<p>In addition to salmon that was already mentioned, you can also try <a href="https://pachterlab.github.io/kallisto/starting" rel="nofollow noreferrer">kallisto</a> or <a href="https://github.com/bli25ucb/RSEM_tutorial" rel="nofollow noreferrer">RSEM</a>, which are also fairly popular/respectable and will work with a transcriptome FASTA.</p>

CC BY-SA 3.0
2017-07-08T19:17:51.123Z
2017-07-08T19:17:51.123Z
burger
<p>It's not possible to compute absolute expression from RNASeq reads if they are processed in the usual way, where a sequencer produces the same number of reads regardless of the input RNA amount. At best, RNASeq will give you an indication of <a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075" rel="nofollow noreferrer">proportional expression</a> within a single sample. For this reason, relative expression (i.e. that used by differential expression tests) is easier to determine than absolute expression.</p>

<p>The closest approximation to absolute expression is to generate an expression relative to the average expression of a set of housekeeping genes, but I don't think there's a universal set that has been decided on. Gene expression, even for common housekeeping genes, can vary depending on the environmental conditions of the cell. For example, GAPDK is involved in <a href="http://www.nature.com/nri/journal/v16/n9/full/nri.2016.70.html" rel="nofollow noreferrer">immune cell activation</a>.</p>

<p>However, as long as experimental conditions are similar, and you're not planning on looking for statistical significance, the proportional expression can still give qualitative insights into how cell populations behave in relation to other populations. DESeq2 provides a <a href="https://rdrr.io/bioc/DESeq2/man/varianceStabilizingTransformation.html" rel="nofollow noreferrer">variance-stabilising transformation function</a> that minimises variation for small-count genes, assuming that each sample has roughly the same total expression. I have found that I get better outcomes / comparisons from this transformation when carrying out a further adjustment to account for gene length (i.e. divide by the length of the longest transcript for each gene). See our <a href="http://jem.rupress.org/content/early/2016/12/01/jem.20160470#materials-methods" rel="nofollow noreferrer">Th2 paper</a>, section "Read mapping and differential expression analysis" for more information. The "transcripts-per-million" values produced by <a href="https://pachterlab.github.io/kallisto/about.html" rel="nofollow noreferrer">Kallisto</a> and <a href="https://combine-lab.github.io/salmon/" rel="nofollow noreferrer">Salmon</a> provide measures similar to this.</p>

<p>If, on the other hand, you were able to modify the experimental design, single cell sequencing (or "known cell count" sequencing) can be used for determining absolute expression: use a spiked-in transcript that is added in proportion to the <em>cell count</em>, so that results can be compared in proportion to the expression for that transcript.</p>

CC BY-SA 3.0
2017-07-08T21:50:31.803Z
2017-07-08T21:50:31.803Z
gringer
<p>It's unclear if your paired-end reads are actually in fasta format, I'll presume that they're in fastq instead.</p>

<p>The easiest tool to use is <a href="http://salmon.readthedocs.io/en/latest/" rel="noreferrer">salmon</a>, which nicely deals with things like multimapping. If you're trying to judge the quality of an assembly, then I recommend having a look at <a href="https://www.ncbi.nlm.nih.gov/pubmed/27252236" rel="noreferrer">transrate</a>, which uses some related methodology for assessing contig quality.</p>

CC BY-SA 3.0
2017-07-08T19:06:49.137Z
Yes they are in fasta format, but they can be easily transformed to fastq with  uniform quality.
hhoomn
CC BY-SA 3.0
2017-07-08T19:22:08.133Z
What happened to the FASTQs? Technically you could convert to FASTQs, but if the reads are not FASTQs to begin with, it's often a red flag indicating other problems.
burger
CC BY-SA 3.0
2017-07-11T14:12:34.340Z
Thank you I used Last aligner for the work, and calculated coverage ratio manually (and considered that as proportional to gene expression).
hhoomn
CC BY-SA 3.0
2017-07-08T16:22:43.643Z
2017-07-08T16:22:43.643Z
Devon Ryan
<p>I'm trying to compute a gene expression profile for an organism. I have gene nucleotide sequences of the mentioned organism stored in a fasta file and a set of paired reads stored in two separate files with the same fasta format. Now I want to compute the coverage of every gene by the given reads. That is, on average how many reads cover a given segment of the sequence.</p>

<p>Is there any tool for this specific purpose?
I'm trying to use the velvet sequencer but I have encountered a <a href="https://bioinformatics.stackexchange.com/questions/1018/how-to-interpret-contig-alignment-psa-produced-by-velvet">few problems</a>.</p>

<rna-seq><rna-alignment>
How can I compute gene expression for a set of RNA reads?
# How can I compute gene expression for a set of RNA reads?

