bio-clinical-databases-somatic-signatures

name	bio-clinical-databases-somatic-signatures
description	Extracts and assigns COSMIC v3.4 mutational signatures (84 SBS / 11 DBS / 18 ID / 24 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.
tool_type	mixed
primary_tool	SigProfilerAssignment

Version Compatibility

Reference examples tested with: SigProfilerMatrixGenerator 1.2+ (Bergstrom 2019), SigProfilerExtractor 1.1.24+ (Islam 2022), SigProfilerAssignment 0.1+ (Diaz-Gay 2023), MutationalPatterns 3.12+ (Manders 2022), MuSiCal 0.7+ (Liu 2024), SigNet (Serrano 2023, bioRxiv), HRDetect (Davies 2017 / Degasperi 2022 implementations), pandas 2.2+, R 4.3+. COSMIC v3.4 (September 2024) is the current reference catalog: 84 SBS, 11 DBS, 18 ID, 24 CN, 16 SV signatures.

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

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. COSMIC signature naming evolves: SBS40 was split to SBS40a/b/c in v3.4 (Senkin 2024); SBS17 split to SBS17a/b (5-FU); SBS10 split to SBS10a-d (POLE/POLD1).

Somatic Mutational Signatures; Etiology, Extraction, Clinical Use

'Extract mutational signatures from this tumor cohort and identify HRD/MMR/APOBEC processes' -> Generate 96-context (or DBS/ID/CN/SV) matrix from VCF; choose de novo extraction (NMF) vs refit-to-COSMIC by cohort size; map dominant signatures to etiology; flag clinical actionability.

• Python (recommended): SigProfilerMatrixGenerator -> SigProfilerExtractor (de novo) or SigProfilerAssignment (refit)
• R alternative: MutationalPatterns::fit_to_signatures() (strict refit) or extract_signatures() (NMF de novo)
• Python (mvNMF for non-uniqueness): MuSiCal (Liu 2024 Nat Genet)
• Python (deep learning low-mutation count): SigNet (Serrano 2023)
• R (HRD-specific): HRDetect (Davies 2017 Nat Med); the 5-feature BRCA-deficiency classifier

COSMIC v3.4 Catalog: Evolution and Composition

Class	Count	Encoding
SBS (Single Base Substitutions)	84	96 trinucleotide contexts (6 substitution types x 16 trinucleotides)
DBS (Doublet Base Substitutions)	11	78 strand-agnostic doublet classes (Bergstrom 2019)
ID (Insertion/Deletion)	18	83 categories (indel length x repeat context x microhomology)
CN (Copy Number)	24	48 channels (total CN x heterozygosity x segment length; Drews 2022 Nature)
SV (Structural Variants)	16	32 channels (cluster x length x type)

Recent splits to know:

• SBS40 -> SBS40a / SBS40b / SBS40c (Senkin 2024 Nature): pan-cancer active (40a); RCC-specific (40b/c).
• SBS17 -> SBS17a (T>C uncertain) / SBS17b (T>G in CTT, 5-FU) (Christensen 2019 Nat Commun; Pich 2019 Nat Genet).
• SBS7 -> SBS7a / 7b / 7c / 7d (UV photoproduct chemistry; Alexandrov 2020).
• SBS10 -> SBS10a (POLE P286R) / 10b (POLE V411L) / 10c / 10d (POLD1) (Mertz 2020 Mol Cell).

