Chang, M.-T. | Search bioRxiv

bioRxiv2018

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↗

bioRxiv2017

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↗