Biology subjects

Amin, M. R.

Publications and source records attributed to Amin, M. R..

6 recordsLinked to original sources

bioRxiv2026

Multi-scale haplotype signatures distinguish among evolutionary processes

Natural selection embeds distinct genomic signatures as populations adapt to environmental pressures. Machine learning approaches, particularly deep neural networks, have emerged as powerful tools to detect adaptive signatures from genomic data. While machine learning has dramatically improved our ability to extract these signals from sequence variation, distinguishing among multiple evolutionary processes remains challenging. We introduce SHADES, a deep learning framework that employs information about sequence differences and their frequencies at various spatial resolutions to capture evolutionary signatures at different genomic scales. We found that SHADES has the capacity to distinguish among neutrality, selective sweeps, balancing selection, and adaptive introgression and outperformed several contemporary methods evaluated for both classification accuracy and power. In addition, SHADES remained robust to a number of confounding factors, with only modest changes in classification performance. Furthermore, application of SHADES to empirical haplotypic variation from European humans recovered previously reported signals for selective sweeps, balancing selection, and adaptive introgression, while also identifying novel genes broadly involved in immune defense, inflammatory regulation, and epithelial barrier biology.

genomics↗

bioRxiv2023

Tensor decomposition based feature extraction and classification to detect natural selection from genomic data

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

genomics↗

bioRxiv2022

Uncovering footprints of natural selection through time-frequency analysis of genomic summary statistics

Natural selection leaves a spatial pattern along the genome, with a distortion in the haplotype distribution near the selected locus that becomes less prominent with increasing distance from the locus. Evaluating the spatial signal of a population-genetic summary statistic across the genome allows for patterns of natural selection to be distinguished from neutrality. Different summary statistics highlight diverse components of genetic variation and, therefore, considering the genomic spatial distribution of multiple summary statistics is expected to aid in uncovering subtle signatures of selection. In recent years, numerous methods have been devised that jointly consider genomic spatial distributions across summary statistics, utilizing both classical machine learning and contemporary deep learning architectures. However, better predictions may be attainable by improving the way in which features used as input to machine learning algorithms are extracted from these summary statistics. To achieve this goal, we apply three time-frequency analysis approaches (wavelet transform, multitaper spectral analysis, and S-transform) to summary statistic arrays. Each analysis method converts a one-dimensional summary statistic arrays to a two-dimensional image of spectral density or visual representation of time-frequency analysis, permitting the simultaneous assessment of temporal and spectral information. We use these images as input to convolutional neural networks and consider combining models across different time-frequency representation approaches through the ensemble stacking technique. Application of our modeling framework to data simulated from neutral and selective sweep scenarios reveals that it achieves almost perfect accuracy and power across a diverse set of evolutionary settings, including population size changes and test sets for which sweep strength, softness, and timing parameters were drawn from a wide range. Moreover, a scan of whole-genome sequencing of central European humans recapitulated previous well-established sweep candidates, as well as predicts novel cancer associated genes as sweeps with high support. Given that this modeling framework is also robust to missing data, we believe that it will represent a welcome addition to the population-genomic toolkit for learning about adaptive processes from genomic data.

genomics↗

bioRxiv2021

Embryonic exposure to cannabidiol disrupts active neural circuits, an effect increased by Δ-tetrahydrocannabinol and involving CB1R and CB2R in zebrafish

In the light of legislative changes and the widespread use of cannabis as a recreational and medicinal drug, delayed effects of cannabis upon brief exposure during embryonic development are of high interest as early pregnancies often go undetected. Here, zebrafish embryos were exposed to cannabidiol (CBD) and {Delta}-9-tetrahydrocannabinol l (THC) until the end of gastrulation (1-10 hours post-fertilization) and analyzed later in development (4-5 days post-fertilization). In order to measure neural activity, we implemented CaMPARI (Calcium-Modulated Photoactivatable Ratiometric Integrator) and optimized the protocol for a 96-well format complemented by locomotor analysis. Our results revealed that neural activity was decreased by CBD more than THC. At higher doses, both cannabinoids could dramatically reduce neural activity and locomotor activity. Interestingly, the decrease was more pronounced when CBD and THC were combined. At the receptor level, CBD-mediated reduction of locomotor activity was partially prevented using cannabinoid type 1 and 2 receptor inhibitors. Overall, we report that CBD toxicity occurs via two cannabinoid receptors and is synergistically enhanced by THC exposure to negatively impact neural activity late in larval development. Future studies are warranted to reveal other cannabinoids and receptors involved in this pathway to understand the subsequent health implications of cannabis consumption on fetal development.

