Add scVelo RNA velocity analysis workflow and IQ-TREE reference documentation

- Introduced a comprehensive RNA velocity analysis pipeline using scVelo, including data loading, preprocessing, velocity estimation, and visualization.
- Added a script for running RNA velocity analysis with customizable parameters and output options.
- Created detailed documentation for IQ-TREE 2 phylogenetic inference, covering command syntax, model selection, bootstrapping methods, and output interpretation.
- Included references for velocity models and their mathematical framework, along with a comparison of different models.
- Enhanced the scVelo skill documentation with installation instructions, use cases, and best practices for RNA velocity analysis.

scientific-skills/bindingdb-database/references/affinity_queries.md

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+# BindingDB Affinity Query Reference
+
+## Affinity Measurement Types
+
+### Ki (Inhibition Constant)
+- **Definition**: Equilibrium constant for inhibitor-enzyme complex dissociation
+- **Equation**: Ki = [E][I]/[EI]
+- **Usage**: Enzyme inhibition; preferred for mechanistic studies
+- **Note**: Independent of substrate concentration (unlike IC50)
+
+### Kd (Dissociation Constant)
+- **Definition**: Thermodynamic binding equilibrium constant
+- **Equation**: Kd = [A][B]/[AB]
+- **Usage**: Direct binding assays (SPR, ITC, fluorescence anisotropy)
+- **Note**: True measure of binding strength; lower = tighter binding
+
+### IC50 (Half-Maximal Inhibitory Concentration)
+- **Definition**: Concentration of inhibitor that reduces target activity by 50%
+- **Usage**: Most common in drug discovery; assay-dependent
+- **Conversion to Ki**: Cheng-Prusoff equation: Ki = IC50 / (1 + [S]/Km)
+- **Note**: Depends on substrate concentration and assay conditions
+
+### EC50 (Half-Maximal Effective Concentration)
+- **Definition**: Concentration that produces 50% of maximal effect
+- **Usage**: Cell-based assays, agonist studies
+
+### Kinetics Parameters
+- **kon**: Association rate constant (M⁻¹s⁻¹); describes how fast complex forms
+- **koff**: Dissociation rate constant (s⁻¹); describes how fast complex dissociates
+- **Residence time**: τ = 1/koff; longer residence = more sustained effect
+- **Kd from kinetics**: Kd = koff/kon
+
+## Common API Query Patterns
+
+### By UniProt ID (REST API)
+
+```python
+import requests
+
+def query_by_uniprot(uniprot_id, affinity_type="Ki"):
+    """
+    REST API query for BindingDB affinities by UniProt target ID.
+    """
+    url = "https://www.bindingdb.org/axis2/services/BDBService/getLigandsByUniprotID"
+    params = {
+        "uniprot_id": uniprot_id,
+        "cutoff": "10000",  # nM threshold
+        "affinity_type": affinity_type,
+        "response": "json"
+    }
+    response = requests.get(url, params=params)
+    return response.json()
+
+# Important targets
+COMMON_TARGETS = {
+    "ABL1": "P00519",    # Imatinib, dasatinib target
+    "EGFR": "P00533",    # Erlotinib, gefitinib target
+    "BRAF": "P15056",    # Vemurafenib, dabrafenib target
+    "CDK2": "P24941",    # Cell cycle kinase
+    "HDAC1": "Q13547",   # Histone deacetylase
+    "BRD4": "O60885",    # BET bromodomain reader
+    "MDM2": "Q00987",    # p53 negative regulator
+    "BCL2": "P10415",    # Antiapoptotic protein
+    "PCSK9": "Q8NBP7",   # Cholesterol regulator
+    "JAK2": "O60674",    # Cytokine signaling kinase
