epitranscriptomics-merip-preprocessing

name	epitranscriptomics-merip-preprocessing
description	Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq c_curve / lc_extrap, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT — see Tips), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for downstream cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for downstream visualisation.
tool_type	cli
primary_tool	STAR

Version Compatibility

Reference examples tested with: STAR 2.7.11+, HISAT2 2.2.1+, samtools 1.19+, deepTools 3.5+, PreSeq 3.2+, fastp 0.23+, Trim Galore 0.6.10+, Picard 3.1+, MultiQC 1.25+, bowtie2 2.5+, BWA-MEM2 2.2+.

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

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

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

STAR --outSAMtype accepts BAM SortedByCoordinate since 2.5.x; check the Log.final.out file for input/output statistics. deepTools bamCompare --operation log2 is the modern syntax (older --ratio log2 still works but is being phased out). PreSeq c_curve and lc_extrap have stable interfaces; HISAT2 reports unique vs multi-mapped in the summary log.

MeRIP-seq Preprocessing

"Get my MeRIP IP and input libraries ready for peak calling" -> Trim adapters with MeRIP-appropriate defaults (do NOT trim UMIs unless the library is UMI-MeRIP — most are not), splice-aware-align IP and input to the GENOME (not transcriptome) with STAR / HISAT2, sort and index, evaluate replicate concordance and IP enrichment with deepTools, build a saturation curve per library so peak counts can be honestly compared across libraries, record antibody clone and lot metadata so cross-batch comparison is later auditable, and produce IP-over-Input log2 bigWig tracks for downstream visualisation. Crucially, do NOT deduplicate non-UMI MeRIP — see the failure-modes section.

• CLI: STAR --runMode alignReads -- splice-aware genome alignment, the field default
• CLI: hisat2 -x index -1 R1.fq.gz -2 R2.fq.gz -- graph-based alternative; lighter memory footprint
• CLI: samtools sort -@ 8 -o sorted.bam in.bam && samtools index sorted.bam -- post-alignment mechanics
• CLI: deeptools multiBamSummary bins -b *.bam -o cov.npz + plotCorrelation -- replicate Spearman
• CLI: deeptools plotFingerprint -b IP.bam Input.bam -o fp.pdf -- IP enrichment QC (ChIP-seq term; transfers cleanly)
• CLI: preseq lc_extrap -B -o curve.txt sorted.bam -- library complexity / saturation
• CLI: deeptools bamCompare -b1 IP.bam -b2 Input.bam --operation log2 -o IP_over_Input.bw -- downstream-ready coverage track

The Single Most Important Modern Insight -- Peak counts are library-size-dependent; saturation curves are the only honest cross-library comparison

A MeRIP library sequenced to 20 million unique reads finds substantially fewer peaks than the same biology at 60 million reads. Per-sample peak counts reported without saturation curves (PreSeq c_curve / lc_extrap; Daley & Smith 2013 Nat Methods 10:325) are uninterpretable across studies and often across replicates within a study. Subsample BAMs to a common unique-read depth before peak calling for any cross-condition peak-count comparison, OR report peaks alongside the saturation curve. A corollary: do NOT deduplicate standard MeRIP — the typical MeRIP protocol (Synaptic Systems 202-003 / Abcam ab151230 / NEB EpiMark E1610 antibody pull-down on fragmented poly(A)-selected RNA) has NO unique molecular identifiers, and picard MarkDuplicates on such libraries collapses real biological replicates of high-coverage transcripts (the opposite of what dedup achieves in DNA ChIP-seq). UMI-MeRIP is the only exception — and most MeRIP libraries in print are NOT UMI. McIntyre et al. 2020 Sci Rep 10:6590 demonstrated that replicate-to-replicate peak overlap is ~80% within a single lab but drops to a median 45% between labs using nominally identical conditions; this irreducible technical noise constrains how strongly any single MeRIP study can support biological claims, and the preprocessing pipeline is where the variance is set.

