Everyday, Scientist Live turns its eyes to the Web around it and highlights news and research across the Internet. Today we look take an extended look at nonnegative matrix factorisation, genomic R&D in Thailand, and the extinct Hadropithecus stenognathus.
COMPUTATIONAL BIOLOGY
In the last decade, advances in high-throughput technologies such as DNA microarrays have made it possible to simultaneously measure the expression levels of tens of thousands of genes and proteins. This has resulted in large amounts of biological data requiring analysis and interpretation. Nonnegative matrix factorisation (NMF) was introduced as an unsupervised, parts-based learning paradigm involving the decomposition of a nonnegative matrix V into two nonnegative matrices, W and H, via a multiplicative updates algorithm. In the context of a p×n gene expression matrix V consisting of observations on p genes from n samples, each column of W defines a metagene, and each column of H represents the metagene expression pattern of the corresponding sample. NMF has been primarily applied in an unsupervised setting in image and natural language processing. More recently, it has been successfully utilized in a variety of applications in computational biology. Examples include molecular pattern discovery, class comparison and prediction, cross-platform and cross-species analysis, functional characterization of genes and biomedical informatics. In this paper, we review this method as a data analytical and interpretive tool in computational biology with an emphasis on these applications.
- Devarajan K (2008) Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology. PLoS Comput Biol 4(7): e1000029. doi:10.1371/journal.pcbi.1000029
RESEARCH AND DEVELOPMENT
With a wealth of biodiversity, a long tradition of agriculture-based industries, and an established medical and biotechnological research and development community, Thailand has become an attractive location for life sciences investment. The large amount of data generated in many areas of life sciences requires visualization, management, and analysis, principally through bioinformatics. To become successful, Thailand's research community should emphasize establishing core technologies, such as genomics and bioinformatics, to boost development of agriculture, food processing, and biomedical research. The Thai government realized the importance of this field and created a national policy to greatly increase Thailand's participation in bioinformatics and genomics, budgeting for specific development goals in research infrastructure, education, and sustainable human resources.
Thailand has not lagged behind in bioinformatics research activity and recognizes the importance of bioinformatics through increased policy awareness, human resources development, and increased research activity involving genomic-scale data generation and computational analyses. Many applications of genomics and bioinformatics to biomedical research and development in Thailand have progressed substantially during the past few years, leading to successful applications in some specific local areas. However, the applications to other important areas, such as agriculture, are hampered by the limited availability of genomic sequence data and the lack of necessary biochemical/physiological information. With the advent of more and more genomic information in public databases, Thailand's research community is striving to adopt comparative genomics to obtain information of direct relevance to the country's health and industrial needs. This article highlights Thailand's contribution to genomics and bioinformatics in the following areas: (1) policy support from the Thai government, (2) capacity building through infrastructure/education/human resources, and (3) research and development in genomics and computational biology.
- Tongsima W, Tongsima S, Palittapongarnpim P (2008) Outlook on Thailand's Genomics and Computational Biology Research and Development. PLoS Comput Biol 4(7): e1000115. doi:10.1371/journal.pcbi.1000115
ANTHROPOLOGY
Franz Sikora found the first specimen and type of the recently extinct Hadropithecus stenognathus in Madagascar in 1899 and sent it to Ludwig Lorenz von Liburnau of the Austrian Imperial Academy of Sciences. Later, he sent several more specimens including a subadult skull that was described by Lorenz von Liburnau in 1902. In 2003, some of us excavated at the locality and found more specimens belonging to this species, including much of a subadult skeleton. Two frontal fragments were found, and these, together with most of the postcranial bones, belong to the skull. CT scans of the skull and other jaw fragments were made in Vienna and those of the frontal fragments at Penn State University. The two fragments have been reunited with the skull in silico, and broken parts from one side of the skull have been replaced virtually by mirror-imaged complete parts from the other side. The parts of the jaw of another individual of a slightly younger dental age have also been reconstructed virtually from CT scans with mirror imaging and by using the maxillary teeth and temporomandibular joints as a guide to finish the reconstruction. Apart from forming a virtual skull for biomechanical and systematic analysis, we were also able to make a virtual endocast. Missing anterior pieces were reconstructed by using part of an endocast of the related Archaeolemur majori. The volume is 115 ml. Hadropithecus and Archaeolemur seem to have had relatively large brains compared with the other large-bodied subfossil lemurs.
- T. M. Ryan, D. A. Burney, L. R. Godfrey, U. B. Göhlich, W. L. Jungers, N. Vasey, Ramilisonina, A. Walker and G. W. Weber. A reconstruction of the Vienna skull of Hadropithecus stenognathus. PNAS July 28, 2008, doi: 10.1073/pnas.0805195105
