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- Member of: ASU Electronic Theses and Dissertations
Description
The processes of a human somatic cell are very complex with various genetic mechanisms governing its fate. Such cells undergo various genetic mutations, which translate to the genetic aberrations that we see in cancer. There are more than 100 types of cancer, each having many more subtypes with aberrations being unique to each. In the past two decades, the widespread application of high-throughput genomic technologies, such as micro-arrays and next-generation sequencing, has led to the revelation of many such aberrations. Known types and subtypes can be readily identified using gene-expression profiling and more importantly, high-throughput genomic datasets have helped identify novel sub-types with distinct signatures. Recent studies showing usage of gene-expression profiling in clinical decision making in breast cancer patients underscore the utility of high-throughput datasets. Beyond prognosis, understanding the underlying cellular processes is essential for effective cancer treatment. Various high-throughput techniques are now available to look at a particular aspect of a genetic mechanism in cancer tissue. To look at these mechanisms individually is akin to looking at a broken watch; taking apart each of its parts, looking at them individually and finally making a list of all the faulty ones. Integrative approaches are needed to transform one-dimensional cancer signatures into multi-dimensional interaction and regulatory networks, consequently bettering our understanding of cellular processes in cancer. Here, I attempt to (i) address ways to effectively identify high quality variants when multiple assays on the same sample samples are available through two novel tools, snpSniffer and NGSPE; (ii) glean new biological insight into multiple myeloma through two novel integrative analysis approaches making use of disparate high-throughput datasets. While these methods focus on multiple myeloma datasets, the informatics approaches are applicable to all cancer datasets and will thus help advance cancer genomics.
ContributorsYellapantula, Venkata (Author) / Dinu, Valentin (Thesis advisor) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Keats, Jonathan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Single molecules in a tunnel junction can now be interrogated reliably using chemically-functionalized electrodes. Monitoring stochastic bonding fluctuations between a ligand bound to one electrode and its target bound to a second electrode ("tethered molecule-pair" configuration) gives insight into the nature of the intermolecular bonding at a single molecule-pair level, and defines the requirements for reproducible tunneling data. Importantly, at large tunnel gaps, there exists a regime for many molecules in which the tunneling is influenced more by the chemical identity of the molecules than by variability in the molecule-metal contact. Functionalizing a pair of electrodes with recognition reagents (the "free analyte" configuration) can generate a distinct tunneling signal when an analyte molecule is trapped in the gap. This opens up a new interface between chemistry and electronics with immediate implications for rapid sequencing of single DNA molecules.
ContributorsChang, Shuai (Author) / Lindsay, Stuart (Thesis advisor) / Ros, Robert (Committee member) / Zhang, Peiming (Committee member) / Tao, Nongjian (Committee member) / Shumway, John (Committee member) / Arizona State University (Publisher)
Created2012
Description
Water quality in surface water is frequently degraded by fecal contamination from human and animal sources, imposing negative implications for recreational water use and public safety. For this reason it is critical to identify the source of fecal contamination in bodies of water in order to take proper corrective actions for controlling fecal pollution. Bacteroides genetic markers have been widely used to differentiate human from other sources of fecal bacteria in water. The results of this study indicate that many assays currently used to detect human-specific Bacteroides produce false positive results in the presence of freshwater fish. To further characterize Bacteroides from fish and human, the fecal samples were cultured, speciated, and identified. As a result, forty six new Bacteroides 16S rRNA gene sequences have been deposited to the NCBI database. These sequences, along with selected animal fecal sample Bacteroides, were aligned against human B. volgatus, B. fragilis, and B. dorei to identify multi-segmented variable regions within the 16S rRNA gene sequence. The collected sequences were truncated and used to construct a cladogram, showing a clear separation between human B. dorei and Bacteroides from other sources. A proposed strategy for source tracking was field tested by collecting water samples from central AZ source water and three different recreational ponds. PCR using HF134 and HF183 primer sets were performed and sequences for positive reactions were then aligned against human Bacteroides to identify the source of contamination. For the samples testing positive using the HF183 primer set (8/13), fecal contamination was determined to be from human sources. To confirm the results, PCR products were sequenced and aligned against the four variable regions and incorporated within the truncated cladogram. As expected, the sequences from water samples with human fecal contamination grouped within the human clade. As an outcome of this study, a tool box strategy for Bacteroides source identification relying on PCR amplification, variable region analysis, human-specific Bacteroides PCR assays, and subsequent truncated cladogram grouping analysis has been developed. The proposed strategy offers a new method for microbial source tracking and provides step-wise methodology essential for identifying sources of fecal pollution.
