LIKELIHOOD ESTIMATION OF CONJUGACY RELATIONSHIPS IN LINEAR MODELS WITH APPLICATIONS TO HIGH-THROUGHPUT GENOMICS

In the simultaneous estimation of a large number of related quantities, multilevel models provide a formal mechanism for efficiently making use of the ensemble of information for deriving individual estimates. In this article we investigate the ability of the likelihood to identify the relationship between signal and noise in multilevel linear mixed models. Specifically, we consider the ability of the likelihood to diagnose conjugacy or independence between the signals and noises. Our work was motivated by the analysis of data from high-throughput experiments in genomics. The proposed model leads to a more flexible family. However, we further demonstrate that adequately capitalizing on the benefits of a well fitting fully-specified likelihood in the terms of gene ranking is difficult.

A HIDDEN MARKOV MODEL FOR JOINT ESTIMATION OF GENOTYPE AND COPY NUMBER IN HIGH-THROUGHPUT SNP CHIPS

Amplifications and deletions of chromosomal DNA, as well as copy-neutral loss of heterozygosity have been associated with diseases processes. High-throughput single nucleotide polymorphism (SNP) arrays are useful for making genome-wide estimates of copy number and genotype calls. Because neighboring SNPs in high throughput SNP arrays are likely to have dependent copy number and genotype due to the underlying haplotype structure and linkage disequilibrium, hidden Markov models (HMM) may be useful for improving genotype calls and copy number estimates that do not incorporate information from nearby SNPs. We improve previous approaches that utilize a HMM framework for inference in high throughput SNP arrays by integrating copy number, genotype calls, and the corresponding confidence scores when available. Using simulated data, we demonstrate how confidence scores control smoothing in a probabilistic framework. Software for fitting HMMs to SNP array data is available in the R package ICE.

Bringing high-throughput techniques to orphan crop of Africa: Highlights from the Tef TILLING Project

Orphan- or understudied-crops are mostly staple food crops in developing world. They are broadly classified under cereals, legumes, root crops, fruits and vegetables. These under-researched crops contribute to the diet of a large portion of resource-poor consumers and at the same time generate income for small-holder farmers in developing countries, particularly in Africa. In addition, they perform better than major crops of the world under extreme soil and climatic conditions. However, orphan crops are not without problems. Due to lack of scientific investigation, most of them produce low yields while others have a variety of toxins that affect the health of consumers. Here, we present some highlights on the status and future perspectives of the Tef Biotechnology Project that employs modern improvement technique in order to genetically improve tef (Eragrostis tef), one of the most important orphan crop in Africa. A reverse genetics approach known as TILLING (Targeting Induced Local Lesions IN Genome) is implemented in order to tackle lodging, the major yield limiting factor in tef.Key words: Orphan crops, underresearched crops, Eragrostis tef, TILLING, semi-dwarf.

cDNA Library Generation for the Analysis of Small RNAs by High-Throughput Sequencing

The RNome of a cell is highly diverse and consists besides messenger RNAs (mRNAs), transfer RNAs (tRNAs), and ribosomal RNAs (rRNAs) also of other small and long transcript entities without apparent coding potential. This class of molecules, commonly referred to as non-protein-coding RNAs (ncRNAs), is involved in regulating numerous biological processes and thought to contribute to cellular complexity. Therefore, much effort is put into their identification and further functional characterization. Here we provide a cost-effective and reliable method for cDNA library construction of small RNAs in the size range of 20-500 residues. The effectiveness of the described method is demonstrated by the analysis of ribosome-associated small RNAs in the eukaryotic model organism Trypanosoma brucei.

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.

NOVEL THERAPEUTIC TARGETS IDENTIFIED BY HIGH-THROUGHPUT TECHNOLOGIES FOR TRIPLE-NEGATIVE BREAST CANCER

Triple-negative breast cancers (TNBC) are characterized by the lack of or reduced expression of the estrogen and progesterone receptors, and normal expression of the human epidermal growth factor receptor 2. The lack of a well-characterized target for treatment leaves only systemic chemotherapy as the mainstay of treatment. Approximately 60-70% of patients are chemosensitive, while the remaining majority does not respond. Targeted therapies that take advantage of the unique molecular perturbations found in triple-negative breast cancer are needed. The genes that are frequently amplified or overexpressed represent potential therapeutic targets for triple-negative breast cancer. The purpose of this study was to identify and validate novel therapeutic targets for triple-negative breast cancers. 681 genes showed consistent and highly significant overexpression in TNBC compared to receptor-positive cancers in 2 data sets. For two genes, 3 of the 4 siRNAs showed preferential growth inhibition in TNBC cells. These two genes were the low density lipoprotein receptor-related protein 8 (LRP8) and very low-density lipoprotein receptor (VLDLR). Exposure to their cognate ligands, reelin and apolipoprotein E isoform 4 (ApoE4), stimulated the growth of TNBC cells in vitro. Suppression of the expression of either LRP8 or VLDLR or exposure to RAP (an inhibitor of ligand binding to LRP8 and VLDLR) abolished this ligand-induced proliferation. High-throughput protein and metabolic arrays revealed that ApoE4 stimulation rescued TNBC cells from serum-starvation induced up-regulation of genes involved in lipid biosynthesis, increased protein expression of oncogenes involved in the MAPK/ERK and DNA repair pathways, and reduced the serum-starvation induction of biochemicals involved in oxidative stress response and glycolytic metabolism. shLRP8 MDA-MB-231 xenografts had reduced tumor volume, in comparison to parental and shCON xenografts. These results indicate that LRP8-APOE signaling confers survival advantages to TNBC tumors under reduced nutrient conditions and during cellular environmental stress. We revealed that the LRP8-APOE receptor-ligand system is overexpressed in human TNBC. We also demonstrated that this receptor system mediates a strong growth promoting and survival function in TNBC cells in vitro and helps to sustain the growth of MDA-MD-231 xenografts. We propose that inhibitors of LRP8-APOE signaling may be clinically useful therapeutic agents for triple-negative breast cancer.

