Bio Clip Seq Stamp Antibody Free

Version Compatibility

Reference examples tested with: STAMP / scSTAMP (Brannan 2021 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 (single-cell readout) is part of the same Brannan 2021 method.

Methods Taxonomy

Methodology evolves; the Brannan lab / Yeo lab 2021 paper is 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 (Guo 2025); 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; high AUC 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)

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.