordBatch. |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. |
8
|
concurrent_fetches
|
int
|
The number of concurrent fetches when reading from an object store. |
1
|
allow_anonymous
|
bool
|
Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
Whether to enable request payer for object storage. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression override. |
'auto'
|
use_zero_based
|
Optional[bool]
|
If True, report 0-based positions. If False, 1-based. If None (default), uses the global configuration. |
None
|
copy_threads
|
Union[int, None]
|
How many threads decode into the result. They write disjoint row ranges, so they never contend. If None (default), this follows |
None
|
Returns:
| Type | Description |
|---|---|
PgenMatrix
|
A |
PgenMatrix
|
array), |
PgenMatrix
|
column). |
Note
Rows are in PVAR order at every partition count: each variant is
written at its own row index rather than in the order it finished
decoding. This differs from read_pgen, whose row order may
interleave above one partition.
Example
Source code in polars_bio/io.py
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read_vcf(path, info_fields=None, format_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, samples=None)
staticmethod
Read a text VCF file into a DataFrame.
Parallelism & Indexed Reads
Indexed parallel reads and predicate pushdown are automatic when a VCF TBI/CSI index is present. See File formats support, Indexed reads, and Automatic parallel partitioning for details.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the VCF file. |
required |
info_fields
|
Union[list[str], None]
|
List of INFO field names to include. If None, all INFO fields from the VCF header are included by default. Use this to limit fields for better performance. |
None
|
format_fields
|
Union[list[str], None]
|
List of FORMAT field names to include (per-sample genotype data). If None, all FORMAT fields are included by default. For single-sample VCFs, FORMAT fields are top-level columns (e.g., |
None
|
samples
|
Union[list[str], None]
|
Optional list of sample names to include from the VCF header. Matching is exact and case-sensitive. Missing sample names are skipped with a warning. The output follows the requested sample order. |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the VCF file. If not specified, it will be detected automatically.. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using VCF TBI/CSI index files for efficient region-based filtering. Index files are auto-discovered (for example, |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Reading VCF with INFO and FORMAT fields
import polars_bio as pb
# Read VCF with both INFO and FORMAT fields
df = pb.read_vcf(
"sample.vcf.gz",
info_fields=["END"], # INFO field
format_fields=["GT", "DP", "GQ"] # FORMAT fields
)
# Single-sample VCF: FORMAT fields are top-level columns (GT, DP, GQ)
print(df.select(["chrom", "start", "ref", "alt", "END", "GT", "DP", "GQ"]))
# Output:
# shape: (10, 8)
# ┌───────┬───────┬─────┬─────┬──────┬─────┬─────┬─────┐
# │ chrom ┆ start ┆ ref ┆ alt ┆ END ┆ GT ┆ DP ┆ GQ │
# │ str ┆ u32 ┆ str ┆ str ┆ i32 ┆ str ┆ i32 ┆ i32 │
# ╞═══════╪═══════╪═════╪═════╪══════╪═════╪═════╪═════╡
# │ 1 ┆ 10009 ┆ A ┆ . ┆ null ┆ 0/0 ┆ 10 ┆ 27 │
# │ 1 ┆ 10015 ┆ A ┆ . ┆ null ┆ 0/0 ┆ 17 ┆ 35 │
# └───────┴───────┴─────┴─────┴──────┴─────┴─────┴─────┘
# Multi-sample VCF: FORMAT data is nested in "genotypes"
df = pb.read_vcf("multisample.vcf", format_fields=["GT", "DP"])
print(df.select(["chrom", "start", "genotypes"]))
Source code in polars_bio/io.py
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read_vcf_zarr(path, info_fields=None, format_fields=None, projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, samples=None, genotype_encoding_raw=True)
staticmethod
Read a local VCF Zarr store into a DataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the VCF Zarr store directory. |
required |
info_fields
|
Union[list[str], None]
|
Optional list of INFO field names to include. If None, local INFO arrays are discovered automatically. Use [] to disable INFO fields. |
None
|
format_fields
|
Union[list[str], None]
|
Optional list of FORMAT field names to include. If None, local FORMAT arrays are discovered automatically. Use [] to disable FORMAT fields. |
None
|
projection_pushdown
|
bool
|
Enable column projection pushdown at the DataFusion level. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown at the DataFusion level. |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None, uses the global configuration. |
None
|
samples
|
Union[list[str], None]
|
Optional list of sample names to include. |
None
|
genotype_encoding_raw
|
bool
|
If True, output GT as raw typed allele calls. If False, output VCF-style GT strings. |
True
|
Source code in polars_bio/io.py
read_bam(path, tag_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, infer_tag_types=True, infer_tag_sample_size=100, tag_type_hints=None)
staticmethod
Read a BAM file into a DataFrame.
Parallelism & Indexed Reads
Indexed parallel reads and predicate pushdown are automatic when a BAI/CSI index is present. See File formats support, Indexed reads, and Automatic parallel partitioning for details.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BAM file. |
required |
tag_fields
|
Union[list[str], None]
|
List of BAM tag names to include as columns (e.g., ["NM", "MD", "AS"]). If None, no optional tags are parsed (default). Common tags include: NM (edit distance), MD (mismatch string), AS (alignment score), XS (secondary alignment score), RG (read group), CB (cell barcode), UB (UMI barcode). |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large-scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large-scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using index files (BAI/CSI) for efficient region-based filtering. Index files are auto-discovered (e.g., |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
infer_tag_types
|
bool
|
If True (default), sample the file to auto-detect types for custom/unknown tags. This prevents integer tags from being decoded as ASCII characters. |
True
|
infer_tag_sample_size
|
int
|
Number of records to sample for tag type inference (default: 100). |
100
|
tag_type_hints
|
Optional[list[str]]
|
Explicit SAM-style type hints for tags (e.g., ["pt:i", "ML:B:C", "FZ:B:S"]). Used as fallback when inference is disabled or a tag is not found in sampled records. Supported forms: TAG:TYPE, TAG:B, or TAG:B:SUBTYPE where TYPE is one of A, c, C, s, S, i, I, f, Z, H and SUBTYPE is one of c, C, s, S, i, I, f. |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Source code in polars_bio/io.py
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read_cram(path, reference_path=None, tag_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, infer_tag_types=True, infer_tag_sample_size=100, tag_type_hints=None)
staticmethod
Read a CRAM file into a DataFrame.
