Biology subjects

Mozaffari, S.

Publications and source records attributed to Mozaffari, S..

4 recordsLinked to original sources

bioRxiv2024

STRPsearch: fast detection of structured tandem repeat proteins

MotivationState-of-the-art prediction methods are generating millions of publicly available protein structures. Structured Tandem Repeats Proteins (STRPs) constitute a subclass of tandem repeats characterized by repetitive structural motifs. STRPs exhibit distinct propensities for secondary structure and form regular tertiary structures, often comprising large molecular assemblies. They can perform important and diverse biological functions due to their highly degenerated sequences, which maintain a similar structure while displaying a variable number of repeat units. This suggests a disconnection between structural size and protein function. However, automatic detection of STRPs remains challenging with current state-of-the-art tools due to their lack of accuracy and long execution times, hindering their application on large datasets. In most cases, manual curation is the most accurate method for detecting and classifying them, making it impossible to inspect millions of structures. ResultsWe present STRPsearch, a novel computational tool for rapid identification, classification, and mapping of STRPs. Leveraging the manually curated entries in RepeatsDB as the known conformational space of the STRPs, STRPsearch utilizes the latest advancements in structural alignment techniques for a fast and accurate detection of repeated structural motifs in protein structures, followed by an innovative approach to map units and insertions through the generation of TM-score graphs. STRPsearch can serve researchers in structural bioinformatics and protein science as an efficient and practical tool for analysis and detection of STRPs. Availability and implementationSTRPsearch is coded in Python, all the scripts and the associated documentation are available at https://github.com/BioComputingUP/STRPsearch. Contactalexander.monzon@unipd.it, silvio.tosatto@unipd.it Supplementary informationSupplementary data are available..

bioinformatics↗

bioRxiv2020

Wide-vergence, multi-spectral adaptive optics scanning laser ophthalmoscope with diffraction-limited illumination and collection

Visualizing and assessing the function of microscopic retinal structures in the human eye is a challenging task that has been greatly facilitated by ophthalmic adaptive optics (AO). Yet, as AO imaging systems advance in functionality by employing multiple spectral channels and larger vergence ranges, achieving optimal resolution and signal-to-noise ratios (SNR) becomes difficult and is often compromised. While current-generation AO retinal imaging systems have demonstrated excellent, near diffraction-limited imaging performance over wide vergence and spectral ranges, a full theoretical and experimental analysis of an AOSLO that includes both the light delivery and collection optics has not been done, and neither has the effects of extending wavefront correction from one wavelength to imaging performance in different spectral channels. Here, we report a methodology and system design for simultaneously achieving diffraction-limited performance in both the illumination and collection paths for a wide-vergence, multi-spectral AO scanning laser ophthalmoscope (SLO) over a 1.2 diopter vergence range while correcting the wavefront in a separate wavelength. To validate the design, an AOSLO was constructed to have three imaging channels spanning different wavelength ranges (543 {+/-} 11 nm, 680 {+/-} 11 nm, and 840 {+/-} 6 nm, respectively) and one near-infrared wavefront sensing channel (940 {+/-} 5 nm). The AOSLO optics and their alignment were determined via simulations in optical and optomechanical design software and then experimentally verified by measuring the AOSLOs illumination and collection point spread functions (PSF) for each channel using a phase retrieval technique. The collection efficiency was then measured for each channel as a function of confocal pinhole size when imaging a model eye achieving near-theoretical performance. Imaging results from healthy human adult volunteers demonstrate the systems ability to resolve the foveal cone mosaic in all three imaging channels despite a wide spectral separation between the wavefront sensing and imaging channels. OCIS codes(110.1080) Active or adaptive optics; (170.4460) Ophthalmic optics and devices; (170.4470) Ophthalmology

bioengineering↗

bioRxiv2018

Versatile Multi-Detector Scheme for Adaptive Optics Scanning Laser Ophthalmoscopy

Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO) is a powerful tool for imaging the retina at high spatial and temporal resolution. In this paper, we present a multi-detector scheme for AOSLO which has two main configurations: pixel reassignment and offset aperture imaging. In this detection scheme, the single element detector of the standard AOSLO is replaced by a fiber bundle which couples the detected light into multiple detectors. The pixel reassignment configuration allows for more light throughput while maintaining optimal confocal resolution. The increase in signal-to-noise ratio (SNR) from this configuration can improve the accuracy of motion registration techniques. The offset aperture imaging configuration enhances the detection of multiply scattered light, which improves the contrast of retinal vasculature and inner retinal layers similar to methods such as nonconfocal split-detector imaging and multi-offset aperture imaging.

bioengineering↗

bioRxiv2018

Heritability Estimation and Differential Analysis with Generalized Linear Mixed Models in Genomic Sequencing Studies

MotivationGenomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large data sets.\n\nResultsHere, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With extensive simulations and comparisons to previous methods, we show that PQLseq is the only method currently available that can produce unbiased heritability estimates for sequencing count data. In addition, we show that PQLseq is well suited for differential analysis in large sequencing studies, providing calibrated type I error control and more power compared to the standard linear mixed model methods. Finally, we apply PQLseq to perform gene expression heritability estimation and differential expression analysis in a large RNA sequencing study in the Hutterites.\n\nAvailability and implementationPQLseq is implemented as an R package with source code freely available at www.xzlab.org/software.html and https://cran.r-project.org/web/packages/PQLseq/index.html.\n\nContactXZ (xzhousph@umich.edu)\n\nSupplementary informationSupplementary data are available online.

genomics↗