How to Handle Multi-Mapped Reads in Ribo-seq

const metadata = ; Introduction Multi-mapping is one of those annoying but common challenges in Ribo-seq data analysis, especially when you're using STAR for read alignment. Since Ribo-seq generates short reads (about 25–30 nt), it’s pretty easy for them to align to more than one location in the genome or transcriptome. This creates ambiguity in read assignment and can mess with the accuracy of your results, especially when calculating translation efficiency (TE). So, here’s the big question: Should we keep or discard multi-mapped reads when calculating TE in Ribo-seq data aligned with STAR? In this tutorial, we’re diving into this issue headfirst. We’ll cover how to install STAR on Linux Ubuntu, show you a minimal STAR alignment example, and walk you through the best practices for handling those pesky multi-mapped reads. We’ll also talk about the trade-offs involved in different strategies for multi-mapping and give you some real-world examples to help guide your decision-making. Let’s get started! Key Takeaways - Multi-mapping is a common challenge in Ribo-seq due to short read lengths (~25–30 nt). Example: In STAR alignment, multi-mapped reads are reported with low mapping quality (MAPQ 0). `bash STAR --runThreadN 4 --genomeDir /path/to/genome_index --readFilesIn sample_R1.fastq sample_R2.fastq --outFileNamePrefix output/ ` - STAR aligner is widely used for aligning Ribo-seq data and requires specific settings for multi-mapped reads. Example: Excluding multi-mapped reads by limiting alignments to 1: `bash STAR --runThreadN 4 --genomeDir /path/to/genome_index --readFilesIn sample_R1.fastq sample_R2.fastq --outFileNamePrefix output/ --outFilterMultimapNmax 1 ` - Multi-mapped reads can be excluded for cleaner TE analysis but may remove valuable biological information from repetitive gene regions. Example: Fractional counts for multi-mapped reads using tools like Salmon or Kallisto: `bash salmon quant -i transcript_index -l A -r sample_R1.fastq -p 8 --validateMappings ` - The best practice is to use uniquely mapped reads for TE calculations, especially if the biological context doesn’t require multi-mapped read inclusion. Example: Filter out multi-mapped reads before calculating TE: `bash ./calculate_TE.sh --excludeMultiMapped --input aligned.bam --output TE_results.txt ` - Always check the alignment log files (e.g., Log.final.out) to understand how multi-mapped reads are impacting your data. Example: Check multi-mapped reads in the Log.final.out: `bash grep "Number of multi-mapped reads" Log.final.out ` Prerequisites/Glossary Multi-mapped Reads Definition: A multi-mapped read aligns to more than one location in the genome or transcriptome, often due to repetitive regions or gene families. - Impact on Analysis: This ambiguity can affect gene expression and TE calculations. STAR (Spliced Transcripts Alignment to a Reference) Definition: STAR is a fast, efficient aligner for RNA-seq and Ribo-seq data, designed to map reads to a reference genome or transcriptome. - Key Features: STAR is known for its speed and accuracy, as well as its ability to align short reads (such as Ribo-seq) and handle large datasets efficiently. Ribo-seq (Ribosome Profiling) Definition: Ribo-seq captures and sequences ribosome-bound mRNA fragments, revealing translation activity across the genome. How It Works: Isolating ribosome-bound mRNA, then sequencing to identify ribosome positions on mRNA, offers insights into gene translation. Translation Efficiency (TE) Definition: TE is the ratio of Ribo-seq reads (indicating translation) to RNA-seq reads (indicating mRNA abundance). TE Calculation: TE=RNA -seq reads per geneRibo /-seq reads per gene Why It Matters: A high TE indicates active translation, while a low TE suggests inactive translation. By comparing TE values across genes, we can infer which genes are being translated more efficiently and which are less active. Reference Genome Definition: A reference genome is a complete mapped genome sequence used for aligning sequencing reads. In Ribo-seq, STAR