bio-sequence-statistics

name	bio-sequence-statistics
description	Calculate sequence statistics (N50, length distribution, GC content, summary reports) using Biopython. Use when analyzing sequence datasets, generating QC reports, or comparing assemblies.
tool_type	python
primary_tool	Bio.SeqIO

Version Compatibility

Reference examples tested with: BioPython 1.83+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Sequence Statistics

"Calculate N50 and other assembly statistics" → Compute sequence count, length distribution, N50/L50, GC content, and nucleotide composition for FASTA datasets.

• Python: SeqIO.parse(), gc_fraction() (BioPython)

Calculate comprehensive statistics for sequence datasets using Biopython.

Required Imports

from Bio import SeqIO
from Bio.SeqUtils import gc_fraction
import statistics

Basic Statistics

Sequence Count and Total Length

records = list(SeqIO.parse('sequences.fasta', 'fasta'))
total_seqs = len(records)
total_bp = sum(len(r.seq) for r in records)
print(f'Sequences: {total_seqs}')
print(f'Total bp: {total_bp:,}')

Length Statistics

lengths = [len(r.seq) for r in SeqIO.parse('sequences.fasta', 'fasta')]

print(f'Count: {len(lengths)}')
print(f'Total: {sum(lengths):,} bp')
print(f'Min: {min(lengths):,} bp')
print(f'Max: {max(lengths):,} bp')
print(f'Mean: {statistics.mean(lengths):,.1f} bp')
print(f'Median: {statistics.median(lengths):,.1f} bp')
print(f'Std Dev: {statistics.stdev(lengths):,.1f} bp')

N50 and Nx Statistics

Calculate N50

def calculate_n50(lengths):
    sorted_lengths = sorted(lengths, reverse=True)
    total = sum(sorted_lengths)
    cumsum = 0
    for length in sorted_lengths:
        cumsum += length
        if cumsum >= total * 0.5:
            return length
    return 0

lengths = [len(r.seq) for r in SeqIO.parse('assembly.fasta', 'fasta')]
n50 = calculate_n50(lengths)
print(f'N50: {n50:,} bp')

Calculate Any Nx Value

def calculate_nx(lengths, x):
    '''Calculate Nx where x is percentage (e.g., 50 for N50, 90 for N90)'''
    sorted_lengths = sorted(lengths, reverse=True)
    total = sum(sorted_lengths)
    threshold = total * (x / 100)
    cumsum = 0
    for length in sorted_lengths:
        cumsum += length
        if cumsum >= threshold:
            return length
    return 0

lengths = [len(r.seq) for r in SeqIO.parse('assembly.fasta', 'fasta')]
print(f'N50: {calculate_nx(lengths, 50):,} bp')
print(f'N75: {calculate_nx(lengths, 75):,} bp')
print(f'N90: {calculate_nx(lengths, 90):,} bp')

L50 (Number of Sequences in N50)

def calculate_l50(lengths):
    sorted_lengths = sorted(lengths, reverse=True)
    total = sum(sorted_lengths)
    cumsum = 0
    for i, length in enumerate(sorted_lengths, 1):
        cumsum += length
        if cumsum >= total * 0.5:
            return i
    return len(sorted_lengths)

lengths = [len(r.seq) for r in SeqIO.parse('assembly.fasta', 'fasta')]
print(f'L50: {calculate_l50(lengths)} sequences')

GC Content Statistics

Overall GC Content

from Bio.SeqUtils import gc_fraction

total_gc = 0
total_len = 0
for record in SeqIO.parse('sequences.fasta', 'fasta'):
    total_gc += gc_fraction(record.seq) * len(record.seq)
    total_len += len(record.seq)

overall_gc = total_gc / total_len
print(f'Overall GC: {overall_gc:.1%}')

Per-Sequence GC Distribution

gc_values = [gc_fraction(r.seq) for r in SeqIO.parse('sequences.fasta', 'fasta')]

print(f'Mean GC: {statistics.mean(gc_values):.1%}')
print(f'Median GC: {statistics.median(gc_values):.1%}')
print(f'Min GC: {min(gc_values):.1%}')
print(f'Max GC: {max(gc_values):.1%}')
print(f'Std Dev: {statistics.stdev(gc_values):.2%}')

GC Content Histogram Data

from collections import Counter

gc_values = [gc_fraction(r.seq) for r in SeqIO.parse('sequences.fasta', 'fasta')]
bins = [int(gc * 100 // 5) * 5 for gc in gc_values]  # 5% bins
histogram = Counter(bins)

for gc_bin in sorted(histogram.keys()):
    count = histogram[gc_bin]
    print(f'{gc_bin}-{gc_bin+5}%: {count} sequences')

