<p>There is an evaluation of PB Honey and Sniffles algorithms for low coverage PacBio datasets in <a href="http://www.biorxiv.org/content/early/2016/12/17/092544.full.pdf+html" rel="nofollow noreferrer">this preprint</a> and another evaluation is shown on <a href="http://www.pacb.com/wp-content/uploads/2016-ashg-poster-andrew-carroll-dnanexus.pdf" rel="nofollow noreferrer">this poster</a>. Both reports agree that optimal is (surprisingly) combination of <a href="https://sourceforge.net/projects/pb-jelly/" rel="nofollow noreferrer">PB Honey</a> and <a href="https://github.com/fritzsedlazeck/Sniffles" rel="nofollow noreferrer">Sniffles</a>.</p>

<p>Author of Sniffles have benchmarked Sniffles against PB Honey, where he shown that Sniffles performs significantly better. Take a look on <a href="http://schatzlab.cshl.edu/presentations/2016/2016.10.28.BIODATA.PacBioSV.pdf" rel="nofollow noreferrer">this presentation (slide 15)</a>.</p>

<p>Another option is <a href="https://github.com/EichlerLab/pacbio_variant_caller" rel="nofollow noreferrer">SMRT-SV</a>, but I am not aware of any benchmarking.</p>

CC BY-SA 3.0
2017-06-01T19:57:26.373Z
SMRT-SV uses assembly-quality coverage of PB reads and sadly of low use. PB Honey and Sniffles are essentially useless for clinical germline studies as neither supports multi-sample calling and when comparing calls from both tools on patients where we also had Illumina X Ten genomes showed promise in PB data but severe problems with existing tools.
Manuel
CC BY-SA 3.0
2017-06-02T15:28:18.997Z
I can imagine that if the SV calls would take into account all the family data in one go, the results would be more reliable. However is it really a problem to do variant calls individually and then compare / merge them? I would guess that it would be still better than Illumina...
Kamil S Jaron
CC BY-SA 3.0
2017-06-04T12:11:41.000Z
Has anyone successfully used either of these programs on MinION generated data?
Matt Bashton
CC BY-SA 3.0
2017-06-04T12:41:00.860Z
@MatthewBashton for this you should open a new question...
Kamil S Jaron
CC BY-SA 3.0
2017-06-01T15:11:07.053Z
2017-06-01T15:11:07.053Z
Kamil S Jaron
<p>I'm aware of the following (very few and suboptimal) options:</p>

<ul>
<li><a href="https://github.com/fritzsedlazeck/Sniffles/releases" rel="nofollow noreferrer">Sniffles</a> -- sadly not very reliable in my experience, also no genotyping step or multi-sample support</li>
<li><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-180" rel="nofollow noreferrer">PB Honey</a> -- no genotyping step or multi-sample support</li>
</ul>

CC BY-SA 3.0
2017-05-31T23:48:37.513Z
2017-05-31T23:48:37.513Z
Manuel
<p>PacBio is selling ~10x PacBio SEQUEL long reads as an upgrade to Illumina data for SV discovery.</p>

<p>In a clinical setting, the main requirements are proper sensitivity and specificity but also the processing of cohorts, at least families. This requires a genotyping step, such that it can be identified whether a given variant is shared by two or more individuals or whether it is not.</p>

<p>What are the tools of the trade for this task?</p>

<p>As having 50-60x PacBio reads is not an option from a economic point of view, one has to make do with 10x coverage.</p>

<structural-variation><long-reads><pacbio>
Structural variant calling for low-coverage PacBio data
# Structural variant calling for low-coverage PacBio data

(Asked by: username_0 on 2017-05-31)

PacBio is selling ~10x PacBio SEQUEL long reads as an upgrade to Illumina data for SV discovery.

In a clinical setting, the main requirements are proper sensitivity and specificity but also the processing of cohorts, at least families. This requires a genotyping step, such that it can be identified whether a given variant is shared by two or more individuals or whether it is not.

What are the tools of the trade for this task?

As having 50-60x PacBio reads is not an option from a economic point of view, one has to make do with 10x coverage.

**Tags:** structural-variation, long-reads, pacbio

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## 2 answers

### Answer by username_1 on 2017-06-01. Score: 2

There is an evaluation of PB Honey and Sniffles algorithms for low coverage PacBio datasets in [this preprint](http://www.biorxiv.org/content/early/2016/12/17/092544.full.pdf+html) and another evaluation is shown on [this poster](http://www.pacb.com/wp-content/uploads/2016-ashg-poster-andrew-carroll-dnanexus.pdf). Both reports agree that optimal is (surprisingly) combination of [PB Honey](https://sourceforge.net/projects/pb-jelly/) and [Sniffles](https://github.com/fritzsedlazeck/Sniffles).

Author of Sniffles have benchmarked Sniffles against PB Honey, where he shown that Sniffles performs significantly better. Take a look on [this presentation (slide 15)](http://schatzlab.cshl.edu/presentations/2016/2016.10.28.BIODATA.PacBioSV.pdf).

Another option is [SMRT-SV](https://github.com/EichlerLab/pacbio_variant_caller), but I am not aware of any benchmarking.

> **Comments:**
> **username_0**: SMRT-SV uses assembly-quality coverage of PB reads and sadly of low use. PB Honey and Sniffles are essentially useless for clinical germline studies as neither supports multi-sample calling and when comparing calls from both tools on patients where we also had Illumina X Ten genomes showed promise in PB data but severe problems with existing tools.  
> **username_1**: I can imagine that if the SV calls would take into account all the family data in one go, the results would be more reliable. However is it really a problem to do variant calls individually and then compare / merge them? I would guess that it would be still better than Illumina...  
> **username_2**: Has anyone successfully used either of these programs on MinION generated data?  
> **username_1**: @MatthewBashton for this you should open a new question...  

---

### Answer by username_0 on 2017-05-31. Score: 1

I'm aware of the following (very few and suboptimal) options:

* [Sniffles](https://github.com/fritzsedlazeck/Sniffles/releases) -- sadly not very reliable in my experience, also no genotyping step or multi-sample support
* [PB Honey](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-180) -- no genotyping step or multi-sample support


---

