Answers.Body: <p>You can do full-length whole-transcriptome sequencing using either the <a href="https://store.nanoporetech.com/catalog/product/view/id/167/s/direct-rna-sequencing-kit/category/28/" rel="nofollow noreferrer">direct RNA kit</a> or a cDNA kit (<a href="https://store.nanoporetech.com/catalog/product/view/id/177/s/cdna-pcr/category/28/" rel="nofollow noreferrer">with</a> or <a href="https://store.nanoporetech.com/catalog/product/view/id/178/s/direct-cdna/category/28/" rel="nofollow noreferrer">without</a> PCR) on a MinION. This will allow you to quantify isoforms directly, rather than estimating by looking at read pairs.</p>
  
  <p>However, it'd be a bit expensive to do it that way if you're looking at a single transcript (and aren't barcoding to multiplex samples on the same run).</p>
  
  <p>If you've already done Illumina whole-transcriptome sequencing (e.g. strand-specific paired-end sequencing), tools like <a href="https://pachterlab.github.io/kallisto/download" rel="nofollow noreferrer">Kallisto</a>, <a href="https://github.com/deweylab/RSEM#introduction" rel="nofollow noreferrer">RSEM</a>, or <a href="https://github.com/COMBINE-lab/salmon/releases" rel="nofollow noreferrer">Salmon</a> can be used to estimate isoform expression based on the mapped reads. These tools use the information about transcript placement and/or similarity to alter the observed read counts in an attempt to better represent reality.</p>
  
  <p>Unfortunately, if you want these tools to quantify intronic sequence (i.e. the $2^4$ possibilities you have suggested), then those sequences need to be included in the sequences provided. They won't estimate frequencies for non-existent sequence.</p>
  
  <p>As an alternative to including every possible gene model, something like a <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1284-1" rel="nofollow noreferrer">SuperTranscript model</a> might work. I'm not familiar with Alicia Oshlack's method, but it at least gives the appearance that it might reduce the complexity of searching for multiple unannotated isoforms within a gene.</p>
  
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Answers.CreationDate: 2017-11-15T01:47:39.407Z
Answers.Text: So the current transcript level quantification is performed with Salmon. Essentially salmon says that the retained intronic transcript DROSHA-203 (ie., ENST00000504133.5) is present in the samples. However when you look at the transcriptome fasta or the actual annotation outlined by Ensembl, it only lists the exonic portions of the transcript. My issue is quantifying which introns are being retained in the resulting transcript. As it stand it looks like there are multiple possibilities based upon the cDNA sequence provided through the annotation.
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Answers.LastEditDate: 2017-11-15T02:33:48.183Z
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Body: <p><em>Note: this question has also been asked on <a href="https://www.biostars.org/p/283612/" rel="nofollow noreferrer">Biostars</a></em></p>
  
  <p>We are seeing a retained intron transcript event for some RNAseq samples, and we want to assess at the sequence level which intron retention events are actually occurring so that we can do protein domain predictions. For example, we have transcript level expression support for DROSHA-203, which according to Ensembl is a retained intron transcript that potential gives rise to multiple splicing variants via the cDNA sequence information: </p>
  
  <p><a href="https://i.sstatic.net/OnAMx.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/OnAMx.png" alt="DROSHA-203 cDNA"></a></p>
  
  <p>In theory, the orange sites indicate where a mutation might occur which could obliterate a splicing recognition site, thereby causing an retained intron event. So in total there are 6 potential mutational events which might give rise to 4 possible retention events. Given that 4 intronic regions might be retained, the number of unique retention events is a combinatorics question equal to 2^4, e.g., intron1 retained only, vs intron1 + 2 retained, all possible unique combination, etc, correct?</p>
  
  <p>Is there a better way to more granularly quantify these possible transcripts? Or do we have to PCR validate all of them to figure out which one's are actually occurring? Is there a way that I can look at the reads mapping to the corresponding intronic regions to make even a probabilistic assessment of which transcripts are present and to what extent they are expressed? The libraries are prepped for total RNA with a ribozero depletion, so I should still be able to quantify any enrichment to these intronic regions, correct?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-11-15T09:40:29.077Z
Comments.Text: Did you look up if there is any kind of LD between these positions? Maybe whenever a mutation in 359 occurs, there's also a mutation in the the previous splicing event.
Comments.UserDisplayName: llrs
Tags: <rna-seq>
Title: Quantification of EST/cDNA based retained intron transcripts
ThreadText: # Quantification of EST/cDNA based retained intron transcripts
  