Algorithmic Taxonomy

Tool / step	Mechanism	Output	Strength	Fails when
STAR 2.7+ (Dobin 2013 Bioinformatics 29:15)	Two-pass splice-aware alignment with on-the-fly splice junction database	Sorted BAM + splice-junction TSV	Field default; multi-mapper retention configurable; STAR splice-junction DB	Memory-heavy (~30 GB human); slower than HISAT2
HISAT2 2.2.1+ (Kim 2019 Nat Biotechnol 37:907)	Hierarchical graph FM-index; splice-aware	Sorted BAM	~5x lighter memory than STAR; comparable accuracy	Less mature splice-junction handling for novel introns
BWA-MEM2 (Vasimuddin 2019 IPDPS 314)	DNA-style local alignment; NO splice awareness	Sorted BAM	Use ONLY for transcriptome-aligned MeRIP (rare)	Splits reads across exon junctions if used on genome
fastp 0.23+ (Chen 2018 Bioinformatics 34:i884)	Streaming adapter detection + quality trim	Trimmed FASTQ + JSON QC	Fast; JSON-readable QC output	UMI handling disabled by default; do NOT pass --umi for standard non-UMI MeRIP (the opposite of the failure direction in some other library types)
Trim Galore	Wrapper over cutadapt with paired-end auto-detect	Trimmed FASTQ	Conservative defaults; widely cited	Slower than fastp on large datasets
samtools sort / index	BAM coordinate sort + .bai index	Sorted BAM + index	Standard	None at default
Picard MarkDuplicates	Identifies PCR duplicates by 5' alignment start	Marked / removed BAM	Standard in DNA / ChIP	The dominant MeRIP convention is to SKIP dedup for non-UMI libraries (collapses real biology at high-coverage transcripts); a minority of pipelines dedup MeRIP — record the choice in metadata
deepTools multiBamSummary + plotCorrelation	Per-bin read counts; Spearman / Pearson matrix	Heatmap + clustering	Standard replicate-concordance plot	Bin size sensitive (use 10 kb for transcriptome-genome)
deepTools plotFingerprint (Diaz 2012 Stat Appl Genet Mol Biol 11:9)	Cumulative read-fraction vs cumulative-bin-fraction Lorenz curve	PDF + raw counts	Direct IP-vs-input enrichment QC; "good" IP has steep tail

Decision Tree by Scenario

Scenario	Recommended	Why wrong choices fail
Standard mammalian MeRIP, downstream exomePeak2	STAR splice-aware -> genome BAM; do NOT deduplicate; build saturation curve	Transcriptome alignment breaks exomePeak2 (expects genome BAM + GTF); dedup collapses biology
Downstream m6Anet (ONT direct RNA)	Defer to m6anet-analysis -- alignment is to TRANSCRIPTOME with minimap2 -ax map-ont -uf -k14	Genome-aligned ONT input breaks m6Anet entirely (signal-level dataprep requires per-transcript coordinates)
Limited memory (<16 GB)	HISAT2 instead of STAR	STAR human genome index requires ~30 GB
UMI-MeRIP (rare)	Trim UMI to read header (umi_tools / fastp --umi_loc), align, then dedup with umi_tools dedup	Standard picard MarkDuplicates ignores UMI; effective dedup rate wrong
Cross-batch comparison (different antibody lots)	Record antibody clone + lot in sample-sheet metadata; include batch factor in downstream design	Pooling cross-batch counts without batch term inflates false-positive differential peaks
Spike-in normalisation (NEB EpiMark control oligos)	Align separately to spike-in reference; report IP-spike-in / Input-spike-in ratio per sample; use for absolute normalisation	Most users discard the NEB EpiMark Gluc / Cluc controls; they are the per-sample IP-efficiency QC anchor
Cross-library peak-count comparison	Subsample BAMs to common unique-read depth with samtools view -s BEFORE downstream peak calling, OR fit saturation curves and compare at common depth	Raw peak counts are sequencing-depth-dependent and not biologically interpretable
Suspect failed IP	Inspect deepTools plotFingerprint AND per-transcript IP/input ratio distribution; failed IP shows shallow Lorenz tail AND median IP/input ~1.0	Trusting raw peak count alone — failed IPs still produce peaks
Viral / contamination-suspect samples	Build a combined host + viral index (and rRNA index) and check unmapped reads	Single-organism indexes hide systematic contamination
Aligning to transcriptome (rare; specific downstream tools)	BWA-MEM2 or bowtie2; defer to read-alignment/

