UNLABELLED: Cordova is an out-of-the-box solution for building and maintaining an online database of genetic variations integrated with pathogenicity prediction results from popular algorithms. Our primary motivation for developing this system is to aid researchers and clinician-scientists in determining the clinical significance of genetic variations. To achieve this goal, Cordova provides an interface to review and manually or computationally curate genetic variation data as well as share it for clinical diagnostics and the advancement of research. AVAILABILITY AND IMPLEMENTATION: Cordova is open source under the MIT license and is freely available for download at https://github.com/clcg/cordova.

Next-generation sequencing technology provides greater sequencing capacity to identify disease-causing variations. Currently there are many alignment tools and variation detection techniques, multiple SNP data sets used to filter out benign polymorphisms with the intent of enriching for disease-causing variations, and many tools and websites for annotating variations and performing pathogenicity prediction of variations. There is no consensus or standard with regards to which tools and corresponding parameter settings for each tool is appropriate for the different application of next-generation sequencing technologies for disease-causing variation identification. We have implemented an annotation pipeline that can easily use the output from different alignment tools (BLAT, BWA, BFAST, Bowtie) typically used to align sequence from next-generation sequencing and Sanger sequencing experiments. The Automated Sequence Analysis Pipeline (ASAP) reports variations using the HGVS standardized nomenclature[1] to facilitate comparison across tools and publications. To date, we have annotated the sequence from 13 exomes in an effort to identify disease-causing mutations.

AudioGene is a software system developed at the University of Iowa to classify and predict gene mutations that indicate causal or increased risk factors of disease. We focus on a concise example — the most likely genetic causes of a particular form of inherited hearing loss — ADNSHL. Whereas the cost and throughput involved in the collection of genomic data have advanced dramatically during the past decade, gathering and interpreting clinical information regarding disease diagnosis remains slow, costly and error-prone. AudioGene employs machine-learning techniques in an iterative procedure to prioritize probable genetic risk factors of disease, which are then verified with a molecular (wet lab) assay. In our current implementation AudioGene achieves 67% first-choice accuracy (versus 23% using a majority classifier). When the top three choices are considered, accuracy increases to 83%. This has numerous implications for reducing the cost of genetic screening as well as increasing the power of novel gene discovery efforts. While AudioGene is focused on hearing loss, the design and underlying mechanisms are generalizable to many other diseases including heart disease, cancer and mental illness.

MOTIVATION: Despite recent improvements in high-throughput or classic molecular biology approaches it is still challenging to identify intermediate resolution genomic variations (50 bp to 50 kb). Although array-based technologies can be used to detect copy number variations in the human genome they are biased to detect only the largest such deletions or duplications. Several studies have identified deletions or duplications occurring within a gene that directly cause or predispose to disease. We have developed a novel computational system, SPeeDD (system to prioritize deletions or duplications) that utilizes machine learning techniques to predict likely candidate regions that delete or duplicate exon(s) within a gene. RESULTS: Data mining and machine learning methods were applied to identify sequence features that were predictive of homologous recombination events. The logistic model tree (LMT) method yielded the best results. Sensitivity varied from 20% to 71.6% depending on the specific machine learning model used, but specificity exceeded 90% for all methods evaluated. In addition, the SPeeDD system successfully predicted and prioritized a recently published novel BRCA1 mutation. CONCLUSIONS: Results suggest that the SPeeDD system is effective at prioritizing candidate deletions and duplications within a gene. Use of SPeeDD enables more focused screening, which reduces the labor and associated costs of the molecular assays and may also lead to targeted design of new array-based screens to focus on candidate areas to accelerate the process of mutation discovery.

MOTIVATION: High accuracy of data always governs the large-scale gene discovery projects. The data should not only be trustworthy but should be correctly annotated for various features it contains. Sequence errors are inherent in single-pass sequences such as ESTs obtained from automated sequencing. These errors further complicate the automated identification of EST-related sequencing. A tool is required to prepare the data prior to advanced annotation processing and submission to public databases. RESULTS: This paper describes ESTprep, a program designed to preprocess expressed sequence tag (EST) sequences. It identifies the location of features present in ESTs and allows the sequence to pass only if it meets various quality criteria. Use of ESTprep has resulted in substantial improvement in accurate EST feature identification and fidelity of results submitted to GenBank. AVAILABILITY: The program is freely available for download from http://genome.uiowa.edu/pubsoft/software.html

MOTIVATION: In gene discovery projects based on EST sequencing, effective post-sequencing identification methods are important in determining tissue sources of ESTs within pooled cDNA libraries. In the past, such identification efforts have been characterized by higher than necessary failure rates due to the presence of errors within the subsequence containing the oligo tag intended to define the tissue source for each EST. RESULTS: A large-scale EST-based gene discovery program at The University of Iowa has led to the creation of a unique software method named UITagCreator usable in the creation of large sets of synthetic tissue identification tags. The identification tags provide error detection and correction capability and, in conjunction with automated annotation software, result in a substantial improvement in the accurate identification of the tissue source in the presence of sequencing and base-calling errors. These identification rates are favorable, relative to past paradigms. AVAILABILITY: The UITagCreator source code and installation instructions, along with detection software usable in concert with created tag sets, is freely available at http://genome.uiowa.edu/pubsoft/software.html CONTACT: [email protected]

Increasingly, feedback of measured run-time information is being used in the optimization of computation execution. This paper introduces a model relating the static view of a computation to its run-time variance that is useful in this context. A notion of uncertainty is then used to provide bounds on key scheduling parameters of the run-time computation. To illustrate the relationship between fidelity in measured information and minimum schedulable, grain size, we apply the bounds to three existing parallel architectures for the case of run-time variance caused by monitoring intrusion. We also outline a hybrid static-dynamic scheduling paradigm-SEDIA-that uses the model of uncertainty to optimize computation for execution in the presence of run-time variance from sources other than monitoring intrusion.