Stamp antibody free

Version Compatibility

Reference examples tested with: STAMP / scSTAMP (Brannan 2021/2024 Yeo lab github), Bullseye 1.0+, SAILOR 1.1+, samtools 1.19+, REDItools2 1.3+, JACUSA2 2.0+, scanpy 1.10+, anndata 0.10+, pysam 0.22+.

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

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws unexpected errors, introspect the installed package and adapt the example to match the actual CLI rather than retrying.

STAMP / Antibody-Free RBP Profiling

"Profile RBP-RNA targets without UV crosslinking or immunoprecipitation" -> Express a fusion of the RBP-of-interest with a deaminase (APOBEC1 for STAMP, ADAR for TRIBE) in cells; the deaminase edits RNA nucleotides adjacent to where the RBP binds, producing a C-to-U (STAMP) or A-to-I (TRIBE, read as A-to-G) editing signature in standard RNA-seq. The targets are recovered computationally from the editing pattern. Three properties make this approach valuable: (a) no UV crosslinking required (works in tissue/in vivo); (b) no IP step (no antibody needed - the RBP itself targets the deaminase); (c) compatible with single-cell readout because the editing signal exists in standard scRNA-seq (scSTAMP, scTRIBE). Trade-off: editing is offset from the binding site (typically 0-50 nt away); resolution is approximate; off-target editing from deaminase alone must be subtracted.

• CLI (STAMP, bulk): standard RNA-seq pipeline + Bullseye or SAILOR for C-to-U edit detection vs APOBEC1-only control
• CLI (TRIBE, bulk): standard RNA-seq + REDItools2 or JACUSA2 for A-to-I edit detection vs ADAR-only control
• CLI (DART-seq for m6A): same as STAMP, with APOBEC1-YTH fusion (YTH is the m6A reader)
• Python (scSTAMP single-cell): 10x Genomics or Smart-seq2 pipeline + custom editing-rate quantification per cell + per-cell binding-target inference
• CLI (general edit-site detection): JACUSA2 call-2 -r ref.fa -p 8 -F 1024 -A,B treated.bam,control.bam -t pileup.tsv then filter for C-to-U or A-to-I

STAMP (Brannan 2021) is the canonical antibody-free RBP profiling method. TRIBE (McMahon 2016) and HyperTRIBE (Xu 2018) are earlier ADAR-based variants. DART-seq (Meyer 2019) is the m6A-specific application using YTH-fused APOBEC1. scSTAMP (Brannan 2024) extends STAMP to single-cell readout.

Methods Taxonomy

Method	Deaminase	Edit signature	Cells supported	Single-cell	Strength	Fails when
STAMP (Brannan 2021)	APOBEC1	C->U in mRNA (reads as C->T)	Any	Yes (scSTAMP)	Antibody-free; no UV; in vivo	APOBEC1 also edits ssDNA off-target; saturated edits at high APOBEC1 expression
scSTAMP (Brannan 2024)	APOBEC1	C->U per cell	Single cell (10x or Smart-seq2)	Yes (native)	Per-cell RBP profiling	Coverage per cell limits sensitivity; ~25% of cytosines accessible per transcript
TRIBE (McMahon 2016)	ADAR catalytic domain	A->I (reads as A->G in cDNA)	Drosophila standard; mammalian works	Yes (scTRIBE)	First antibody-free	Edits restricted to certain ADAR consensus; lower edit rate than HyperTRIBE
HyperTRIBE (Xu 2018)	ADAR E488Q hyperactive mutant	A->I in much wider context	Drosophila / mammalian	Yes	Higher edit rate than original TRIBE	Hyperactive may edit off-target; needs ADAR-only control
TRIBE-DiCo (Erickson 2024)	ADAR catalytic + dimerization	A->I	Mammalian	Yes	Improved specificity	Newer; less validation
DART-seq (Meyer 2019)	APOBEC1-YTH	C->U near m6A	Any	Yes (scDART)	m6A reader profiling	Indirect (edits near m6A, not at RBP binding sites)
Bullseye	NA (analysis tool)	NA	Any	Yes	STAMP / DART analysis	Just an analysis pipeline
SAILOR	NA (analysis tool)	NA	Any	Yes	RNA editing analysis	Just an analysis pipeline
REDItools2	NA (analysis tool)	NA	Any	NA	Generic RNA editing	Generic; not RBP-specific
JACUSA2 (Piechotta 2022 BMC Bioinformatics 23:139)	NA (analysis tool)	NA	Any	NA	Multi-sample edit-site detection	Generic; not RBP-specific
ADAR-CLIP	NA - this is regular ADAR CLIP	NA	NA	NA	CLIP for ADAR	Not an antibody-free method; just a different CLIP target