Length Distribution

Length Histogram Data

from collections import Counter

lengths = [len(r.seq) for r in SeqIO.parse('sequences.fasta', 'fasta')]

# Define bins (e.g., 0-100, 100-200, etc.)
bin_size = 100
bins = [(l // bin_size) * bin_size for l in lengths]
histogram = Counter(bins)

for length_bin in sorted(histogram.keys()):
    count = histogram[length_bin]
    print(f'{length_bin}-{length_bin + bin_size}: {count}')

Length Percentiles

import statistics

lengths = sorted(len(r.seq) for r in SeqIO.parse('sequences.fasta', 'fasta'))

percentiles = [10, 25, 50, 75, 90, 95, 99]
for p in percentiles:
    idx = int(len(lengths) * p / 100)
    print(f'P{p}: {lengths[idx]:,} bp')

Comprehensive Summary Report

Goal: Generate a complete QC summary (counts, lengths, N50, GC) for any FASTA file.

Approach: Load all records, compute length and GC arrays, derive N50/L50 from cumulative sorted lengths, and package into a dictionary.

Reference (BioPython 1.83+):

from Bio import SeqIO
from Bio.SeqUtils import gc_fraction
import statistics

def sequence_summary(fasta_file):
    records = list(SeqIO.parse(fasta_file, 'fasta'))
    lengths = [len(r.seq) for r in records]
    gc_values = [gc_fraction(r.seq) for r in records]

    sorted_lengths = sorted(lengths, reverse=True)
    total_bp = sum(lengths)

    cumsum = 0
    n50 = 0
    l50 = 0
    for i, length in enumerate(sorted_lengths, 1):
        cumsum += length
        if cumsum >= total_bp * 0.5 and n50 == 0:
            n50 = length
            l50 = i

    return {
        'file': fasta_file,
        'sequences': len(records),
        'total_bp': total_bp,
        'min_length': min(lengths),
        'max_length': max(lengths),
        'mean_length': statistics.mean(lengths),
        'median_length': statistics.median(lengths),
        'n50': n50,
        'l50': l50,
        'gc_mean': statistics.mean(gc_values),
        'gc_std': statistics.stdev(gc_values) if len(gc_values) >   
    }

stats = sequence_summary()
()
()
()
()
()
()
()
()

Compare Multiple Assemblies

Goal: Generate a side-by-side comparison table of key metrics across multiple assembly files.

Approach: Run sequence_summary on each file and format results into an aligned table.

Reference (BioPython 1.83+):

from pathlib import Path

def compare_assemblies(fasta_files):
    results = []
    for fasta_file in fasta_files:
        stats = sequence_summary(str(fasta_file))
        results.append(stats)
    return results

files = list(Path('assemblies/').glob('*.fasta'))
comparisons = compare_assemblies(files)

print(f'{"File":<30} {"Seqs":>10} {"Total bp":>15} {"N50":>12}')
print('-' * 70)
for stats in comparisons:
    print(f'{Path(stats["file"]).name:<30} {stats["sequences"]:>10,} {stats["total_bp"]:>15,} {stats["n50"]:>12,}')

Export to CSV

import csv
from pathlib import Path

def export_stats_csv(fasta_files, output_csv):
    fieldnames = ['file', 'sequences', 'total_bp', 'min_length', 'max_length',
                  'mean_length', 'median_length', 'n50', 'l50', 'gc_mean', 'gc_std']

    with open(output_csv, 'w', newline='') as f:
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        writer.writeheader()
        for fasta_file in fasta_files:
            stats = sequence_summary(str(fasta_file))
            writer.writerow(stats)

files = list(Path('assemblies/').glob('*.fasta'))
export_stats_csv(files, 'assembly_stats.csv')

Nucleotide Composition

from collections import Counter

def nucleotide_composition(fasta_file):
    total_counts = Counter()
    for record in SeqIO.parse(fasta_file, 'fasta'):
        total_counts.update(str(record.seq).upper())

    total = sum(total_counts.values())
    return {base: count / total for base, count in total_counts.items()}

comp = nucleotide_composition('sequences.fasta')
for base in ['A', 'T', 'G', 'C', 'N']:
    if base in comp:
        print(f'{base}: {comp[base]:.2%}')

Common Metrics Reference

Metric	Description
N50	Length where 50% of total bases are in sequences >= this length
L50	Number of sequences needed to reach N50
N90	Length where 90% of total bases are in sequences >= this length
GC%	Proportion of G+C bases
Contiguity	Higher N50 = more contiguous assembly

Related Skills

• read-sequences - Parse sequences for statistics calculation
• batch-processing - Calculate stats across multiple files
• fastq-quality - Quality score statistics for FASTQ files
• sequence-manipulation/sequence-properties - Per-sequence GC content and properties
• alignment-files - samtools stats/flagstat for alignment statistics