GSE104501 Processing Pipeline

Publication

Nature structural & molecular biology (2017) — PMID 29106412

Dataset

Poly(A) Tail Length of L4 C. elegans

1. 1

   Base calling, trimming of adapter sequences, removal of duplicated reads and determination of poly(A) tail sizes were performed by tailseeker2.

   tailseeker2 v2.0.0

   $ Bash example

   # Install tailseeker2 (example, actual installation might vary)
   # conda create -n tailseeker2_env python=3.8
   # conda activate tailseeker2_env
   # pip install tailseeker2 # Or via bioconda if available
   
   # Placeholder for input and output files
   INPUT_FASTQ="raw_nanopore_reads.fastq.gz"
   OUTPUT_TSV="polyA_tail_lengths.tsv"
   REFERENCE_GENOME="path/to/human_hg38.fasta" # Using hg38 as a common placeholder for reference genome
   
   # The description states: "Base calling, trimming of adapter sequences, removal of duplicated reads and determination of poly(A) tail sizes were performed by tailseeker2."
   # tailseeker2 processes base-called FASTQ files; it does not perform base calling from raw .fast5 files. It is assumed the input FASTQ is already base-called.
   # While tailseeker2 performs adapter trimming (--trim-adapters) and poly(A) tail size determination, it does not have a direct command-line option for explicit removal of duplicated reads.
   # It is assumed that duplicate removal is either handled by a preceding step or is implicitly managed by tailseeker2's internal filtering, or the description is generalized.
   
   tailseeker2 \
       --input "${INPUT_FASTQ}" \
       --output "${OUTPUT_TSV}" \
       --reference "${REFERENCE_GENOME}" \
       --trim-adapters \
       --min-tail-length 10 \
       --min-read-length 50 \
       --threads $(nproc)

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2. 2

   Reads were analyzed by mapping to the WS247 assembly of the C. elegans genome using RNA-STAR.

   STAR v2.7.10a (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install STAR (example using conda)
   # conda install -c bioconda star
   
   # --- Reference Data Preparation (C. elegans WS247) ---
   # Download C. elegans WS247 genome FASTA and GTF annotation files.
   # WS247 is an older release. For current data, refer to WormBase (e.g., WS289).
   # For WS247, you would typically access older WormBase FTP archives or specific project repositories.
   # Example placeholder paths for downloaded files:
   # GENOME_FASTA="/path/to/c_elegans.WS247.genomic.fa"
   # GTF_ANNOTATION="/path/to/c_elegans.WS247.annotations.gtf"
   
   # Create a directory for the STAR genome index
   mkdir -p C_elegans_WS247_STAR_index
   
   # Build STAR genome index for C. elegans WS247
   # Replace /path/to/c_elegans.WS247.genomic.fa and /path/to/c_elegans.WS247.annotations.gtf with actual paths
   STAR --runMode genomeGenerate \
        --genomeDir C_elegans_WS247_STAR_index \
        --genomeFastaFiles /path/to/c_elegans.WS247.genomic.fa \
        --sjdbGTFfile /path/to/c_elegans.WS247.annotations.gtf \
        --sjdbOverhang 100 \
        --runThreadN 8
   
   # --- Reads Alignment ---
   # Replace reads_R1.fastq.gz and reads_R2.fastq.gz with your actual input FASTQ files.
   # Assuming paired-end reads. For single-end, remove the second file from --readFilesIn.
   STAR --runMode alignReads \
        --genomeDir C_elegans_WS247_STAR_index \
        --readFilesIn reads_R1.fastq.gz reads_R2.fastq.gz \
        --readFilesCommand zcat \
        --outFileNamePrefix aligned_reads_ \
        --outSAMtype BAM SortedByCoordinate \
        --outSAMunmapped Within \
        --outFilterType BySJout \
        --outFilterMultimapNmax 20 \
        --outFilterMismatchNmax 999 \
        --outFilterMismatchNoverLmax 0.1 \
        --alignIntronMin 20 \
        --alignIntronMax 1000000 \
        --alignMatesGapMax 1000000 \
        --sjdbScore 1 \
        --runThreadN 8
   

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3. 3

   Poly(A) lengths were then assigned to individual coding genes by intersecting the mapped sequences with WormBase.org WS247 gene annotations using BEDTools.

   bedtools v2.30.0 GitHub

   $ Bash example

   # Install bedtools (if not already installed)
   # conda install -c bioconda bedtools
   
   # --- Reference Data Setup ---
   # Download WormBase WS247 C. elegans gene annotations (GFF3 format).
   # The specific URL for WS247 GFF3 might vary slightly or be hosted differently over time.
   # This is a representative path for a past release.
   # wget -O c_elegans.PRJNA13758.WS247.gff3.gz "ftp://ftp.wormbase.org/pub/wormbase/releases/WS247/GFF/c_elegans.PRJNA13758.WS247.gff3.gz"
   # gunzip c_elegans.PRJNA13758.WS247.gff3.gz
   
   # Convert GFF3 to BED format for bedtools intersect.
   # This example filters for 'gene' features and converts to 0-based start, 1-based end BED format.
   # It extracts chromosome, start-1, end, gene_id (from Name= or ID= attribute), score (placeholder), strand.
   # A more robust GFF3 to BED conversion might use tools like gff2bed from BEDOPS.
   # awk '$3 == "gene"' c_elegans.PRJNA13758.WS247.gff3 | \
   #     awk -v OFS='\t' '{
   #         chr=$1; start=$4-1; end=$5; strand=$7;
   #         gene_id=".";
   #         if ($9 ~ /ID=([^;]+)/) { gene_id = substr($9, RSTART+3, RLENGTH-3); }
   #         else if ($9 ~ /Name=([^;]+)/) { gene_id = substr($9, RSTART+5, RLENGTH-5); }
   #         print chr, start, end, gene_id, "0", strand
   #     }' | \
   #     grep -P "^[[:alnum:]]+" > wormbase_ws247_genes.bed
   
   # --- Input Files ---
   # Placeholder for mapped sequences (e.g., poly(A) site reads, or 3'UTR reads).
   # These sequences should be in BED format (e.g., chr, start, end, name, score, strand).
   # Example: echo -e "chrI\t1000\t1050\tread1\t0\t+\nchrI\t2000\t2030\tread2\t0\t-" > mapped_sequences.bed
   mapped_sequences_bed="mapped_sequences.bed"
   
   # Placeholder for WormBase WS247 gene annotations in BED format.
   # This file is assumed to have been prepared using the steps above or similar.
   wormbase_genes_bed="wormbase_ws247_genes.bed"
   
   # --- Execution Command ---
   # Intersect mapped sequences with WormBase WS247 gene annotations.
   # -wao: Write the original entry in A and B for each overlap, plus the number of overlapping bases.
   # This output allows associating each poly(A) sequence with its overlapping gene and its attributes,
   # which is crucial for subsequent assignment of poly(A) lengths to individual coding genes.
   bedtools intersect -a "${mapped_sequences_bed}" -b "${wormbase_genes_bed}" -wao > polyA_sequences_and_overlapping_genes.bed
   
   # Further processing (e.g., using awk, Python, or R) would be needed to group
   # by gene (from the B file columns in the output) and calculate poly(A) lengths
   # (derived from the A file columns in the output) per gene.

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Tools Used