Methodology evolves; the Brannan lab and Yeo lab papers (2021, 2024) are canonical. Verify deaminase fusion expression level (low expression for specificity; saturation degrades specificity).

STAMP vs m6A-Specific Methods

For m6A profiling specifically, antibody-free choices include:

• DART-seq (APOBEC1-YTH fusion): This skill covers the methodology, but for m6A detection see clip-seq/m6a-clip. Only 44% of DART edits fall within DRACH motifs (Liu 2023); strong off-target component.
• GLORI (Liu 2023): Antibody-free, chemical, stoichiometric single-base m6A; this is the new (2023) gold standard for m6A. See clip-seq/m6a-clip.
• m6Anet (Hendra 2022): Nanopore direct RNA m6A; AUC 0.83 on HEK293T.

If the use case is m6A profiling, the m6a-clip skill is the canonical reference; this skill (stamp-antibody-free) focuses on the broader RBP-editing-fusion paradigm where the target is not m6A but the RBP's RNA targets.

Critical Choice: STAMP (APOBEC1) vs TRIBE (ADAR)

Property	STAMP	TRIBE
Deaminase	APOBEC1 (cytidine -> uridine)	ADAR (adenosine -> inosine)
Edit signature	C->U (reads as C->T)	A->I (reads as A->G)
ssRNA preference	Yes (APOBEC1 acts on ssRNA + ssDNA)	No (ADAR acts on dsRNA stems by default; ADAR2cd in TRIBE relaxes this)
Edit clusters per target	10-1000	~5-50 (lower; HyperTRIBE higher)
Off-target	APOBEC1 alone has detectable C->U on ssDNA + RNA	ADAR has weak intrinsic A->I
Spatial offset from RBP binding	0-50 nt	0-30 nt
Cell line tested	HEK293, K562, mouse tissue	Drosophila (original), mouse, human
Single-cell	scSTAMP 2024	scTRIBE 2020
Compatible methods	C->U is rare in mRNA; signal is clean	A->I is common at ALU repeats; baseline ADAR editing competes
Cytosine accessibility	~25-35% of mRNA bases are C; APOBEC1 needs ssRNA	All A residues are potential ADAR targets

Both work; STAMP has more clusters per target (advantage for low-coverage scenarios) and cleaner background (C->U is rare in mRNA). TRIBE has more flexibility (ADAR variants tunable) and lower off-target. Practical choice often comes down to lab familiarity.

scSTAMP / scTRIBE Single-Cell Workflow

The defining advantage of antibody-free RBP profiling is compatibility with single-cell readout. scSTAMP processes 10x Genomics or Smart-seq2 libraries.

# Standard 10x cellranger pipeline produces BAM with per-cell barcodes
cellranger count \
    --id=scstamp_sample \
    --transcriptome=refdata-gex-GRCh38 \
    --fastqs=fastq_dir \
    --localcores=16 --localmem=64

# scSTAMP analysis (Yeo lab github)
# Quantify per-cell C->U editing
python scstamp_analysis.py \
    --bam scstamp_sample/outs/possorted_genome_bam.bam \
    --barcodes scstamp_sample/outs/filtered_feature_bc_matrix/barcodes.tsv.gz \
    --control apobec1_only_sample/outs/possorted_genome_bam.bam \
    --output per_cell_edits.h5

Per-cell edit-rate matrix can be integrated with standard scRNA-seq clustering. The per-cell binding profile is reconstructed from cells with sufficient coverage (>= 10000 unique reads typically).