Parallelism & Indexed Reads
Indexed parallel reads and predicate pushdown are automatic when a CRAI index is present. See File formats support, Indexed reads, and Automatic parallel partitioning for details.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the CRAM file (local or cloud storage: S3, GCS, Azure Blob). |
required |
reference_path
|
str
|
Optional path to external FASTA reference file (local path only, cloud storage not supported). If not provided, the CRAM file must contain embedded reference sequences. The FASTA file must have an accompanying index file (.fai) in the same directory. Create the index using: |
None
|
tag_fields
|
Union[list[str], None]
|
List of CRAM tag names to include as columns (e.g., ["NM", "MD", "AS"]). If None, no optional tags are parsed (default). Common tags include: NM (edit distance), MD (mismatch string), AS (alignment score), XS (secondary alignment score), RG (read group), CB (cell barcode), UB (UMI barcode). |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
projection_pushdown
|
bool
|
Enable column projection pushdown optimization. When True, only requested columns are processed at the DataFusion execution level, improving performance and reducing memory usage. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using index files (CRAI) for efficient region-based filtering. Index files are auto-discovered (e.g., |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
infer_tag_types
|
bool
|
If True (default), sample the file to auto-detect types for custom/unknown tags. This prevents integer tags from being decoded as ASCII characters. |
True
|
infer_tag_sample_size
|
int
|
Number of records to sample for tag type inference (default: 100). |
100
|
tag_type_hints
|
Optional[list[str]]
|
Explicit SAM-style type hints for tags (e.g., ["pt:i", "ML:B:C", "FZ:B:S"]). Used as fallback when inference is disabled or a tag is not found in sampled records. Supported forms: TAG:TYPE, TAG:B, or TAG:B:SUBTYPE where TYPE is one of A, c, C, s, S, i, I, f, Z, H and SUBTYPE is one of c, C, s, S, i, I, f. |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Known Limitation: MD and NM Tags
Due to a limitation in the underlying noodles-cram library, MD (mismatch descriptor) and NM (edit distance) tags are not accessible from CRAM files, even when stored in the file. These tags can be seen with samtools but are not exposed through the noodles-cram record.data() interface.
Other optional tags (RG, MQ, AM, OQ, etc.) work correctly. This issue is tracked at: https://github.com/biodatageeks/datafusion-bio-formats/issues/54
Workaround: Use BAM format if MD/NM tags are required for your analysis.
Using External Reference
Public CRAM File Example
Download and read a public CRAM file from 42basepairs:
# Download the CRAM file and reference
wget https://42basepairs.com/download/s3/gatk-test-data/wgs_cram/NA12878_20k_hg38/NA12878.cram
wget https://storage.googleapis.com/genomics-public-data/resources/broad/hg38/v0/Homo_sapiens_assembly38.fasta
# Create FASTA index (required)
samtools faidx Homo_sapiens_assembly38.fasta
Creating CRAM with Embedded Reference
To create a CRAM file with embedded reference using samtools:
Returns:
| Type | Description |
|---|---|
DataFrame
|
A Polars DataFrame with the following schema: - name: Read name (String) - chrom: Chromosome/contig name (String) - start: Alignment start position, 1-based (UInt32) - end: Alignment end position, 1-based (UInt32) - flags: SAM flags (UInt32) - cigar: CIGAR string (String) - mapping_quality: Mapping quality (UInt32) - mate_chrom: Mate chromosome/contig name (String) - mate_start: Mate alignment start position, 1-based (UInt32) - sequence: Read sequence (String) - quality_scores: Base quality scores (String) |
Source code in polars_bio/io.py
1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 | |
read_sam(path, tag_fields=None, projection_pushdown=True, use_zero_based=None, infer_tag_types=True, infer_tag_sample_size=100, tag_type_hints=None)
staticmethod
Read a SAM file into a DataFrame.
SAM (Sequence Alignment/Map) is the plain-text counterpart of BAM. This function reuses the BAM reader, which auto-detects the format from the file extension.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the SAM file. |
required |
tag_fields
|
Union[list[str], None]
|
List of SAM tag names to include as columns (e.g., ["NM", "MD", "AS"]). If None, no optional tags are parsed (default). |
None
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance. |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration. |
None
|
infer_tag_types
|
bool
|
If True (default), sample the file to auto-detect types for custom/unknown tags. |
True
|
infer_tag_sample_size
|
int
|
Number of records to sample for tag type inference (default: 100). |
100
|
tag_type_hints
|
Optional[list[str]]
|
Explicit SAM-style type hints for tags (e.g., ["pt:i", "ML:B:C", "FZ:B:S"]). Supported forms: TAG:TYPE, TAG:B, or TAG:B:SUBTYPE where TYPE is one of A, c, C, s, S, i, I, f, Z, H and SUBTYPE is one of c, C, s, S, i, I, f. |
None
|
Note
By default, coordinates are output in 1-based closed format.
Source code in polars_bio/io.py
read_fastq(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True)
staticmethod
Read a FASTQ file into a DataFrame.
