Answers.Body: <p>This kit uses the dUTP method to preserve strandedness. That (to my knowledge) always produces a reverse library. Not sure where you have your information of a forward library from, but we used this kit for many years and it is reverse.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-10-21T13:04:38.247Z
Answers.LastActivityDate: 2023-10-21T13:04:38.247Z
Answers.UserDisplayName: ATpoint
Body: <p>My RNAseq analysis pipeline is as follows: fastqc (read quality is good, some overrepresentation of adaptor sequence) → trimmomatic (trimmed adaptor sequence, qc report after trimming suggests the overrepresented adaptors are gone) → HISAT2 (used a forward stranded alignment protocol) → featurecounts</p>
  <p>My HISAT 2 command line:</p>
  <pre><code>hisat2 --rna-strandness F -x HISAT2/grch38/genome -U &quot;<span class="math-container">$fastq_file" | samtools sort -o "HISAT2/$</span>{sample_name}_aligned.bam&quot;
  </code></pre>
  <p>My featurecounts command line:</p>
  <pre><code>featureCounts -s 2 -a hg38/gencode.v44.chr_patch_hapl_scaff.annotation.gtf -o &quot;quant/<span class="math-container">${sample_name}_featureCounts.txt" "$</span>bam_file&quot;
  </code></pre>
  <p>However, the strange thing is that if I use a forward stranded protocol in feature counts (with the forward stranded alignment I generated with HISAT2), the assignment rate is 5%, with the majority of unassigned reads being having no features. But if I use a reverse stranded protocol in feature counts, the assignment rate is 70%. Does this suggest my data is reverse stranded?</p>
  <p>My library was prepared by the NEB directional RNA library prep kit that claims to produce forward stranded libraries, so I’m really confused by this result. How should I proceed with this data? Should I run an alignment with a reverse strand protocol and just proceed with a reverse strand assignment? Or should I proceed with the forward alignment and forward strand assignment data because that’s what the library is supposed to be?</p>
  <p>Thank you so much for your help!</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-10-20T07:14:02.130Z
Comments.Text: Not a direct answer but an alternative approach: The `nfcore/rnaseq` `nextflow` pipeline infers strandedness automatically and feeds in the "right" parameters to the tools used in the pipeline so you do not have to worry about this. You can even find the inferred strand info int he output directory of the `nextflow` run. And if you want to check your reads, you can give a try with `Salmon` or `RSeqQC`.
Comments.UserDisplayName: haci
Tags: <rna-seq><rna-alignment><featurecounts><strandedness>
Title: Discrepancy with featurecounts analysis using a forward stranded and reverse stranded protocol
ThreadText: # Discrepancy with featurecounts analysis using a forward stranded and reverse stranded protocol
  
  (Asked by: username_0 on 2023-10-19)
  
  My RNAseq analysis pipeline is as follows: fastqc (read quality is good, some overrepresentation of adaptor sequence) → trimmomatic (trimmed adaptor sequence, qc report after trimming suggests the overrepresented adaptors are gone) → HISAT2 (used a forward stranded alignment protocol) → featurecounts
  
  My HISAT 2 command line:
  
  ```
  hisat2 --rna-strandness F -x HISAT2/grch38/genome -U "$fastq_file" | samtools sort -o "HISAT2/${sample_name}_aligned.bam"
  
  ```
  
  My featurecounts command line:
  
  ```
  featureCounts -s 2 -a hg38/gencode.v44.chr_patch_hapl_scaff.annotation.gtf -o "quant/${sample_name}_featureCounts.txt" "$bam_file"
  
  ```
  
  However, the strange thing is that if I use a forward stranded protocol in feature counts (with the forward stranded alignment I generated with HISAT2), the assignment rate is 5%, with the majority of unassigned reads being having no features. But if I use a reverse stranded protocol in feature counts, the assignment rate is 70%. Does this suggest my data is reverse stranded?
  
  My library was prepared by the NEB directional RNA library prep kit that claims to produce forward stranded libraries, so I’m really confused by this result. How should I proceed with this data? Should I run an alignment with a reverse strand protocol and just proceed with a reverse strand assignment? Or should I proceed with the forward alignment and forward strand assignment data because that’s what the library is supposed to be?
  
  Thank you so much for your help!
  
  **Tags:** rna-seq, rna-alignment, featurecounts, strandedness
  
  > **Comments:**
  > **username_1**: Not a direct answer but an alternative approach: The `nfcore/rnaseq` `nextflow` pipeline infers strandedness automatically and feeds in the "right" parameters to the tools used in the pipeline so you do not have to worry about this. You can even find the inferred strand info int he output directory of the `nextflow` run. And if you want to check your reads, you can give a try with `Salmon` or `RSeqQC`.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-10-21. Score: 3
  
  This kit uses the dUTP method to preserve strandedness. That (to my knowledge) always produces a reverse library. Not sure where you have your information of a forward library from, but we used this kit for many years and it is reverse.
  
  
  ---
  
  