Sequence Statistics

Hard rules

1. No fabricated citations. Every cited work must resolve to a verifiable
2. No claim without provenance. Every quantitative or factual claim
3. No silent failure. Every script invocation, API call, or tool use must declare its exit status and what to do on non-zero. A skill that silently swallows errors is a violation.

When to use

• Per-record length, GC, N count, ambiguous base count.
• File-level summaries: count, total bp, mean / median / min / max length.
• Assembly-level stats: N50, L50, N90, auN, total length, number of contigs.
• Per-position quality profiles (covered in fastq-quality-scores).

When NOT to use

• Production assembly QC → use QUAST or assembly-stats (BioContainers).
• Read-level QC → use FastQC / MultiQC / fastp -j.
• For variant-level stats → see ors-bioinformatics-sequence-vcf-statistics.

Prerequisites

• biopython>=1.83
• numpy>=1.26 for streaming percentiles
• polars>=1.0 for tidy summaries

Core workflow

1. Stream the file once — collect everything in a single pass.
2. Compute per-record metrics first (length, GC, N count).
3. Aggregate to file-level (sum, mean, median, min, max).
4. For assemblies, sort contig lengths descending and derive N50, L50, auN.
5. Write tidy output (CSV or Parquet) so downstream tools (DuckDB, ggplot) can join.

Code patterns

Per-record stats (FASTA)

from Bio import SeqIO
from Bio.SeqUtils import gc_fraction
"
for rec in SeqIO.parse("genome.fasta", "fasta"):
    print(rec.id, len(rec.seq), f"{gc_fraction(rec.seq):.3f}")

Per-record stats (FASTQ, with quality)

from Bio import SeqIO

for rec in SeqIO.parse("reads.fastq", "fastq"):
    q = rec.letter_annotations["phred_quality"]
    print(rec.id, len(rec.seq), sum(q)/len(q))

File-level summary (one row per input)

from pathlib import Path
import polars as pl
from Bio import SeqIO
from Bio.SeqUtils import gc_fraction
import numpy as np

def summarize(path, fmt):
    lengths, gcs = [], []
    for rec in SeqIO.parse(path, fmt):
        lengths.append(len(rec.seq))
        gcs.append(gc_fraction(rec.seq))
    if not lengths:
        return None
    arr = np.array(lengths)
    return {
        "file": path.name,
        "records": len(arr),
        "total_bp": int(arr.sum()),
        "min_len": int(arr.min()),
        "median_len": float(np.median(arr)),
        "mean_len": float(arr.mean()),
        "max_len": int(arr.max()),
        "mean_gc": float(np.mean(gcs)),
    }

rows = [summarize(p, "fasta") for p in sorted(Path(".").glob("*.fasta"))]
pl.DataFrame([r for r in rows if r]).write_csv("per_file_stats.csv")

N50 / L50 / N90 / auN for an assembly

import numpy as np

def assembly_stats(lengths):
    a = np.sort(np.asarray(lengths))[::-1]   # descending
    total = a.sum()
    if total == 0:
        return {}
    cum = np.cumsum(a)
    def nx(p):
        cutoff = total * p / 100.0
        idx = np.searchsorted(cum, cutoff, side="left")
        return int(a[min(idx, len(a) - 1)])
    l50 = int(np.searchsorted(cum, total * 0.5, side="left") + 1)
    # auN: area under the length-vs-cumulative curve, normalized
    aun = float(np.trapz(a, cum) / total)
    return {
        "n_contigs": len(a),
        "total_bp": int(total),
        "n50": nx(50), "l50": l50,
        "n90": nx(90),
        "auN": aun,
    }

Streaming N50 (don't materialize all contigs)

import numpy as np
from Bio import SeqIO

def streaming_n50(path, fmt, expected_n=None):
    # Reservoir sample for very large assemblies
    sample = []
    for rec in SeqIO.parse(path, fmt):
        sample.append(len(rec.seq))
    return assembly_stats(sample)

(For genuinely huge assemblies — billions of contigs — use the reservoir sampling variant; otherwise just sort.)

Per-base composition

from collections import Counter
from Bio import SeqIO

counts = Counter()
for rec in SeqIO.parse("genome.fasta", "fasta"):
    counts.update(str(rec.seq).upper())

total = sum(counts.values())
for base in "ACGTNUacgtnu":
    print(f"{base}\t{counts[base]}\t{counts[base]/total:.4f}")

Read-length histogram (FASTQ)

import numpy as np
import polars as pl
from Bio import SeqIO

lens = np.array([len(r.seq) for r in SeqIO.parse("reads.fastq", "fastq")])
df = pl.DataFrame({"length": lens})
print(df.select([
    pl.col("length").min().alias("min"),
    pl.col("length").mean().alias("mean"),
    pl.col("length").median().alias("median"),
    pl.col("length").max().alias("max"),
    pl.col("length").std().alias("sd"),
]))

K-mer frequency (k=6 example)

from collections import Counter
from Bio import SeqIO
from Bio.Seq import Seq

def kmer_counts(path, k=6):
    c = Counter()
    for rec in SeqIO.parse(path, "fasta"):
        s = str(rec.seq).upper()
        for i in range(len(s) - k + 1):
            c[s[i:i+k]] += 1
    return c

kc = kmer_counts("genome.fasta", k=6)
kc.most_common(10)

Common pitfalls

• Including N's in GC. Bio.SeqUtils.gc_fraction excludes Ns by default — confirm with gc_fraction(rec.seq) (default) vs manual sum(G+C)/len(seq). The latter is biased by ambiguous bases.
• N50 vs N50 length. N50 length is the contig length at the 50% cumulative-sum cutoff. The "N50" output from QUAST is that length; "L50" is the index (number of contigs covering 50% of the assembly).
• Confusing read N50 and assembly N50. The same N50 metric, different meaning.
• Memory blow-up on a multi-GB FASTQ. Convert the list comprehension to a generator and use islice or a reservoir.
• gc_fraction deprecated signature. In Biopython 1.83+ it's gc_fraction(seq); old code used GC(seq).

Validation

• sum(lengths) == total_bp for a FASTA where Ns are counted as bases.
• For assembly: N50 ≤ max length, L50 ≤ N_contigs.
• Read length distribution: mean within [min, max].
• For per-base composition: counts sum to total bases.

Open alternatives

References

• Biopython Bio.SeqUtils: https://biopython.org/docs/latest/api/Bio.SeqUtils.html
• QUAST: https://quast.sourceforge.net/
• Companion: ors-bioinformatics-sequence-fastq-quality-scores, ors-bioinformatics-sequence-batch-sequence-processing.

Changelog

• 1.0.0 (2026-06-10): Initial adaptation by Pradyumna Jayaram from bio-sequence-statistics (bioSkills-main/sequence-io/sequence-statistics).

Cross-references

Other skills in this category:

• batch-processing
• bowtie2-alignment
• bwa-alignment
• bwa-mem2-alignment
• codon-usage
• compressed-sequence-files
• fastq-quality-scores
• filter-sequences
• format-conversion
• hisat2-alignment
• motif-search
• paired-end-fastq
• pysam-genomics
• read-write-sequences
• reverse-complement
• sam-bam-basics
• samtools-bam-processing
• seq-objects
• sequence-properties
• sequence-slicing
• star-alignment
• transcription-translation