Etiology Table (Postdoc-grade)

Signature	Etiology	Clinical implication	Notes
SBS1	Spontaneous 5mC deamination at CpG	Age-correlated; mitotic-rate biomarker	Clock-like
SBS5	UNKNOWN, clock-like; age-correlated	--	Reviewer-accepted: "unknown, clock-like"; NOT polymerase fidelity errors
SBS2 / SBS13	APOBEC (APOBEC3A dominant per Petljak 2022)	Often co-occur; kataegis; ICI response signal	A3A vs A3B via YTCA vs RTCA tetranucleotide ratio
SBS3	HRD (BRCA1/2 deficient flat profile)	PARP inhibitor eligibility	HRDetect 98.7% sensitivity (Davies 2017)
ID6	HRD microhomology-mediated deletions	PARP eligibility	Pairs with SBS3
CN17 (HRD-CN1)	HRD chromosomal instability	PARP eligibility; also BRCA1 promoter hypermethylation	Drews 2022
SBS6 / 14 / 15 / 20 / 21 / 26 / 44 + ID1 / 2	MMR-D	ICI eligibility	Lynch typically 6/15/26/44; sporadic MLH1-hyperMet typically 21/26
SBS14 + SBS20	POLE+MMR or POLD1+MMR double defect	Ultra-hypermutator; ICI excellent response	>500 mut/Mb
SBS10a / 10b	POLE-exo P286R / V411L	Hypermutator; ICI excellent response	100-300 mut/Mb pure POLE
SBS10c / 10d	POLD1	--	Less common
SBS28	POLE indirect	Often co-extracted with SBS10	--
SBS4 + DBS2	Tobacco smoking; benzo[a]pyrene-G adducts	Lung cancer	C>A bias
SBS7a/b/c/d + DBS1	UV (CPD vs 6-4 photoproduct chemistry)	Melanoma	CC>TT dipyrimidine, CC>AA
SBS24	Aflatoxin	HCC (geographic)	C>A at CpC; Schulze 2015 Nat Genet

CRITICAL CORRECTION: The widely-cited "SBS33 = FFPE artifact" is wrong. Modern literature attributes FFPE artifact to a signature resembling SBS30 (NTHL1-BER-deficiency profile); after enzymatic uracil repair the artifact instead resembles SBS1.

Tool Taxonomy

Tool	Approach	Class coverage	When to use	Fails when
SigProfilerSuite (Alexandrov lab; Bergstrom 2019 BMC Genomics; Islam 2022 Cell Genomics; Diaz-Gay 2023 Bioinformatics)	Matrix gen -> NMF de novo / forward-backward refit	SBS / DBS / ID / CN / SV	Field standard; CPIC-equivalent for signatures	Heavy compute for de novo (100 NMF replicates)
MutationalPatterns (Manders 2022 BMC Genomics)	R-based; strict refit + NMF de novo	SBS / DBS / ID; lesion segregation	R workflows; reproducible refit	Lacks SV signatures
MuSiCal (Liu 2024 Nat Genet)	mvNMF (minimum-volume NMF) addressing NMF non-uniqueness	SBS / DBS / ID	Mid-size cohorts; novel signatures suspected	Less benchmarking at very large scale
SigNet (Serrano 2023, bioRxiv)	ANN-based signature attribution	SBS	Low mutation counts (best in 13-tool benchmark)	New tool; reproducibility data still maturing
YAPSA (Hubschmann 2020)	Linear combination decomposition	SBS	Comparison runs	Less widely used
MutSignatures (Fantini 2020)	Probabilistic refits	SBS	--	--
deconstructSigs (Rosenthal 2016)	NNLS (unregularized)	SBS	DEPRECATED; never use	NNLS overfits onto reference set; superseded by SigProfilerAssignment
mSigAct	Signatures from RNA-seq	SBS	RNA-seq only contexts	Limited resolution
Helmsman (Carlson 2018)	Fast matrix construction	SBS / DBS / ID	Preprocessing step only	Not for extraction/refit
HRDetect (Davies 2017 Nat Med)	Lasso logistic on 6 features (SBS3 / ID6 / RS3 / RS5 / HRD-LOH)	HRD-specific	BRCA1/2 deficiency classifier	Breast/ovarian-trained; cross-cancer needs revalidation

The deprecation: deconstructSigs is the most-cited signature tool in publications but is operationally deprecated. NNLS without regularization overfits onto the ~70-signature reference; reviewers flag manuscripts using it without SigProfilerAssignment sensitivity. Replace with SigProfilerAssignment or MutationalPatterns strict refit.