Methodology evolves; before any high-stakes preprocessing pipeline, web-search "STAR vs HISAT2 MeRIP 2024" and "MeRIP saturation curve preseq" for current consensus parameters.

Adapter Trimming for MeRIP

Goal: Remove sequencing adapters and low-quality 3' ends WITHOUT removing biological signal (UMI-MeRIP must keep UMI sequences; standard MeRIP does not have UMIs and trimming should be minimal).

Approach: Use fastp or Trim Galore with adapter auto-detection; require minimum read length 25-30 nt (shorter reads multi-map and confound exomePeak2); for standard non-UMI MeRIP, do NOT pass --umi flags; preserve random-hexamer-priming artifacts ONLY if downstream pipeline expects them (most do not).

mkdir -p trimmed

for sample in IP_rep1 IP_rep2 IP_rep3 Input_rep1 Input_rep2 Input_rep3; do
    fastp \
        --in1 raw/${sample}_R1.fastq.gz \
        --in2 raw/${sample}_R2.fastq.gz \
        --out1 trimmed/${sample}_R1.fq.gz \
        --out2 trimmed/${sample}_R2.fq.gz \
        --html qc/${sample}_fastp.html \
        --json qc/${sample}_fastp.json \
        --length_required 25 \
        --detect_adapter_for_pe \
        --thread 8
done

For UMI-MeRIP (rare), insert --umi --umi_loc read1 --umi_len 8 BEFORE the alignment step. Default fastp output preserves base quality information needed by downstream variant-aware tools; do NOT pass --disable_quality_filtering for MeRIP libraries.

STAR Splice-Aware Alignment for IP and Input

Goal: Produce coordinate-sorted, indexed GENOME BAM files for each IP and input library with splice-junction-aware mapping, retaining a moderate number of multi-mappers for accurate per-window read counts at multi-isoform loci.

Approach: Build STAR genome index once with the matched GENCODE / Ensembl GTF used downstream; loop IP and input samples with identical parameters; retain up to 20 multi-mappers per read (MeRIP read counts at multi-isoform genes need this); request explicit BAM SortedByCoordinate; emit splice-junction tables for QC.

STAR \
    --runMode genomeGenerate \
    --genomeDir star_index \
    --genomeFastaFiles genome.fa \
    --sjdbGTFfile annotation.gtf \
    --sjdbOverhang 100 \
    --runThreadN 12

mkdir -p aligned

for sample in IP_rep1 IP_rep2 IP_rep3 Input_rep1 Input_rep2 Input_rep3; do
    STAR \
        --runMode alignReads \
        --genomeDir star_index \
        --readFilesIn trimmed/${sample}_R1.fq.gz trimmed/${sample}_R2.fq.gz \
        --readFilesCommand zcat \
        --outSAMtype BAM SortedByCoordinate \
        --outFilterMultimapNmax 20 \
        --outSAMattributes NH HI AS nM NM MD \
        --outFileNamePrefix aligned/${sample}_ \
        --runThreadN 12

    samtools index -@ 4 aligned/${sample}_Aligned.sortedByCoord.out.bam
done

--outFilterMultimapNmax 20 is intentional: MeRIP at rRNA / snoRNA / pseudogene-rich loci needs multi-mapper retention. Reduce to 1 only if downstream analysis explicitly cannot tolerate multi-mappers. --sjdbOverhang should equal (read length - 1) but 100 is the common-enough default for 100-150 bp reads.