A quantitative, high-throughput screen for protein stability

In proteomic research, it is often necessary to screen a large number of polypeptides for the presence of stable structure. Described here is a technique (referred to as SUPREX, stability of unpurified proteins from rates of H/D exchange) for measuring the stability of proteins in a rapid, high-throughput fashion. The method uses hydrogen exchange to estimate the stability of microgram quantities of unpurified protein extracts by using matrix-assisted laser desorption/ionization MS. The stabilities of maltose binding protein and monomeric λ repressor variants determined by SUPREX agree well with stability data obtained from conventional CD denaturation of purified protein. The method also can detect the change in stability caused by the binding of maltose to maltose binding protein. The results demonstrate the precision of the method over a wide range of stabilities.

PCR candidate region mismatch scanning: adaptation to quantitative, high-throughput genotyping

Linkage and association analyses were performed to identify loci affecting disease susceptibility by scoring previously characterized sequence variations such as microsatellites and single nucleotide polymorphisms. Lack of markers in regions of interest, as well as difficulty in adapting various methods to high-throughput settings, often limits the effectiveness of the analyses. We have adapted the Escherichia coli mismatch detection system, employing the factors MutS, MutL and MutH, for use in PCR-based, automated, high-throughput genotyping and mutation detection of genomic DNA. Optimal sensitivity and signal-to-noise ratios were obtained in a straightforward fashion because the detection reaction proved to be principally dependent upon monovalent cation concentration and MutL concentration. Quantitative relationships of the optimal values of these parameters with length of the DNA test fragment were demonstrated, in support of the translocation model for the mechanism of action of these enzymes, rather than the molecular switch model. Thus, rapid, sequence-independent optimization was possible for each new genomic target region. Other factors potentially limiting the flexibility of mismatch scanning, such as positioning of dam recognition sites within the target fragment, have also been investigated. We developed several strategies, which can be easily adapted to automation, for limiting the analysis to intersample heteroduplexes. Thus, the principal barriers to the use of this methodology, which we have designated PCR candidate region mismatch scanning, in cost-effective, high-throughput settings have been removed.

An automated multiplex oligonucleotide synthesizer: development of high-throughput, low-cost DNA synthesis.

An automated oligonucleotide synthesizer has been developed that can simultaneously and rapidly synthesize up to 96 different oligonucleotides in a 96-well microtiter format using phosphoramidite synthesis chemistry. A modified 96-well plate is positioned under reagent valve banks, and appropriate reagents are delivered into individual wells containing the growing oligonucleotide chain, which is bound to a solid support. Each well has a filter bottom that enables the removal of spent reagents while retaining the solid support matrix. A seal design is employed to control synthesis environment and the entire instrument is automated via computer control. Synthesis cycle times for 96 couplings are < 11 min, allowing a plate of 96 20-mers to be synthesized in < 5 hr. Oligonucleotide synthesis quality is comparable to commercial machines, with average coupling efficiencies routinely > 98% across the entire 96-well plate. No significant well-to-well variations in synthesis quality have been observed in > 6000 oligonucleotides synthesized to date. The reduced reagent usage and increased capacity allow the overall synthesis cost to drop by at least a factor of 10. With the development of this instrument, it is now practical and cost-effective to synthesize thousands to tens of thousands of oligonucleotides.

The first extrasolar planet discovered with a new-generation high-throughput Doppler instrument

We report the detection of the first extrasolar planet, ET-1 (HD 102195b), using the Exoplanet Tracker (ET), a new-generation Doppler instrument. The planet orbits HD 102195, a young star with solar metallicity that may be part of the local association. The planet imparts radial velocity variability to the star with a semiamplitude of 63.4 ± 2.0 m s^-1 and a period of 4.11 days. The planetary minimum mass (m sin i) is 0.488MJ ± 0.015M_J. The planet was initially detected in the spring of 2005 with the Kitt Peak National Observatory (KPNO) 0.9 m coudé feed telescope. The detection was confirmed by radial velocity observations with the ET at the KPNO 2.1 m telescope and also at the 9 m Hobby-Eberly Telescope (HET) with its High Resolution Spectrograph. This planetary discovery with a 0.9 m telescope around a V = 8.05 magnitude star was made possible by the high throughput of the instrument: 49% measured from the fiber output to the detector. The ET's interferometer-based approach is an effective method for planet detection. In addition, the ET concept is adaptable to multiple-object Doppler observations or very high precision observations with a cross-dispersed echelle spectrograph to separate stellar fringes over a broad wavelength band. In addition to spectroscopic observations of HD 102195, we obtained brightness measurements with one of the automated photometric telescopes at Fairborn Observatory. Those observations reveal that HD 102195 is a spotted variable star with an amplitude of ~0.015 mag and a 12.3 ± 0.3 day period. This is consistent with spectroscopically observed Ca II H and K emission levels and line-broadening measurements but inconsistent with rotational modulation of surface activity as the cause of the radial velocity variability. Our photometric observations rule out transits of the planetary companion.

High throughput determination of sequence-function relationships in protein and RNA

Thesis (Ph.D.)--University of Washington, 2016-06