DAIRRy-BLUP: A High-Performance Computing Approach to Genomic Prediction

In genomic prediction, common analysis methods rely on a linear mixed-model framework to estimate SNP marker effects and breeding values of animals or plants. Ridge regression–best linear unbiased prediction (RR-BLUP) is based on the assumptions that SNP marker effects are normally distributed, are uncorrelated, and have equal variances. We propose DAIRRy-BLUP, a parallel, Distributed-memory RR-BLUP implementation, based on single-trait observations (y), that uses the Average Information algorithm for restricted maximum-likelihood estimation of the variance components. The goal of DAIRRy-BLUP is to enable the analysis of large-scale data sets to provide more accurate estimates of marker effects and breeding values. A distributed-memory framework is required since the dimensionality of the problem, determined by the number of SNP markers, can become too large to be analyzed by a single computing node. Initial results show that DAIRRy-BLUP enables the analysis of very large-scale data sets (up to 1,000,000 individuals and 360,000 SNPs) and indicate that increasing the number of phenotypic and genotypic records has a more significant effect on the prediction accuracy than increasing the density of SNP arrays.     

A self-managing wide-area data streaming service

Efficient and robust data streaming services are a critical requirement of emerging Grid applications, which are based on seamless interactions and coupling between geographically distributed application components. Furthermore the dynamism of Grid environments and applications requires that these services be able to continually manage and optimize their operation based on system state and application requirements. This paper presents a design and implementation of such a self-managing data-streaming service based on online control strategies. A Grid-based fusion workflow scenario is used to evaluate the service and demonstrate its feasibility and performance.

Personal adaptive clusters as containers for scientific jobs

We describe a system for creating personal clusters in user-space to support the submission and management of thousands of compute-intensive serial jobs to the network-connected compute resources on the NSF TeraGrid. The system implements a robust infrastructure that submits and manages job proxies across a distributed computing environment. These job proxies contribute resources to personal clusters created dynamically for a user on-demand. The personal clusters then adapt to the prevailing job load conditions at the distributed sites by migrating job proxies to sites expected to provide resources more quickly. Furthermore, the system allows multiple instances of these personal clusters to be created as containers for individual scientific experiments, allowing the submission environment to be customized for each instance. The version of the system described in this paper allows users to build large personal Condor and Sun Grid Engine clusters on the TeraGrid. Users then manage their scientific jobs, within each personal cluster, with a single uniform interface using the feature-rich functionality found in these job management environments.

Combining data sharing with the master–worker paradigm in the common component architecture

Software component technologies are being accepted as an adequate solution for handling the complexity of applications. However, existing software component models tend to be specialized to some types of resource architectures (e.g. in-process, distributed environments, etc.) and/or do not provide a very high level of abstraction. This paper focuses on handling data sharing on operation invocations between components as a solution allowing applications to be efficiently executed on all kinds of resources. In particular, the data sharing pattern appears in master–worker applications, when workers need to access only a part of a large piece of data, either in read or write mode. This approach is applied to the Common Component Architecture model. Its benefits are discussed using an image rendering application.

Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments?

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a 'landscape' representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems 'hard', but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the 'best' experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes.     

Membrane computing: brief introduction, recent results and applications

The internal organization and functioning of living cells, as well as their cooperation in tissues and higher order structures, can be a rich source of inspiration for computer science, not fully exploited at the present date. Membrane computing is an answer to this challenge, well developed at the theoretical (mathematical and computability theory) level, already having several applications (via usual computers), but without having yet a bio-lab implementation. After briefly discussing some general issues related to natural computing, this paper provides an informal introduction to membrane computing, focused on the main ideas, the main classes of results and of applications. Then, three recent achievements, of three different types, are briefly presented, with emphasis on the usefulness of membrane computing as a framework for devising models of interest for biological and medical research.     