HISAT2 Alternative for Memory-Constrained Environments

Goal: Achieve splice-aware alignment in ~5x less memory than STAR (12-16 GB suffices for human), with comparable accuracy for MeRIP applications.

Approach: Build HISAT2 graph index; align with --dta for downstream-transcript-assembly compatibility; pipe directly to samtools sort.

hisat2-build genome.fa hisat2_index/genome

for sample in IP_rep1 IP_rep2 IP_rep3 Input_rep1 Input_rep2 Input_rep3; do
    hisat2 \
        -x hisat2_index/genome \
        -1 trimmed/${sample}_R1.fq.gz \
        -2 trimmed/${sample}_R2.fq.gz \
        --dta \
        --summary-file qc/${sample}_hisat2.log \
        -p 12 | \
    samtools sort -@ 8 -o aligned/${sample}.sorted.bam -

    samtools index -@ 4 aligned/${sample}.sorted.bam
done

HISAT2 multi-mapper handling is governed by -k; the default reports the primary alignment only. For MeRIP, pass -k 5 if multi-mapper-aware downstream counting is required.

Per-Sample QC: flagstat and idxstats

mkdir -p qc

for bam in aligned/*sortedByCoord.out.bam aligned/*sorted.bam; do
    name=$(basename ${bam} .bam)
    samtools flagstat ${bam} > qc/${name}.flagstat
    samtools idxstats ${bam} > qc/${name}.idxstats
done

Inspect flagstat for properly-paired rate (>=85% indicates good pairing); inspect idxstats for unexpected chromosome-level read piles (rRNA bleed-through, mitochondrial domination — both are MeRIP red flags).

Replicate Concordance via deepTools

Goal: Quantify how similar replicate IP libraries are to each other (and likewise input libraries) using a Spearman correlation matrix; flag a divergent replicate before it propagates into peak calling.

Approach: Compute genome-wide per-bin read counts at 10 kb resolution across all IP and input BAMs; convert to a clustered Spearman heatmap with deepTools plotCorrelation.

multiBamSummary bins \
    --bamfiles aligned/IP_rep1*.bam aligned/IP_rep2*.bam aligned/IP_rep3*.bam \
                aligned/Input_rep1*.bam aligned/Input_rep2*.bam aligned/Input_rep3*.bam \
    --binSize 10000 \
    --numberOfProcessors 8 \
    --outRawCounts qc/raw_bin_counts.tab \
    -o qc/cov.npz

plotCorrelation \
    --corData qc/cov.npz \
    --corMethod spearman \
    --skipZeros \
    --whatToPlot heatmap \
    --colorMap RdYlBu_r \
    --plotNumbers \
    -o qc/replicate_correlation.pdf

IP replicates within a condition should cluster (Spearman >= 0.85 typical); input replicates should cluster with each other; IP and input should NOT cluster together. A failed IP looks like input.

IP Enrichment via plotFingerprint

Goal: Confirm IP libraries are enriched (a few transcripts have many reads) and input libraries are uniform (reads spread across transcripts); fail-fast on poor IP before peak calling.

Approach: deepTools plotFingerprint builds a cumulative Lorenz-style curve; a steep tail = signal concentrated in few regions (good IP); a diagonal = uniform coverage (input or failed IP). The framework is from ChIP-seq (Diaz 2012 Stat Appl Genet Mol Biol 11:9) and transfers cleanly to MeRIP.

plotFingerprint \
    --bamfiles aligned/IP_rep1*.bam aligned/IP_rep2*.bam aligned/IP_rep3*.bam \
                aligned/Input_rep1*.bam aligned/Input_rep2*.bam aligned/Input_rep3*.bam \
    --labels IP1 IP2 IP3 In1 In2 In3 \
    --numberOfProcessors 8 \
    --skipZeros \
    --outQualityMetrics qc/fingerprint_metrics.tab \
    -o qc/fingerprint.pdf

Good MeRIP IP: cumulative-fraction-of-reads vs cumulative-fraction-of-bins curve sits well below the diagonal in the right half (top-X% of bins capture >50% of reads). Input: near-diagonal. The --outQualityMetrics file reports JS distance and synthetic JS distance; the IP-vs-Input JS distance is a single-number IP-quality summary (higher = more concentrated signal).