Parallelism & Compression
See File formats support, Compression, and Automatic parallel partitioning for details on parallel reads and supported compression types.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the FASTQ file. |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the FASTQ file. If not specified, it will be detected automatically based on the file extension. BGZF and GZIP compressions are supported ('bgz', 'gz'). |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
Source code in polars_bio/io.py
read_fasta(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True)
staticmethod
Read a FASTA file into a DataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the FASTA file. |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the FASTA file. If not specified, it will be detected automatically based on the file extension. BGZF and GZIP compressions are supported ('bgz', 'gz'). |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown optimization. When True, only requested columns are processed at the DataFusion execution level, improving performance and reducing memory usage. |
True
|
Example
shape: (1, 3)
┌─────────────────────────┬─────────────────────────────────┬─────────────────────────────────┐
│ name ┆ description ┆ sequence │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞═════════════════════════╪═════════════════════════════════╪═════════════════════════════════╡
│ ENA|BK006935|BK006935.2 ┆ TPA_inf: Saccharomyces cerevis… ┆ CCACACCACACCCACACACCCACACACCAC… │
└─────────────────────────┴─────────────────────────────────┴─────────────────────────────────┘
Source code in polars_bio/io.py
read_gff(path, attr_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Read a GFF file into a DataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the GFF file. |
required |
attr_fields
|
Union[list[str], None]
|
List of attribute field names to extract as separate columns. If None, attributes will be kept as a nested structure. Use this to extract specific attributes like 'ID', 'gene_name', 'gene_type', etc. as direct columns for easier access. |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the GFF file. If not specified, it will be detected automatically.. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using index files (TBI/CSI) for efficient region-based filtering. Index files are auto-discovered (e.g., |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Source code in polars_bio/io.py
read_gtf(path, attr_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Read a GTF file into a DataFrame.
GTF (Gene Transfer Format) shares the same 9-column structure as GFF but uses
different attribute syntax (key "value" vs GFF's key=value).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the GTF file. |
required |
attr_fields
|
Union[list[str], None]
|
List of attribute field names to extract as separate columns. If None, attributes will be kept as a nested structure. |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large-scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the GTF file. If not specified, it will be detected automatically. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using index files (TBI/CSI) for efficient region-based filtering. Index files are auto-discovered (e.g., |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Source code in polars_bio/io.py
read_bed(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, use_zero_based=None)
staticmethod
Read a BED file into a DataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BED file. |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the BED file. If not specified, it will be detected automatically based on the file extension. BGZF compressions is supported ('bgz'). |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
Note
Only BED4 format is supported. It extends the basic BED format (BED3) by adding a name field, resulting in four columns: chromosome, start position, end position, and name. Also unlike other text formats, GZIP compression is not supported.
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Source code in polars_bio/io.py
read_pairs(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Read a Pairs (Hi-C) file into a DataFrame.
The Pairs format (4DN project) stores chromatin contact data with columns: readID, chr1, pos1, chr2, pos2, strand1, strand2.
Parallelism & Indexed Reads
Indexed parallel reads and predicate pushdown are automatic when a TBI index is present. See File formats support and Indexed reads for details.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the Pairs file (.pairs, .pairs.gz, .pairs.bgz). |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. |
8
|
concurrent_fetches
|
int
|
The number of concurrent fetches when reading from an object store. |
1
|
allow_anonymous
|
bool
|
Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
Whether to enable request payer for object storage. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type. If not specified, it will be detected automatically. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using index files (TBI) for efficient region-based filtering. Index files are auto-discovered (e.g., |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Source code in polars_bio/io.py
read_bigwig(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Read a BigWig file into a DataFrame.
BigWig rows are exposed as chrom, start, end, and value.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BigWig file. |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the BigWig file. If not specified, it will be detected automatically based on the file extension. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown optimization. When True, only requested columns are processed at the DataFusion execution level, improving performance and reducing memory usage. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown on the genomic coordinate columns so range filters are evaluated at the DataFusion execution level. |
True
|
use_zero_based
|
Optional[bool]
|
Coordinate system override. BigWig is natively 0-based half-open; set to False to emit 1-based closed coordinates, or None to use the global default. |
None
|
Source code in polars_bio/io.py
read_bigbed(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, schema='auto')
staticmethod
Read a BigBed file into a DataFrame.
schema="auto" uses supported autoSQL fields when available.
schema="rest" exposes the raw trailing fields in rest.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BigBed file. |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the BigBed file. If not specified, it will be detected automatically based on the file extension. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown optimization. When True, only requested columns are processed at the DataFusion execution level, improving performance and reducing memory usage. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown on the genomic coordinate columns so range filters are evaluated at the DataFusion execution level. |
True
|
use_zero_based
|
Optional[bool]
|
Coordinate system override. BigBed is natively 0-based half-open; set to False to emit 1-based closed coordinates, or None to use the global default. |
None
|
schema
|
str
|
Schema mode. |
'auto'
|
Source code in polars_bio/io.py
read_cool(path, resolution=None, join_bins=True, include_weights=False, projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Read a Cooler (.cool/.mcool) Hi-C contact matrix into a DataFrame.
See scan_cool for parameter semantics.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the |
required |
resolution
|
Optional[int]
|
Bin size selecting an |
None
|
join_bins
|
bool
|
If True (default), join pixels with bin coordinates ( |
True
|
include_weights
|
bool
|
If True, expose balancing weights as |
False
|
projection_pushdown
|
bool
|
Enable column projection pushdown optimization. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown on the first-axis genomic columns ( |
True
|
use_zero_based
|
Optional[bool]
|
Coordinate system override. Cooler is natively 0-based half-open; set to False to emit 1-based closed coordinates, or None to use the global default. |
None
|
Source code in polars_bio/io.py
read_table(path, schema=None, **kwargs)
staticmethod
Read a tab-delimited (i.e. BED) file into a Polars DataFrame. Tries to be compatible with Bioframe's read_table but faster. Schema should follow the Bioframe's schema format.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the file. |
required |
schema
|
Dict
|
Schema should follow the Bioframe's schema format. |
None
|
Source code in polars_bio/io.py
scan_bcf(path, info_fields=None, format_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, samples=None, genotype_output='string')
staticmethod
Lazily read a BCF file into a LazyFrame.
