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.

Enzyme Assays: High-throughput Screening, Genetic Selection and Fingerprinting

Edited by one of the leading experts in the field, this book fills the need for a book presenting the most important methods for high-throughput screenings and functional characterization of enzymes. It adopts an interdisciplinary approach, making it indispensable for all those involved in this expanding field, and reflects the major advances made over the past few years. For biochemists, analytical, organic and catalytic chemists, and biotechnologists.

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.

Stable expression of Escherichia coli beta-glucuronidase A (GusA) in Giardia lamblia: application to high-throughput drug susceptibility testing

OBJECTIVES: In order to create a suitable model for high-throughput drug screening, a Giardia lamblia WB C6 strain expressing Escherichia coli glucuronidase A (GusA) was created and tested with respect to susceptibility to the anti-giardial drugs nitazoxanide and metronidazole. METHODS: GusA, a well-established reporter gene in other systems, was cloned into the vector pPacVInteg allowing stable expression in G. lamblia under control of the promoter from the glutamate dehydrogenase (gdh) gene. The resulting transgenic strain was compared with the wild-type strain in a vitality assay, characterized with respect to susceptibility to nitazoxanide, metronidazole and -- as assessed in a 96-well plate format -- to a panel of 15 other compounds to be tested for anti-giardial activity. RESULTS: GusA was stably expressed in G. lamblia. Using a simple glucuronidase assay protocol, drug efficacy tests yielded results similar to those from cell counting. CONCLUSIONS: G. lamblia WB C6 GusA is a suitable tool for high-throughput anti-giardial drug screening.