Library Complexity / Saturation Curves via PreSeq

Goal: Compute per-library complexity so peak counts can be honestly compared across libraries and conditions of different sequencing depth.

Approach: PreSeq c_curve (interpolation up to observed depth) and lc_extrap (extrapolation beyond observed) on the sorted BAM. Daley & Smith 2013 Nat Methods 10:325 capture-recapture model.

mkdir -p complexity

for bam in aligned/*.bam; do
    name=$(basename ${bam} .bam)

    preseq c_curve -B -o complexity/${name}_c_curve.txt ${bam}

    preseq lc_extrap -B -o complexity/${name}_lc_extrap.txt ${bam}
done

Inspect: the lc_extrap curve plots distinct molecules vs total reads; a plateau indicates saturation. For cross-condition peak-count comparison: pick a common depth (often 30M unique reads), subsample with samtools view -s 0.<frac> to that depth, THEN call peaks.

IP-over-Input bigWig for Downstream Visualisation

Goal: Produce a per-bin log2 (IP / Input) coverage track per replicate, ready for downstream metagene / browser plots.

Approach: deepTools bamCompare with --operation log2; choose a sensible pseudocount to avoid divide-by-zero at low-coverage bins.

mkdir -p tracks

paste -d ' ' \
    <(printf '%s\n' IP_rep1 IP_rep2 IP_rep3) \
    <(printf '%s\n' Input_rep1 Input_rep2 Input_rep3) | \
while read ip input; do
    bamCompare \
        -b1 aligned/${ip}_Aligned.sortedByCoord.out.bam \
        -b2 aligned/${input}_Aligned.sortedByCoord.out.bam \
        --operation log2 \
        --pseudocount 1 \
        --binSize 25 \
        --normalizeUsing CPM \
        --numberOfProcessors 8 \
        -o tracks/${ip}_over_${input}.bw
done

--pseudocount 1 prevents division-by-zero at zero-coverage bins; --binSize 25 is fine-grained enough to preserve peak topology while keeping bigWig files reasonably sized.

Per-Method Failure Modes

Dedup applied to non-UMI MeRIP

Trigger: picard MarkDuplicates REMOVE_DUPLICATES=true invoked on a standard MeRIP BAM that has no UMI.

Mechanism: Standard MeRIP libraries have no unique molecular identifiers. PCR duplicates and biological re-sampling at high-coverage transcripts look identical at the alignment level. Dedup removes both, collapsing real coverage at the most-abundant transcripts to an artificially flat profile. This is the opposite of dedup's intent in DNA ChIP-seq.

Symptom: Coverage at housekeeping mRNAs (e.g., GAPDH, ACTB) drops 5-20x after dedup; downstream peak counts at highly-expressed transcripts collapse; volcano plot of differential peaks shows expression-driven false positives.

Fix: Skip dedup for standard non-UMI MeRIP. If the library is UMI-MeRIP, use umi_tools dedup (Smith 2017 Genome Res 27:491) which respects UMI rather than alignment position alone. Record dedup status in sample-sheet metadata.

Transcriptome alignment for downstream peak calling

Trigger: STAR or bowtie2 aligned to transcriptome FASTA, then BAM passed to exomePeak2 / MeTPeak / MACS3.

Mechanism: exomePeak2 and MeTPeak expect a GENOME BAM plus GTF; they project peaks back to transcript features internally. A transcriptome BAM has reads in per-transcript coordinates which the GTF cannot resolve back to genome coordinates without re-alignment.