ContributorsKabiri-Badr, Leila (Author) / Abbaszadegan, Morteza (Thesis advisor) / Bingham, Scott (Committee member) / Rock, Channah (Committee member) / Fox, Peter (Committee member) / Mclain, Jean (Committee member) / Arizona State University (Publisher)
Created2012
Description
While techniques for reading DNA in some capacity has been possible for decades,
the ability to accurately edit genomes at scale has remained elusive. Novel techniques
have been introduced recently to aid in the writing of DNA sequences. While writing
DNA is more accessible, it still remains expensive, justifying the increased interest in
in silico predictions of cell behavior. In order to accurately predict the behavior of
cells it is necessary to extensively model the cell environment, including gene-to-gene
interactions as completely as possible.
Significant algorithmic advances have been made for identifying these interactions,
but despite these improvements current techniques fail to infer some edges, and
fail to capture some complexities in the network. Much of this limitation is due to
heavily underdetermined problems, whereby tens of thousands of variables are to be
inferred using datasets with the power to resolve only a small fraction of the variables.
Additionally, failure to correctly resolve gene isoforms using short reads contributes
significantly to noise in gene quantification measures.
This dissertation introduces novel mathematical models, machine learning techniques,
and biological techniques to solve the problems described above. Mathematical
models are proposed for simulation of gene network motifs, and raw read simulation.
Machine learning techniques are shown for DNA sequence matching, and DNA
sequence correction.
Results provide novel insights into the low level functionality of gene networks. Also
shown is the ability to use normalization techniques to aggregate data for gene network
inference leading to larger data sets while minimizing increases in inter-experimental
noise. Results also demonstrate that high error rates experienced by third generation
sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.
the ability to accurately edit genomes at scale has remained elusive. Novel techniques
have been introduced recently to aid in the writing of DNA sequences. While writing
DNA is more accessible, it still remains expensive, justifying the increased interest in
in silico predictions of cell behavior. In order to accurately predict the behavior of
cells it is necessary to extensively model the cell environment, including gene-to-gene
interactions as completely as possible.
Significant algorithmic advances have been made for identifying these interactions,
but despite these improvements current techniques fail to infer some edges, and
fail to capture some complexities in the network. Much of this limitation is due to
heavily underdetermined problems, whereby tens of thousands of variables are to be
inferred using datasets with the power to resolve only a small fraction of the variables.
Additionally, failure to correctly resolve gene isoforms using short reads contributes
significantly to noise in gene quantification measures.
This dissertation introduces novel mathematical models, machine learning techniques,
and biological techniques to solve the problems described above. Mathematical
models are proposed for simulation of gene network motifs, and raw read simulation.
Machine learning techniques are shown for DNA sequence matching, and DNA
sequence correction.
Results provide novel insights into the low level functionality of gene networks. Also
shown is the ability to use normalization techniques to aggregate data for gene network
inference leading to larger data sets while minimizing increases in inter-experimental
noise. Results also demonstrate that high error rates experienced by third generation
sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.
ContributorsFaucon, Philippe Christophe (Author) / Liu, Huan (Thesis advisor) / Wang, Xiao (Committee member) / Crook, Sharon M (Committee member) / Wang, Yalin (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2017
Description
My research centers on the design and fabrication of biomolecule-sensing devices that combine top-down and bottom-up fabrication processes and leverage the unique advantages of each approach. This allows for the scalable creation of devices with critical dimensions and surface properties that are tailored to target molecules at the nanoscale.
