Alex Lancaster
I use an evolutionary systems biology approach to investigate interplay of evolvability and robustness: how organisms can simultaneously generate phenotypic novelty, yet be buffered against the forces of genetic and environmental change. I seek to understand the cellular networks underpinning the genotype-phenotype map and to apply these insights to help identify mechanistic underpinnings of human diseases.
less
InterestsView All (38)
Uploads
Papers by Alex Lancaster
of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS
will not be realized until robust and routine whole genome sequencing data can be accurately rendered to
medically actionable reports within a time window of hours and at scales of economy in the 10’s of dollars.
Results: We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow
management system, to develop GenomeKey, an NGS whole genome analysis workflow. COSMOS implements
complex workflows making optimal use of high-performance compute clusters. Here we show that the Amazon
Web Service (AWS) implementation of GenomeKey via COSMOS provides a fast, scalable, and cost-effective analysis
of both public benchmarking and large-scale heterogeneous clinical NGS datasets.
Conclusions: Our systematic benchmarking reveals important new insights and considerations to produce clinical turn-around of whole genome analysis optimization and workflow management including strategic batching of individual genomes and efficient cluster resource configuration.
Availability and Implementation: http://plaac.wi.mit.edu/. The Ruby-based web framework, and the command-line software (implemented in Java, with visualization routines in R) are available at: http://github.com/whitehead/plaac under the MIT license. All software can be run under OS X, Windows, and Unix.
Results: We de novo assembled a transcriptome for the Western black widow (Latrodectus hesperus) from deeply sequenced cDNAs of three tissue types. Our multi-tissue assembly contained ~100,000 unique transcripts, of which > 27,000 were annotated by homology. Comparing transcript abundance among the different tissues, we identified 647 silk gland-specific transcripts, including the few known silk fiber components (e.g. six spider fibroins, spidroins). Silk gland specific transcripts are enriched compared to the entire transcriptome in several functions, including protein degradation, inhibition of protein degradation, and oxidation-reduction. Phylogenetic analyses of 37 gene families containing silk gland specific transcripts demonstrated novel gene expansions within silk glands, and multiple co-options of silk specific expression from paralogs expressed in other tissues.
Conclusions: We propose a transcriptional program for the silk glands that involves regulating gland specific synthesis of silk fiber and glue components followed by protecting and processing these components into functional fibers and glues. Our black widow silk gland gene repertoire provides extensive expansion of resources for biomimetic applications of silk in industry and medicine. Furthermore, our multi-tissue transcriptome facilitates evolutionary analysis of arachnid genomes and adaptive protein systems.
of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS
will not be realized until robust and routine whole genome sequencing data can be accurately rendered to
medically actionable reports within a time window of hours and at scales of economy in the 10’s of dollars.
Results: We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow
management system, to develop GenomeKey, an NGS whole genome analysis workflow. COSMOS implements
complex workflows making optimal use of high-performance compute clusters. Here we show that the Amazon
Web Service (AWS) implementation of GenomeKey via COSMOS provides a fast, scalable, and cost-effective analysis
of both public benchmarking and large-scale heterogeneous clinical NGS datasets.
Conclusions: Our systematic benchmarking reveals important new insights and considerations to produce clinical turn-around of whole genome analysis optimization and workflow management including strategic batching of individual genomes and efficient cluster resource configuration.
Availability and Implementation: http://plaac.wi.mit.edu/. The Ruby-based web framework, and the command-line software (implemented in Java, with visualization routines in R) are available at: http://github.com/whitehead/plaac under the MIT license. All software can be run under OS X, Windows, and Unix.
Results: We de novo assembled a transcriptome for the Western black widow (Latrodectus hesperus) from deeply sequenced cDNAs of three tissue types. Our multi-tissue assembly contained ~100,000 unique transcripts, of which > 27,000 were annotated by homology. Comparing transcript abundance among the different tissues, we identified 647 silk gland-specific transcripts, including the few known silk fiber components (e.g. six spider fibroins, spidroins). Silk gland specific transcripts are enriched compared to the entire transcriptome in several functions, including protein degradation, inhibition of protein degradation, and oxidation-reduction. Phylogenetic analyses of 37 gene families containing silk gland specific transcripts demonstrated novel gene expansions within silk glands, and multiple co-options of silk specific expression from paralogs expressed in other tissues.
Conclusions: We propose a transcriptional program for the silk glands that involves regulating gland specific synthesis of silk fiber and glue components followed by protecting and processing these components into functional fibers and glues. Our black widow silk gland gene repertoire provides extensive expansion of resources for biomimetic applications of silk in industry and medicine. Furthermore, our multi-tissue transcriptome facilitates evolutionary analysis of arachnid genomes and adaptive protein systems.
increasing detail. The first part provides an introductory treatment and description of Swarm. The second part provides a deeper survey of the anatomy of a swarm program. The third part goes into significantly greater detail on some elements of programming in Swarm that users are likely to enounter as they build programs with Swarm. Users are encouraged to explore the Swarm sample programs and to visit the Swarm home page (http://www.swarm.org), where they can find out the latest news and join the Swarm e-mail community.