Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for clinicians

Background

In diagnosis of rare genetic diseases we face a decision as to the degree to which the sequencing lab offers one or more diagnoses based on clinical input provided by the clinician, or the clinician reaches a diagnosis based on the complete set of variants provided by the lab. We tested a software approach to assist the clinician in making the diagnosis based on clinical findings and an annotated genomic variant table, using cases already solved using less automated processes.

Results

For the 81 cases studied (involving 216 individuals), 70 had genetic abnormalities with phenotypes previously described in the literature, and 11 were not described in the literature at the time of analysis (“discovery genes”). These included cases beyond a trio, including ones with different variants in the same gene. In 100% of cases the abnormality was recognized. Of the 70, the abnormality was ranked #1 in 94% of cases, with an average rank 1.1 for all cases. Large CNVs could be analyzed in an integrated analysis, performed in 24 of the cases. The process is rapid enough to allow for periodic reanalysis of unsolved cases.

Conclusions

A clinician-friendly environment for clinical correlation can be provided to clinicians who are best positioned to have the clinical information needed for this interpretation.

Multi-species integrative biclustering

We describe an algorithm, multi-species cMonkey, for the simultaneous biclustering of heterogeneous multiple-species data collections and apply the algorithm to a group of bacteria containing Bacillus subtilis, Bacillus anthracis, and Listeria monocytogenes. The algorithm reveals evolutionary insights into the surprisingly high degree of conservation of regulatory modules across these three species and allows data and insights from well-studied organisms to complement the analysis of related but less well studied organisms.

FiberID-A technique to identify fibrous protein subclasses

Fibrous proteins such as collagen, silk, and elastin play critical biological roles, yet they have been the subject of few projects that use computational techniques to predict either their class or their structure. In this article, we present FiberID, a simple yet effective method for identifying and distinguishing three fibrous protein subclasses from their primary sequences. Using a combination of amino acid composition and fast Fourier measurements, FiberID can classify fibrous proteins belonging to these subclasses with high accuracy by using two standard machine learning techniques (decision trees and Naïve Bayesian classifiers). After presenting our results, we present several fibrous sequences that are regularly misclassified by FiberID as sequences of potential interest for further study. Finally, we analyze the decision trees developed by FiberID for potential insights regarding the structure of these proteins.

Interpreter of maladies: redescription mining applied to biomedical data analysis

Comprehensive, systematic and integrated data-centric statistical approaches to disease modeling can provide powerful frameworks for understanding disease etiology. Here, one such computational framework based on redescription mining in both its incarnations, static and dynamic, is discussed. The static framework provides bioinformatic tools applicable to multifaceted datasets, containing genetic, transcriptomic, proteomic, and clinical data for diseased patients and normal subjects. The dynamic redescription framework provides systems biology tools to model complex sets of regulatory, metabolic and signaling pathways in the initiation and progression of a disease. As an example, the case of chronic fatigue syndrome (CFS) is considered, which has so far remained intractable and unpredictable in its etiology and nosology. The redescription mining approaches can be applied to the Centers for Disease Control and Prevention's Wichita (KS, USA) dataset, integrating transcriptomic, epidemiological and clinical data, and can also be used to study how pathways in the hypothalamic-pituitary-adrenal axis affect CFS patients.

A competition model with dynamically allocated inhibitor production

The chemostat is a basic model for competition in an open system and a model for the laboratory bio-reactor (CSTR). Inhibitors in open systems are studied with a view of detoxification in natural systems and of control in bio-reactors. This study allows the amount of resource devoted to inhibitor production to depend on the state of the system. The feasibility of one dependence is provided by quorum sensing. In contrast to the constant allocation case, a much wider set of outcomes is possible including interior, stable rest points and stable limit cycles. These outcomes are important contrasts to competitive exclusion or bistable attractors that are often the outcomes for competitive systems. The model consists of four non-linear ordinary differential equations and computer software is used for most of the stability calculations.

Competition in the presence of a lethal external inhibitor

The study considers two organisms competing for a nutrient in an open system in the presence of an inhibitor (or toxicant). The inhibitor is input at a constant rate and is lethal to one competitor while being taken up by the other without harm. This is in contrast to previous studies, where the inhibitor decreases the reproductive rate of one of the organisms. The mathematical result of the lethal effect, modeled by a mass action term, is that the system cannot be reduced to a monotone dynamical system of one order lower as is common with chemostat-like problems. The model is described by four non-linear, ordinary differential equations and we seek to describe the asymptotic behavior as a function of the parameters of the system. Several global exclusion results are presented with mathematical proofs. However, in the case of coexistence, oscillatory behavior is possible and the study proceeds with numerical examples. The model is relevant to bioremediation problems in nature and to laboratory bio-reactors.

The gradostat: A model of competition along a nutrient gradient

The general mathematical theory of the gradostat is presented for two competitors. The gradostat provides a mechanism for studying competition along a nutrient gradient. In the two vessel case, the results are complete and the conditions are testable. In then-vessel case, the relevant conditions are stated in terms of the stability modulii of certain matrices and are testable for any specific case.