Zoe Ward (She/Her)
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zoe-ward.bsky.social
Zoe Ward (She/Her)
@zoe-ward.bsky.social
25 followers 80 following 1 posts
Bioinformatician at Auckland Uni • Variant curation • Inherited cardiac disease • Rare disease • Human genomics • Coffee ❤️
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zoe-ward.bsky.social
If you're at #hgsa2025 swing by poster 130 to see how we delivered a diagnosis for a family who had been on a 20 year unsolved genetic journey with @polona.bsky.social #raredisease #inheritedcardiac #humangenetics
August 17, 2025 at 4:07 AM
Reposted by Zoe Ward (She/Her)
ewanbirney.bsky.social
Another set of uORF impact mutations very likely to be causal in this set (uORFs - changes in UTR sequences which make or destroy an upstream ORF - uORF - often key for regulation)
July 29, 2025 at 12:19 PM
Reposted by Zoe Ward (She/Her)
pnrigenetics.bsky.social
Scientific advances are making it possible to detect #DNA changes that once went unnoticed. The Carvalho Lab @claucarvalho.bsky.social‬ is studying structural variants—like inversions—that may may disrupt genes and contribute to #rarediseases. Explore the latest research highlights: bit.ly/454JDZF
July 21, 2025 at 9:22 PM
Reposted by Zoe Ward (She/Her)
samarendra-pani.bsky.social
[1/8] *New Open-Access Long Read Resource*. We sequenced 1,019 genomes from the 1000 Genomes Project sample cohort using @nanoporetech.com long-read sequencing (LRS) to median 17x coverage. Publication at go.nature.com/4ffPb8f.

@hhu.de @crg.eu @embl.org @impvienna.bsky.social
July 24, 2025 at 9:26 AM
Reposted by Zoe Ward (She/Her)
tommytang.bsky.social
Toward clinical long-read genome sequencing for rare diseases www.nature.com/articles/s4...
Toward clinical long-read genome sequencing for rare diseases
Nature Genetics - Long-read genome sequencing has the potential to dramatically impact genetic diagnostics of rare diseases. This Perspective discusses the key challenges and benefits of the...
www.nature.com
June 30, 2025 at 2:15 PM
Reposted by Zoe Ward (She/Her)
natrevgenet.nature.com
Evolution of splicing model architectures go.nature.com/4eweliE
Figure from our recent Review: From computational models of the splicing code to regulatory mechanisms and therapeutic implications (free to read here: rdcu.be/dVNV4)
a, Early models built sequence motifs to describe the consensus sequences of individual core splicing elements, such as splice sites (SSs) and intronic and/or exonic enhancers and silencers. Statistical and machine-learning models were built to output the probability of a novel sequence acting as a core splicing element. The sequence logos shown for 5′SS and 3′SS were generated from Human hg38 RefSeq annotations (code available at https://www.github.com/ulelab/splicelogos). b, As our understanding of splicing mechanisms progressed, expert-selected features were extracted from sequences and used to train integrative models to predict splicing outcomes. c, With the advent of deep-learning, models could jointly learn features directly from raw sequence input. Although theoretically, sequence context could be as large as shown in part d, in practice smaller windows of up to 30 kb have been used. d, Supervised models with convolutional and transformer layers produce multimodal genome-wide data. These models use a much larger sequence context and can predict genome-wide data including RNA sequencing coverage, which can be further processed to evaluate splicing. e, By learning how to reconstruct partially masked genomic sequences across multiple species, self-supervised masked language models capture evolutionarily conserved sequence elements and their functional context in a very generic and flexible fashion. The informative numerical representations obtained by large language models can be used for splicing prediction tasks. Here 3′SS within different sequence contexts from multiple species are shown aligned for easier interpretation, but in practice sequences do not have to be aligned. Current masked language models with application to splicing use variable context windows from 1,000 to 1 million base pairs; however, it is currently unclear whether larger context windows confer better performance
July 2, 2025 at 10:21 AM
Reposted by Zoe Ward (She/Her)
natrevgenet.nature.com
ICYMI: New online! Structural variants in the 3D genome as drivers of disease
Structural variants in the 3D genome as drivers of disease
Nature Reviews Genetics, Published online: 30 June 2025; doi:10.1038/s41576-025-00862-xDisruption of the 3D genome caused by structural variation contributes to developmental disorders and cancer. The authors review the causes and molecular and clinical consequences of position effects arising from disruptions to the genome architecture.
www.nature.com
July 7, 2025 at 11:32 AM
Reposted by Zoe Ward (She/Her)
hggadvances.bsky.social
📣New research from @gregor-research.bsky.social!
📄 Long-Read Sequencing is Required for Precision Diagnosis of Incontinentia Pigmenti
👉http://bit.ly/4kQLRCo
June 20, 2025 at 6:15 PM
Reposted by Zoe Ward (She/Her)
hggadvances.bsky.social
📣New from Ng et al!
📄Identification of technically challenging variants – whole genome sequencing improves diagnostic yield in patients with high clinical suspicion of rare diseases
👉http://bit.ly/4lkXEci
June 20, 2025 at 6:11 PM
Reposted by Zoe Ward (She/Her)
tommytang.bsky.social
DiffMethylTools: a toolbox of the detection, annotation and visualization of differential DNA methylation www.biorxiv.org/content/10....
the github link is dead, though..
July 6, 2025 at 2:15 PM
Reposted by Zoe Ward (She/Her)
nechamawieder.bsky.social
Super excited to share our new review paper - The role of untranslated region variants in Mendelian disease!
www.nature.com/articles/s41...
The role of untranslated region variants in Mendelian disease: a review - European Journal of Human Genetics
European Journal of Human Genetics - The role of untranslated region variants in Mendelian disease: a review
www.nature.com
July 4, 2025 at 8:08 AM