  (Asked by: username_0 on 2017-11-14)
  
  *Note: this question has also been asked on [Biostars](https://www.biostars.org/p/283612/)*
  
  We are seeing a retained intron transcript event for some RNAseq samples, and we want to assess at the sequence level which intron retention events are actually occurring so that we can do protein domain predictions. For example, we have transcript level expression support for DROSHA-203, which according to Ensembl is a retained intron transcript that potential gives rise to multiple splicing variants via the cDNA sequence information:
  
  [![DROSHA-203 cDNA](https://i.sstatic.net/OnAMx.png)](https://i.sstatic.net/OnAMx.png)
  
  In theory, the orange sites indicate where a mutation might occur which could obliterate a splicing recognition site, thereby causing an retained intron event. So in total there are 6 potential mutational events which might give rise to 4 possible retention events. Given that 4 intronic regions might be retained, the number of unique retention events is a combinatorics question equal to 2^4, e.g., intron1 retained only, vs intron1 + 2 retained, all possible unique combination, etc, correct?
  
  Is there a better way to more granularly quantify these possible transcripts? Or do we have to PCR validate all of them to figure out which one's are actually occurring? Is there a way that I can look at the reads mapping to the corresponding intronic regions to make even a probabilistic assessment of which transcripts are present and to what extent they are expressed? The libraries are prepped for total RNA with a ribozero depletion, so I should still be able to quantify any enrichment to these intronic regions, correct?
  
  **Tags:** rna-seq
  
  > **Comments:**
  > **username_1**: Did you look up if there is any kind of LD between these positions? Maybe whenever a mutation in 359 occurs, there's also a mutation in the the previous splicing event.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2017-11-14. Score: 1
  
  You can do full-length whole-transcriptome sequencing using either the [direct RNA kit](https://store.nanoporetech.com/catalog/product/view/id/167/s/direct-rna-sequencing-kit/category/28/) or a cDNA kit ([with](https://store.nanoporetech.com/catalog/product/view/id/177/s/cdna-pcr/category/28/) or [without](https://store.nanoporetech.com/catalog/product/view/id/178/s/direct-cdna/category/28/) PCR) on a MinION. This will allow you to quantify isoforms directly, rather than estimating by looking at read pairs.
  
  However, it'd be a bit expensive to do it that way if you're looking at a single transcript (and aren't barcoding to multiplex samples on the same run).
  
  If you've already done Illumina whole-transcriptome sequencing (e.g. strand-specific paired-end sequencing), tools like [Kallisto](https://pachterlab.github.io/kallisto/download), [RSEM](https://github.com/deweylab/RSEM#introduction), or [Salmon](https://github.com/COMBINE-lab/salmon/releases) can be used to estimate isoform expression based on the mapped reads. These tools use the information about transcript placement and/or similarity to alter the observed read counts in an attempt to better represent reality.
  
  Unfortunately, if you want these tools to quantify intronic sequence (i.e. the $2^4$ possibilities you have suggested), then those sequences need to be included in the sequences provided. They won't estimate frequencies for non-existent sequence.
  
  As an alternative to including every possible gene model, something like a [SuperTranscript model](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1284-1) might work. I'm not familiar with Alicia Oshlack's method, but it at least gives the appearance that it might reduce the complexity of searching for multiple unannotated isoforms within a gene.
  
  > **Comments:**
  > **username_0**: So the current transcript level quantification is performed with Salmon. Essentially salmon says that the retained intronic transcript DROSHA-203 (ie., ENST00000504133.5) is present in the samples. However when you look at the transcriptome fasta or the actual annotation outlined by Ensembl, it only lists the exonic portions of the transcript. My issue is quantifying which introns are being retained in the resulting transcript. As it stand it looks like there are multiple possibilities based upon the cDNA sequence provided through the annotation.  
  
  ---
  
  