Biology subjects

Rokhsar, D.

Publications and source records attributed to Rokhsar, D..

3 recordsLinked to original sources

bioRxiv2025

Major cell type differences between larval and adult hemichordate body plans

A major gap in our understanding of animal development is how adult body plans arise in animals with indirect development, where adults emerge from the transformation of a distinct larval form during metamorphosis. We address this question by examining cellular changes in the enteropneust hemichordate Schizocardium californicum, a species with a complex lifecycle and dramatic metamorphosis. Employing whole-body single-cell RNA sequencing, we chart the cellular composition and transcriptional dynamics of larval, metamorphosis, and adult stages. Our tissue level atlas reveals that ectodermal and endodermal cell types in larvae and adults occupy distinct transcriptional spaces, showing greater similarity to other cell types within the same life stage than to their counterparts in the opposite stage. In contrast, mesodermal cell types from both larvae and adults cluster closely together, indicating conserved transcriptional profiles. These findings demonstrate that the extensive morphological reorganization during metamorphosis is paralleled by profound shifts in cell-type specific transcriptional programs, highlighting the complexity of the larva-to-adult transition.

developmental biology↗

bioRxiv2019

The Setaria viridis genome and diversity panel enables discovery of a novel domestication gene

Diverse wild and weedy crop relatives hold genetic variants underlying key evolutionary innovations of crops under domestication. Here, we provide genome resources and probe the genetic basis of domestication traits in green millet (Setaria viridis), a close wild relative of foxtail millet (S. italica). Specifically, we develop and exploit a platinum-quality genome assembly and de novo assemblies for 598 wild accessions to identify loci underlying a) response to climate, b) a key loss of shattering trait that permits mechanical harvest, and c) leaf angle, a major predictor of yield in many grass crops. With CRISPR-Cas9 genome editing, we validated Less Shattering1 (SvLES1) as a novel gene for seed shattering, which is rendered non-functional via a retrotransposon insertion in SiLes1, the domesticated loss-of-shattering allele of S. italica. Together these results and resources project S. viridis as a key model species for complex trait dissection and biotechnological improvement of panicoid crops.

genomics↗

bioRxiv2018

BELLA: Berkeley Efficient Long-Read to Long-Read Aligner and Overlapper

Recent advances in long-read sequencing enable the characterization of genome structure and its intra- and inter-species variation at a resolution that was previously impossible. Detecting overlaps between reads is integral to many long-read genomics pipelines, such as de novo genome assembly. While longer reads simplify genome assembly and improve the contiguity of the reconstruction, current long-read technologies come with high error rates. We present Berkeley Long-Read to Long-Read Aligner and Overlapper (BELLA), a novel algorithm for computing overlaps and alignments via sparse matrix-matrix multiplication that balances the goals of recall and precision, performing well on both. We present a probabilistic model that demonstrates the feasibility of using short k-mers for detecting candidate overlaps. We then introduce a notion of reliable k-mers based on our probabilistic model. Combining reliable k-mers with our binning mechanism eliminates both the k-mer set explosion that would otherwise occur with highly erroneous reads and the spurious overlaps from k-mers originating in repetitive regions. Finally, we present a new method based on Chernoff bounds for separating true overlaps from false positives using a combination of alignment techniques and probabilistic modeling. Our methodologies aim at maximizing the balance between precision and recall. On both real and synthetic data, BELLA performs amongst the best in terms of F1 score, showing performance stability which is often missing for competitor software. BELLAs F1 score is consistently within 1.7% of the top entry. Notably, we show improved de novo assembly results on synthetic data when coupling BELLA with the Miniasm assembler.

bioinformatics↗