<p>If you only have one gene and you only need to do this once then the simplest possible workflow is to generate the aliment using STAR (optimally with the two pass method) and open the two resultant <code>.bam</code> files in IGV, the coverage column at the stop above your alignment track should clearly show the counts of reference and non-reference supporting reads.  The IGV <a href="http://software.broadinstitute.org/software/igv/alignmentdata#coverage" rel="nofollow noreferrer">documentation</a> shows a screen shot the pop-up coverage indicator and allele specific read count.</p>

CC BY-SA 4.0
2018-08-07T19:30:03.563Z
2018-08-07T19:30:03.563Z
Matt Bashton
<p>For counting reads I use mpileup, e.g. <code>samtools mpileup --reference hg38.fa -r Chr10:18000-45500 input.bam</code>, which will give base-resolution coverage for a BAM file.</p>

<p>I've written <a href="https://github.com/gringer/bioinfscripts/blob/master/readstomper.pl" rel="nofollow noreferrer">my own script</a> to process mpileup output and make it easier to understand. By default it reports read coverage as a proportion of total coverage, but this can be modified by using the <code>-counts</code> command line argument:</p>

<p><code>$ samtools mpileup -r tig00018708_tig00000379:210665-240664:2107-2130 --reference trimMmerged_tig00018708_other.fasta local_Sampled_50M_vs_trimMmerged_tig00018708_other.bam  | /bioinf/scripts/readstomper.pl -counts</code></p>

<pre><code>[mpileup] 1 samples in 1 input files
&lt;mpileup&gt; Set max per-file depth to 8000
Assembly,Position,Coverage,ref,cR,pR,A,C,G,T,d,i,InsMode
tig00018708_tig00000379:210665-240664,2107,10,C,10,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2108,11,G,11,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2109,11,T,11,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2110,11,T,11,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2111,12,G,12,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2112,14,G,14,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2113,13,C,13,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2114,13,G,13,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2115,13,C,2,0.15,0,0,0,11,0,0
tig00018708_tig00000379:210665-240664,2116,15,T,14,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2117,15,G,14,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2118,18,A,17,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2119,19,T,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2120,19,C,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2121,18,A,7,0.41,0,0,10,0,0,0
tig00018708_tig00000379:210665-240664,2122,18,G,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2123,18,T,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2124,18,C,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2125,18,C,9,0.50,0,0,0,9,0,0
tig00018708_tig00000379:210665-240664,2126,18,C,14,0.78,2,0,1,1,0,0
tig00018708_tig00000379:210665-240664,2127,18,C,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2128,18,T,15,0.83,0,0,3,0,0,0
tig00018708_tig00000379:210665-240664,2129,17,A,13,0.76,0,0,4,0,0,0
tig00018708_tig00000379:210665-240664,2130,16,A,9,0.56,0,6,1,0,0,0
</code></pre>

<p>Note that only <em>non-reference</em> coverage is reported in the A/C/G/T/d/i columns. I have found that this makes it easier for subsequent data analysis, but your mileage may vary.</p>

CC BY-SA 3.0
2017-12-18T19:26:19.140Z
2017-12-18T19:26:19.140Z
gringer
<p>SCmut is a method to detect cell-level mutation from single-cell RNA-sequencing, published by <a href="https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz288/5480302" rel="nofollow noreferrer">Vu et al. (2019)</a></p>

<p><a href="https://github.com/nghiavtr/SCmut" rel="nofollow noreferrer">https://github.com/nghiavtr/SCmut</a></p>

CC BY-SA 4.0
2019-09-08T18:29:00.987Z
2019-09-08T18:29:00.987Z
Peter
<p>We have RNA-seq fastq data from control (WT) patients and a patient with a point mutation at a known location in one gene.
I'd like to retrieve the reads aligning to that gene and show the presence of the mutation.</p>

<p>I can think of 2 strategies:</p>

<h2>A)</h2>

<p>Alignment and visualization, based on <a href="https://gatkforums.broadinstitute.org/gatk/discussion/3891/calling-variants-in-rnaseq" rel="noreferrer">GATK</a>:</p>

<ol>
<li><p>Run STAR 2-pass</p></li>
<li><p><a href="https://www.biostars.org/p/48719/" rel="noreferrer">Extract alignments of interest</a>, e.g.:</p>

<p><code>samtools view input.bam "Chr10:18000-45500" &gt; output.sam</code></p></li>
<li><p>Visualize:</p>

<ul>
<li>IGV</li>
<li><a href="https://wiki.transvar.org/display/igbman/Visualizing+read+alignments#Visualizingreadalignments-Loadsequencedatatoviewmismatches" rel="noreferrer">IGB</a></li>
<li><a href="http://asciigenome.readthedocs.io/en/latest/description.html" rel="noreferrer">ASCIIGenome</a> (see also <a href="https://bioinformatics.stackexchange.com/a/176/208">this</a> SE answer)</li>
</ul></li>
</ol>