Symptom: exomePeak2 throws errors on TxDb-genome consistency; MeTPeak returns zero peaks; MACS3 calls peaks on transcript IDs as if they were chromosomes.

Fix: Align to GENOME with STAR / HISAT2 for downstream MeRIP peak calling. Transcriptome alignment is correct only for m6anet-analysis (ONT DRS) and rare quantification-only downstream tools.

Failed IP indistinguishable from input

Trigger: A single replicate IP library has IP/input ratio distribution centred at 1.0 across all transcripts (no enrichment); fingerprint Lorenz curve sits at the diagonal.

Mechanism: Failed IP — antibody-RNA binding did not enrich m6A-containing fragments. Causes include antibody-batch defect, insufficient pulldown wash, RNA degradation during IP, or accidental mock IgG IP.

Symptom: plotFingerprint shows IP overlaying input on the Lorenz plot; per-transcript IP/input ratio histogram is centred at 1.0; downstream peak callers find few or no peaks AT THE FAILED REPLICATE while other replicates produce normal counts.

Fix: Identify the failed replicate via plotFingerprint AND IP/input ratio distribution BEFORE peak calling; exclude or re-do. Single failed IP in a 3-replicate design routinely produces "differential" peaks driven entirely by the failure.

Antibody lot mismatch across samples

Trigger: A multi-condition MeRIP study uses Synaptic Systems 202-003 antibody lot A for the control IPs and lot B for the treatment IPs (because lot A ran out mid-study).

Mechanism: Anti-m6A polyclonals (Synaptic Systems 202-003, Abcam ab151230, NEB EpiMark E1610, Cell Signaling 56593, Active Motif 61755) have batch-to-batch variability in pulldown efficiency and m6A-vs-m6Am cross-reactivity. Pooling lot-A and lot-B counts in a downstream differential model attributes lot-effect to condition.

Symptom: "Differential" peaks at high abundance transcripts; effect sizes track antibody lot rather than condition; reanalysis with lot in the design matrix removes most differential peaks.

Fix: Record antibody_clone and antibody_lot per sample in metadata; include lot as a fixed effect in downstream differential analysis. Within a single study, ideally use a single lot for ALL replicates and ALL conditions.

Peak counts compared across libraries of different depth

Trigger: "Condition A has 14,000 peaks; condition B has 22,000 peaks; condition B has more m6A."

Mechanism: Peak count is library-size-dependent. A library at 60M unique reads finds more peaks than 30M. Without rarefaction or saturation correction, peak-count comparisons across libraries are dominated by sequencing depth.

Symptom: Peak counts track total mapped reads more closely than they track biological condition; downstream "biological m6A change" claims do not survive rarefaction-to-common-depth.

Fix: Either rarefy all BAMs to common unique-read depth before peak calling, OR fit saturation curves with PreSeq lc_extrap and compare at matched depth, OR report peak count alongside the saturation curve.

Random hexamer priming over-trim

Trigger: Aggressive 5' trimming of the first 6-12 nt to remove "random hexamer priming bias" applied to MeRIP libraries.

Mechanism: Random hexamer priming bias affects the 5' nucleotide composition of reads but does NOT degrade downstream peak-calling accuracy. Over-trimming removes biological signal and shortens reads enough to inflate multi-mapper fraction.

Fix: Standard adapter trimming with --length_required 25 is sufficient; do not 5'-trim for hexamer bias unless downstream tooling explicitly requires unbiased 5' ends (most do not). The bias is a known artifact in the RNA-seq community and is robust to standard analytical pipelines.

