<p>I will take the liberty of giving one possible answers to my own question – but I’m very interested in other answers.</p>

<p>One analysis type that such data enables is the analysis of transcript switches with predicted potential consequences. </p>

<p>I myself have recently developed such a tool called <a href="http://bioconductor.org/packages/IsoformSwitchAnalyzeR/" rel="nofollow noreferrer">IsoformSwitchAnalyzeR</a>. IsoformSwitchAnalyzeR enables statistical identification (via <a href="https://bioconductor.org/packages/release/bioc/html/DRIMSeq.html" rel="nofollow noreferrer">DRIMSeq</a>) of isoform switches with predicted functional consequences. The consequences analyzed can be chosen from a long list which includes gain/loss of protein domains, signal peptides changes in NMD sensitivity etc. The R package also enables easy visualization of isoform switches along with their consequences and it directly supports the output of <a href="http://cole-trapnell-lab.github.io/cufflinks/" rel="nofollow noreferrer">Cufflinks/Cuffdiff</a>, <a href="http://deweylab.github.io/RSEM/" rel="nofollow noreferrer">RSEM</a>, <a href="https://github.com/COMBINE-lab/salmon" rel="nofollow noreferrer">Salmon</a> and <a href="http://pachterlab.github.io/kallisto/" rel="nofollow noreferrer">Kallisto</a>.</p>

<p>Apart from enabling identification of interesting examples switch identification also enables systematic analysis of what genes are affected. For inspiration I will recommend <a href="http://mcr.aacrjournals.org/content/early/2017/06/02/1541-7786.MCR-16-0459" rel="nofollow noreferrer">one of my own articles</a> (describing results obtained with IsoformSwitchAnalyzeR) as well Hector et al’s <a href="http://biorxiv.org/content/early/2016/09/21/076653" rel="nofollow noreferrer">recent paper</a> (which is not using IsoformSwitchAnalyzeR). Of particular interest and finesse is Hector’s analysis of how isoform switches can disrupt protein-protein interactions.</p>

<p>Looking forward to hear more ideas. </p>

CC BY-SA 3.0
2017-06-16T11:38:59.103Z
2017-06-16T11:55:50.510Z
2017-06-16T11:55:50.510Z
Kristoffer Vitting-Seerup
<p>I am not sure if this has been done, is common, or are research lines, but here is what I think can be done with transcripts' differences (aside from comparing the change of the expression of each transcript):</p>
<p>Having the different transcripts, enables us to study also the relation between transcripts, which are co-expressed together or differently co-expressed. Maybe at the individual level the switch between transcripts of a gene is not related to a condition, but together with other transcripts it is.</p>
<p>Transcripts usage could be also linked to structural or genomic changes. Either short/small changes or big. At genomic level SNP, inversions or methylation..., or structural changes such as compression of a region, different distances between chromosomes/regions...</p>

CC BY-SA 3.0
2017-06-21T08:55:01.680Z
Could you elaborate on what you mean by "differently co-expressed" ?
Kristoffer Vitting-Seerup
CC BY-SA 3.0
2017-06-21T09:02:25.040Z
The way a transcript (or a gene) is expressed with other transcripts can change between conditions. If in a condition transcript A is co-expressed with B, C and D, in another condition it can be co-expressed with D, E and F. See this [paper](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-497)
llrs
CC BY-SA 4.0
2017-06-21T08:07:33.170Z
2020-06-20T13:36:55.597Z
2020-06-20T13:36:55.597Z
llrs
<p>Additional insights can frequently be made in research by studying things at a lower level. By peeling back the higher layers, effects can be seen that are hidden (or reversed) when looking at aggregate data. This is generally referred to as <a href="https://en.wikipedia.org/wiki/Simpson%27s_paradox" rel="nofollow noreferrer">Simpson's Paradox</a>.</p>

<p>We know that RNA is functional at an isoform/transcript level, and in some cases the expression of different transcripts from the same gene can have different biologically-relevant outcomes. By looking at transcript-level information, even if the transcripts for a gene average out to be a similar level between different treatment conditions, variation at an individual transcript level can still be discovered. This is important when looking at differential expression of <em>populations</em> of cells, because it can clue researchers into finding out that a particular cell sub-population might be of interest; single cells typically will only produce single isoforms for each gene at a specific point in time.</p>

CC BY-SA 3.0
2018-01-11T04:06:30.943Z
2018-01-11T04:06:30.943Z
gringer
<p>I understand that this post is rather old but there has been some development in the field and <a href="https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC6178912.2/" rel="nofollow noreferrer">this 2019 paper</a> might be a good resource for interested individuals.</p>

CC BY-SA 4.0
2022-01-06T17:00:55.717Z
2022-01-06T17:00:55.717Z
stefanH
<p>Recent breakthroughs in bioinformatics tools for quantification (e.g.
<a href="http://cole-trapnell-lab.github.io/cufflinks/" rel="nofollow noreferrer">Cufflinks</a>/<a href="http://pachterlab.github.io/kallisto/" rel="nofollow noreferrer">Kallisto</a>/<a href="https://github.com/COMBINE-lab/salmon" rel="nofollow noreferrer">Salmon</a> etc.) and tools which can identify differential transcript usage (DTU) (e.g. <a href="https://bioconductor.org/packages/release/bioc/html/DRIMSeq.html" rel="nofollow noreferrer">DRIMSeq</a>, <a href="http://cole-trapnell-lab.github.io/cufflinks/" rel="nofollow noreferrer">Cufflinks</a> etc.) mean that from RNA-seq data we can now relatively easy obtain a genome wide analysis of transcripts that are differentially used betwen conditions.</p>