BCF CSI range pushdown, projection pushdown, and configured input
partition parallelism are preserved. genotype_output="string" returns
VCF-style GT calls and remains the default. genotype_output="dosage"
returns the number of ALT alleles per sample as nullable Int8 (normally
0, 1, or 2 for diploid calls); any missing allele yields null. Dosage
requires GT to be the only selected FORMAT field and requires biallelic
records. When format_fields is None, all header-defined FORMAT
fields are selected, so pass format_fields=["GT"] when the header
declares additional fields. Multiallelic records are rejected.
Source code in polars_bio/io.py
scan_bgen(path, genotype_output='probability', probability_layout='nested', samples=None, genotype_fields=None, sample_path=None, bgi_path=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Lazily read a BGEN file into a LazyFrame.
BGI range pushdown, projection pushdown, and configured input partition
parallelism are preserved. See read_bgen for the parameters.
Source code in polars_bio/io.py
scan_pgen(path, genotype_fields=('GT',), samples=None, missing_sample_policy='error', psam_id_mode='iid', pvar_path=None, psam_path=None, pgi_path=None, max_range_gap=None, max_range_bytes=None, batch_soft_byte_limit=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Lazily read a PLINK 2 PGEN fileset into a LazyFrame.
Projection pushdown and configured input partition parallelism are
preserved. See read_pgen for the parameters.
Source code in polars_bio/io.py
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scan_vcf(path, info_fields=None, format_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, samples=None)
staticmethod
Lazily read a text VCF file into a LazyFrame.
Parallelism & Indexed Reads
Indexed parallel reads and predicate pushdown are automatic when a VCF TBI/CSI index is present. See File formats support, Indexed reads, and Automatic parallel partitioning for details.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the VCF file. |
required |
info_fields
|
Union[list[str], None]
|
List of INFO field names to include. If None, all INFO fields from the VCF header are included by default. Use this to limit fields for better performance. |
None
|
format_fields
|
Union[list[str], None]
|
List of FORMAT field names to include (per-sample genotype data). If None, all FORMAT fields are included by default. For single-sample VCFs, FORMAT fields are top-level columns (e.g., |
None
|
samples
|
Union[list[str], None]
|
Optional list of sample names to include from the VCF header. Matching is exact and case-sensitive. Missing sample names are skipped with a warning. The output follows the requested sample order. |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the VCF file. If not specified, it will be detected automatically.. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using VCF TBI/CSI index files for efficient region-based filtering. Index files are auto-discovered (for example, |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Lazy scanning VCF with INFO and FORMAT fields
import polars_bio as pb
# Lazily scan VCF with both INFO and FORMAT fields
lf = pb.scan_vcf(
"sample.vcf.gz",
info_fields=["END"], # INFO field
format_fields=["GT", "DP", "GQ"] # FORMAT fields
)
# Apply filters and collect only what's needed
df = lf.filter(pl.col("DP") > 20).select(
["chrom", "start", "ref", "alt", "GT", "DP", "GQ"]
).collect()
# Single-sample VCF: FORMAT fields are top-level columns (GT, DP, GQ)
# Multi-sample VCF: FORMAT data is nested in "genotypes"
Source code in polars_bio/io.py
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scan_vcf_zarr(path, info_fields=None, format_fields=None, projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, samples=None, genotype_encoding_raw=True)
staticmethod
Lazily read a local VCF Zarr store into a LazyFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the VCF Zarr store directory. |
required |
info_fields
|
Union[list[str], None]
|
Optional list of INFO field names to include. If None, local INFO arrays are discovered automatically. Use [] to disable INFO fields. |
None
|
format_fields
|
Union[list[str], None]
|
Optional list of FORMAT field names to include. If None, local FORMAT arrays are discovered automatically. Use [] to disable FORMAT fields. |
None
|
projection_pushdown
|
bool
|
Enable column projection pushdown at the DataFusion level. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown at the DataFusion level. |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None, uses the global configuration. |
None
|
samples
|
Union[list[str], None]
|
Optional list of sample names to include. |
None
|
genotype_encoding_raw
|
bool
|
If True, output GT as raw typed allele calls. If False, output VCF-style GT strings. |
True
|
Source code in polars_bio/io.py
scan_bam(path, tag_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, infer_tag_types=True, infer_tag_sample_size=100, tag_type_hints=None)
staticmethod
Lazily read a BAM file into a LazyFrame.
Parallelism & Indexed Reads
Indexed parallel reads and predicate pushdown are automatic when a BAI/CSI index is present. See File formats support, Indexed reads, and Automatic parallel partitioning for details.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BAM file. |
required |
tag_fields
|
Union[list[str], None]
|
List of BAM tag names to include as columns (e.g., ["NM", "MD", "AS"]). If None, no optional tags are parsed (default). Common tags include: NM (edit distance), MD (mismatch string), AS (alignment score), XS (secondary alignment score), RG (read group), CB (cell barcode), UB (UMI barcode). |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using index files (BAI/CSI) for efficient region-based filtering. Index files are auto-discovered (e.g., |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
infer_tag_types
|
bool
|
If True (default), sample the file to auto-detect types for custom/unknown tags. This prevents integer tags from being decoded as ASCII characters. |
True
|
infer_tag_sample_size
|
int
|
Number of records to sample for tag type inference (default: 100). |
100
|
tag_type_hints
|
Optional[list[str]]
|
Explicit SAM-style type hints for tags (e.g., ["pt:i", "ML:B:C", "FZ:B:S"]). Used as fallback when inference is disabled or a tag is not found in sampled records. Supported forms: TAG:TYPE, TAG:B, or TAG:B:SUBTYPE where TYPE is one of A, c, C, s, S, i, I, f, Z, H and SUBTYPE is one of c, C, s, S, i, I, f. |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Source code in polars_bio/io.py
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scan_cram(path, reference_path=None, tag_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, infer_tag_types=True, infer_tag_sample_size=100, tag_type_hints=None)
staticmethod
Lazily read a CRAM file into a LazyFrame.