Reconciliation: When QC Signals Disagree

Pattern	Likely cause	Action
plotFingerprint diagonal but IP/input ratio shows enrichment	Mismatched chromosome naming (chr1 vs 1) between samples	Verify `samtools view -H bam
Replicate Spearman 0.95 but plotFingerprint diverges	Replicates correlate in bulk but differ in IP enrichment depth	Check per-sample sequencing depth; reduce to common depth
Saturation curve plateaus early but peak count low	Library complexity exhausted (e.g., over-amplified PCR)	Inspect duplicate rate; re-prep library if possible
MultiQC reports input has higher mapping rate than IP	IP enriches non-canonical sequences (m6A on intronic RNA, mt-RNA) that map differently	Acceptable if STAR multi-mapper retention is on; verify with idxstats
Properly-paired rate < 60%	Insert size distribution off (RNA degradation; library prep failure)	Inspect samtools view -f 0x2 count; re-prep if severe
HISAT2 reports many discordant pairs	Splice-junction not captured in index	Re-build with --dta and confirm GTF matches genome
plotFingerprint synthetic JS distance < 0.5	Marginal IP enrichment; borderline failed	Inspect per-transcript IP/input ratio distribution; consider exclusion

Quantitative Thresholds

Quantity	Threshold	Source / rationale
Minimum read length after trimming	25 nt	Below this, multi-mapping fraction inflates; downstream peak callers lose specificity
STAR --outFilterMultimapNmax for MeRIP	20	Retains multi-isoform mapping; tighten to 1 only when downstream cannot tolerate
--sjdbOverhang	read length - 1	STAR convention; 100 is common for 100-150 bp reads
Properly-paired rate (samtools flagstat)	>=85%	Below indicates degraded RNA or library-prep failure
Replicate Spearman correlation (multiBamSummary 10 kb bins)	>=0.85 (IP-vs-IP within condition)	Below suggests one replicate is anomalous
plotFingerprint IP-vs-input JS distance	>=0.5	Higher indicates better IP enrichment; <0.3 suggests failed IP
Saturation curve plateau depth	~30-60M unique reads typical	Below this, peak calling under-samples; depth depends on cell type / antibody
Per-transcript IP/input ratio median	>1.5 (genome-wide median)	Lower suggests failed IP; conditions / cell lines vary
Minimum biological replicates	3 (4-5 preferred) per condition	McIntyre 2020 Sci Rep 10:6590 — N=2 routinely under-powered
Dedup status for non-UMI MeRIP	OFF	Standard convention; UMI-MeRIP is the only exception
BAM sort order for downstream tools	Coordinate (SortedByCoordinate)	exomePeak2, MeTPeak, MACS3, deepTools all expect coordinate sort
bamCompare --binSize for downstream metagene	25	Fine enough to preserve peak topology; coarser only for whole-chromosome browser views
bamCompare --pseudocount	1	Prevents divide-by-zero at zero-coverage bins; larger values flatten signal

Common Errors

Error / symptom	Cause	Solution
STAR runs out of memory on human genome	--genomeDir build needs ~30 GB RAM	Use HISAT2 (~12 GB) or run STAR on a high-memory node
samtools index fails with "is not coordinate sorted"	BAM is name-sorted or unsorted	Re-run samtools sort (not sort -n)
exomePeak2 errors on TxDb chromosome mismatch	BAM uses chr1, GTF uses 1 (or reverse)	Verify with `samtools view -H bam
deepTools bamCompare --ratio log2 deprecation warning	Newer deepTools uses --operation log2	Switch to --operation log2
PreSeq lc_extrap rejects with "low complexity"	Library too shallow OR genome too small (BAM under 1M unique reads)	Use c_curve only; or sequence deeper
MultiQC misses STAR Log.final.out	STAR output naming non-standard	Re-run with --outFileNamePrefix and rerun MultiQC; check multiqc_config.yaml search patterns
picard MarkDuplicates collapses all reads to 1 per position	Tiny BAM or single read pair per fragment	Verify BAM has many properly-paired reads; do NOT dedup non-UMI MeRIP regardless
Empty fingerprint output	All BAMs have identical bin coverage	Verify BAMs are different files; check multiBamSummary --outRawCounts
bigWig file size too large	Bin size too small at deep coverage	Increase --binSize from 25 to 50; bigWig is lossy at large bin sizes
Saturation curve never plateaus	Library deeply under-sampled	Sequence deeper OR accept curve does not plateau and report accordingly
fastp --umi errors on non-UMI library	UMI flag passed but library has no UMI	Drop --umi flag for standard non-UMI MeRIP

