TOWARDS EFFICIENT PROCESSING OF NEIGHBOURHOOD ANALYTICS FOR ADVANCED APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH

Differentially expressed genes match bill morphology and plumage despite largely undifferentiated genomes in a Holarctic songbird

© 2015 John Wiley & Sons Ltd. Understanding the patterns and processes that contribute to phenotypic diversity and speciation is a central goal of evolutionary biology. Recently, high-throughput sequencing has provided unprecedented phylogenetic resolution in many lineages that have experienced rapid diversification. The Holarctic redpoll finches (Genus: Acanthis) provide an intriguing example of a recent, phenotypically diverse lineage; traditional sequencing and genotyping methods have failed to detect any genetic differences between currently recognized species, despite marked variation in plumage and morphology within the genus. We examined variation among 20 712 anonymous single nucleotide polymorphisms (SNPs) distributed throughout the redpoll genome in combination with 215 825 SNPs within the redpoll transcriptome, gene expression data and ecological niche modelling to evaluate genetic and ecological differentiation among currently recognized species. Expanding upon previous findings, we present evidence of (i) largely undifferentiated genomes among currently recognized species; (ii) substantial niche overlap across the North American Acanthis range; and (iii) a strong relationship between polygenic patterns of gene expression and continuous phenotypic variation within a sample of redpolls from North America. The patterns we report may be caused by high levels of ongoing gene flow between polymorphic populations, incomplete lineage sorting accompanying very recent or ongoing divergence, variation in cis-regulatory elements, or phenotypic plasticity, but do not support a scenario of prolonged isolation and subsequent secondary contact. Together, these findings highlight ongoing theoretical and computational challenges presented by recent, rapid bouts of phenotypic diversification and provide new insight into the evolutionary dynamics of an intriguing, understudied non-model system. See also the Perspective by Lifjel

Harvesting far-red light:Lessons from Photosystem I

Oxygenic photosynthesis is the fundamental process by which sunlight energy is stored as chemical energy in organic compounds and oxygen is released in the atmosphere. It starts with the capture of a photon by one of the pigments embedded within one of the two photosystems, Photosystem I (PSI) or II (PSII). These photosystems are large assemblies of many pigments held together by the protein scaffold. The absorption of the photon brings the pigment to an electronic excited state. The excitation energy is then transferred from pigment-to-pigment to the reaction center (RC) of the photosystem, where it is used to perform charge separation (CS). The pigment-to-pigment energy transfer within photosynthetic complexes occurs on a very fast, femtosecond (fs, 10^(-15) second) to picosecond (ps, 10^(-12) second) timescale, which ensures that the photosystems are extremely efficient in using the energy for charge separation. In this thesis, aspects of the light-harvesting of photosynthetic pigment-protein complexes were investigated. The spectroscopic properties (absorption, emission) and energy-transfer processes were studied with a variety of different techniques, including advanced ultrafast time-resolved spectroscopic methods (two-dimensional electronic spectroscopy (2DES) and time-resolved fluorescence spectroscopy). In these time-resolved experiments, the complexes are excited with ultrashort (fs temporal width) pulses of light, after which the optical response (photon-echo, fluorescence) is monitored in time. By measuring these signals, excitation energy transfer (EET) and energy trapping within these complexes can be determined. Oxygenic photosynthesis is mainly powered by visible light in the 400–700 nm range. Expanding the absorption range to 750 nm would result in 19% more photons available for photosynthesis [Chen, M. & Blankenship, R. E. (2011) Trends Plant Sci., 16, 427–431]. Moreover, improved far-red light-harvesting can be advantageous in shaded environments. For these reasons extention of the absorption beyond the 400–700 nm range is an important approach in the global aim to improve crop productivity to meet the increasing global demands for food production. This thesis focuses on the far-red light-harvesting properties of PSI from higher plants and cyanobacteria. The aim is to understand underlying aspects that are important for far-red light (FRL, 700–800 nm) absorption and EET within PSI. These aspects can be useful to enhance the far-red light-harvesting in photosynthesis of other organism, such as plants. The chapters of this thesis contain several insights that can be generally important in the goal to enhance the far-red light-harvesting abilities of photosynthetic complexes. The integration of new low-energy states, such as from long-wavelength Chlorophylls (Chls) or new Chl a red form states, is a viable strategy to enhance the absorption of far-red light of these complexes. However, additional alterations to the light-harvesting mechanism may be required to obtain a highly efficient complex with optimally enhanced far-red light absorption. Notably, as shown by the investigated natural (light-harvesting complex I and FRL-specific PSI complex) and artificial (Chl f-containing hybrid PSI) complexes the protein scaffold is a determining factor in optimization of the far-red light-harvesting properties of photosynthetic complexes. Conclusively, in this thesis we provide several important lessons for the aim to effectively enhance the far-red light-harvesting capacity of other photosynthetic organisms

The application of proteomics to Pseudomonas putida F1

Through years of technology development, the drug industry has been able to synthesize many valuable medicines to provide better health care. However, any time a new medication (or industrial chemical for that matter) is made; it must be tested to ensure that it is not carcinogenic. For years, scientists have worked to design a screening method that is fast, efficient, and reliable. Currently, the most widely-used method used is the Ames test [1]. This test has both strengths and weaknesses which will be discussed. In this project, Two Dimensional Electrophoresis (2DE) is used to monitor protein expression in Pseudomonas putida F1 grown on different carbon sources with the purpose of finding a set of carcinogenic indicator proteins, which will lead to a replacement for Ames test. 2DE provides a molecular approach to carcinogenesis, thus is more detailed and potentially more reliable. The result of the project as well as possible future directions for this research will be discussed

Towards Data-Driven Large Scale Scientific Visualization and Exploration

Technological advances have enabled us to acquire extremely large datasets but it remains a challenge to store, process, and extract information from them. This dissertation builds upon recent advances in machine learning, visualization, and user interactions to facilitate exploration of large-scale scientific datasets. First, we use data-driven approaches to computationally identify regions of interest in the datasets. Second, we use visual presentation for effective user comprehension. Third, we provide interactions for human users to integrate domain knowledge and semantic information into this exploration process. Our research shows how to extract, visualize, and explore informative regions on very large 2D landscape images, 3D volumetric datasets, high-dimensional volumetric mouse brain datasets with thousands of spatially-mapped gene expression profiles, and geospatial trajectories that evolve over time. The contribution of this dissertation include: (1) We introduce a sliding-window saliency model that discovers regions of user interest in very large images; (2) We develop visual segmentation of intensity-gradient histograms to identify meaningful components from volumetric datasets; (3) We extract boundary surfaces from a wealth of volumetric gene expression mouse brain profiles to personalize the reference brain atlas; (4) We show how to efficiently cluster geospatial trajectories by mapping each sequence of locations to a high-dimensional point with the kernel distance framework. We aim to discover patterns, relationships, and anomalies that would lead to new scientific, engineering, and medical advances. This work represents one of the first steps toward better visual understanding of large-scale scientific data by combining machine learning and human intelligence