<p><strong>How can I count the reads showing mutation vs wild type?</strong></p>

<hr>

<h2>B)</h2>

<p>The second approach would simply look for the count numbers mapping (e.g. salmon) to the transcript ID(s) corresponding to mutation and WT, based on <a href="https://bioinformatics.stackexchange.com/a/2070/208">this</a> SE answer.</p>

<p><strong>Is there a better way to do this?</strong></p>

<rna-seq><phylogenetics><snp><rna-alignment><mutations>
Show presence of known mutation in RNA-seq data
# Show presence of known mutation in RNA-seq data

(Asked by: username_0 on 2017-12-18)

We have RNA-seq fastq data from control (WT) patients and a patient with a point mutation at a known location in one gene.
I'd like to retrieve the reads aligning to that gene and show the presence of the mutation.

I can think of 2 strategies:

## A)

Alignment and visualization, based on [GATK](https://gatkforums.broadinstitute.org/gatk/discussion/3891/calling-variants-in-rnaseq):

1. Run STAR 2-pass
2. [Extract alignments of interest](https://www.biostars.org/p/48719/), e.g.:

   `samtools view input.bam "Chr10:18000-45500" > output.sam`
3. Visualize:

   * IGV
   * [IGB](https://wiki.transvar.org/display/igbman/Visualizing+read+alignments#Visualizingreadalignments-Loadsequencedatatoviewmismatches)
   * [ASCIIGenome](http://asciigenome.readthedocs.io/en/latest/description.html) (see also [this](https://bioinformatics.stackexchange.com/a/176/208) SE answer)

**How can I count the reads showing mutation vs wild type?**

---

## B)

The second approach would simply look for the count numbers mapping (e.g. salmon) to the transcript ID(s) corresponding to mutation and WT, based on [this](https://bioinformatics.stackexchange.com/a/2070/208) SE answer.

**Is there a better way to do this?**

**Tags:** rna-seq, phylogenetics, snp, rna-alignment, mutations

---

## 3 answers

### Answer by username_1 on 2018-08-07. Score: 3

If you only have one gene and you only need to do this once then the simplest possible workflow is to generate the aliment using STAR (optimally with the two pass method) and open the two resultant `.bam` files in IGV, the coverage column at the stop above your alignment track should clearly show the counts of reference and non-reference supporting reads. The IGV [documentation](http://software.broadinstitute.org/software/igv/alignmentdata#coverage) shows a screen shot the pop-up coverage indicator and allele specific read count.


---

### Answer by username_2 on 2017-12-18. Score: 3

For counting reads I use mpileup, e.g. samtools mpileup --reference hg38.fa -r Chr10:18000-45500 input.bam, which will give base-resolution coverage for a BAM file.
I've written my own script to process mpileup output and make it easier to understand. By default it reports read coverage as a proportion of total coverage, but this can be modified by using the -counts command line argument:
$ samtools mpileup -r tig00018708_tig00000379:210665-240664:2107-2130 --reference trimMmerged_tig00018708_other.fasta local_Sampled_50M_vs_trimMmerged_tig00018708_other.bam  | /bioinf/scripts/readstomper.pl -counts
[mpileup] 1 samples in 1 input files
 Set max per-file depth to 8000
Assembly,Position,Coverage,ref,cR,pR,A,C,G,T,d,i,InsMode
tig00018708_tig00000379:210665-240664,2107,10,C,10,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2108,11,G,11,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2109,11,T,11,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2110,11,T,11,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2111,12,G,12,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2112,14,G,14,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2113,13,C,13,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2114,13,G,13,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2115,13,C,2,0.15,0,0,0,11,0,0
tig00018708_tig00000379:210665-240664,2116,15,T,14,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2117,15,G,14,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2118,18,A,17,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2119,19,T,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2120,19,C,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2121,18,A,7,0.41,0,0,10,0,0,0
tig00018708_tig00000379:210665-240664,2122,18,G,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2123,18,T,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2124,18,C,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2125,18,C,9,0.50,0,0,0,9,0,0
tig00018708_tig00000379:210665-240664,2126,18,C,14,0.78,2,0,1,1,0,0
tig00018708_tig00000379:210665-240664,2127,18,C,18,1.00,0,0,0,0,0,0
tig00018708_tig00000379:210665-240664,2128,18,T,15,0.83,0,0,3,0,0,0
tig00018708_tig00000379:210665-240664,2129,17,A,13,0.76,0,0,4,0,0,0
tig00018708_tig00000379:210665-240664,2130,16,A,9,0.56,0,6,1,0,0,0

Note that only non-reference coverage is reported in the A/C/G/T/d/i columns. I have found that this makes it easier for subsequent data analysis, but your mileage may vary.


---

### Answer by username_0 on 2019-09-08. Score: 0

SCmut is a method to detect cell-level mutation from single-cell RNA-sequencing, published by [Vu et al. (2019)](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz288/5480302)

<https://github.com/nghiavtr/SCmut>


---