uses it to align reads and identify ribosome positions. In Ribo-seq: The reference genome is used by STAR to align Ribo-seq reads, which helps identify ribosome positions on specific mRNA molecules. Input/Output Formats Definition: Input: For Ribo-seq and RNA-seq analysis, the input files are typically FASTQ files, which contain raw sequence data from the sequencing machine. Output: The output files are typically BAM or SAM files, which contain aligned read data. Additionally, STAR produces log files (like Log.final.out) that provide detailed statistics on the alignment process, including the number of multi-mapped reads. How-to Guide This section will provide step-by-step instructions for: 1. Installing STAR on Linux Ubuntu 2. Running a Minimal STAR Alignment Example 3. Handling Multi-Mapped Reads in Ribo-seq Data Let’s begin with the Installation Instructions for STAR Installing STAR on Linux Ubuntu Step 1: Update your System Before starting with the installation of STAR, make sure your system is up-to-date: `bash sudo apt-get update sudo apt-get upgrade ` Step 2: Install Dependencies STAR requires build-essential and zlib1g-dev libraries for compiling. Install them using: `bash sudo apt-get install build-essential zlib1g-dev ` Step 3: Download STAR Now, download the STAR aligner from GitHub: `bash wget https://github.com/alexdobin/STAR/archive/2.7.11b.zip ` Step 4: Extract the ZIP file Extract the downloaded ZIP file: `bash unzip STAR-2.7.11b.zip cd STAR-2.7.11b ` Step 5: Compile STAR To compile STAR from the source, use the make command: `bash make ` Step 6: Verify Installation Once compiled, verify the installation by checking the STAR version: `bash ./STAR --version ` Expected output: `bash [STAR] Version: 2.7.11b ` Running a Minimal STAR Alignment Example Step 1: Prepare Input Files Ensure you have Ribo-seq FASTQ files and a reference genome. Let’s assume the files are named sample_R1.fastq and sample_R2.fastq. Step 2: Run the STAR Alignment To align the Ribo-seq data with STAR, use the following command. This command will use 4 threads to speed up the alignment: `bash ./STAR --runThreadN 4 --genomeDir /path/to/genome_index --readFilesIn sample_R1.fastq sample_R2.fastq --outFileNamePrefix output/ ` Step 3: Check Alignment Output STAR will output several files, including: - Aligned.out.bam: The aligned read file. - Log.final.out: Alignment statistics, including the number of multi-mapped reads. Check the Log.final.out file to see the alignment summary and multi-mapped read statistics. Handling Multi-Mapped Reads in Ribo-seq Data Multi-mapped reads are common in Ribo-seq due to the short length of the reads and the repetitive nature of many gene regions. You can handle these reads in a few ways, depending on your analysis goals. Option 1: Exclude Multi-Mapped Reads To exclude multi-mapped reads during alignment, use the --outFilterMultimapNmax parameter. For example, set it to 1 to allow only unique alignments: `bash ./STAR --runThreadN 4 --genomeDir /path/to/genome_index --readFilesIn sample_R1.fastq sample_R2.fastq --outFileNamePrefix output/ --outFilterMultimapNmax 1 ` This will exclude all multi-mapped reads from the alignment. Option 2: Fractional Counting If you want to retain multi-mapped reads, you can use tools like Salmon or Kallisto to fractionally count multi-mapped reads across genes. These tools distribute the count of each multi-mapped read proportionally across all potential loci it could map to, minimizing bias. Example with Salmon: `bash salmon quant -i transcript_index -l A -r sample_R1.fastq -p 8 --validateMappings ` Next Steps STAR Alignment Example: We’ve now aligned the data and filtered multi-mapped reads. The next step is to validate the data using the Log.final.out and aligned BAM files. Handling Multi-Mapping Strategy: After aligning, we can discuss whether to exclude multi-mapped reads or use fractional counting to ensure TE analysis is accurate. “Ah, multi-mapped reads... like finding your keys in 5 places at once. It’s confusing, but with the right strategy, we can sort out the mess and find the one that really matters.” Minimal STAR Alignment Example 1. Running STAR Alignment: After installing STAR, the next step is aligning the Ribo-seq reads using the following command: `bash ./STAR --runThreadN 4 --genomeDir /path/to/genome_index --readFilesIn sample_R1.fastq sample_R2.fastq --outFileNamePrefix output/ ` This will align the FASTQ files and produce output files with the prefix output/. These files will include: - Aligned.out.bam: The aligned reads in BAM format. - Log.final.out: A summary of the alignment process, including multi-mapping statistics. 