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Biology subjects

Wang, Y.-T.

Publications and source records attributed to Wang, Y.-T..

3 recordsLinked to original sources

Glucose intake hampers PKA-regulated HSP90 chaperone activity

Aging is an intricate phenomenon associated with the gradual loss of physiological functions, and both nutrient sensing and proteostasis control lifespan. Although multiple approaches have facilitated the identification of candidate genes that govern longevity, the molecular mechanisms that link aging pathways are still elusive. Here, we conducted a quantitative mass spectrometry screen and identified all phosphorylation/dephosphorylation sites on yeast proteins that significantly responded to calorie restriction, a well-established approach to extend lifespan. Functional screening of 135 potential regulators uncovered that Ids2 is activated by PP2C under CR and inactivated by PKA under glucose intake. ids2{Delta} or ids2 phosphomimetic cells displayed heat sensitivity and lifespan shortening. Ids2 serves as a co-chaperone to form a complex with Hsc82 or the redundant Hsp82, and phosphorylation of Ids2 impedes its association with chaperone HSP90. Thus, PP2C and PKA orchestrate glucose sensing and protein folding to enable cells to maintain protein quality for sustained longevity.

cell biology

GraphSeq: Accelerating String Graph Construction for De Novo Assembly on Spark

Summary: De novo genome assembly is an important application on both uncharacterized genome assembly and variant identification in a reference-unbiased way. In comparison with de Brujin graph, string graph is a lossless data representation for de novo assembly. However, string graph construction is computational intensive. We propose GraphSeq to accelerate string graph construction by leveraging the distributed computing framework.\n\nAvailability and Implementation: GraphSeq is implemented with Scala on Spark and freely available at https://www.atgenomix.com/blog/graphseq.\n\nSupplementary information: Supplementary data are available at Bioinformatics online.

bioinformatics

SeqsLab: an integrated platform for cohort-based annotation and interpretation of genetic variants on Spark

SummarySeqsLab is a platform that helps researchers to easily annotate and interpret genetic variants derived from a large quantity of personal genomes. It provides an integrated interface to annotate the variants based on curated databases as well as in silico estimation on the effects of the variants. SeqsLab adopts the scalable cluster computing framework, Spark, and incorporates several customized algorithms to speed up the process of variant annotation and interpretation. The key features of SeqsLab include efficient annotation on large structural variations, diverse combinations of variant filters, easy incorporation with a vast amount of public databases, and scalable architecture of analyzing hundreds of human whole genomes simultaneously.\n\nAvailability and ImplementationSeqsLab is implemented with JAVA. The generated annotation will then be stored in Elasticsearch for real-time query and exploratory analysis. SeqsLab can be accessed by web browsers and is freely available at http://portal.seqslab.net/.\n\nContactchungtsai_su@atgenomix.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics