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How to feed HISAT2 results into expression tools?

Guide on processing HISAT2 results into expression tools. Convert and sort BAM files, choose your quantification tool, set up annotations, and integrate results into analyses.

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How to feed HISAT2 results into expression tools?

 

Prepare HISAT2 Aligned Reads

 

  • HISAT2 generates SAM format output by default. First, convert the SAM file to BAM format using SAMtools for efficient processing.
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  • Sort the BAM file by coordinates using SAMtools, as many expression tools require sorted BAM files.
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  • Index the sorted BAM file using SAMtools to facilitate quick access to the data during downstream analysis.

 

Select an Expression Quantification Tool

 

  • Common expression quantification tools include StringTie, featureCounts, and HTSeq. Choose one that best fits your needs in terms of features and compatibility with downstream analyses.
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  • Ensure your chosen tool is properly installed in your environment. Follow specific installation guides for each software to avoid compatibility issues.

 

Set Up Annotation Files

 

  • Download the reference annotation file (GTF or GFF format) for your organism. This file contains genomic feature information necessary for counting reads.
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  • Ensure the annotation file is compatible with your BAM file in terms of reference names and versions.

 

Run Expression Quantification Tool

 

  • For StringTie: Run StringTie with the sorted BAM file and the annotation GTF file to estimate expression levels. Use the `-e` option to enable more accurate expression estimates by limiting read assignment to specified features.
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  • For featureCounts: Run featureCounts with the sorted BAM file and the annotation file. Ensure you specify appropriate options for strand-specific counting if your data require it.
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  • For HTSeq: Run HTSeq-count with the sorted BAM file and the annotation file. Specify options for stranded data if necessary, and choose the appropriate mode such as intersection-strict or union.

 

Output and Format Results

 

  • For StringTie: The output will be a GTF file containing expression levels. You can convert this file into a tabular format using additional StringTie utilities if needed.
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  • For featureCounts: The output is a tab-delimited text file listing counts for each feature. You may further process this file to normalize counts using R packages like DESeq2 or edgeR.
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  • For HTSeq: The output is a counts file, which you can feed into statistical analysis packages for differential expression analysis.

 

Post-Processing and Quality Checks

 

  • Check the alignment rates and feature counting statistics to ensure data quality. This helps identify issues such as low mapping rates or unexpected biases.
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  • Perform additional normalization and batch correction if necessary, using tools and methods suitable for your data and study design.

 

Integrate into Downstream Analyses

 

  • Use the expression data in downstream analyses such as differential expression analysis, clustering, or pathway analysis. Ensure your data is appropriately normalized and corrected for systematic biases before further analysis.
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  • Document all data processing steps for reproducibility, and consider sharing your pipeline and scripts within your research community.

 

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