2. STAR Output Example (Log.final.out): After running the alignment, you can check the Log.final.out file for a summary of the alignment statistics: `bash cat output/Log.final.out ` This file will give you an overview of the alignment process, including: - Number of uniquely mapped reads. - Number of multi-mapped reads. - Total mapped reads. - Mapping quality. For example: `bash Number of input reads 1000000 Average input read length 150 Uniquely mapped reads 300000 Number of multi-mapped reads 200000 Total aligned reads 500000 ` These statistics help you assess the quality of the alignment and identify how many reads were multi-mapped. 3. Validating the Alignment with samtools flagstat To verify the alignment quality and check the number of multi-mapped reads, use samtools flagstat on the BAM file: `bash samtools flagstat output/Aligned.out.bam ` This will give a detailed report like: `bash 1000000 + 0 in total (QC-passed reads + QC-failed reads) 0 + 0 duplicates 500000 + 0 mapped (50.00% : N/A) 500000 + 0 paired in sequencing 250000 + 0 read1 250000 + 0 read2 200000 + 0 properly paired (40.00% : N/A) 0 + 0 with itself and mate mapped 0 + 0 singletons (0.00% : N/A) 200000 + 0 with mate mapped to a different chr 0 + 0 with mate mapped to a different chr (mapQ>=5) ` The mapped percentage and the multi-mapped read count will help you assess the quality of the alignment and ensure the expected number of multi-mappers. “Don’t just trust your alignment. If STAR’s output had a personality, it’d be that one friend who insists, ‘Trust me, I got this,’ but you check the results anyway. Better safe than sorry!” Common Mistakes & Fixes Mistake 1: Ribo-seq Contaminated with rRNA Reads Fix: rRNA contamination is a common issue in Ribo-seq. If rRNA reads are not removed before alignment, they dominate the data, leading to multi-mapping. Solution: Perform rRNA depletion during library preparation or filter out reads that align to rRNA sequences post-alignment using a tool like Samtools or Picard. Mistake 2: Adapter Sequences in the Reads Fix: Adapter contamination can cause poor alignment and unnecessary multi-mapping. Solution: Use tools like bbduk to trim adapter sequences from FASTQ files before alignment. Always check for adapters using FastQC. Mistake 3: Incorrect STAR Alignment Parameters Fix: Using inappropriate alignment parameters can lead to incorrect mappings or unnecessary multi-mapping. Solution: Ensure you are using appropriate STAR parameters, like --outFilterMultimapNmax to limit multi-mapped reads or --alignEndsType EndToEnd to avoid partial alignments. Mistake 4: Discarding Multi-Mapped Reads Without Proper Consideration Fix: Simply discarding multi-mapped reads without considering their biological relevance can lead to loss of data, especially in repetitive gene regions. Solution: Carefully consider whether multi-mapped reads should be excluded or handled using fractional counting (e.g., using Salmon or Kallisto) to retain more information without over-counting. Mistake 5: Not Verifying STAR Output Fix: Failure to validate STAR’s output using samtools flagstat and Log.final.out can result in overlooked alignment issues. Solution: Regularly verify STAR’s output to check for alignment quality, multi-mapped read counts, and overall mapping efficiency. “Don’t skip adapter trimming. It’s like setting up a race without checking your car’s tires. If you skip it, your results won’t be pretty!” Verification of STAR Installation After installing STAR, it's important