De Novo vs Refit: The Field's Most-Contested Choice

Degasperi 2022 Science (12,222 WGS, UK 100k Genomes) argued refitting underestimates novel signatures because variance is forced onto existing references. They identified 40 additional SBS and 18 DBS signatures by full de novo extraction.

Operational rule:

Mutation count per sample	Cohort size	Approach
> 200 (SBS96)	N >= 50 (or 100 for DBS/ID/CN)	De novo extraction (SigProfilerExtractor, MuSiCal); validate via split-sample CV + bootstrap stability
> 200	N < 50	Refit (SigProfilerAssignment)
50-200	Any	Refit only; flag low confidence
< 50	Any	Do not attempt single-sample signature analysis

SigProfilerExtractor stability gates:

• nmf_replicates = 100 (default 100, do not reduce)
• minimum stability >= 0.2 per signature
• minimum average stability >= 0.8 across signatures
• combined stability == 1.0 for selected rank

Manuscripts reporting extraction without these stability values are unreviewable.

Decision Tree by Scenario

Scenario	Recommended path	Why
Single tumor WGS, > 200 mutations	SigProfilerAssignment refit	Single-sample de novo is unstable
Cohort >= 50 WGS, novel etiology suspected	SigProfilerExtractor de novo + cross-validate	Capture potentially novel signatures
Cohort >= 50 WGS, established cancer type	SigProfilerAssignment refit	Field consensus; fast
Mid-size cohort with novel signatures	MuSiCal mvNMF	Handles NMF non-uniqueness
Low mutation count (<100/sample)	SigNet (Serrano 2023)	Best low-count performance per Pancotti et al 2025 Brief Bioinform benchmark
BRCA1/2 deficiency screen	HRDetect (Davies 2017)	6-feature lasso classifier; 98.7% sensitivity
Tumor evolution / mutation timing	MutationTimer (Gerstung 2020)	Requires Battenberg CN; PCAWG-validated
FFPE samples	SigProfilerAssignment with explicit FFPE-artifact handling	SBS30-like artifact; matched fresh-frozen controls ideal
WES (not WGS)	SigProfilerMatrixGenerator with exome=True	Trinucleotide-context correction for capture bias
RNA-seq only	mSigAct	Limited resolution; supplement with DNA-seq if available
HRD CN signatures	SigProfilerExtractor CN mode + CN17 (HRD-CN1)	Drews 2022 framework
Cross-cancer signature comparison	SigProfilerSuite with strand bias on	Aristolochic-acid SBS22 shows strong transcribed-strand bias

Standard Workflow: SigProfilerSuite

Goal: Generate 96-context mutation matrix, extract de novo signatures with stability validation, and decompose to COSMIC v3.4 reference.

Approach: Three-step pipeline with explicit version pinning and stability gates.

# Step 1: Install reference genome (one-time)
from SigProfilerMatrixGenerator import install as genInstall
genInstall.install('GRCh38')

# Step 2: Generate matrix
from SigProfilerMatrixGenerator.scripts import SigProfilerMatrixGeneratorFunc as matGen
matrices = matGen.SigProfilerMatrixGeneratorFunc(
    project='cohort_2026',
    genome='GRCh38',
    vcfFiles='/path/to/vcf_directory',
    plot=True,
    exome=False,  # True if WES; corrects trinucleotide capture bias
    bed_file=None,  # Restrict to BED region if panel
    chrom_based=False,
    tsb_stat=True  # Transcribed-strand statistics
)