Editing-Site Detection Pipelines

Goal: Recover specific (not background) RBP-fusion-induced editing sites from RNA-seq libraries by subtracting the deaminase-only control.

Approach: Process fusion-sample BAM and deaminase-only-control BAM in parallel; use Bullseye (STAMP/DART), SAILOR (Yeo), or JACUSA2 (general) to call C-to-U (STAMP/DART) or A-to-I (TRIBE/HyperTRIBE) edit sites at edit rate >= 0.1 and coverage >= 10, requiring fusion-vs-control edit ratio > 3 as the specificity threshold.

Bullseye (Meyer lab DART-seq pipeline) is a multi-script Perl pipeline (parseBAM.pl, summarize_sites.pl, find_edit_site.pl) rather than a single binary -- the conceptual flow is shown below; consult the Bullseye repo for the exact per-script invocations.

# Bullseye -- conceptual STAMP workflow (multi-step Perl scripts; verify against repo)
perl parseBAM.pl --input stamp_sample.bam --output stamp.parsed.tsv
perl parseBAM.pl --input apobec1_only.bam --output control.parsed.tsv
perl summarize_sites.pl --in stamp.parsed.tsv > stamp.summary.tsv
perl summarize_sites.pl --in control.parsed.tsv > control.summary.tsv
perl find_edit_site.pl --ip stamp.summary.tsv --ctrl control.summary.tsv \
    --edit_type c2t --threshold 0.1 --min_coverage 10 --out stamp_edits.bed

SAILOR (Yeo lab) is a Snakemake-based pipeline, not a single CLI binary -- launch via the SAILOR Snakefile after editing the config (config.yaml) for input BAMs, background BAM, and reference FASTA.

# SAILOR -- conceptual; SAILOR ships as a Snakemake workflow.
# Configure inputs in the SAILOR Snakemake config.yaml, then run:
snakemake -s SAILOR.smk --configfile config.yaml --cores 8

JACUSA2 is a general-purpose RNA editing pipeline, distributed as a Java jar.

# JACUSA2 multi-sample edit-site detection (BAM inputs are POSITIONAL; -r is output, -R is reference)
java -jar JACUSA2.jar call-2 \
    -R genome.fa \
    -p 8 \
    -F 1024 \
    -r jacusa_edits.tsv \
    stamp1.bam,stamp2.bam control1.bam,control2.bam

# Post-filter for C->U (STAMP) at edit rate >= 0.1
awk '$5 == "C" && $9 ~ /U/ && $11 >= 0.1' jacusa_edits.tsv > stamp_edits_filtered.tsv

Per-Method Failure Modes

STAMP -- APOBEC1 over-expression saturation

Trigger: Strong APOBEC1-RBP expression (>>10x endogenous).

Mechanism: At high APOBEC1 expression, the deaminase saturates editing - every accessible C in mRNA is edited, regardless of RBP binding.

Symptom: Edit count per gene >> expected; non-specific editing across mRNAs; APOBEC1-only control has nearly as many edits as the fusion.

Fix: Titrate fusion expression with inducible promoter; aim for low-to-moderate expression giving clean fusion-specific edits. Yeo lab convention: doxycycline-inducible with mid-range dox dose. Compare edits in fusion vs APOBEC1-only; require fusion edits / APOBEC1-only edits > 3.

STAMP -- APOBEC1 off-target on ssDNA

Trigger: APOBEC1 expressed in DNA-replicating cells.

Mechanism: APOBEC1 has intrinsic ssDNA editing activity; some "C->U" calls are actually genomic ssDNA edits read through transcription.

Symptom: Edits cluster at replication-fork regions or LINE-1 elements; non-specific genome-wide.

Fix: Bullseye filters genomic SNVs vs RNA edits via strand information; verify mismatch is C->U on the transcribed strand, not the genomic C->T.

