seqnames
stringclasses 17
values | start
float64 0
1.53M
| end
int32 1
1.53M
| pileup
int32 1
490k
|
---|---|---|---|
chrI
| 43 | 44 | 1 |
chrI
| 202 | 203 | 1 |
chrI
| 254 | 255 | 1 |
chrI
| 281 | 282 | 1 |
chrI
| 288 | 289 | 1 |
chrI
| 322 | 323 | 1 |
chrI
| 455 | 456 | 1 |
chrI
| 471 | 472 | 1 |
chrI
| 479 | 480 | 1 |
chrI
| 484 | 485 | 1 |
chrI
| 489 | 490 | 1 |
chrI
| 505 | 506 | 1 |
chrI
| 518 | 519 | 1 |
chrI
| 522 | 523 | 1 |
chrI
| 559 | 560 | 1 |
chrI
| 589 | 590 | 1 |
chrI
| 592 | 593 | 1 |
chrI
| 701 | 702 | 1 |
chrI
| 703 | 704 | 1 |
chrI
| 725 | 726 | 1 |
chrI
| 828 | 829 | 1 |
chrI
| 934 | 935 | 1 |
chrI
| 947 | 948 | 1 |
chrI
| 949 | 950 | 1 |
chrI
| 973 | 974 | 1 |
chrI
| 1,090 | 1,091 | 1 |
chrI
| 1,103 | 1,104 | 1 |
chrI
| 1,124 | 1,125 | 1 |
chrI
| 1,157 | 1,158 | 2 |
chrI
| 1,253 | 1,254 | 1 |
chrI
| 1,255 | 1,256 | 1 |
chrI
| 1,260 | 1,261 | 1 |
chrI
| 1,261 | 1,262 | 1 |
chrI
| 1,291 | 1,292 | 2 |
chrI
| 1,334 | 1,335 | 2 |
chrI
| 1,380 | 1,381 | 1 |
chrI
| 1,417 | 1,418 | 1 |
chrI
| 1,429 | 1,430 | 1 |
chrI
| 1,442 | 1,443 | 1 |
chrI
| 1,446 | 1,447 | 2 |
chrI
| 1,447 | 1,448 | 3 |
chrI
| 1,448 | 1,449 | 2 |
chrI
| 1,449 | 1,450 | 1 |
chrI
| 1,458 | 1,459 | 2 |
chrI
| 1,501 | 1,502 | 4 |
chrI
| 1,505 | 1,506 | 1 |
chrI
| 1,558 | 1,559 | 1 |
chrI
| 1,563 | 1,564 | 1 |
chrI
| 1,566 | 1,567 | 1 |
chrI
| 1,669 | 1,670 | 1 |
chrI
| 1,671 | 1,672 | 1 |
chrI
| 1,698 | 1,699 | 1 |
chrI
| 1,761 | 1,762 | 1 |
chrI
| 1,793 | 1,794 | 1 |
chrI
| 1,813 | 1,814 | 1 |
chrI
| 1,833 | 1,834 | 1 |
chrI
| 1,848 | 1,849 | 1 |
chrI
| 1,890 | 1,891 | 1 |
chrI
| 1,891 | 1,892 | 1 |
chrI
| 1,902 | 1,903 | 1 |
chrI
| 1,908 | 1,909 | 1 |
chrI
| 1,932 | 1,933 | 1 |
chrI
| 1,956 | 1,957 | 1 |
chrI
| 1,964 | 1,965 | 1 |
chrI
| 1,976 | 1,977 | 1 |
chrI
| 2,050 | 2,051 | 1 |
chrI
| 2,055 | 2,056 | 1 |
chrI
| 2,060 | 2,061 | 1 |
chrI
| 2,117 | 2,118 | 1 |
chrI
| 2,139 | 2,140 | 1 |
chrI
| 2,242 | 2,243 | 1 |
chrI
| 2,277 | 2,278 | 1 |
chrI
| 2,303 | 2,304 | 1 |
chrI
| 2,317 | 2,318 | 1 |
chrI
| 2,318 | 2,319 | 1 |
chrI
| 2,393 | 2,394 | 1 |
chrI
| 2,404 | 2,405 | 1 |
chrI
| 2,427 | 2,428 | 1 |
chrI
| 2,441 | 2,442 | 1 |
chrI
| 2,458 | 2,459 | 1 |
chrI
| 2,462 | 2,463 | 1 |
chrI
| 2,463 | 2,464 | 1 |
chrI
| 2,511 | 2,512 | 1 |
chrI
| 2,555 | 2,556 | 1 |
chrI
| 2,586 | 2,587 | 1 |
chrI
| 2,594 | 2,595 | 1 |
chrI
| 2,603 | 2,604 | 1 |
chrI
| 2,747 | 2,748 | 1 |
chrI
| 2,752 | 2,753 | 1 |
chrI
| 2,897 | 2,898 | 1 |
chrI
| 2,969 | 2,970 | 1 |
chrI
| 2,979 | 2,980 | 1 |
chrI
| 3,043 | 3,044 | 1 |
chrI
| 3,081 | 3,082 | 1 |
chrI
| 3,082 | 3,083 | 1 |
chrI
| 3,089 | 3,090 | 1 |
chrI
| 3,143 | 3,144 | 1 |
chrI
| 3,149 | 3,150 | 1 |
chrI
| 3,151 | 3,152 | 1 |
chrI
| 3,164 | 3,165 | 1 |
Barkai Compendium
This collects the ChEC-seq data from the following GEO series:
The metadata for each is parsed out from the SraRunTable, or in the case of GSE222268, the NCBI series matrix file (the genotype isn't in the SraRunTable)
The Barkai lab refers to this set as their binding compendium.