# Step 3a (cohort >= 50): de novo extraction with stability gates
from SigProfilerExtractor import sigpro as sig
sig.sigProfilerExtractor(
    input_type='matrix',
    input_data='cohort_2026/output/SBS/cohort_2026.SBS96.all',
    output='extraction_output',
    reference_genome='GRCh38',
    opportunity_genome='GRCh38',
    minimum_signatures=1,
    maximum_signatures=12,
    nmf_replicates=100,            # Required for stability
    cpu=-1,
    seeds='random',
    matrix_normalization='gmm',
    resample=True,
    batch_size=1,
    refit_denovo_signatures=True,
    cosmic_version=3.4              # Match to current COSMIC release
)

# Step 3b (single sample or cohort < 50): refit to COSMIC
from SigProfilerAssignment import Analyzer as Analyze
Analyze.cosmic_fit(
    samples='cohort_2026/output/SBS/cohort_2026.SBS96.all',
    output='assignment_output',
    input_type='matrix',
    genome_build='GRCh38',
    cosmic_version=3.4,
    signature_database='SBS_GRCh38_GRCh38',  # Verify against the SigProfilerAssignment release; the bundled
                                              # COSMIC signature-database identifiers change between versions.
    nnls_add_penalty=0.05,         # Forward-add gate
    nnls_remove_penalty=0.01,      # Backward-remove gate
    initial_remove_penalty=0.05,
    refit_denovo_signatures=False,
    make_plots=True,
    sample_reconstruction_plots=True
)

MutationalPatterns Strict Refit (R Alternative)

Goal: Same as SigProfilerAssignment but in R; suited for Bioconductor pipelines.

Approach: Cosine-based stopping reduces overfitting vs deconstructSigs.

library(MutationalPatterns)
library(BSgenome.Hsapiens.UCSC.hg38)

# Load VCFs as GRanges
vcf_files <- list.files('vcf_dir', pattern = '\\.vcf$', full.names = TRUE)
sample_names <- gsub('\\.vcf$', '', basename(vcf_files))
vcfs <- read_vcfs_as_granges(vcf_files, sample_names,
                              ref_genome = 'BSgenome.Hsapiens.UCSC.hg38')

# Generate 96-context matrix
mut_mat <- mut_matrix(vcf_list = vcfs, ref_genome = 'BSgenome.Hsapiens.UCSC.hg38')

# Fit to COSMIC v3.4 with strict refit (cosine-stopping; avoids deconstructSigs overfit)
signatures <- get_known_signatures(muttype = 'snv', source = 'COSMIC_v3.4',
                                    sig_type = 'reference' genome  
strict_refit  fit_to_signatures_strictmut_mat signatures max_delta  


plot_contributionstrict_refitfit_rescontribution signatures mode  

HRDetect for BRCA1/2 Deficiency

Goal: Classify tumors as HRD vs HR-proficient using the 6-feature Davies 2017 lasso.

Approach: Compute SBS3, ID6 (microhomology-mediated deletions), RS3 (rearrangement signature 3), RS5, HRD-LOH score; apply lasso classifier.

# Davies 2017 HRDetect framework
# Features: SBS3, ID6/ID8, RS3, RS5, HRD-LOH
# Output: probability of HRD; threshold 0.7 = HRD-positive
library(signature.tools.lib)

hrdetect <- HRDetect_pipeline(
    SNV_vcf_files = snv_vcfs,
    Indels_vcf_files = indel_vcfs,
    SV_bedpe_files = sv_bedpes,
    CNV_tab_files = cnv_tables,
    genome.v = 'hg38',
    nparallel = 8
)

# hrdetect$hrdetect_output has BRCA_prob per sample
# >= 0.7 = HRD-positive; consider PARP inhibitor

Per-Operation Failure Modes

1. Single-sample de novo extraction

• Trigger: Run SigProfilerExtractor on a cohort of 1.
• Mechanism: NMF requires multiple samples to find stable rank; single-sample 96-context spectrum has unstable signature decomposition.
• Symptom: Tool runs but signatures are noisy and inconsistent across replicates.
• Fix: Use refit (SigProfilerAssignment) for cohorts < 50; never de novo on single samples.

