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---
license: mit
tags:
- transcription-factor
- binding
- chipexo
- genomics
- biology
language:
- en
pretty_name: Rossi ChIP-exo 2021
configs:
- config_name: metadata
description: Metadata describing the tagged regulator in each experiment
data_files:
- split: train
path: rossi_2021_metadata.parquet
dataset_info:
features:
- name: regulator_locus_tag
dtype: string
description: Systematic gene name (ORF identifier) of the transcription factor
- name: regulator_symbol
dtype: string
description: Standard gene symbol of the transcription factor
- name: run_accession
dtype: string
description: GEO run accession identifier for the sample
- name: yeastepigenome_id
dtype: string
description: Sample identifier used by yeastepigenome.org
- config_name: genome_map
description: ChIP-exo 5' tag coverage data partitioned by sample accession
data_files:
- split: train
path: genome_map/*/*.parquet
dataset_info:
features:
- name: chr
dtype: string
description: Chromosome name (e.g., chrI, chrII, etc.)
- name: pos
dtype: int32
description: Genomic position of the 5' tag
- name: pileup
dtype: int32
description: Depth of coverage (number of 5' tags) at this genomic position
- config_name: rossi_annotated_features
description: ChIP-exo regulator-target binding features with peak statistics
dataset_type: annotated_features
default: true
metadata_fields:
- regulator_locus_tag
- regulator_symbol
- target_locus_tag
- target_symbol
data_files:
- split: train
path: yeastepigenome_annotatedfeatures.parquet
dataset_info:
features:
- name: sample_id
dtype: int32
description: >-
Unique identifier for each ChIP-exo experimental sample.
- name: pss_id
dtype: float64
description: >-
Current brentlab promotersetsig table id. This will eventually be removed.
- name: binding_id
dtype: float64
description: >-
Current brentlab binding table id. This will eventually be removed.
- name: yeastepigenome_id
dtype: float64
description: >-
Unique identifier in the yeastepigenome database.
- name: regulator_locus_tag
dtype: string
description: >-
Systematic ORF name of the regulator.
role: regulator_identifier
- name: regulator_symbol
dtype: string
description: >-
Common gene name of the regulator.
role: regulator_identifier
- name: target_locus_tag
dtype: string
description: >-
The systematic ID of the feature to which the effect/pvalue is
assigned. See hf/BrentLab/yeast_genome_resources
role: target_identifier
- name: target_symbol
dtype: string
description: >-
The common name of the feature to which the effect/pvalue is
assigned. If there is no common name, the `target_locus_tag` is
used.
role: target_identifier
- name: n_sig_peaks
dtype: float64
description: >-
Number of peaks in the promoter region of the the target gene
role: quantitative_measure
- name: max_fc
dtype: float64
description: >-
If there are multiple peaks in the promoter region, then the maximum is
reported. Otherwise, it is the fold change of the single peak in the
promoter.
role: quantitative_measure
- name: min_pval
dtype: float64
description: >-
The most significant p-value among peaks for this interaction.
role: quantitative_measure
---
# Rossi 2021
This data is gathered from [yeastepigenome.org](https://yeastepigenome.org/). This work was published in
[Rossi MJ, Kuntala PK, Lai WKM, Yamada N, Badjatia N, Mittal C, Kuzu G, Bocklund K, Farrell NP, Blanda TR, Mairose JD, Basting AV, Mistretta KS, Rocco DJ, Perkinson ES, Kellogg GD, Mahony S, Pugh BF. A high-resolution protein architecture of the budding yeast genome. Nature. 2021 Apr;592(7853):309-314. doi: 10.1038/s41586-021-03314-8. Epub 2021 Mar 10. PMID: 33692541; PMCID: PMC8035251.](https://doi.org/10.1038/s41586-021-03314-8)
## Dataset details
`genome_map` is fully reprocessed data from the sequence files. I used the nf-core/chipseq pipeline, details for which can be found in `scripts/`. With
those bams, I filtered the reads using `samtools` and the same settings specified in Rossi et al 2021, and then counted 5' ends using bedtools. See
`scripts/count_tags.sh`.
## Data Structure
### Metadata
| Field | Description |
|-----------------------|-------------------------------------------------------------------|
| `regulator_locus_tag` | Systematic gene name (ORF identifier) of the transcription factor |
| `regulator_symbol` | Standard gene symbol of the transcription factor |
| `run_accession` | GEO run accession identifier for the sample |
| `yeastepigenome_id` | Sample identifier used by yeastepigenome.org |
### Genome Map
| Field | Description |
|----------|----------------------------------------------------------------|
| `chr` | Chromosome name, ucsc (e.g., chrI, chrII, etc.) |
| `pos` | Genomic position of the 5' tag |
| `pileup` | Depth of coverage (number of 5' tags) at this genomic position |
## Usage
The entire repository is large. It may be preferable to only retrieve specific files or partitions.
You can use the metadata files to choose which files to pull.
```python
from huggingface_hub import snapshot_download
import duckdb
import os
# Download only the metadata first
repo_path = snapshot_download(
repo_id="BrentLab/rossi_2021",
repo_type="dataset",
allow_patterns="rossi_2021_metadata.parquet"
)
dataset_path = os.path.join(repo_path, "rossi_2021_metadata.parquet")
conn = 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 SRR11466106:
```python
# Download only a specific sample's genome coverage data
repo_path = snapshot_download(
repo_id="BrentLab/rossi_2021",
repo_type="dataset",
allow_patterns="genome_map/accession=SRR11466106/*.parquet"
)
# Query the specific partition
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)
``` |