AutoPhy: Automated phylogenetic identification of novel protein subfamilies.

Phylogenetic analysis of protein sequences provides a powerful means of identifying novel protein functions and subfamilies, and for identifying and resolving annotation errors. However, automation of functional clustering based on phylogenetic trees has been challenging and most of it is done manually. Clustering phylogenetic trees usually requires the delineation of tree-based thresholds (e.g., distances), leading to an ad hoc problem. We propose a new phylogenetic clustering approach that identifies clusters without using ad hoc distances or other pre-defined values. Our workflow combines uniform manifold approximation and projection (UMAP) with Gaussian mixture models as a k-means like procedure to automatically group sequences into clusters. We then apply a "second pass" clade identification algorithm to resolve non-monophyletic groups. We tested our approach with several well-curated protein families (outer membrane porins, acyltransferase, and nuclear receptors) and showed our automated methods recapitulated known subfamilies. We also applied our methods to a broad range of different protein families from multiple databases, including Pfam, PANTHER, and UniProt, and to alignments of RNA viral genomes. Our results showed that AutoPhy rapidly generated monophyletic clusters (subfamilies) within phylogenetic trees evolving at very different rates both within and among phylogenies. The phylogenetic clusters generated by AutoPhy resolved misannotations and identified new protein functional groups and novel viral strains.

Hold that pose: capturing cervical dystonia's head deviation severity from video.

OBJECTIVE: Deviated head posture is a defining characteristic of cervical dystonia (CD). Head posture severity is typically quantified with clinical rating scales such as the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS). Because clinical rating scales are inherently subjective, they are susceptible to variability that reduces their sensitivity as outcome measures. The variability could be circumvented with methods to measure CD head posture objectively. However, previously used objective methods require specialized equipment and have been limited to studies with a small number of cases. The objective of this study was to evaluate a novel software system-the Computational Motor Objective Rater (CMOR)-to quantify multi-axis directionality and severity of head posture in CD using only conventional video camera recordings. METHODS: CMOR is based on computer vision and machine learning technology that captures 3D head angle from video. We used CMOR to quantify the axial patterns and severity of predominant head posture in a retrospective, cross-sectional study of 185 patients with isolated CD recruited from 10 sites in the Dystonia Coalition. RESULTS: The predominant head posture involved more than one axis in 80.5% of patients and all three axes in 44.4%. CMOR's metrics for head posture severity correlated with severity ratings from movement disorders neurologists using both the TWSTRS-2 and an adapted version of the Global Dystonia Rating Scale (rho = 0.59-0.68, all p <0.001). CONCLUSIONS: CMOR's convergent validity with clinical rating scales and reliance upon only conventional video recordings supports its future potential for large scale multisite clinical trials.

Head tremor in cervical dystonia: Quantifying severity with computer vision.

BACKGROUND: Head tremor (HT) is a common feature of cervical dystonia (CD), usually quantified by subjective observation. Technological developments offer alternatives for measuring HT severity that are objective and amenable to automation. OBJECTIVES: Our objectives were to develop CMOR (Computational Motor Objective Rater; a computer vision-based software system) to quantify oscillatory and directional aspects of HT from video recordings during a clinical examination and to test its convergent validity with clinical rating scales. METHODS: For 93 participants with isolated CD and HT enrolled by the Dystonia Coalition, we analyzed video recordings from an examination segment in which participants were instructed to let their head drift to its most comfortable dystonic position. We evaluated peak power, frequency, and directional dominance, and used Spearman's correlation to measure the agreement between CMOR and clinical ratings. RESULTS: Power averaged 0.90 (SD 1.80) deg2/Hz, and peak frequency 1.95 (SD 0.94) Hz. The dominant HT axis was pitch (antero/retrocollis) for 50%, roll (laterocollis) for 6%, and yaw (torticollis) for 44% of participants. One-sided t-tests showed substantial contributions from the secondary (t = 18.17, p < 0.0001) and tertiary (t = 12.89, p < 0.0001) HT axes. CMOR's HT severity measure positively correlated with the HT item on the Toronto Western Spasmodic Torticollis Rating Scale-2 (Spearman's rho = 0.54, p < 0.001). CONCLUSIONS: We demonstrate a new objective method to measure HT severity that requires only conventional video recordings, quantifies the complexities of HT in CD, and exhibits convergent validity with clinical severity ratings.