Stress sensitivity and the development of affective disorders

Depressive disorders are the most common form of mental illness in America, affecting females twice as often as males. The great variability of symptoms and responses to therapeutic treatment emphasize the complex underlying neurobiology of disease onset and progression. Evidence from human and animal studies reveals a vital link between individual stress sensitivity and the predisposition toward mood disorders. While the stress response is essential for maintenance of homeostasis and survival, chronic stress and maladaptive responses to stress insults can lead to depression or other affective disorders. A key factor in the mediation of stress responsivity is the neuropeptide corticotropin-releasing factor (CRF). Studies in animal models of heightened stress sensitivity have illustrated the involvement of CRF downstream neurotransmitter targets, including serotonin and norepinephrine, in the profound neurocircuitry failure that may underlie maladaptive coping strategies. Stress sensitivity may also be a risk factor in affective disorder development susceptibility. As females show an increased stress response and recovery time compared to males, they may be at an increased vulnerability for disease. Therefore, examination of sex differences in CRF and downstream targets may aid in the elucidation of the underlying causes of the increased disease presentation in females. While we continue to make progress in our understanding of mood disorder etiology, we still have miles to go before we sleep. As an encouraging number of new animal models of altered stress sensitivity and negative stress coping strategies have been developed, the future looks extremely promising for the possibility of a new generation of drug targets to be developed.     

Future affective technology for autism and emotion communication

People on the autism spectrum often experience states of emotional or cognitive overload that pose challenges to their interests in learning and communicating. Measurements taken from home and school environments show that extreme overload experienced internally, measured as autonomic nervous system (ANS) activation, may not be visible externally: a person can have a resting heart rate twice the level of non-autistic peers, while outwardly appearing calm and relaxed. The chasm between what is happening on the inside and what is seen on the outside, coupled with challenges in speaking and being pushed to perform, is a recipe for a meltdown that may seem to come ‘out of the blue’, but in fact may have been steadily building. Because ANS activation both influences and is influenced by efforts to process sensory information, interact socially, initiate motor activity, produce meaningful speech and more, deciphering the dynamics of ANS states is important for understanding and helping people on the autism spectrum. This paper highlights advances in technology that can comfortably sense and communicate ANS arousal in daily life, allowing new kinds of investigations to inform the science of autism while also providing personalized feedback to help individuals who participate in the research.     

不同压缩程序对海量生物信息数据压缩效率的比较分析

海量生物信息数据的不断涌现迫切需要在数据压缩技术方面进行更多研究,以减轻服务器存储压力和提高网络传输及数据分析的效率。目前虽然已开发出大量数据压缩软件,但对于海量生物信息数据而言,应该选用何种软件和方法进行数据压缩,尚缺乏详细的综合比较分析。本文选择生物信息学领域中GenBank数据库中的典型核酸和蛋白质序列数据库以及典型生物信息软件Blast和EMBOSS为例,采用不同数据压缩软件进行综合比较分析,结果发现经典压缩软件compress的总体压缩效率很高,除压缩比率可接受之外,其压缩时间相对其他软件而言显著减少,甚至比并行化的hzip2（pbzip2）和gzip（pigz）软件的时间还少很多,故可优先考虑使用。7-Zip软件虽然具有最高的压缩比率,但压缩过程十分耗时,可用于数据的长期储存;而采用bzip2、rar以及gzip等软件压缩的文件,虽然压缩比率较7-Zip的偏低,但压缩过程相对而言还比较快速。具体应用中推荐使用经典压缩软件compress以及并行化运行的pbzip2和pigz软件,三者可作为同时兼顾压缩比率和压缩时间的优选。     

In vitro molecular pattern classification via DNA-based weighted-sum operation

Recent progress in molecular computation suggests the possibility of pattern classification in vitro. Weighted sum is a primitive operation required by many pattern classification problems. Here we present a DNA-based molecular computation method for implementing the weighted-sum operation and its use for molecular pattern classification in a test tube. The weights of the classifier are encoded as the mixing ratios of the differentially labeled probe DNA molecules, which are competitively hybridized with the input-encoding target molecules to compute the decision boundary of classification. The computation result is detected by fluorescence signals. We experimentally verify the underlying weight encoding scheme and demonstrate successful discrimination of two-group labels of synthetic DNA mixture patterns. The method can be used for direct computation on biomolecular data in a liquid state.