+}
+```
+
+### By PubChem CID (REST API)
+
+```python
+def query_by_pubchem_cid(pubchem_cid):
+    """Get all binding data for a specific compound by PubChem CID."""
+    url = "https://www.bindingdb.org/axis2/services/BDBService/getAffinitiesByCID"
+    params = {"cid": pubchem_cid, "response": "json"}
+    response = requests.get(url, params=params)
+    return response.json()
+
+# Example: Imatinib PubChem CID = 5291
+imatinib_data = query_by_pubchem_cid(5291)
+```
+
+### By Target Name
+
+```python
+def query_by_target_name(target_name, affinity_cutoff=100):
+    """Query BindingDB by target name."""
+    url = "https://www.bindingdb.org/axis2/services/BDBService/getAffinitiesByTarget"
+    params = {
+        "target_name": target_name,
+        "cutoff": affinity_cutoff,
+        "response": "json"
+    }
+    response = requests.get(url, params=params)
+    return response.json()
+```
+
+## Dataset Download Guide
+
+### Available Files
+
+| File | Size | Contents |
+|------|------|---------|
+| `BindingDB_All.tsv.zip` | ~3.5 GB | All data: ~2.9M records |
+| `BindingDB_All.sdf.zip` | ~7 GB | All data with 3D structures |
+| `BindingDB_IC50.tsv` | ~1.5 GB | IC50 data only |
+| `BindingDB_Ki.tsv` | ~0.8 GB | Ki data only |
+| `BindingDB_Kd.tsv` | ~0.2 GB | Kd data only |
+| `BindingDB_EC50.tsv` | ~0.5 GB | EC50 data only |
+| `tdc_bindingdb_*` | Various | TDC-formatted subsets |
+
+### Efficient Loading
+
+```python
+import pandas as pd
+
+# For large files, use chunking
+def load_bindingdb_chunked(filepath, uniprot_ids, affinity_col="Ki (nM)", chunk_size=100000):
+    """Load BindingDB in chunks to filter for specific targets."""
+    results = []
+    for chunk in pd.read_csv(filepath, sep="\t", chunksize=chunk_size,
+                              low_memory=False, on_bad_lines='skip'):
+        # Filter for target
+        mask = chunk["UniProt (SwissProt) Primary ID of Target Chain"].isin(uniprot_ids)
+        if mask.any():
+            results.append(chunk[mask])
+
+    if results:
+        return pd.concat(results)
+    return pd.DataFrame()
+```
+
+## pKi / pIC50 Conversion
+
+Converting raw affinity to logarithmic scale (common in ML):
+
+```python
+import numpy as np
+
+def to_log_affinity(affinity_nM):
+    """Convert nM affinity to pAffinity (negative log molar)."""
+    affinity_M = affinity_nM * 1e-9  # Convert nM to M
+    return -np.log10(affinity_M)
+
+# Examples:
+# 1 nM   → pAffinity = 9.0
+# 10 nM  → pAffinity = 8.0
+# 100 nM → pAffinity = 7.0
+# 1 μM   → pAffinity = 6.0
+# 10 μM  → pAffinity = 5.0
+```
+
+## Quality Filters
+
+When using BindingDB data for ML or SAR:
+
+```python
+def filter_quality(df):
+    """Apply quality filters to BindingDB data."""
+    # 1. Require valid SMILES
+    df = df[df["Ligand SMILES"].notna() & (df["Ligand SMILES"] != "")]
+
+    # 2. Require valid affinity
+    df = df[df["Ki (nM)"].notna() | df["IC50 (nM)"].notna()]
+
+    # 3. Filter extreme values (artifacts)
+    for col in ["Ki (nM)", "IC50 (nM)", "Kd (nM)"]:
+        if col in df.columns:
+            df = df[~(df[col] > 1e6)]  # Remove > 1 mM (non-specific)
+
+    # 4. Use only human targets
+    if "Target Source Organism According to Curator or DataSource" in df.columns:
+        df = df[df["Target Source Organism According to Curator or DataSource"].str.contains(
+            "Homo sapiens", na=False
+        )]
+
+    return df
+```