(Asked by: username_0 on 2017-07-08)

I'm trying to compute a gene expression profile for an organism. I have gene nucleotide sequences of the mentioned organism stored in a fasta file and a set of paired reads stored in two separate files with the same fasta format. Now I want to compute the coverage of every gene by the given reads. That is, on average how many reads cover a given segment of the sequence.

Is there any tool for this specific purpose?
I'm trying to use the velvet sequencer but I have encountered a [few problems](https://bioinformatics.stackexchange.com/questions/1018/how-to-interpret-contig-alignment-psa-produced-by-velvet).

**Tags:** rna-seq, rna-alignment

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## 3 answers

### Answer by username_3 on 2017-07-08. Score: 8 | Accepted answer

It's unclear if your paired-end reads are actually in fasta format, I'll presume that they're in fastq instead.

The easiest tool to use is [salmon](http://salmon.readthedocs.io/en/latest/), which nicely deals with things like multimapping. If you're trying to judge the quality of an assembly, then I recommend having a look at [transrate](https://www.ncbi.nlm.nih.gov/pubmed/27252236), which uses some related methodology for assessing contig quality.

> **Comments:**
> **username_0**: Yes they are in fasta format, but they can be easily transformed to fastq with  uniform quality.  
> **username_1**: What happened to the FASTQs? Technically you could convert to FASTQs, but if the reads are not FASTQs to begin with, it's often a red flag indicating other problems.  
> **username_0**: Thank you I used Last aligner for the work, and calculated coverage ratio manually (and considered that as proportional to gene expression).  

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### Answer by username_1 on 2017-07-08. Score: 3

In addition to salmon that was already mentioned, you can also try [kallisto](https://pachterlab.github.io/kallisto/starting) or [RSEM](https://github.com/bli25ucb/RSEM_tutorial), which are also fairly popular/respectable and will work with a transcriptome FASTA.


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### Answer by username_2 on 2017-07-08. Score: 2

It's not possible to compute absolute expression from RNASeq reads if they are processed in the usual way, where a sequencer produces the same number of reads regardless of the input RNA amount. At best, RNASeq will give you an indication of [proportional expression](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075) within a single sample. For this reason, relative expression (i.e. that used by differential expression tests) is easier to determine than absolute expression.

The closest approximation to absolute expression is to generate an expression relative to the average expression of a set of housekeeping genes, but I don't think there's a universal set that has been decided on. Gene expression, even for common housekeeping genes, can vary depending on the environmental conditions of the cell. For example, GAPDK is involved in [immune cell activation](http://www.nature.com/nri/journal/v16/n9/full/nri.2016.70.html).

However, as long as experimental conditions are similar, and you're not planning on looking for statistical significance, the proportional expression can still give qualitative insights into how cell populations behave in relation to other populations. DESeq2 provides a [variance-stabilising transformation function](https://rdrr.io/bioc/DESeq2/man/varianceStabilizingTransformation.html) that minimises variation for small-count genes, assuming that each sample has roughly the same total expression. I have found that I get better outcomes / comparisons from this transformation when carrying out a further adjustment to account for gene length (i.e. divide by the length of the longest transcript for each gene). See our [Th2 paper](http://jem.rupress.org/content/early/2016/12/01/jem.20160470#materials-methods), section "Read mapping and differential expression analysis" for more information. The "transcripts-per-million" values produced by [Kallisto](https://pachterlab.github.io/kallisto/about.html) and [Salmon](https://combine-lab.github.io/salmon/) provide measures similar to this.

If, on the other hand, you were able to modify the experimental design, single cell sequencing (or "known cell count" sequencing) can be used for determining absolute expression: use a spiked-in transcript that is added in proportion to the *cell count*, so that results can be compared in proportion to the expression for that transcript.


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