2. Sub-100-mutation sample analyzed individually

• Trigger: Calculate signatures for a tumor with <100 mutations.
• Mechanism: 96-context SBS spectrum needs 200-500 mutations for stable estimation; sub-100 produces signal-to-noise dominated by stochastic context distribution.
• Symptom: Random or implausible signature contributions.
• Fix: Aggregate samples in a meta-tumor for cohort analysis; for single-sample at low count consider SigNet which is optimized for low counts.

3. FFPE artifact misclassified as SBS30 / SBS33

• Trigger: Pipeline reports SBS33 (or SBS30) as biologically meaningful.
• Mechanism: FFPE-induced C>T deamination produces a profile resembling SBS30 (~0.90 cosine); after enzymatic uracil repair resembles SBS1. Pre-2022 literature incorrectly cited SBS33.
• Symptom: False NTHL1-BER-deficiency or "unknown SBS33" reports in cohorts using FFPE samples without matched controls.
• Fix: Run matched fresh-frozen controls in cohort; flag FFPE samples for separate analysis; use enzymatic-uracil pretreatment; expect SBS30-like artifact, not SBS33.

4. WES + signature analysis without trinucleotide correction

• Trigger: Run SigProfilerExtractor on WES VCFs with exome=False.
• Mechanism: WES capture has biased trinucleotide composition vs whole genome.
• Symptom: Apparent signature differences from WGS-derived signatures are artifactual.
• Fix: Set exome=True; this triggers trinucleotide-context correction.

5. Refit chosen for cohort with novel etiology

• Trigger: Tropical-region cohort with putative novel mutagen exposure; refit to COSMIC.
• Mechanism: Refit constrains variance onto existing catalog; novel signatures appear as residual or are decomposed onto closest-cosine known signatures.
• Symptom: Apparent absence of novel etiology despite biological hypothesis.
• Fix: For cohorts >= 50 run de novo extraction with cross-validation; Senkin 2024 kidney cancer cohort exemplifies the gain.

6. APOBEC SBS2 vs SBS13 conflation; A3A vs A3B

• Trigger: Report "APOBEC activity" without subtype.
• Mechanism: Petljak 2022 Nature established APOBEC3A as dominant active deaminase; the SBS2/SBS13 ratio reflects REV1-dependent translesion synthesis.
• Symptom: Lose mechanistic insight; potential mis-attribution.
• Fix: Distinguish A3A (YTCA 5' tetranucleotide preference) vs A3B (RTCA); cite Petljak 2022.

7. SBS5 attributed to "polymerase fidelity errors"

• Trigger: Manuscript claims SBS5 = replication errors.
• Mechanism: SBS5 etiology is contested; clock-like, age-correlated, modulated by ERCC2/TC-NER but not established as polymerase errors.
• Symptom: Reviewers reject.
• Fix: Report as "unknown, clock-like" until field consensus.

8. Cross-version comparison without re-extraction

• Trigger: Compare SBS40 contributions from v3.2 (single signature) vs v3.4 (split into 40a/b/c).
• Mechanism: Signature splits/merges occur between versions; cross-version exposures are not directly comparable.
• Symptom: Apparent "loss" or "gain" of activity due to renaming.
• Fix: Re-run with current COSMIC version; document version in methods.

9. Strand bias ignored

• Trigger: SBS22 (aristolochic acid) reported without transcribed-strand bias.
• Mechanism: Aristolochic-acid mutagenesis strongly biased toward transcribed strand; SigProfiler handles via tsb_stat=True, MutationalPatterns supports, deconstructSigs ignores.
• Symptom: Mis-attribution; loss of mechanistic evidence.
• Fix: Use SigProfiler or MutationalPatterns; enable strand-bias output.