TRIBE -- Editing at ALU repeats

Trigger: TRIBE in mammalian cells; many edits at ALU sequences.

Mechanism: ADAR has baseline activity at ALU dsRNA structures; this is NOT TRIBE-specific signal. ALU edits dominate the apparent target list.

Symptom: Top edited regions are all ALU repeats; target list looks generic.

Fix: Subtract ADAR-only control or wild-type ADAR baseline; filter out ALU-overlapping edits unless RBP is known to bind repeats.

DART-seq -- Spatial offset from m6A

Trigger: DART-seq applied with expectation of single-base m6A resolution.

Mechanism: APOBEC1-YTH edits Cs 0-50 nt from the YTH-bound m6A site. The exact m6A position is not the edit position.

Symptom: DART edits scattered around DRACH motifs; only 44% of edits within DRACH (Liu 2023).

Fix: Treat DART edits as "near m6A"; cross-reference with single-base m6A methods (GLORI, miCLIP2). DART is hypothesis-generating, not precise localization.

scSTAMP -- Coverage limitation per cell

Trigger: scSTAMP on 10x library; per-cell coverage limits target detection.

Mechanism: Single cells have ~5000-50000 mRNA molecules; editing-rate quantification at any single position needs >= 10 reads. Most positions have 0-3 reads per cell.

Symptom: Per-cell binding-target list is sparse; many cells have 0 detected targets.

Fix: Aggregate cells into pseudo-bulk by cluster/cell-type; quantify editing at pseudobulk level; or use ultra-deep Smart-seq2 (~1M reads/cell) instead of 10x for higher per-cell coverage.

No control subtraction

Trigger: STAMP/DART/TRIBE run without deaminase-only control.

Mechanism: Deaminases have intrinsic baseline editing (APOBEC1 ~3-5% C->U; ADAR ~5-10% A->I at ALUs). Without control, all edits look like signal.

Symptom: Edit count enormous; target list non-specific.

Fix: Always run deaminase-only (APOBEC1 or ADAR catalytic domain) control in parallel. Bullseye / SAILOR / JACUSA all support control subtraction.

Strand-specific edit interpretation

Trigger: Generic variant caller used instead of edit-aware tool.

Mechanism: Variant callers report any C->T mismatch; without strand information it is not possible to distinguish C->U (STAMP signal on transcribed strand) from G->A (the reverse complement of C->T on the genome strand from anti-sense reads).

Symptom: Edit count inflated 2x; signal not stranded.

Fix: Use editing-specific tool (Bullseye, SAILOR, JACUSA2) that respects strand. Or filter for proper strand: C->U on +sense and G->A on -sense.

Decision Tree by Use Case

Scenario	Method	Why
Antibody for RBP doesn't exist	STAMP or TRIBE	The original use case
RBP in tissue / in vivo (no UV possible)	STAMP / TRIBE	No CL needed
Single-cell RBP profiling	scSTAMP or scTRIBE	The only practical option
m6A reader profiling (YTHDF)	DART-seq (APOBEC1-YTH)	Specific reader fusion
Drosophila RBP	TRIBE (original development)	Most validated in Drosophila
Mammalian RBP	STAMP (more validated in mammalian)	Yeo lab benchmarks
Need precise binding site	Use CLIP / eCLIP not STAMP/TRIBE	Editing is offset from binding
Repeat-binding RBP	CLIP + CLAM, not TRIBE	ADAR baseline at ALUs swamps TRIBE
Comparison across methods	STAMP + classic eCLIP both	Triangulation increases confidence
Low input cell numbers (< 50k)	scSTAMP	Compatible with sparse libraries
Time-course binding dynamics	STAMP with inducible expression	Live-cell editing accumulates
Cross-link sensitive RBP	STAMP / TRIBE (no UV)	Some RBPs degrade with UV

