GSE277047 Processing Pipeline

Publication

Molecular cell (2024) — PMID 39303722

Dataset

Integrated multi-omics analysis of zinc finger proteins uncovers roles in RNA regulation [RBNS]

1. 1

   Raw data provided is demultiplexed reads with adapters removed and only unique synthetic reads.

   umi_tools (dedup subcommand) (Inferred with models/gemini-2.5-flash) v1.1.2 GitHub

   $ Bash example

   # Install umi_tools if not already installed
   # conda install -c bioconda umi_tools
   
   # This command assumes 'input.bam' is an aligned BAM file with Unique Molecular Identifiers (UMIs) 
   # in the read names (e.g., after processing with umi_tools extract and alignment).
   # It performs UMI-based deduplication to retain only unique synthetic reads.
   # The input BAM is derived from demultiplexed, adapter-trimmed raw reads.
   umi_tools dedup \
       --extract-method=string \
       --method=unique \
       --output-stats=deduplication_stats.tsv \
       --log=deduplication.log \
       --paired \
       --stdin=input.bam \
       --stdout=output_unique.bam

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2. 2

   Library strategy: RBNS

   RBNS vN/A GitHub

   $ Bash example

   # RBNS (RNA Bind-n-Seq) is an experimental method to determine RNA-binding protein (RBP) binding specificities.
   # The computational analysis of RBNS data typically involves several steps:
   # 1. Alignment of sequencing reads to a defined RNA library (e.g., using Bowtie2 or BWA).
   # 2. Quantification of reads for each RNA sequence and identification of enriched sequences.
   # 3. Motif discovery on the identified enriched sequences to find the RBP's binding preferences.
   
   # This code block focuses on the motif discovery step, which is a key output of RBNS analysis.
   # It assumes that a FASTA file of enriched RNA sequences has already been generated from upstream processing.
   
   # Placeholder for input and output files
   # Replace with the actual FASTA file containing RNA sequences identified as enriched for RBP binding.
   # This file is typically generated after aligning RBNS reads to a synthetic RNA library and quantifying enrichment.
   ENRICHED_SEQUENCES_FASTA="rbns_enriched_sequences.fasta"
   OUTPUT_DIR="rbns_motif_discovery_output"
   
   mkdir -p "${OUTPUT_DIR}"
   
   # Execute MEME for motif discovery on enriched sequences
   # MEME (Multiple Em for Motif Elicitation) is a widely used tool for discovering novel, ungapped motifs.
   # Parameters:
   #   -dna: Specifies that the input sequences are DNA (or RNA, treated as DNA for motif finding).
   #   -mod zoops: Specifies the ZOOPS (Zero or One Occurrence Per Sequence) model, suitable for motifs that may appear once or not at all in each sequence.
   #   -nmotifs 3: Search for up to 3 motifs.
   #   -minw 6 -maxw 15: Specifies the minimum and maximum width of motifs to find.
   #   -o: Output directory.
   # conda install -c bioconda meme
   meme "${ENRICHED_SEQUENCES_FASTA}" -o "${OUTPUT_DIR}" -dna -mod zoops -nmotifs 3 -minw 6 -maxw 15

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Tools Used