Parallelism & Indexed Reads
Indexed parallel reads and predicate pushdown are automatic when a CRAI index is present. See File formats support, Indexed reads, and Automatic parallel partitioning for details.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the CRAM file (local or cloud storage: S3, GCS, Azure Blob). |
required |
reference_path
|
str
|
Optional path to external FASTA reference file (local path only, cloud storage not supported). If not provided, the CRAM file must contain embedded reference sequences. The FASTA file must have an accompanying index file (.fai) in the same directory. Create the index using: |
None
|
tag_fields
|
Union[list[str], None]
|
List of CRAM tag names to include as columns (e.g., ["NM", "MD", "AS"]). If None, no optional tags are parsed (default). Common tags include: NM (edit distance), MD (mismatch string), AS (alignment score), XS (secondary alignment score), RG (read group), CB (cell barcode), UB (UMI barcode). |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
projection_pushdown
|
bool
|
Enable column projection pushdown optimization. When True, only requested columns are processed at the DataFusion execution level, improving performance and reducing memory usage. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using index files (CRAI) for efficient region-based filtering. Index files are auto-discovered (e.g., |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
infer_tag_types
|
bool
|
If True (default), sample the file to auto-detect types for custom/unknown tags. This prevents integer tags from being decoded as ASCII characters. |
True
|
infer_tag_sample_size
|
int
|
Number of records to sample for tag type inference (default: 100). |
100
|
tag_type_hints
|
Optional[list[str]]
|
Explicit SAM-style type hints for tags (e.g., ["pt:i", "ML:B:C", "FZ:B:S"]). Used as fallback when inference is disabled or a tag is not found in sampled records. Supported forms: TAG:TYPE, TAG:B, or TAG:B:SUBTYPE where TYPE is one of A, c, C, s, S, i, I, f, Z, H and SUBTYPE is one of c, C, s, S, i, I, f. |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Known Limitation: MD and NM Tags
Due to a limitation in the underlying noodles-cram library, MD (mismatch descriptor) and NM (edit distance) tags are not accessible from CRAM files, even when stored in the file. These tags can be seen with samtools but are not exposed through the noodles-cram record.data() interface.
Other optional tags (RG, MQ, AM, OQ, etc.) work correctly. This issue is tracked at: https://github.com/biodatageeks/datafusion-bio-formats/issues/54
Workaround: Use BAM format if MD/NM tags are required for your analysis.
Using External Reference
Public CRAM File Example
Download and read a public CRAM file from 42basepairs:
# Download the CRAM file and reference
wget https://42basepairs.com/download/s3/gatk-test-data/wgs_cram/NA12878_20k_hg38/NA12878.cram
wget https://storage.googleapis.com/genomics-public-data/resources/broad/hg38/v0/Homo_sapiens_assembly38.fasta
# Create FASTA index (required)
samtools faidx Homo_sapiens_assembly38.fasta
import polars_bio as pb
import polars as pl
# Lazy scan and filter for chromosome 20 reads
df = pb.scan_cram(
"NA12878.cram",
reference_path="Homo_sapiens_assembly38.fasta"
).filter(
pl.col("chrom") == "chr20"
).select(
["name", "chrom", "start", "end", "mapping_quality"]
).limit(10).collect()
print(df)
Creating CRAM with Embedded Reference
To create a CRAM file with embedded reference using samtools:
Returns:
| Type | Description |
|---|---|
LazyFrame
|
A Polars LazyFrame with the following schema: - name: Read name (String) - chrom: Chromosome/contig name (String) - start: Alignment start position, 1-based (UInt32) - end: Alignment end position, 1-based (UInt32) - flags: SAM flags (UInt32) - cigar: CIGAR string (String) - mapping_quality: Mapping quality (UInt32) - mate_chrom: Mate chromosome/contig name (String) - mate_start: Mate alignment start position, 1-based (UInt32) - sequence: Read sequence (String) - quality_scores: Base quality scores (String) |
Source code in polars_bio/io.py
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scan_sam(path, tag_fields=None, projection_pushdown=True, use_zero_based=None, infer_tag_types=True, infer_tag_sample_size=100, tag_type_hints=None)
staticmethod
Lazily read a SAM file into a LazyFrame.
SAM (Sequence Alignment/Map) is the plain-text counterpart of BAM. This function reuses the BAM reader, which auto-detects the format from the file extension.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the SAM file. |
required |
tag_fields
|
Union[list[str], None]
|
List of SAM tag names to include as columns (e.g., ["NM", "MD", "AS"]). If None, no optional tags are parsed (default). |
None
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance. |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration. |
None
|
infer_tag_types
|
bool
|
If True (default), sample the file to auto-detect types for custom/unknown tags. |
True
|
infer_tag_sample_size
|
int
|
Number of records to sample for tag type inference (default: 100). |
100
|
tag_type_hints
|
Optional[list[str]]
|
Explicit SAM-style type hints for tags (e.g., ["pt:i", "ML:B:C", "FZ:B:S"]). Supported forms: TAG:TYPE, TAG:B, or TAG:B:SUBTYPE where TYPE is one of A, c, C, s, S, i, I, f, Z, H and SUBTYPE is one of c, C, s, S, i, I, f. |
None
|
Note
By default, coordinates are output in 1-based closed format.
