Browsing by Author "Xu, Z."

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Open Access
Multi-level regulation of even-skipped stripes by the ubiquitous factor Zelda
(The Company of Biologists, 2023-11-23) Önal, Pınar; Bishop, T.R.; Xu, Z.; Zheng, M.; Gunasinghe, M.; Nien, C.Y.; Small, S.; Datta, R.R.
The zinc-finger protein Zelda (Zld) is a key activator of zygotic transcription in early Drosophila embryos. Here, we study Zld dependent regulation of the seven-striped pattern of the pair-rule gene even-skipped (eve). Individual stripes are regulated by discrete enhancers that respond to broadly distributed activators; stripe boundaries are formed by localized repressors encoded by the gap genes. The strongest effects of Zld are on stripes 2, 3 and 7, which are regulated by two enhancers in a 3.8 kb genomic fragment that includes the eve basal promoter. We show that Zld facilitates binding of the activator Bicoid and the gap repressors to this fragment, consistent with its proposed role as a pioneer protein. To test whether the effects of Zld are direct, we mutated all canonical Zld sites in the 3.8 kb fragment, which reduced expression but failed to phenocopy the abolishment of stripes caused by removing Zld in trans. We show that Zld also indirectly regulates the eve stripes by establishing specific gap gene expression boundaries, which provides the embryonic spacing required for proper stripe activation
Open Access
A privacy-preserving solution for compressed storage and selective retrieval of genomic data
(Cold Spring Harbor Laboratory Press, 2016) Huang Z.; Ayday, E.; Lin, H.; Aiyar, R. S.; Molyneaux, A.; Xu, Z.; Fellay, J.; Steinmetz, L. M.; Hubaux, Jean-Pierre
In clinical genomics, the continuous evolution of bioinformatic algorithms and sequencing platforms makes it beneficial to store patients' complete aligned genomic data in addition to variant calls relative to a reference sequence. Due to the large size of human genome sequence data files (varying from 30 GB to 200 GB depending on coverage), two major challenges facing genomics laboratories are the costs of storage and the efficiency of the initial data processing. In addition, privacy of genomic data is becoming an increasingly serious concern, yet no standard data storage solutions exist that enable compression, encryption, and selective retrieval. Here we present a privacy-preserving solution named SECRAM (Selective retrieval on Encrypted and Compressed Reference-oriented Alignment Map) for the secure storage of compressed aligned genomic data. Our solution enables selective retrieval of encrypted data and improves the efficiency of downstream analysis (e.g., variant calling). Compared withBAM, thede factostandard for storing aligned genomic data, SECRAM uses 18%less storage. Compared with CRAM, one of the most compressed nonencrypted formats (using 34% less storage than BAM), SECRAM maintains efficient compression and downstream data processing, while allowing for unprecedented levels of security in genomic data storage. Compared with previous work, the distinguishing features of SECRAM are that (1) it is position-based insteadofread-based,and(2)itallowsrandomqueryingofasubregionfromaBAM-likefileinanencryptedform.Ourmethod thus offers a space-saving, privacy-preserving, and effective solution for the storage of clinical genomic data.