Anticipated Reviewer Pushback

Pushback	Response
"Was deduplication applied?"	No — standard non-UMI MeRIP protocol; PCR duplicate vs biological resampling indistinguishable without UMI; dedup collapses real coverage at high-expression transcripts
"What is the IP enrichment QC?"	deepTools plotFingerprint reported per replicate; JS distance >=0.5 vs input
"Are the replicates concordant?"	Spearman correlation matrix reported via deepTools plotCorrelation on 10 kb bins; IP-IP within condition >=0.85
"Saturation curve?"	PreSeq lc_extrap per library; libraries rarefied to common depth before downstream peak calling
"What antibody clone and lot?"	Recorded per sample in metadata; same lot for all replicates within study
"Why STAR instead of HISAT2?"	STAR splice-junction-DB-based vs HISAT2 graph-based; both valid for MeRIP; choice driven by memory budget
"How many biological replicates?"	N >=3 per condition (per McIntyre 2020); N=2 is under-powered for differential downstream
"Was alignment to genome or transcriptome?"	Genome (required for exomePeak2 / MeTPeak / MACS3 downstream); transcriptome alignment is for m6anet-analysis only

References

• Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15-21. doi:10.1093/bioinformatics/bts635
• Kim D, Paggi JM, Park C, Bennett C, Salzberg SL (2019) Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37(8):907-915. doi:10.1038/s41587-019-0201-4
• Vasimuddin Md, Misra S, Li H, Aluru S (2019) Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems. IPDPS 314-324. doi:10.1109/IPDPS.2019.00041
• Chen S, Zhou Y, Chen Y, Gu J (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17):i884-i890. doi:10.1093/bioinformatics/bty560
• Ramírez F, Ryan DP, Grüning B et al (2016) deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 44(W1):W160-W165. doi:10.1093/nar/gkw257
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• Daley T, Smith AD (2013) Predicting the molecular complexity of sequencing libraries. Nat Methods 10(4):325-327. doi:10.1038/nmeth.2375
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Related Skills

• m6a-peak-calling - Immediate downstream consumer of the IP/input BAM pairs
• m6a-differential - Downstream differential analysis on peak count matrices; design matrix relies on IP/input pairing recorded here
• m6anet-analysis - ONT DRS alternative; uses TRANSCRIPTOME alignment with minimap2, NOT the genome BAMs produced here
• modification-visualization - Uses the bigWig output of bamCompare for metagene plots and browser tracks
• read-qc/quality-reports - FastQC / MultiQC upstream of trimming
• read-alignment/star-alignment - General STAR splice-aware alignment patterns
• read-alignment/hisat2-alignment - HISAT2 graph-based alternative; general usage
• alignment-files/sam-bam-basics - General BAM mechanics, samtools fundamentals
• alignment-files/bam-statistics - flagstat / idxstats / per-chromosome counts
• alignment-files/duplicate-handling - General dedup philosophy (note: NOT applicable to non-UMI MeRIP)
• chip-seq/chipseq-qc - ChIP-seq IP QC concepts (FRiP, fingerprint, library complexity) that transfer directly
• chip-seq/peak-calling - General IP-vs-input peak-calling concepts
• clip-seq/clip-preprocessing - Antibody-RNA crosslink protocols (miCLIP / m6A-CLIP) overlap with MeRIP design
• rna-quantification/featurecounts-counting - Count matrix construction for downstream differential
• workflows/rnaseq-to-de - End-to-end pipeline orchestration patterns