to verify that it's correctly installed and ready to use. This can be done by checking the version of the STAR aligner. Step 1: Run the STAR Version Command To ensure STAR is installed correctly, open the terminal and run the following command `bash ./STAR --version ` Expected Output: If STAR is installed correctly, the terminal should display the version of STAR you installed. It will look something like this: `bash [STAR] Version: 2.7.11b ` This confirms that STAR is installed and ready for alignment tasks. Tying Back to the Biostars Article Handling multi-mapped reads in Ribo-seq data is a common challenge, and the Biostars discussion titled “Multi-mapped reads in Ribo-Seq data, discard or keep?” provides valuable insights from the bioinformatics community. The STAR aligner can report multi-mapped reads with low mapping quality (MAPQ 0), which requires proper handling for accurate TE calculations. The Biostars article offers recommendations on managing multi-mapped reads, including discarding them or using fractional counting methods (with tools like Salmon or Kallisto) to mitigate their impact on downstream analysis. These strategies help ensure that multi-mapping does not distort TE measurements. You can check out the full discussion on Biostars for a more in-depth understanding of best practices for handling multi-mapped reads: Biostars Discussion on Multi-Mapped Reads. Checklist for Bioinformaticians Here’s a simple, actionable checklist summarizing all the steps bioinformaticians should follow to set up their Ribo-seq analysis pipeline, especially when working with multi-mapped reads. Bioinformatics Pipeline Checklist for Ribo-seq Analysis 1. Install STAR: Ensure STAR is installed correctly on your system. Run STAR --version to verify installation. 2. Prepare Input Files: FASTQ files (Ribo-seq reads) for alignment. Reference genome or transcriptome for alignment. 3. Remove Contaminants: rRNA depletion or filtering is critical to avoid contamination. Adapter trimming to improve read quality (e.g., using bbduk). 4. Run STAR Alignment: Align the Ribo-seq data using STAR with appropriate parameters: `bash ./STAR --runThreadN 4 --genomeDir /path/to/genome_index --readFilesIn sample_R1.fastq sample_R2.fastq --outFileNamePrefix output/ ` 5. Handle Multi-Mapped Reads: Decide whether to exclude or fractionally count multi-mapped reads based on your analysis needs. Example to exclude multi-mappers: `bash ./STAR --runThreadN 4 --genomeDir /path/to/genome_index --readFilesIn sample_R1.fastq sample_R2.fastq --outFileNamePrefix output/ --outFilterMultimapNmax 1 ` 6. Review Alignment Output: Check the Log.final.out for alignment statistics, including multi-mapped read counts. 7. TE Calculation: Calculate Translation Efficiency (TE) using the ratio of Ribo-seq to RNA-seq reads: TE=RNA-seq reads per geneRib / -seq reads per gene​ 8. Post-Processing: Consider using fractional counting for multi-mapped reads if needed (tools like Salmon, Kallisto, or RSEM). 9. Verify Consistency: Compare Ribo-seq and RNA-seq processing pipelines for consistency in handling multi-mapped reads. 10. Document Your Pipeline: Ensure all steps, settings, and parameters are well-documented for reproducibility. Conclusion Handling multi-mapped reads in Ribo-seq data is critical, especially with tools like STAR. Bioinformaticians can achieve accurate TE analysis by either excluding multi-mappers or using fractional counting methods. Multi-mapped reads are common in Ribo-seq due to short read lengths, and while STAR is great for alignment, careful handling of multi-mappers is essential for reliable results. Excluding multi-mapped reads can give cleaner data but might miss information from repetitive gene regions. Alternatively, fractional counting with tools like Salmon or Kallisto helps retain more data without over-counting. Always verify STAR installation and review log files to ensure the pipeline is working correctly. With the right strategies, handling multi-mapped reads won’t be a roadblock, and it will lead to meaningful insights in Ribo-seq analysis.