Biopython

# Advanced Biopython Features

## Sequence Motifs with Bio.motifs

### Creating Motifs

```python
from Bio import motifs
from Bio.Seq import Seq

# Create motif from instances
instances = [
    Seq("TACAA"),
    Seq("TACGC"),
    Seq("TACAC"),
    Seq("TACCC"),
    Seq("AACCC"),
    Seq("AATGC"),
    Seq("AATGC"),
]

motif = motifs.create(instances)
```

### Motif Consensus and Degenerate Sequences

```python
# Get consensus sequence
print(motif.counts.consensus)

# Get degenerate consensus (IUPAC ambiguity codes)
print(motif.counts.degenerate_consensus)

# Access counts matrix
print(motif.counts)
```

### Position Weight Matrix (PWM)

```python
# Create position weight matrix
pwm = motif.counts.normalize(pseudocounts=0.5)
print(pwm)

# Calculate information content
ic = motif.counts.information_content()
print(f"Information content: {ic:.2f} bits")
```

### Searching for Motifs

```python
from Bio.Seq import Seq

# Search sequence for motif
test_seq = Seq("ATACAGGACAGACATACGCATACAACATTACAC")

# Get Position Specific Scoring Matrix (PSSM)
pssm = pwm.log_odds()

# Search sequence
for position, score in pssm.search(test_seq, threshold=5.0):
    print(f"Position {position}: score = {score:.2f}")
```

### Reading Motifs from Files

```python
# Read motif from JASPAR format
with open("motif.jaspar") as handle:
    motif = motifs.read(handle, "jaspar")

# Read multiple motifs
with open("motifs.jaspar") as handle:
    for m in motifs.parse(handle, "jaspar"):
        print(m.name)

# Supported formats: jaspar, meme, transfac, pfm
```

### Writing Motifs

```python
# Write motif in JASPAR format
with open("output.jaspar", "w") as handle:
    handle.write(motif.format("jaspar"))
```

## Population Genetics with Bio.PopGen

### Working with GenePop Files

```python
from Bio.PopGen import GenePop

# Read GenePop file
with open("data.gen") as handle:
    record = GenePop.read(handle)

# Access populations
print(f"Number of populations: {len(record.populations)}")
print(f"Loci: {record.loci_list}")

# Iterate through populations
for pop_idx, pop in enumerate(record.populations):
    print(f"\nPopulation {pop_idx + 1}:")
    for individual in pop:
        print(f"  {individual[0]}: {individual[1]}")
```

### Calculating Population Statistics

```python
from Bio.PopGen.GenePop.Controller import GenePopController

# Create controller
ctrl = GenePopController()

# Calculate basic statistics
result = ctrl.calc_allele_genotype_freqs("data.gen")

# Calculate Fst
fst_result = ctrl.calc_fst_all("data.gen")
print(f"Fst: {fst_result}")

# Test Hardy-Weinberg equilibrium
hw_result = ctrl.test_hw_pop("data.gen", "probability")
```

## Sequence Utilities with Bio.SeqUtils

### GC Content

```python
from Bio.SeqUtils import gc_fraction
from Bio.Seq import Seq

seq = Seq("ATCGATCGATCG")
gc = gc_fraction(seq)
print(f"GC content: {gc:.2%}")
```

### Molecular Weight

```python
from Bio.SeqUtils import molecular_weight

# DNA molecular weight
dna_seq = Seq("ATCG")
mw = molecular_weight(dna_seq, seq_type="DNA")
print(f"DNA MW: {mw:.2f} g/mol")

# Protein molecular weight
protein_seq = Seq("ACDEFGHIKLMNPQRSTVWY")
mw = molecular_weight(protein_seq, seq_type="protein")
print(f"Protein MW: {mw:.2f} Da")
```

### Melting Temperature

```python
from Bio.SeqUtils import MeltingTemp as mt

# Calculate Tm using nearest-neighbor method
seq = Seq("ATCGATCGATCG")
tm = mt.Tm_NN(seq)
print(f"Tm: {tm:.1f}°C")

# Use different salt concentration
tm = mt.Tm_NN(seq, Na=50, Mg=1.5)  # 50 mM Na+, 1.5 mM Mg2+

# Wallace rule (for primers)
tm_wallace = mt.Tm_Wallace(seq)
```

### GC Skew

```python
from Bio.SeqUtils import gc_skew

# Calculate GC skew
seq = Seq("ATCGATCGGGCCCAAATTT")
skew = gc_skew(seq, window=100)
print(f"GC skew: {skew}")
```

### ProtParam - Protein Analysis

```python
from Bio.SeqUtils.ProtParam import ProteinAnalysis

protein_seq = "ACDEFGHIKLMNPQRSTVWY"
analyzed_seq = ProteinAnalysis(protein_seq)

# Molecular weight
print(f"MW: {analyzed_seq.molecular_weight(