From 4bf14f0019d1b489904efc59448b32adc9fcef98 Mon Sep 17 00:00:00 2001 From: pdimens Date: Thu, 1 Aug 2024 14:16:49 -0400 Subject: [PATCH] add blog --- blog/filteringsnps.md | 2 +- blog/softwareenvironments.md | 2 +- blog/svpooling.md | 91 +++++++++++++++++++++++++++++++++++ retype.yml | 2 +- static/pca.png | Bin 0 -> 297333 bytes 5 files changed, 94 insertions(+), 3 deletions(-) create mode 100644 blog/svpooling.md create mode 100644 static/pca.png diff --git a/blog/filteringsnps.md b/blog/filteringsnps.md index 08c320d46..dda6c0e9a 100644 --- a/blog/filteringsnps.md +++ b/blog/filteringsnps.md @@ -4,7 +4,7 @@ date: 2024-06-05 category: guides description: A gentle introduction to the wild world of filtering SNPs icon: filter -image: https://vre.eucanshare.bsc.es/vre/tools/SNP_Filtering/assets/home/logo.png +image: https://visualpharm.com/assets/479/Filter-595b40b65ba036ed117d440c.svg --- # :icon-filter: Filtering Variants diff --git a/blog/softwareenvironments.md b/blog/softwareenvironments.md index 23aa8e5e9..57773aac8 100644 --- a/blog/softwareenvironments.md +++ b/blog/softwareenvironments.md @@ -4,7 +4,7 @@ date: 2024-06-21 category: guides description: Deciding between using Conda or Containers icon: container -image: https://cdn-icons-png.freepik.com/512/7115/7115168.png +image: https://visualpharm.com/assets/917/Docker-595b40b65ba036ed117d3f62.svg --- # :icon-container: Choosing a software runtime method diff --git a/blog/svpooling.md b/blog/svpooling.md new file mode 100644 index 000000000..9d3ac5787 --- /dev/null +++ b/blog/svpooling.md @@ -0,0 +1,91 @@ +--- +author: Pavel Dimens +date: 2024-08-01 +category: guides +description: Why pool samples for SV calling and when to do it +icon: sync +image: https://visualpharm.com/assets/214/Merge%20Files-595b40b75ba036ed117d8636.svg +--- + +# :icon-sync: Pooling samples for SV calling +One of the cool benefits of linked-read data is the fact that you +can call structural variants with it. Depending on the depth of +your data, you may want (or need) to pool samples together. This +page walks you through why you may want to do that and the logic of +doing so. + +## Sample depth +### Depth, explained +In bioinformatics, the terms "coverage" and "depth" and often used +interchangeably, which is incorrect and leads to confusion. _Coverage_ +refers to the proportion of a genome that is sequenced, and _depth_ +refers to the number of sequences present at a given genomic position. +Think of coverage as horizontal and depth as vertical. If depth is given +as "15X", that means every sequenced genomic position has about 15 sequences +per position (a.k.a. 15 replicates per locus). For clarity, when we say "depth", +we are referring to this "vertical" description of replicates per locus. +### Depth, in context +Historically, one would have wanted to sequence fewer individuals at higher depth +to get confident genotype calls, rather than sequence more individuals at lower depth. +Recent advances in bioinformatics have enabled low-coverage whole genome sequencing +a.k.a. _lcWGS_ to be a viable options for studies of outbred non-model systems. More +and more studies are emerging that have adopted lcWGS, which is pretty cool. However, +calling structural variants at low depth is challenging, especially with short reads. + +## The problem +It's recommended to have at least 10X-12X depth to get decent structural variant calls +(definitely read that in a paper that I would like to link here, but I can't seem to find +it). If your data already has a minimum of 10X for each individual, great! Feel free to use +variant callers like `naibr` and `leviathan` to identify structural variants. Hwoever, if +you opted to sequence more individuals at lower coverage (lcWGS is often between 0.5-5X), +then calling structural variants in individuals may be a challenge. + +## The solution +One way to get your low-coverage data and still call structural variants is to pool +samples together, which would effectively boost the depth. By doing this, you will +no longer be able to make per-individual assessments, which can be fine depending on +the nature of your study. + +## Pooling considerations +If pooling samples, you must pool them sensibly and with a biological context to do so. +In other words, you don't just pool random samples together to inflate depth. Since +haplotag data is just whole genome sequence data plus a little extra information, you should +use the SNPs of your data to first identify genetic clusters as per standard population +genetic practices. Once population structure/stratification has been identified, you can use +that as a basis to pool together samples from these groups. As an example, you can use +[pcangsd](https://github.com/Rosemeis/pcangsd) to perform a PCA on low-coverage SNP data and +get results like these: +![PCA of Alosa sapidissima (SNPs from low-coverage haplotag dataset)](/static/pca.png) + +Given these results, a sensible pooling strategy would be: +- **Pool 1**: Miramichi samples +- **Pool 2**: Annapolis samples +- **Pool 3**: St_Johns samples +- **Pool 4**: Santee_Cooper samples +- **Pool 5**: Roanoke + Chowan-Blackwater samples +- **Pool 6**: Potomac samples +- **Pool 7**: Delaware samples +- **Pool 8**: Hudson samples +- **Pool 9**: Connecticut samples +- **Pool 10**: Merrimack + Kennebec samples + +!!!danger Do not concatenate manually! +It would seem natural to use `samtools cat` to combine alignment files +quickly and easily, but **do not do this**. The reason is, samples aligned +using Harpy have their linked-read barcodes deconvolved and supplemented with +a unique molecule ID, given as an `MI:i` SAM tag in the alignment records. The +molecule ID's start at `1` for each sample and increment for every identified +unique molecule. What this means is that doing a typical concatenation via +`samtools cat` **will not resolve conflicting molecule IDs**. Think of it this way, +every sample has MI's from `1` to `N`, and as soon as you do a basic concatenation, +the MI 1..N from sample2 will immediately conflict with 1..N from sample1. Why? +Well, it's impossible that `MI:i:1` from sample1 and `MI:i:1` from sample2 came from +the same molecule. A basic concatenation will flood the resulting file with clashing `MI` +tags, which will make it impossible for a linked-read aware SV caller to make sense +of the data and do its job well. + +The [!badge corners="pill" text="harpy sv"](/Modules/SV/SV.md) workflows will intelligently concatenate files and will make sure +every individual will have unique `MI` values that are not shared with any +other individual in the pool. If you need to concatenate linked-read alignment files outside +of a workflow, use `concatenate_bam.py` shipped with Harpy instead of `samtools cat` or other similar tools. +!!! diff --git a/retype.yml b/retype.yml index 663552d78..f6eba188b 100644 --- a/retype.yml +++ b/retype.yml @@ -28,7 +28,7 @@ footer: copyright: "© Copyright {{ year }}. 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