Assistant professor at the University of Rhode Island. Interested in human impacts on the evolution of marine populations. #NewPI [he, his, him]
Checkout the lab account @MarineEvoEcoLab
| Website | https://MarineEvoEco.com |
| Github | https://github.com/jpuritz |
| Google Scholar | https://scholar.google.com/citations?user=l3CNKC4AAAAJ&hl=en&oi=ao |
| ORCID | https://orcid.org/0000-0003-1404-4680 |
I didn’t know until now, but I was born to decline faculty meeting calendar invites.
The
@MarineEvoEcoLab
just rocked it last semester. I'm so proud.
https://www.marineevoeco.com/post/2024-06-01-what-a-semester/
As I sit and reflect on the Spring 2024 semester, I am just amazed at all the incredible achievements of the PLOMEE team! We started the Spring semester with Gabe Barret defending his Master’s thesis, “Variant Graphs Improve Accuracy of Downstream Analysis In Restriction Site-Associated DNA Sequencing.
Out now! A dynamic web resource for robust and #reproducible #genomics in nonmodel species
https://marineomics.github.io/
This paper in @MethodsEcolEvol describes our web resource for genomics pipelines. Examples include functional genomics, population genomics #PopGen, and genome-phenome. More pages are coming soon.
Many examples use #rstats and datasets for marine organisms. Consider contributing to the site! It's a great way to achieve broader impacts for your work.
JUST PUBLISHED: "A second unveiling: Haplotig masking of the eastern oyster genome improves population-level inference" #Evolution #Genomics #PopGen #oyster https://onlinelibrary.wiley.com/doi/10.1111/1755-0998.13801
A haplotig in an assembly occurs when a single region is split into two places in the reference. We had them everywhere in our first eastern oyster assembly! This causes a bimodal distribution of coverage. This study shows how haplotigs affect pop gen inference and how to improve the reference by masking haplotigs.
We had a lot of SNPs and used strict filtering, and I suspect these effects are more pronounced with higher tolerances for missing data.
The paper itself is paywalled (but see free link above), but the nearly identical preprint is @ https://doi.org/10.1101/2022.08.29.505626
#Haplotigs inflate assembled #genome size and they lower coverage because each haplotig gets a portion of the true genome coverage. This give a bimodal pattern on coverage across the genome. See this publication https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2485-7
See gif: https://twitter.com/jonpuritz/status/1651218353615241221?s=46
Background Recent developments in third-gen long read sequencing and diploid-aware assemblers have resulted in the rapid release of numerous reference-quality assemblies for diploid genomes. However, assembly of highly heterozygous genomes is still problematic when regional heterogeneity is so high that haplotype homology is not recognised during assembly. This results in regional duplication rather than consolidation into allelic variants and can cause issues with downstream analysis, for example variant discovery, or haplotype reconstruction using the diploid assembly with unpaired allelic contigs. Results A new pipeline—Purge Haplotigs—was developed specifically for third-gen sequencing-based assemblies to automate the reassignment of allelic contigs, and to assist in the manual curation of genome assemblies. The pipeline uses a draft haplotype-fused assembly or a diploid assembly, read alignments, and repeat annotations to identify allelic variants in the primary assembly. The pipeline was tested on a simulated dataset and on four recent diploid (phased) de novo assemblies from third-generation long-read sequencing, and compared with a similar tool. After processing with Purge Haplotigs, haploid assemblies were less duplicated with minimal impact on genome completeness, and diploid assemblies had more pairings of allelic contigs. Conclusions Purge Haplotigs improves the haploid and diploid representations of third-gen sequencing based genome assemblies by identifying and reassigning allelic contigs. The implementation is fast and scales well with large genomes, and it is less likely to over-purge repetitive or paralogous elements compared to alignment-only based methods. The software is available at https://bitbucket.org/mroachawri/purge_haplotigs under a permissive MIT licence.
Katie Lotterhos