<p>What can you use these results for? What systematic analysis does this transcript level data enable?</p>

CC BY-SA 3.0
2017-06-21T07:42:17.260Z
I would recommend to remove the software-recommendation tag. Here you don't ask for a tool to analyze this type of data, but what can be done analyzing this kind of data.
llrs
CC BY-SA 3.0
2017-06-21T08:53:30.023Z
But I'm also asking for tools that can do it...?
Kristoffer Vitting-Seerup
<rna-seq><transcriptome><software-recommendation>
Analysis of differential transcript usage (DTU)
# Analysis of differential transcript usage (DTU)

(Asked by: username_0 on 2017-06-16)

Recent breakthroughs in bioinformatics tools for quantification (e.g.
[Cufflinks](http://cole-trapnell-lab.github.io/cufflinks/)/[Kallisto](http://pachterlab.github.io/kallisto/)/[Salmon](https://github.com/COMBINE-lab/salmon) etc.) and tools which can identify differential transcript usage (DTU) (e.g. [DRIMSeq](https://bioconductor.org/packages/release/bioc/html/DRIMSeq.html), [Cufflinks](http://cole-trapnell-lab.github.io/cufflinks/) etc.) mean that from RNA-seq data we can now relatively easy obtain a genome wide analysis of transcripts that are differentially used betwen conditions.

What can you use these results for? What systematic analysis does this transcript level data enable?

**Tags:** rna-seq, transcriptome, software-recommendation
> **Comments:**
> **username_1**: I would recommend to remove the software-recommendation tag. Here you don't ask for a tool to analyze this type of data, but what can be done analyzing this kind of data.  
> **username_0**: But I'm also asking for tools that can do it...?  

---

## 4 answers

### Answer by username_0 on 2017-06-16. Score: 4

I will take the liberty of giving one possible answers to my own question – but I’m very interested in other answers.

One analysis type that such data enables is the analysis of transcript switches with predicted potential consequences.

I myself have recently developed such a tool called [IsoformSwitchAnalyzeR](http://bioconductor.org/packages/IsoformSwitchAnalyzeR/). IsoformSwitchAnalyzeR enables statistical identification (via [DRIMSeq](https://bioconductor.org/packages/release/bioc/html/DRIMSeq.html)) of isoform switches with predicted functional consequences. The consequences analyzed can be chosen from a long list which includes gain/loss of protein domains, signal peptides changes in NMD sensitivity etc. The R package also enables easy visualization of isoform switches along with their consequences and it directly supports the output of [Cufflinks/Cuffdiff](http://cole-trapnell-lab.github.io/cufflinks/), [RSEM](http://deweylab.github.io/RSEM/), [Salmon](https://github.com/COMBINE-lab/salmon) and [Kallisto](http://pachterlab.github.io/kallisto/).

Apart from enabling identification of interesting examples switch identification also enables systematic analysis of what genes are affected. For inspiration I will recommend [one of my own articles](http://mcr.aacrjournals.org/content/early/2017/06/02/1541-7786.MCR-16-0459) (describing results obtained with IsoformSwitchAnalyzeR) as well Hector et al’s [recent paper](http://biorxiv.org/content/early/2016/09/21/076653) (which is not using IsoformSwitchAnalyzeR). Of particular interest and finesse is Hector’s analysis of how isoform switches can disrupt protein-protein interactions.

Looking forward to hear more ideas.


---

### Answer by username_1 on 2017-06-21. Score: 3

I am not sure if this has been done, is common, or are research lines, but here is what I think can be done with transcripts' differences (aside from comparing the change of the expression of each transcript):

Having the different transcripts, enables us to study also the relation between transcripts, which are co-expressed together or differently co-expressed. Maybe at the individual level the switch between transcripts of a gene is not related to a condition, but together with other transcripts it is.

Transcripts usage could be also linked to structural or genomic changes. Either short/small changes or big. At genomic level SNP, inversions or methylation..., or structural changes such as compression of a region, different distances between chromosomes/regions...

> **Comments:**
> **username_0**: Could you elaborate on what you mean by "differently co-expressed" ?  
> **username_1**: The way a transcript (or a gene) is expressed with other transcripts can change between conditions. If in a condition transcript A is co-expressed with B, C and D, in another condition it can be co-expressed with D, E and F. See this [paper](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-497)  

---

### Answer by username_2 on 2018-01-11. Score: 1

Additional insights can frequently be made in research by studying things at a lower level. By peeling back the higher layers, effects can be seen that are hidden (or reversed) when looking at aggregate data. This is generally referred to as [Simpson's Paradox](https://en.wikipedia.org/wiki/Simpson%27s_paradox).

We know that RNA is functional at an isoform/transcript level, and in some cases the expression of different transcripts from the same gene can have different biologically-relevant outcomes. By looking at transcript-level information, even if the transcripts for a gene average out to be a similar level between different treatment conditions, variation at an individual transcript level can still be discovered. This is important when looking at differential expression of *populations* of cells, because it can clue researchers into finding out that a particular cell sub-population might be of interest; single cells typically will only produce single isoforms for each gene at a specific point in time.


---

### Answer by username_3 on 2022-01-06. Score: 0

I understand that this post is rather old but there has been some development in the field and [this 2019 paper](https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC6178912.2/) might be a good resource for interested individuals.


---