Source code in polars_bio/io.py
scan_fastq(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True)
staticmethod
Lazily read a FASTQ file into a LazyFrame.
Parallelism & Compression
See File formats support, Compression, and Automatic parallel partitioning for details on parallel reads and supported compression types.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the FASTQ file. |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the FASTQ file. If not specified, it will be detected automatically based on the file extension. BGZF and GZIP compressions are supported ('bgz', 'gz'). |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
Source code in polars_bio/io.py
scan_fasta(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True)
staticmethod
Lazily read a FASTA file into a LazyFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the FASTA file. |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the FASTA file. If not specified, it will be detected automatically based on the file extension. BGZF and GZIP compressions are supported ('bgz', 'gz'). |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
Example
shape: (1, 3)
┌─────────────────────────┬─────────────────────────────────┬─────────────────────────────────┐
│ name ┆ description ┆ sequence │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞═════════════════════════╪═════════════════════════════════╪═════════════════════════════════╡
│ ENA|BK006935|BK006935.2 ┆ TPA_inf: Saccharomyces cerevis… ┆ CCACACCACACCCACACACCCACACACCAC… │
└─────────────────────────┴─────────────────────────────────┴─────────────────────────────────┘
Source code in polars_bio/io.py
scan_gff(path, attr_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Lazily read a GFF file into a LazyFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the GFF file. |
required |
attr_fields
|
Union[list[str], None]
|
List of attribute field names to extract as separate columns. If None, attributes will be kept as a nested structure. Use this to extract specific attributes like 'ID', 'gene_name', 'gene_type', etc. as direct columns for easier access. |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large-scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the GFF file. If not specified, it will be detected automatically. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using index files (TBI/CSI) for efficient region-based filtering. Index files are auto-discovered (e.g., |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Source code in polars_bio/io.py
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scan_gtf(path, attr_fields=None, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Lazily read a GTF file into a LazyFrame.
GTF (Gene Transfer Format) shares the same 9-column structure as GFF but uses
different attribute syntax (key "value" vs GFF's key=value).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the GTF file. |
required |
attr_fields
|
Union[list[str], None]
|
List of attribute field names to extract as separate columns. If None, attributes will be kept as a nested structure. |
None
|
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large-scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the GTF file. If not specified, it will be detected automatically. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using index files (TBI/CSI) for efficient region-based filtering. Index files are auto-discovered (e.g., |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Source code in polars_bio/io.py
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scan_bed(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, use_zero_based=None)
staticmethod
Lazily read a BED file into a LazyFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BED file. |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the BED file. If not specified, it will be detected automatically based on the file extension. BGZF compressions is supported ('bgz'). |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance by only reading the necessary columns at the DataFusion level. |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
Note
Only BED4 format is supported. It extends the basic BED format (BED3) by adding a name field, resulting in four columns: chromosome, start position, end position, and name. Also unlike other text formats, GZIP compression is not supported.
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Source code in polars_bio/io.py
scan_pairs(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Lazily read a Pairs (Hi-C) file into a LazyFrame.
The Pairs format (4DN project) stores chromatin contact data with columns: readID, chr1, pos1, chr2, pos2, strand1, strand2.
Parallelism & Indexed Reads
Indexed parallel reads and predicate pushdown are automatic when a TBI index is present. See File formats support and Indexed reads for details.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the Pairs file (.pairs, .pairs.gz, .pairs.bgz). |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. |
8
|
concurrent_fetches
|
int
|
The number of concurrent fetches when reading from an object store. |
1
|
allow_anonymous
|
bool
|
Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
Whether to enable request payer for object storage. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type. If not specified, it will be detected automatically. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown to optimize query performance. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown using index files (TBI) for efficient region-based filtering. Index files are auto-discovered (e.g., |
True
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based half-open coordinates. If False, output 1-based closed coordinates. If None (default), uses the global configuration |
None
|
Note
By default, coordinates are output in 1-based closed format. Use use_zero_based=True or set pb.set_option(pb.POLARS_BIO_COORDINATE_SYSTEM_ZERO_BASED, True) for 0-based half-open coordinates.
Source code in polars_bio/io.py
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scan_bigwig(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Lazily read a BigWig file into a LazyFrame.
BigWig is natively 0-based half-open. Set use_zero_based=False to emit
1-based closed coordinates.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BigWig file. |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the BigWig file. If not specified, it will be detected automatically based on the file extension. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown optimization. When True, only requested columns are processed at the DataFusion execution level, improving performance and reducing memory usage. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown on the genomic coordinate columns so range filters are evaluated at the DataFusion execution level. |
True
|
use_zero_based
|
Optional[bool]
|
Coordinate system override. BigWig is natively 0-based half-open; set to False to emit 1-based closed coordinates, or None to use the global default. |
None
|
Source code in polars_bio/io.py
scan_bigbed(path, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', projection_pushdown=True, predicate_pushdown=True, use_zero_based=None, schema='auto')
staticmethod
Lazily read a BigBed file into a LazyFrame.
BigBed is natively 0-based half-open. Set use_zero_based=False to emit
1-based closed coordinates.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BigBed file. |
required |
chunk_size
|
int
|
The size in MB of a chunk when reading from an object store. The default is 8 MB. For large scale operations, it is recommended to increase this value to 64. |
8
|
concurrent_fetches
|
int
|
[GCS] The number of concurrent fetches when reading from an object store. The default is 1. For large scale operations, it is recommended to increase this value to 8 or even more. |
1
|
allow_anonymous
|
bool
|
[GCS, AWS S3] Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
[AWS S3] Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file from object storage. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file from object storage. |
300
|
compression_type
|
str
|
The compression type of the BigBed file. If not specified, it will be detected automatically based on the file extension. |
'auto'
|
projection_pushdown
|
bool
|
Enable column projection pushdown optimization. When True, only requested columns are processed at the DataFusion execution level, improving performance and reducing memory usage. |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown on the genomic coordinate columns so range filters are evaluated at the DataFusion execution level. |
True
|
use_zero_based
|
Optional[bool]
|
Coordinate system override. BigBed is natively 0-based half-open; set to False to emit 1-based closed coordinates, or None to use the global default. |
None
|
schema
|
str
|
Schema mode. |
'auto'
|
Source code in polars_bio/io.py
scan_cool(path, resolution=None, join_bins=True, include_weights=False, projection_pushdown=True, predicate_pushdown=True, use_zero_based=None)
staticmethod
Lazily read a Cooler (.cool/.mcool) Hi-C contact matrix into a LazyFrame.