My first project focuses on a new strategy for preparing solid-state nanopore sensors for DNA sequencing. Challenges for existing nanopore approaches include specificity of detection, controllability of translocation, and scalability of fabrication. In a new solid-state pore architecture, top-down fabrication of an initial electrode gap embedded in a sealed nanochannel is followed by feedback-controlled electrochemical deposition of metal to shrink the gap and define the nanopore size. The resulting structure allows for the use of an electric field to control the motion of DNA through the pore and the direct detection of a tunnel current through a DNA molecule.
My second project focuses on top-down fabrication strategies for a fixed nanogap device to explore the electronic conductance of proteins. Here, a metal-insulator-metal junction can be fabricated with top-down fabrication techniques, and the subsequent electrode surfaces can be chemically modified with molecules that bind strongly to a target protein. When proteins bind to molecules on either side of the dielectric gap, a molecular junction is formed with observed conductances on the order of nanosiemens. These devices can be used in applications such as DNA sequencing or to gain insight into fundamental questions such as the mechanism of electron transport in proteins.
My first project focuses on a new strategy for preparing solid-state nanopore sensors for DNA sequencing. Challenges for existing nanopore approaches include specificity of detection, controllability of translocation, and scalability of fabrication. In a new solid-state pore architecture, top-down fabrication of an initial electrode gap embedded in a sealed nanochannel is followed by feedback-controlled electrochemical deposition of metal to shrink the gap and define the nanopore size. The resulting structure allows for the use of an electric field to control the motion of DNA through the pore and the direct detection of a tunnel current through a DNA molecule.
My second project focuses on top-down fabrication strategies for a fixed nanogap device to explore the electronic conductance of proteins. Here, a metal-insulator-metal junction can be fabricated with top-down fabrication techniques, and the subsequent electrode surfaces can be chemically modified with molecules that bind strongly to a target protein. When proteins bind to molecules on either side of the dielectric gap, a molecular junction is formed with observed conductances on the order of nanosiemens. These devices can be used in applications such as DNA sequencing or to gain insight into fundamental questions such as the mechanism of electron transport in proteins.
ContributorsSadar, Joshua Stephen (Author) / Qing, Quan (Thesis advisor) / Lindsay, Stuart (Committee member) / Vaiana, Sara (Committee member) / Ros, Robert (Committee member) / Arizona State University (Publisher)
Created2019
Description
Driven by the curiosity for the secret of life, the effort on sequencing of DNAs and other large biopolymers has never been respited. Advanced from recent sequencing techniques, nanotube and nanopore based sequencing has been attracting much attention. This thesis focuses on the study of first and crucial compartment of the third generation sequencing technique, the capture and translocation of biopolymers, and discuss the advantages and obstacles of two different nanofluidic pathways, nanotubes and nanopores for single molecule capturing and translocation. Carbon nanotubes with its constrained structure, the frictionless inner wall and strong electroosmotic flow, are promising materials for linearly threading DNA and other biopolymers for sequencing. Solid state nanopore on the other hand, is a robust chemical, thermal and mechanical stable nanofluidic device, which has a high capturing rate and, to some extent, good controllable threading ability for DNA and other biomolecules. These two different but similar nanofluidic pathways both provide a good preparation of analyte molecules for the sequencing purpose. In addition, more and more research interests have move onto peptide chains and protein sensing. For proteome is better and more direct indicators for human health, peptide chains and protein sensing have a much wider range of applications on bio-medicine, disease early diagnoses, and etc. A universal peptide chain nanopore sensing technique with universal chemical modification of peptides is discussed in this thesis as well, which unifies the nanopore capturing process for vast varieties of peptides. Obstacles of these nanofluidic pathways are also discussed. In the end of this thesis, a proposal of integration of solid state nanopore and fixed-gap recognition tunneling sequencing technique for a more accurate DNA and peptide readout is discussed, together with some early study work, which gives a new direction for nanopore based sequencing.
ContributorsSong, Weisi (Author) / Lindsay, Stuart (Thesis advisor) / Ros, Robert (Committee member) / Qing, Quan (Committee member) / Zhang, Peiming (Committee member) / Arizona State University (Publisher)
Created2015