The genotypes for GSE222268 are not clear enough to me currently to parse well.
Dataset Details
genome_map
stores the pileup of 5' end tags. See the Series and associated cited paper for details, but it is a
standard processing pipeline to count 5' ends.
The <series_accession>_metadata.parquet
files store metadata. You may use the field accession
to extract the corresponding
data.
See scripts/
for more parsing details.
Data Structure
genome_map/
This is a parquet dataset which is partitioned by Series and Accession
Field | Description |
---|---|
seqnames |
Chromosome or sequence name (e.g., chrI, chrII, etc.) |
start |
Start position of the genomic interval (1-based coordinates) |
end |
End position of the genomic interval (1-based coordinates) |
pileup |
Number of reads or signal intensity at this genomic position |
GSE178430
Field | Description |
---|---|
accession |
Sample accession identifier |
regulator_locus_tag |
Systematic gene name (ORF identifier) of the tagged transcription factor |
regulator_symbol |
Standard gene symbol of the tagged transcription factor |
strainid |
Strain identifier used in the experiment |
instrument |
Sequencing instrument used for data generation |
genotype |
Full genotype description of the experimental strain |
dbd_donor_symbol |
Gene symbol of the DNA-binding domain donor (for chimeric constructs) |
ortholog_donor |
Ortholog donor information for cross-species constructs |
paralog_deletion_symbol |
Gene symbol of deleted paralog in the strain background |
paralog_resistance_cassette |
Antibiotic resistance cassette used for paralog deletion |
GSE209631
Field | Description |
---|---|
accession |
Sample accession identifier |
regulator_locus_tag |
Systematic gene name (ORF identifier) of the tagged transcription factor |
regulator_symbol |
Standard gene symbol of the tagged transcription factor |
variant_type |
Type of transcription factor variant tested in the experiment |
GSE222268
Field | Description |
---|---|
title |
Experiment title or sample description |
accession |
GEO sample accession identifier |
extract_protocol_ch1 |
Protocol used for sample extraction and preparation |
description |
Detailed description of the experimental sample or condition |
instrument_model |
Model of sequencing instrument used for data generation |
Usage
The entire repository is large. It may be preferrable to only retrieve specific files or partitions. You can use the metadata files to choose which files to pull.
from huggingface_hub import snapshot_download
import duckdb
import os
# Download only the partitioned dataset directory
repo_path = snapshot_download(
repo_id="BrentLab/barkai_compendium",
repo_type="dataset",
allow_patterns="_metadata.parquet"
)
dataset_path = os.path.join(repo_path, "GSE178430_metadata.parquet")
con = duckdb.connect()
meta_res = conn.execute("SELECT * FROM read_parquet(?) LIMIT 10", [dataset_path]).df()
print(meta_res)
We might choose to take a look at the file with accession GSM5417602
# Download only the partitioned dataset directory
repo_path = snapshot_download(
repo_id="BrentLab/barkai_compendium",
repo_type="dataset",
allow_patterns="genome_map/series=GSE179430/accession=GSM5417602/*parquet" # Only the parquet data
)
# The rest works the same
dataset_path = os.path.join(repo_path, "genome_map")
result = conn.execute("SELECT * FROM read_parquet(?) LIMIT 10",
[f"{dataset_path}/**/*.parquet"]).df()
print(result)
Dataset Author and Contact: Chase Mateusiak @cmatKhan
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