One row per stored pixel (upper-triangle contact), joined with bin
coordinates by default. .mcool files store one data collection per
resolution: select one with resolution or the cooler URI syntax
file.mcool::/resolutions/10000; an .mcool with several
resolutions and no selection raises an error listing the available
ones. Only local filesystem paths are supported.
Cooler is natively 0-based half-open. Set use_zero_based=False to
emit 1-based closed coordinates.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the |
required |
resolution
|
Optional[int]
|
Bin size selecting an |
None
|
join_bins
|
bool
|
If True (default), join pixels with bin coordinates ( |
True
|
include_weights
|
bool
|
If True, expose balancing weights as |
False
|
projection_pushdown
|
bool
|
Enable column projection pushdown optimization. Only HDF5 datasets required by the requested columns are read, and |
True
|
predicate_pushdown
|
bool
|
Enable predicate pushdown on the first-axis genomic columns ( |
True
|
use_zero_based
|
Optional[bool]
|
Coordinate system override. Cooler is natively 0-based half-open; set to False to emit 1-based closed coordinates, or None to use the global default. |
None
|
Example
Source code in polars_bio/io.py
scan_table(path, schema=None, **kwargs)
staticmethod
Lazily read a tab-delimited (i.e. BED) file into a Polars LazyFrame. Tries to be compatible with Bioframe's read_table but faster and lazy. Schema should follow the Bioframe's schema format.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the file. |
required |
schema
|
Dict
|
Schema should follow the Bioframe's schema format. |
None
|
Source code in polars_bio/io.py
describe_bcf(path, allow_anonymous=True, enable_request_payer=False, compression_type='auto')
staticmethod
Describe a BCF INFO and FORMAT schema.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BCF file. The path must end in |
required |
allow_anonymous
|
bool
|
Whether to allow anonymous access to object storage (GCS and S3 supported). |
True
|
enable_request_payer
|
bool
|
Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
compression_type
|
str
|
The compression override. The default detects BCF automatically. |
'auto'
|
Source code in polars_bio/io.py
describe_bgen(path, allow_anonymous=True, enable_request_payer=False, compression_type='auto', sample_path=None, bgi_path=None)
staticmethod
Describe the schema a BGEN file produces.
BGEN has no INFO/FORMAT header, so instead of a field dictionary this
returns one row per emitted column, plus the file-level properties the
provider records in the Arrow schema metadata: the BGEN layout, whether
a .bgi index was used, whether sample identifiers were generated, and
the coordinate system.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BGEN file. The path must end in |
required |
allow_anonymous
|
bool
|
Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
Whether to enable request payer for object storage. |
False
|
compression_type
|
str
|
The compression override. |
'auto'
|
sample_path
|
Union[str, None]
|
An explicit Oxford |
None
|
bgi_path
|
Union[str, None]
|
An explicit |
None
|
Note
The reported schema is the one the default probability_layout="nested"
produces, because that layout describes every BGEN file. Reading with
probability_layout="fixed" gives genotypes.GP a fixed-width state
list instead.
Source code in polars_bio/io.py
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describe_pgen(path, allow_anonymous=True, enable_request_payer=False, compression_type='auto', pvar_path=None, psam_path=None, pgi_path=None)
staticmethod
Describe the schema a PLINK 2 PGEN fileset produces.
PGEN has no embedded header, so instead of a field dictionary this returns one row per emitted column, plus the file-level properties the provider records in the Arrow schema metadata: the storage mode, whether the index is embedded or external, the specification baseline, and the coordinate system.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the PGEN file. The path must end in |
required |
allow_anonymous
|
bool
|
Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
Whether to enable request payer for object storage. |
False
|
compression_type
|
str
|
The compression override. |
'auto'
|
pvar_path
|
Union[str, None]
|
An explicit |
None
|
psam_path
|
Union[str, None]
|
An explicit |
None
|
pgi_path
|
Union[str, None]
|
An explicit |
None
|
Note
The reported schema is the one the default genotype_fields=("GT",)
produces. Selecting other genotype fields changes the children of
the genotypes struct.
Source code in polars_bio/io.py
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describe_vcf(path, allow_anonymous=True, enable_request_payer=False, compression_type='auto')
staticmethod
Describe a text VCF INFO and FORMAT schema.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the text VCF file. |
required |
allow_anonymous
|
bool
|
Whether to allow anonymous access to object storage (GCS and S3 supported). |
True
|
enable_request_payer
|
bool
|
Whether to enable request payer for object storage. This is useful for reading files from AWS S3 buckets that require request payer. |
False
|
compression_type
|
str
|
The compression type of the VCF file. If not specified, it will be detected automatically.. |
'auto'
|
Source code in polars_bio/io.py
describe_vcf_zarr(path)
staticmethod
Describe VCF Zarr INFO and FORMAT schema.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the local VCF Zarr store directory. |
required |
Source code in polars_bio/io.py
describe_bam(path, sample_size=100, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', use_zero_based=None)
staticmethod
Get schema information for a BAM file with automatic tag discovery.