pharmacology and toxicology↗

bioRxiv2020

Novel Mutations in NSP1 and PLPro of SARS-CoV-2 NIB-1 Genome Mount for Effective Therapeutics

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the etiologic agent of Coronavirus Disease-2019 (COVID-19), is rapidly accumulating new mutations. Analysis of these mutations is necessary for gaining knowledge regarding different aspects of therapeutic development. Recently, we have reported a Sanger method based genome sequence of a viral isolate named SARS-CoV-2 NIB-1, circulating in Bangladesh. The genome has four novel mutations in V121D, V843F, A889V and G1691C positions. V121D substitution has the potential to destabilize the Non-Structural Protein (NSP-1) which inactivates the type-1 Interferon-induced antiviral system hence this mutant could be the basis of attenuated vaccines against SARS-CoV-V843F, A889V and G1691C are all located in NSP3. G1691C can decrease the flexibility of the protein while V843F and A889V changed the binding pattern of SARS-CoV-2 Papain-Like protease (PLPro) inhibitor GRL0617. V843F PLPro showed reduced affinity for Interferon Stimulating Gene-15 (ISG-15) protein whereas V843F+A889V double mutants exhibited the same binding affinity as wild type PLPro. Here, V843F is a conserved position of PLPro that damaged the structure but A889V, a less conserved residue, most probably neutralized that damage. Mutants of NSP1 could provide attenuated vaccines against coronavirus. Also, these mutations of PLPro could be targeted to develop anti-SARS therapeutics.

bioinformatics↗

bioRxiv2017

HPViewer: Sensitive and specific genotyping of human papillomavirus in metagenomic DNA

BackgroundShotgun DNA sequencing provides sensitive detection of all 182 HPV types in tissue and body fluid. However, existing computational methods either produce false positives misidentifying HPV types due to shared sequences among HPV, human, and prokaryotes, or produce false negative since they identify HPV by assembled contigs requiring large abundant of HPV reads.\n\nResultsWe show that HPV shares extensive simple repeats with human and prokaryotes and homologous sequences among different HPV types. The shared sequences caused errors in HPV genotyping and the repeats of human origin caused false positives in HPVDetector. Programs, such as VirusTAP and Vipie, which require de novo assembly of shotgun reads into contigs, eliminated false positives at a cost of substantial reduction in sensitivity. Here, we designed HPViewer with two custom HPV reference databases masking simple repeats and homology sequences respectively and one homology distance matrix to hybridize these two databases. It directly identified HPV from short DNA reads rather than assembled contigs. Using 100,100 simulated samples, we revealed that HPViewer was robust for samples containing either high or low number of HPV reads. Using 12 shotgun sequencing samples from respiratory papillomatosis, HPViewer was equal to VirusTAP, and Vipie and better than HPVDetector with the respect to specificity and was the most sensitive method in the detection of HPV types 6 and 11. We demonstrated that contigs-based approaches had disadvantages of detection of HPV. In 1,573 sets of metagenomic data from 18 human body sites, HPViewer identified 104 types of HPV in a body-site associated pattern and 89 types of HPV co-occurring in one sample with other types of HPV at least once.\n\nConclusionsWe demonstrated HPViewer was sensitive and specific for HPV detection in metagenomic data. It was also suggested that masking shared sequences is an effective approach to avoid false positive detection and identifying HPV from short metagenomic reads is more sensitive than assembled contigs. The innovative homology distance matrix connecting two HPV databases, repeat-mask and homology-mask, optimized the balance of sensitivity and specificity. HPViewer can be accessed at https://github.com/yuhanH/HPViewer/.

bioinformatics↗