CLIP-seq Preprocessing
UMI Extraction (eCLIP/iCLIP)
# Extract UMI from read 1
umi_tools extract \
--stdin=reads_R1.fastq.gz \
--read2-in=reads_R2.fastq.gz \
--bc-pattern=NNNNNNNNNN \
--stdout=R1_umi.fastq.gz \
--read2-out=R2_umi.fastq.gz
# bc-pattern: UMI barcode pattern
# N = UMI base
# For eCLIP: typically 10-nt UMI in read 1
Adapter Trimming
# Trim adapters after UMI extraction
cutadapt \
-a AGATCGGAAGAGCACACGTCT \
-A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT \
-m 18 \
-o trimmed_R1.fastq.gz \
-p trimmed_R2.fastq.gz \
R1_umi.fastq.gz R2_umi.fastq.gz
Two-Pass Trimming (eCLIP)
# eCLIP protocol has inline adapters
# First pass: trim 3' adapter
cutadapt -a AGATCGGAAGAGC -m 18 -o pass1.fq.gz input.fq.gz
# Second pass: trim 5' adapter (read-through)
cutadapt -g AGATCGGAAGAGC -m 18 -o pass2.fq.gz pass1.fq.gz
PCR Duplicate Removal
# After alignment, deduplicate using UMIs
umi_tools dedup \
--stdin=aligned.bam \
--stdout=deduped.bam \
--paired \
--method=unique
# Methods:
# unique: Exact UMI match
# cluster: Allow UMI mismatches (default)
# adjacency: Network-based clustering
Python Preprocessing
from umi_tools import UMIClusterer
import pysam
def count_umis_per_position(bam_path):
'''Count unique UMIs at each genomic position'''
from collections import defaultdict
position_umis = defaultdict(set)
with pysam.AlignmentFile(bam_path, 'rb') as bam:
for read in bam:
if read.is_unmapped:
continue
# Extract UMI from read name (added by umi_tools extract)
umi = read.query_name.split('_')[-1]
pos = (read.reference_name, read.reference_start)
position_umis[pos].add(umi)
return {pos: len(umis) for pos, umis in position_umis.items()}
Quality Control
def clip_qc(bam_path):
'''CLIP-seq specific QC metrics'''
import pysam
total = 0
unique_positions = set()
read_lengths = []
with pysam.AlignmentFile(bam_path, 'rb') as bam:
for read in bam:
if read.is_unmapped:
continue
total += 1
unique_positions.add((read.reference_name, read.reference_start))
read_lengths.append(read.query_length)
return {
'total_reads': total,
'unique_positions': len(unique_positions),
'mean_read_length': sum(read_lengths) / len(read_lengths),
'complexity': len(unique_positions) / total
}
Related Skills
- clip-alignment - Align preprocessed reads
- read-qc/umi-processing - General UMI handling
- clip-peak-calling - Call peaks from aligned reads
FridrichMethod