<p>There are <em>a lot</em> of reasons why identifying the precise location of breakpoints is difficult, especially with short read technologies. <a href="https://www.cell.com/ajhg/fulltext/S0002-9297%2821%2900098-7" rel="nofollow noreferrer">Here</a> is a review with some important considerations.</p>
<p>Some of the reasons why it's difficult to accurately identify breakpoints:</p>
<ol>
<li>reads often do not span both breakpoints</li>
<li>accumulation of differences between reference and sample means mapping becomes more difficult</li>
<li>sequencing errors</li>
<li>increased ploidy makes inference considerably more difficult, especially if there are complex and/or overlapping SVs</li>
</ol>
<p>To overcome these, you can try to use long read sequencing, use multiple SV callers and only take overlapping results. <a href="https://github.com/fritzsedlazeck/SURVIVOR" rel="nofollow noreferrer">SURVIVOR</a> is a useful tool for the latter approach. I would suggest using multiple callers that have different underlying techniques to identify SVs.</p>

CC BY-SA 4.0
2024-12-13T13:45:06.050Z
2024-12-13T13:45:06.050Z
dthorbur
<p>I am working on identifying large structural variants in gene-edited pig genomes. I am finding it difficult to refine and filter out true breakpoints in control samples compared to gene-edited samples. Is there any program or tool that identifies such true breakpoints? I have been using repeat masker to annotate the breakpoints with repetitive elements and finding that some regions have very high coverage indicating a mapping issue, however, do we have to remove such false positives manually or do we have a way for such kind of mapping issues as well through some tools or R package. I have been using the genomic Ranges package to identify the overlaps between the breakpoints and repetitive elements through repeat masker.</p>

CC BY-SA 4.0
2024-12-09T03:56:59.780Z
What technology was used to sequence the reads? I presume because you're having difficulty with mapping these are short Illumina reads?
gringer
<genomics><genomicranges><repeatmasker>
How do we refine true breakpoints in short read whole genome sequencing data?
# How do we refine true breakpoints in short read whole genome sequencing data?

(Asked by: username_0 on 2024-12-09)

I am working on identifying large structural variants in gene-edited pig genomes. I am finding it difficult to refine and filter out true breakpoints in control samples compared to gene-edited samples. Is there any program or tool that identifies such true breakpoints? I have been using repeat masker to annotate the breakpoints with repetitive elements and finding that some regions have very high coverage indicating a mapping issue, however, do we have to remove such false positives manually or do we have a way for such kind of mapping issues as well through some tools or R package. I have been using the genomic Ranges package to identify the overlaps between the breakpoints and repetitive elements through repeat masker.

**Tags:** genomics, genomicranges, repeatmasker
> **Comments:**
> **username_1**: What technology was used to sequence the reads? I presume because you're having difficulty with mapping these are short Illumina reads?  

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## 1 answers

### Answer by username_2 on 2024-12-13. Score: 1

There are *a lot* of reasons why identifying the precise location of breakpoints is difficult, especially with short read technologies. [Here](https://www.cell.com/ajhg/fulltext/S0002-9297%2821%2900098-7) is a review with some important considerations.

Some of the reasons why it's difficult to accurately identify breakpoints:

1. reads often do not span both breakpoints
2. accumulation of differences between reference and sample means mapping becomes more difficult
3. sequencing errors
4. increased ploidy makes inference considerably more difficult, especially if there are complex and/or overlapping SVs

To overcome these, you can try to use long read sequencing, use multiple SV callers and only take overlapping results. [SURVIVOR](https://github.com/fritzsedlazeck/SURVIVOR) is a useful tool for the latter approach. I would suggest using multiple callers that have different underlying techniques to identify SVs.


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