Reconciliation: When Methods Disagree

Pattern	Likely cause	Action
SigProfiler de novo vs refit different signatures	De novo finds novel + refit forces onto COSMIC	If cohort >= 50, prefer de novo; document
MutationalPatterns vs SigProfilerAssignment different exposures	NNLS vs forward-backward selection differences	Compare cosine similarity to mut_mat; pick better reconstruction
HRDetect calls HR-deficient + BRCA wildtype	BRCA1 promoter hypermethylation; PALB2 / FBXW7 / CDK12 alterations	Confirm with HRD-LOH score; assay BRCA1 methylation
Cosine to SBS3 high but ID6 absent	Single-feature HRD signal insufficient	Use HRDetect 6-feature classifier, not SBS3 alone
APOBEC signature present + low TMB	Cohort has APOBEC but not hypermutator	Both can coexist; YTCA/RTCA discriminates A3A vs A3B
FFPE samples produce SBS30 / SBS33-like signal	Almost always artifact	Run matched FF controls; use enzymatic uracil pretreatment
Cohort signature contributions implausible	Sub-100-mutation samples included	Stratify by mutation count; report >=200 separately

Quantitative Thresholds and Conventions

Threshold	Convention	Source
SBS96 stable extraction	>=200 mutations per sample	Alexandrov 2020
De novo extraction cohort	N >= 50 (SBS); N >= 100 (DBS/ID/CN)	Field consensus
nmf_replicates	100 (default; do not reduce)	SigProfilerExtractor
Stability gate	minimum stability >= 0.2; average >= 0.8	SigProfilerExtractor defaults
Cosine similarity for "same signature"	> 0.85 (some use 0.90)	Convention
SBS-FFPE-artifact cosine to SBS30	~0.90	formalin-induced C>T characterization (mutational-signatures literature; specific primary citation pending verification)
HRDetect threshold	BRCA_prob >= 0.7 = HRD-positive	Davies 2017
POLE-exo + MMR mutation count	>500 mut/Mb (ultra-hypermutator)	Alexandrov 2020
Pure POLE-exo mutation count	100-300 mut/Mb	Alexandrov 2020
MMR-D typical mutation count	30-50 mut/Mb	Salem 2018 Cancer Discov
COSMIC version	3.4 (September 2024)	COSMIC database

Common Errors

Symptom	Cause	Solution
Tool reports SBS33 in FFPE cohort	Mis-attribution of FFPE artifact	Confirm by examining trinucleotide pattern; FFPE artifact resembles SBS30 in modern catalog
Single tumor signature attribution unstable	Sub-200-mutation sample analyzed alone	Aggregate; use SigNet for low-count
Refit ignores novel etiology	Forced onto COSMIC reference	Run de novo on cohort if N >= 50
HRDetect false negative	Missing one of 6 features (ID6, RS3, RS5, HRD-LOH)	Confirm all features computed; assay BRCA1 methylation
Strand bias not detected	Tool/setting ignores transcribed-strand	Use SigProfilerSuite with tsb_stat=True or MutationalPatterns
WES vs WGS signatures differ	Capture-bias trinucleotide composition	Set exome=True in SigProfilerMatrixGenerator
Platinum-treated tumor: SBS31 vs SBS35 confusion	Both attributed to platinum; SBS35 closer to direct Drost lab signature	Report both; cosine to direct
Aristolochic-acid signature in non-exposure context	Bias from highly-expressed transcribed-strand artifacts	Check geographic + clinical history

