Lift&Add is live 🙂 suprised that trying to the answer the simple problem - how do I get more marsupials in my alignments? - has let to a method paper, but happy nonetheless.
academic.oup.com/bioinformati...
first cancer conference #CBA2026! not too bad at all
🧪🖥️🧬 It's here!! Our second MPRA, which is totally different from the first. For starters, there's no human sequences anywhere!
Instead there's marsupials, wolves, pandas and a lot of hard work from lab members past & present, chief amongst them @navya-shukla.bsky.social (looking for a postdoc btw)
Researchers from the Human Genomics & Evolution Lab have helped uncover how DNA inherited from extinct human relatives continues to influence our biology today.
Find out more: www.svi.edu.au/news-events/...
Hi yes I will have more to say about this in a few hours but please enjoy this paper. It's been a huge labour of love and effort for the last four years, and a significant part of our research efforts, and I'm so so so thrilled it's finally ready to share.
Tldr: scRNA-seq in Indonesia hard but fun
My non-MPRA phd work! 😄
Happy to share new manuscript I completed with @ee-reh-neh.bsky.social & @davisjmcc.bsky.social back in Melbourne. The work originally conceived by @ijbeasley.bsky.social focuses on how we can reconcile and meta-analyse eQTL studies across studies cohorts and ancestries. doi.org/10.64898/202...
AbstractMotivation. Identifying sequence constraint across long evolutionary distances is a powerful method for the discovery of functional genomic sequenc
The phenotypic effects of germline variants are often mediated through gene regulation. Expression quantitative trait loci (eQTLs) are genetic variants associated with changes in gene expression. Understanding how eQTLs vary across populations is essential for characterising the genetic and regulatory drivers of trait diversity. Meta-analysing eQTL studies from multiple populations enables more robust detection of eQTLs and can reveal regulatory mechanisms shaped by population-specific environmental or ancestry-related factors. However, across the multi-ancestry eQTL literature, a wide range of methods have been used to quantify eQTL portability across ancestry groups. Because different studies employ different portability metrics, it is challenging to form a coherent view of the regulatory landscape across populations. In this work, we analyse eQTL summary statistics from ten datasets matched on tissue type and sequencing technology. We compare portability metrics used previously and show that they can yield markedly different patterns of apparent regulatory conservation or divergence. We then examine the statistical determinants of portability across metrics and demonstrate that sample size, minor allele frequency, and linkage disequilibrium are major drivers of the observed differences in eQTL portability across studies. These findings highlight that differences in statistical power stemming from factors such as population size and allele frequency must be accounted for when evaluating eQTL portability. To address this issue, we introduce a new approach designed to correct for these factors when calling eQTL portability. Finally, we show that empirical Bayes multivariate adaptive shrinkage provides a powerful framework for meta-analysing multiple eQTL studies, with the ability to pool signals across populations to produce more robust effect-size estimates within each population. ### Competing Interest Statement The authors have declared no competing interest. National Health and Medical Research Council, https://ror.org/011kf5r70, Ideas Grant 2020501, Investigator Grant 1195595
