tooluniverse-data-wrangling

# Data Wrangling: Universal Access Patterns

Reference for downloading and parsing scientific data from any source. Write and run Python code via Bash for every step.

## When to Use

- ToolUniverse tool returned metadata/search results but you need **raw or bulk data**
- Data is in a format tools don't parse (VCF, h5ad, BAM, SDF, GCT)
- You need a **multi-step API workflow** (search -> filter -> download -> parse)
- The data source has **no ToolUniverse tool** at all
- You need **thousands of records**, not the 10-100 a tool returns

## Decision: Tool vs Code

| Situation | Use |
|-----------|-----|
| Single record lookup, simple search, <100 results | ToolUniverse tool (`execute_tool`) |
| Bulk download, custom filtering, format conversion | Write Python code |
| Tool exists but returns truncated results | Write code using the same API the tool wraps |
| No tool exists for this source | Write code directly |

---

## Section A: Format Cookbook

### Tabular
```python
import pandas as pd, io

df = pd.read_csv("data.csv")                                # CSV
df = pd.read_csv("data.tsv", sep="\t")                      # TSV
df = pd.read_sas(io.BytesIO(content), format="xport")       # SAS Transport (XPT) — NHANES, CDC
df = pd.read_sas("data.sas7bdat", format="sas7bdat")        # SAS native
df = pd.read_stata("data.dta")                               # Stata — ICPSR, HRS
df = pd.read_parquet("data.parquet")                         # Parquet — MIMIC-IV
df = pd.read_excel("data.xlsx")                              # Excel
df = pd.read_spss("data.sav")                                # SPSS
df = pd.read_fwf("data.dat")                                 # Fixed-width — legacy surveys
```

### Genomics
```python
from Bio import SeqIO
records = list(SeqIO.parse("seqs.fasta", "fasta"))           # FASTA
records = list(SeqIO.parse("reads.fastq", "fastq"))          # FASTQ

# VCF (no cyvcf2 needed)
vcf_lines = [l for l in open("vars.vcf") if not l.startswith("##")]
df = pd.read_csv(io.StringIO("".join(vcf_lines)), sep="\t")

df = pd.read_csv("genes.gff3", sep="\t", comment="#",        # GFF/GTF
     names=["seqid","source","type","start","end","score","strand","phase","attrs"])
df = pd.read_csv("regions.bed", sep="\t", header=None,       # BED
     names=["chrom","start","end","name","score","strand"])

import pysam                                                  # BAM (requires pysam)
bam = pysam.AlignmentFile("aligned.bam", "rb")
for read in bam.fetch("chr1", 1000, 2000): print(read.query_name)
```

### Structural
```python
from Bio.PDB import PDBParser, MMCIFParser
parser = PDBParser(QUIET=True)
structure = parser.get_structure("prot", "structure.pdb")     # PDB

parser = MMCIFParser(QUIET=True)
structure = parser.get_structure("prot", "structure.cif")     # mmCIF

from rdkit import Chem                                        # SDF/MOL (requires rdkit)
supplier = Chem.SDMolSupplier("compounds.sdf")
mols = [m for m in supplier if m is not None]
```

### Omics Matrices
```python
import anndata
adata = anndata.read_h5ad("expression.h5ad")                 # AnnData (scRNA-seq, spatial)

import scipy.io
mat = scipy.io.mmread("matrix.mtx")                          # 10X Genomics MTX
barcodes = pd.read_csv("barcodes.tsv", header=None)[0].tolist()
features = pd.read_csv("features.tsv", sep="\t", header=None)[1].tolist()

df = pd.read_csv("expression.gct", sep="\t", skiprows=2)     # GCT (gene expression)

import loompy                                                 # Loom (legacy single-cell)
ds = loompy.connect("data.loom")
```

### Mass Spectrometry & Flow Cytometry
```python
from pyteomics import mzml                                    # mzML (proteomics, requires pyteomics)
spectra = list(mzml.read("spectra.mzML"))

import fcsparser                                              # FCS (flow cytometry, requires fcsparser)
meta, data = fcsparser.parse("sample.fcs", reformat_meta=True)
```

### Neuroimaging
```python
import nibabel as nib                                         # NIfTI (requires nibabel)
img = nib.load("brain.nii.gz")
data = img.get_fdata()  # 3D/4D numpy array

# DICOM (requires pydicom)
import pydicom
dcm = pydicom.dcmread("scan.dcm")
pixel_data = dcm.pixel_array
```

### Phylogenetics & Systems Biology
```python
from Bio import Phylo                                         # Newick/Nexus (BioPython)
tree = Phylo.read("tree.nwk", "newick")
tree = Phylo.read("tree.nex", "nexus")

import libsbml                                                # SBML (systems biology, requires python-libsbml)
reader = libsbml.SBMLReader()
doc = reader.readSBML("model.xml")
model = doc.getModel()
```

### Serialized
```python
import json, xml.etree.ElementTree as ET, h5py

data = json.load(open("data.json"))                           # JSON
df = pd.read_json("records.json")                             # JSON -> DataFrame
tree = ET.parse("data.xml"); root = tree.getroot()            # XML
f = h5py.File("data.h5", "r"); dataset = f["group/data"][:]   # HDF5
```

### Compressed
```python
df = pd.read_csv("data.csv.gz")                              # gzip (pandas auto-detects)
df = pd.read_csv("data.tsv.gz", sep="\t")                    # gzip TSV

import zipfile
with zipfile.ZipFile(io.BytesIO(content)) as z:               # ZIP
    df = pd.read_csv(z.open(z.namelist()[0]))

import tarfile
with tarfile.open("archive.tar.gz") as t:                     # tar.gz
    f = t.extractfile(t.getnames()[0])
    df = pd.read_csv(f)
```

---

## Section B: API Patterns by Domain

Each category shows: which ToolUniverse tools exist, and how to go beyond them with direct API calls.

### 1. NCBI E-utilities (Gene, Nucleotide, Protein, SRA, GEO)
Tools: `NCBIGene_search`, `NCBI_search_nucleotide`, `SRA_search_experiments`, `geo_search_datasets`
```python
import requests
base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"
# Search -> get IDs -> fetch records in batches
ids = requests.get(f"{base}/esearch.fcgi?db=gene&term=BRCA