Anticipated Reviewer Pushback

Pushback	Standard response
"Why not deconstructSigs?"	Operationally deprecated; NNLS overfits. Use SigProfilerAssignment or MutationalPatterns strict refit.
"Single tumor signatures meaningless?"	Below 200 mutations: yes. We aggregate cohorts and run refit for low-mutation samples.
"Your de novo NMF rank choice?"	nmf_replicates=100 with stability gates; minimum >=0.2, average >=0.8, combined =1.0; SigProfilerExtractor defaults.
"FFPE samples bias signatures"	Modern attribution: FFPE artifact resembles SBS30 (not SBS33); we run matched FF controls or use enzymatic uracil pretreatment.
"SBS5 etiology; 'unknown' is unsatisfying"	Tomasetti-Vogelstein clock model; Druck 2026 FHIT + TC-NER; field has not converged; we report "unknown, clock-like".
"Why no APOBEC subtype distinction?"	Reported via YTCA vs RTCA tetranucleotide ratio per Petljak 2022; not all tools surface this; we used SigProfilerTopography.
"Cohort cross-comparison with old paper"	Re-extracted with COSMIC v3.4; signature splits (SBS40 -> 40a/b/c, SBS17 -> 17a/b) make pre-2024 exposures non-comparable.
"HRDetect cross-cancer validation"	Original Davies 2017 trained on breast cancer; we revalidated in our cohort with cross-cancer HRD-LOH score.

References

• Alexandrov LB et al. 2020. The repertoire of mutational signatures in human cancer. Nature 578:94. (PCAWG)
• Tate JG et al. 2019. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res 47:D941. (COSMIC v89)
• Senkin S et al. 2024. Geographic variation of mutagenic exposures in kidney cancer genomes. Nature 629:910. (SBS40a/b/c split)
• Drews RM et al. 2022. A pan-cancer compendium of chromosomal instability. Nature 606:976. (CN signatures)
• Petljak M et al. 2022. Mechanisms of APOBEC3 mutagenesis in human cancer cells. Nature 607:799. (A3A dominance)
• Bergstrom EN et al. 2019. SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genomics 20:685.
• Islam SMA et al. 2022. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell Genomics 2:100179.
• Diaz-Gay M et al. 2023. SigProfilerAssignment: an R + Python package for assigning known mutational signatures to individual samples. Bioinformatics 39:btad756.
• Manders F et al. 2022. MutationalPatterns: the one-stop shop for the analysis of mutational processes. BMC Genomics 23:134.
• Liu Q et al. 2024. Identifying mutational signatures with minimum volume non-negative matrix factorization. Nat Genet.
• Davies H et al. 2017. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med 23:517.
• Degasperi A et al. 2022. Substitution mutational signatures in whole-genome-sequenced cancers in the UK population. Science 376:abl9283.
• Christensen S et al. 2019. 5-Fluorouracil treatment induces characteristic T>G mutations in human cancer. Nat Commun 10:4571. (SBS17b)
• Pich O et al. 2019. The mutational footprints of cancer therapies. Nat Genet 51:1732.
• Hayward NK et al. 2017. Whole-genome landscapes of major melanoma subtypes. Nature 545:175. (UV signatures)
• Schulze K et al. 2015. Exome sequencing of hepatocellular carcinomas. Nat Genet 47:505. (Aflatoxin SBS24)
• Hoang ML et al. 2013. Mutational signature of aristolochic acid exposure as revealed by whole-exome sequencing. Sci Transl Med 5:197ra102.
• Pleguezuelos-Manzano C et al. 2020. Mutational signature in colorectal cancer caused by genotoxic pks+ E. coli. Nature 580:269. (Colibactin SBS88)
• Gerstung M et al. 2020. The evolutionary history of 2,658 cancers. Nature 578:122. (MutationTimer)
• Mertz TM et al. 2020. POLE mutation spectra are shaped by the mutant allele identity. 78:1116.

Related Skills

• clinical-databases/tumor-mutational-burden - TMB and ICI biomarker
• clinical-databases/msi-detection - MSI as MMR-D biomarker (paired with SBS6/15/26/44)
• variant-calling/variant-calling - Somatic VCF input
• variant-calling/variant-calling - Mutect2 / Strelka2 somatic upstream
• data-visualization/heatmaps-clustering - Signature contribution visualization