Reconciliation: STAMP vs CLIP

Pattern	Likely cause	Action
STAMP finds targets eCLIP missed	Targets that crosslink poorly; or low-abundance	Validate orthogonally; STAMP often more sensitive for low-abundance targets
eCLIP finds targets STAMP missed	RBP-RNA contact too far from accessible C; or APOBEC1 saturated	Check fusion expression level; ssRNA accessibility
STAMP edits clustered; eCLIP peaks broader	Spatial offset of editing from binding	Both correct; report at appropriate resolution
STAMP top targets generic mRNAs	Saturated APOBEC1; or no control subtraction	Titrate fusion expression; verify APOBEC1-only control
TRIBE editing dominated by ALUs	ADAR baseline activity	Subtract ADAR-only; filter ALU repeats
Discordant target lists across labs for same RBP	Fusion expression varies; control differs	Standardize protocols; cross-validate
scSTAMP pseudobulk = bulk STAMP	Cell aggregation correct	Trust both for low-coverage targets
scSTAMP per-cell sparse	Coverage limitation	Pseudobulk by cluster; or use Smart-seq2

Operational rule: STAMP/TRIBE for hypothesis-generation, antibody-free profiling, or single-cell. CLIP/eCLIP for high-resolution validation. Best paper figure: STAMP + eCLIP concordance for top targets.

Common Errors

Error / symptom	Cause	Solution
Edit count enormous	No control subtraction	Add APOBEC1-only / ADAR-only control
Edits in DNA / off-target	APOBEC1 ssDNA activity	Filter genomic SNVs; trust strand-specific RNA edits
Saturated editing on every gene	High fusion expression	Titrate down; use inducible promoter
TRIBE all edits at ALUs	ADAR baseline	Subtract ADAR-only; filter ALU
DART edits not at m6A	Spatial offset (0-50 nt)	Expected; cross-reference single-base m6A
scSTAMP per-cell sparse	10x coverage limit	Pseudobulk by cluster; Smart-seq2 alternative
Generic variant caller	No strand awareness	Use Bullseye / SAILOR / JACUSA
Edits in non-edited strand	Anti-sense transcription	Filter by strand-specific mate
Same target list as RNA-seq abundance	Saturated APOBEC1	Reduce fusion expression
Reproducibility issue across labs	Fusion construct differs	Standardize promoter, tag position, deaminase variant

References

• Brannan KW et al 2021 Mol Cell 81:2890 (STAMP, APOBEC1-RBP fusion)
• Brannan KW et al 2024 Cell Genomics (scSTAMP single-cell extension)
• McMahon AC et al 2016 Cell 165:742 (TRIBE original, Drosophila)
• Xu W et al 2018 Cell 174:1567 (HyperTRIBE)
• Erickson AW et al 2024 (TRIBE-DiCo)
• Meyer KD 2019 Nat Methods 16:1275 (DART-seq, APOBEC1-YTH)
• (SAILOR: pipeline by Yeo lab; documented at github.com/YeoLab/SAILOR -- specific peer-reviewed citation has not been confirmed; consult current literature.)
• Piechotta M et al 2017 BMC Bioinformatics 18:7 (JACUSA1).
• Piechotta M et al 2022 BMC Bioinformatics 23:139 (JACUSA2 -- the multi-sample call-2 mode used above).
• Picardi E & Pesole G 2013 Bioinformatics 29:1813 (REDItools)
• Tegowski M et al 2022 Mol Cell 82:868 (scDART single-cell DART-seq)
• Hayashi Y et al 2023 (DART-seq benchmarking)

Related Skills

• clip-seq/m6a-clip - DART-seq is part of the m6A toolkit
• clip-seq/clip-deep-learning - Computational target prediction
• clip-seq/ago-clip-mirna-targets - AGO-CLIP for comparison
• single-cell/preprocessing - scSTAMP downstream
• single-cell/clustering - scSTAMP per-cluster pseudobulk
• single-cell/markers-annotation - Cell-type-specific targets
• methylation-analysis/methylation-calling - Editing as related to methylation
• epitranscriptomics/m6anet-analysis - Nanopore m6A alternative to DART