Samples the first N records to discover all available tags and their types. Returns detailed schema information including column names, data types, nullability, category (standard/tag), SAM type, and descriptions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the BAM file. |
required |
sample_size
|
int
|
Number of records to sample for tag discovery (default: 100). Use higher values for more comprehensive tag discovery. |
100
|
chunk_size
|
int
|
The size in MB of a chunk when reading from object storage. |
8
|
concurrent_fetches
|
int
|
The number of concurrent fetches when reading from object storage. |
1
|
allow_anonymous
|
bool
|
Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
Whether to enable request payer for object storage. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file. |
300
|
compression_type
|
str
|
The compression type of the file. If "auto" (default), compression is detected automatically. |
'auto'
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based coordinates. If False, 1-based coordinates. |
None
|
Returns:
| Type | Description |
|---|---|
DataFrame
|
DataFrame with columns: |
DataFrame
|
|
DataFrame
|
|
DataFrame
|
|
DataFrame
|
|
DataFrame
|
|
DataFrame
|
|
Example
import polars_bio as pb
# Auto-discover all tags present in the file
schema = pb.describe_bam("file.bam", sample_size=100)
print(schema)
# Output:
# shape: (15, 6)
# ┌─────────────┬───────────┬──────────┬──────────┬──────────┬──────────────────────┐
# │ column_name ┆ data_type ┆ nullable ┆ category ┆ sam_type ┆ description │
# │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
# │ str ┆ str ┆ bool ┆ str ┆ str ┆ str │
# ╞═════════════╪═══════════╪══════════╪══════════╪══════════╪══════════════════════╡
# │ name ┆ Utf8 ┆ true ┆ core ┆ null ┆ Query name │
# │ chrom ┆ Utf8 ┆ true ┆ core ┆ null ┆ Reference name │
# │ ... ┆ ... ┆ ... ┆ ... ┆ ... ┆ ... │
# │ NM ┆ Int32 ┆ true ┆ tag ┆ i ┆ Edit distance │
# │ AS ┆ Int32 ┆ true ┆ tag ┆ i ┆ Alignment score │
# └─────────────┴───────────┴──────────┴──────────┴──────────┴──────────────────────┘
Source code in polars_bio/io.py
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describe_sam(path, sample_size=100, use_zero_based=None)
staticmethod
Get schema information for a SAM file with automatic tag discovery.
Samples the first N records to discover all available tags and their types. Reuses the BAM describe logic, which auto-detects SAM from the file extension.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the SAM file. |
required |
sample_size
|
int
|
Number of records to sample for tag discovery (default: 100). |
100
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based coordinates. If False, 1-based coordinates. |
None
|
Returns:
| Type | Description |
|---|---|
DataFrame
|
DataFrame with columns: column_name, data_type, nullable, category, sam_type, description |
Source code in polars_bio/io.py
describe_cram(path, reference_path=None, sample_size=100, chunk_size=8, concurrent_fetches=1, allow_anonymous=True, enable_request_payer=False, max_retries=5, timeout=300, compression_type='auto', use_zero_based=None)
staticmethod
Get schema information for a CRAM file with automatic tag discovery.
Samples the first N records to discover all available tags and their types. Returns detailed schema information including column names, data types, nullability, category (core/tag), SAM type, and descriptions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the CRAM file. |
required |
reference_path
|
str
|
Optional path to external FASTA reference file. |
None
|
sample_size
|
int
|
Number of records to sample for tag discovery (default: 100). |
100
|
chunk_size
|
int
|
The size in MB of a chunk when reading from object storage. |
8
|
concurrent_fetches
|
int
|
The number of concurrent fetches when reading from object storage. |
1
|
allow_anonymous
|
bool
|
Whether to allow anonymous access to object storage. |
True
|
enable_request_payer
|
bool
|
Whether to enable request payer for object storage. |
False
|
max_retries
|
int
|
The maximum number of retries for reading the file. |
5
|
timeout
|
int
|
The timeout in seconds for reading the file. |
300
|
compression_type
|
str
|
The compression type of the file. If "auto" (default), compression is detected automatically. |
'auto'
|
use_zero_based
|
Optional[bool]
|
If True, output 0-based coordinates. If False, 1-based coordinates. |
None
|
Returns:
| Type | Description |
|---|---|
DataFrame
|
DataFrame with columns: |
DataFrame
|
|
DataFrame
|
|
DataFrame
|
|
DataFrame
|
|
DataFrame
|
|
DataFrame
|
|
Known Limitation: MD and NM Tags
Due to a limitation in the underlying noodles-cram library, MD (mismatch descriptor) and NM (edit distance) tags are not discoverable from CRAM files, even when stored. Automatic tag discovery will not include MD/NM tags. Other optional tags (RG, MQ, AM, OQ, etc.) are discovered correctly. See: https://github.com/biodatageeks/datafusion-bio-formats/issues/54
Example
Source code in polars_bio/io.py
1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 | |
describe_cool(path)
staticmethod
Describe the data collections of a Cooler (.cool/.mcool) file.
Returns one row per stored data collection (one for .cool, one per
resolution for .mcool) with group_path, resolution (bin size),
bin_type, format_version, assembly, nbins, nnz, sum, and
nchroms, read from file metadata without scanning pixel data. sum
is Int64/UInt64 for integer-count collections and Float64 for
float-count collections. Files mixing those storage classes use an
exact Decimal column (or an exact string for values outside Arrow's
Decimal128 range), preserving wide integer totals alongside fractions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path
|
str
|
The path to the |
required |
Source code in polars_bio/io.py
from_polars(name, df)
staticmethod
Register a Polars DataFrame as a DataFusion table.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
name
|
str
|
The name of the table. |
required |
df
|
Union[DataFrame, LazyFrame]
|
The Polars DataFrame. |
required |