GSE92602 Processing Pipeline

Publication

Genes & development (2020) — PMID 32616519

Dataset

Identification of islet-enriched long non-coding RNAs contributing to beta-cell failure in type 2 diabetes

1. 1

   Base calling was with Illumina GAP Pipeline Software v1.90

   Illumina GAP Pipeline Software v1.90

   $ Bash example

   # Base calling is an initial processing step performed by the Illumina sequencing instrument's onboard software.
   # It converts raw signal data (intensities) from the sequencer into base calls (A, T, C, G) and quality scores.
   # This process is typically not executed by the user via a command-line tool post-sequencing.
   # The specified software, Illumina GAP Pipeline Software v1.90, is proprietary to Illumina.
   # No user-executable command is available for this step.

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2. 2

   Sequences were aligned to mm9 reference genome using Tophat 2.0.8 with option -g with mm9 reference GTF

   TopHat v2.0.8 GitHub

   $ Bash example

   # Install TopHat 2.0.8 and its dependencies (e.g., Bowtie 1.x)
   # Note: TopHat is an older tool and may require specific environment setup.
   # conda create -n tophat2_env tophat=2.0.8 bowtie=1.1.2 -c bioconda -c conda-forge
   # conda activate tophat2_env
   
   # Define input and output files (placeholders - replace with actual paths)
   READS_1="input_reads_R1.fastq.gz" # Path to input FASTQ file(s) for read 1
   # READS_2="input_reads_R2.fastq.gz" # Uncomment and provide path if paired-end reads
   OUTPUT_DIR="tophat_alignment_output"
   
   # Define reference files (placeholders - replace with actual paths)
   # mm9 reference genome FASTA file
   GENOME_FASTA="/path/to/mm9.fa"
   # mm9 reference GTF file
   GTF_FILE="/path/to/mm9.gtf"
   # Prefix for the Bowtie index files built from the mm9 genome
   BOWTIE_INDEX_PREFIX="/path/to/mm9_bowtie_index/mm9"
   
   # --- Pre-computation: Build Bowtie index if not already present ---
   # TopHat requires a Bowtie index for the reference genome.
   # If the index for mm9 is not already built at BOWTIE_INDEX_PREFIX, uncomment and run the following:
   # mkdir -p $(dirname "$BOWTIE_INDEX_PREFIX")
   # bowtie-build "$GENOME_FASTA" "$BOWTIE_INDEX_PREFIX"
   
   # --- Run TopHat 2.0.8 for alignment ---
   # Align sequences to the mm9 reference genome using the provided GTF for splice junction discovery.
   # The -g option specifies the GTF file.
   tophat2 \
       -o "$OUTPUT_DIR" \
       -g "$GTF_FILE" \
       "$BOWTIE_INDEX_PREFIX" \
       "$READS_1" # Add "$READS_2" here if using paired-end reads
   

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3. 3

   Novel transcripts were predicted with Cufflinks 2.1.1

   Cufflinks v2.1.1 GitHub

   $ Bash example

   # Install Cufflinks (example using conda)
   # conda install -c bioconda cufflinks=2.1.1
   
   # Define input and output paths
   # Replace 'path/to/aligned_reads.bam' with your actual input BAM file
   INPUT_BAM="path/to/aligned_reads.bam"
   # Replace 'path/to/reference_annotation.gtf' with your actual reference GTF/GFF file (e.g., from GENCODE or Ensembl)
   REFERENCE_GTF="path/to/Homo_sapiens.GRCh38.109.gtf" 
   OUTPUT_DIR="cufflinks_novel_transcripts_output"
   
   # Create output directory if it doesn't exist
   mkdir -p "${OUTPUT_DIR}"
   
   # Run Cufflinks to predict novel transcripts
   # -o: Output directory
   # -g: Reference annotation to guide assembly and identify novel transcripts
   # --frag-bias-correct: Correct for sequence-specific bias
   # --multi-read-correct: Correct for reads mapping to multiple locations
   # -p: Number of threads (adjust as needed)
   cufflinks \
     -o "${OUTPUT_DIR}" \
     -g "${REFERENCE_GTF}" \
     --frag-bias-correct \
     --multi-read-correct \
     -p 8 \
     "${INPUT_BAM}"

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4. 4

   Novel transcripts predictions were merged with mm9 reference genome using Cuffmerge 2.1.1 with option -G with mm9 reference GTF

   Cuffmerge v2.1.1 GitHub

   $ Bash example

   # Install Cufflinks suite (which includes Cuffmerge)
   # conda install -c bioconda cufflinks=2.1.1
   
   # Define reference paths
   # Placeholder paths for mm9 reference GTF and FASTA.
   # These files can typically be downloaded from UCSC Genome Browser (e.g., http://hgdownload.soe.ucsc.edu/goldenPath/mm9/bigZips/)
   # or Ensembl (e.g., ftp://ftp.ensembl.org/pub/release-54/gtf/mus_musculus/)
   MM9_REFERENCE_GTF="/path/to/mm9.ncbiRefSeq.gtf"
   MM9_REFERENCE_FASTA="/path/to/mm9.fa"
   
   # Define input file(s) for novel transcript predictions.
   # This should be a text file where each line is the path to a GTF file
   # containing novel transcript predictions (e.g., from Cufflinks assembly output).
   NOVEL_TRANSCRIPTS_GTF_LIST="novel_transcript_assemblies.txt"
   
   # Define output file for the merged GTF
   OUTPUT_MERGED_GTF="merged_novel_transcripts.gtf"
   
   # Execute Cuffmerge to merge novel transcript predictions with the mm9 reference GTF
   # The -g option specifies the reference annotation GTF file.
   # The -s option specifies the reference genome FASTA file.
   cuffmerge -g "${MM9_REFERENCE_GTF}" -s "${MM9_REFERENCE_FASTA}" "${NOVEL_TRANSCRIPTS_GTF_LIST}" -o "${OUTPUT_MERGED_GTF}"

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5. 5

   Counts were generated using htseq-count v0.5.4p3

   HTSeq v0.5.4p GitHub

   $ Bash example

   # Install HTSeq (if not already installed)
   # conda install -c bioconda htseq
   
   # Example usage of htseq-count for generating gene counts from an alignment file and a GTF annotation.
   # Parameters are inferred based on common usage for RNA-seq data.
   # -f bam: Input file format is BAM.
   # -r pos: Reads are sorted by position.
   # -s no: Data is unstranded (use 'yes' or 'reverse' for stranded data).
   # -a 10: Minimum alignment quality score is 10.
   # -t exon: Feature type to count is 'exon'.
   # -i gene_id: Attribute in the GTF file to use as feature ID (e.g., gene_id).
   # aligned_reads.bam: Placeholder for the input alignment file.
   # gencode.vXX.annotation.gtf: Placeholder for the GTF annotation file (e.g., for human GRCh38/hg38, use a recent Gencode version).
   # > gene_counts.txt: Output file for the generated counts.
   htseq-count \
     -f bam \
     -r pos \
     -s no \
     -a 10 \
     -t exon \
     -i gene_id \
     aligned_reads.bam \
     gencode.vXX.annotation.gtf \
     > gene_counts.txt

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Tools Used