A Proposal for Dynamic Access Lists for TCP/IP Packet Filering

The use of IP filtering to improve system security is well established, and although limited in what it can achieve has proved to be efficient and effective. In the design of a security policy there is always a trade-off between usability and security. Restricting access means that legitimate use of the network is prevented; allowing access means illegitimate use may be allowed. Static access list make finding a balance particularly stark -- we pay the price of decreased security 100% of the time even if the benefit of increased usability is only gained 1% of the time. Dynamic access lists would allow the rules to change for short periods of time, and to allow local changes by non-experts. The network administrator can set basic security guide-lines which allow certain basic services only. All other services are restricted, but users are able to request temporary exceptions in order to allow additional access to the network. These exceptions are granted depending on the privileges of the user. This paper covers the following topics: (1) basic introduction to TCP/IP filtering; (2) semantics for dynamic access lists and; (3) a proposed protocol for allowing dynamic access; and (4) a method for representing access lists so that dynamic update and look-up can be done efficiently performed.Comment: 12 pages. Shortened version appeared in SAICSIT 200

An overview of the wcd EST clustering tool

Summary: The wcd system is an open source tool for clustering expressed sequence tags (EST) and other DNA and RNA sequences. wcd allows efficient all-versus-all comparison of ESTs using either the d 2 distance function or edit distance, improving existing implementations of d 2. It supports merging, refinement and reclustering of clusters. It is ‘drop in’ compatible with the StackPack clustering package. wcd supports parallelization under both shared memory and cluster architectures. It is distributed with an EMBOSS wrapper allowing wcd to be installed as part of an EMBOSS installation (and so provided by a web server)

Fast packet filtering using N-ary decision diagrams

Abstrac

Establishing an academic biobank in a resource-challenged environment

Past practices of informal sample collections and spreadsheets for data and sample management fall short of best-practice models for biobanking, and are neither cost effective nor efficient to adequately serve the needs of large research studies. The biobank of the Sydney Brenner Institute for Molecular Bioscience serves as a bioresource for institutional, national and international research collaborations. It provides high-quality human biospecimens from African populations, secure data and sample curation and storage, as well as monitored sample handling and management processes, to promote both non-communicable and infectious-disease research. Best-practice guidelines have been adapted to align with a low-resource setting and have been instrumental in the development of a quality-management system, including standard operating procedures and a quality-control regimen. Here, we provide a summary of 10 important considerations for initiating and establishing an academic research biobank in a low-resource setting. These include addressing ethical, legal, technical, accreditation and/or certification concerns and financial sustainability

Population-specific common SNPs reflect demographic histories and highlight regions of genomic plasticity with functional relevance

Abstract Background Population differentiation is the result of demographic and evolutionary forces. Whole genome datasets from the 1000 Genomes Project (October 2012) provide an unbiased view of genetic variation across populations from Europe, Asia, Africa and the Americas. Common population-specific SNPs (MAF > 0.05) reflect a deep history and may have important consequences for health and wellbeing. Their interpretation is contextualised by currently available genome data. Results The identification of common population-specific (CPS) variants (SNPs and SSV) is influenced by admixture and the sample size under investigation. Nine of the populations in the 1000 Genomes Project (2 African, 2 Asian (including a merged Chinese group) and 5 European) revealed that the African populations (LWK and YRI), followed by the Japanese (JPT) have the highest number of CPS SNPs, in concordance with their histories and given the populations studied. Using two methods, sliding 50-SNP and 5-kb windows, the CPS SNPs showed distinct clustering across large genome segments and little overlap of clusters between populations. iHS enrichment score and the population branch statistic (PBS) analyses suggest that selective sweeps are unlikely to account for the clustering and population specificity. Of interest is the association of clusters close to recombination hotspots. Functional analysis of genes associated with the CPS SNPs revealed over-representation of genes in pathways associated with neuronal development, including axonal guidance signalling and CREB signalling in neurones. Conclusions Common population-specific SNPs are non-randomly distributed throughout the genome and are significantly associated with recombination hotspots. Since the variant alleles of most CPS SNPs are the derived allele, they likely arose in the specific population after a split from a common ancestor. Their proximity to genes involved in specific pathways, including neuronal development, suggests evolutionary plasticity of selected genomic regions. Contrary to expectation, selective sweeps did not play a large role in the persistence of population-specific variation. This suggests a stochastic process towards population-specific variation which reflects demographic histories and may have some interesting implications for health and susceptibility to disease

Genetic substructure and complex demographic history of South African Bantu speakers

Abstract: outh Eastern Bantu-speaking (SEB) groups constitute more than 80% of the population in South Africa. Despite clear linguistic and geographic diversity, the genetic differences between these groups have not been systematically investigated. Based on genome-wide data of over 5000 individuals, representing eight major SEB groups, we provide strong evidence for fine-scale population structure that broadly aligns with geographic distribution and is also congruent with linguistic phylogeny (separation of Nguni, Sotho-Tswana and Tsonga speakers). Although differential Khoe-San admixture plays a key role, the structure persists after Khoe-San ancestry-masking. The timing of admixture, levels of sex-biased gene flow and population size dynamics also highlight differences in the demographic histories of individual groups. The comparisons with five Iron Age farmer genomes further support genetic continuity over ~400 years in certain regions of the country. Simulated trait genomewide association studies further show that the observed population structure could have major implications for biomedical genomics research in South Africa

Genetic-substructure and complex demographic history of South African Bantu speakers

South Eastern Bantu-speaking (SEB) groups constitute more than 80% of the population in South Africa. Despite clear linguistic and geographic diversity, the genetic differences between these groups have not been systematically investigated. Based on genome-wide data of over 5000 individuals, representing eight major SEB groups, we provide strong evidence for fine-scale population structure that broadly aligns with geographic distribution and is also congruent with linguistic phylogeny (separation of Nguni, Sotho-Tswana and Tsonga speakers). Although differential Khoe-San admixture plays a key role, the structure persists after Khoe-San ancestry-masking. The timing of admixture, levels of sex-biased gene flow and population size dynamics also highlight differences in the demographic histories of individual groups. The comparisons with five Iron Age farmer genomes further support genetic continuity over ∼400 years in certain regions of the country. Simulated trait genome-wide association studies further show that the observed population structure could have major implications for biomedical genomics research in South Africa

Development of Bioinformatics Infrastructure for Genomics Research:

Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community