Biology subjects

Sohail, M. S.

Publications and source records attributed to Sohail, M. S..

5 recordsLinked to original sources

bioRxiv2026

Linkage-aware inference of fitness from short-read time-series genomic data

Inferring the fitness effect of mutations is a basic problem in understanding the evolution of populations over time. When multiple mutations are present in a population simultaneously, genetic linkage comes into play, and the fate of an individual mutation depends on both its fitness as well as the background on which it occurs. Accurate inference of fitness effects for evolutionary systems with multiple competing mutations is therefore contingent on resolving the confounding effects of genetic linkage, captured by the covariance between allele-pairs. Increasingly, evolutionary studies are using short-read sequencing technologies to produce detailed snapshots of evolving populations. This presents a problem as the frequencies of allele-pairs are not known beyond the read-length, hampering any attempt to resolve the effects of genetic linkage between pairs of loci residing on different reads. Here we present a computationally efficient pipeline for inferring selection from short-read time-series data with partial allele-pair frequency information, while accounting for linkage. Simulation results show that the method has good performance and is scalable to systems with several thousand variants. Additionally, we demonstrate the pipelines utility on real datasets of within-host HIV and SARS-CoV-2 evolution, showcasing its applicability in resolving linkage effects from complex evolutionary histories.

bioinformatics↗

bioRxiv2025

Back-projection improves inference from sparsely sampled genomic surveillance data

Highly transmissible SARS-CoV-2 variants have emerged throughout the COVID-19 pandemic, driving new waves of infections. Genomic surveillance data can provide insights into the viruss evolution and biology. However, delayed and limited regional data can introduce biases in epidemiological models, potentially obscuring transmission patterns. To address this issue, we used a novel, variant-specific back-projection model to estimate a distribution of likely infection times from sample collection times. We combined this approach with epidemiological modeling to estimate selection for increased transmission in a way that accounts for the uncertainty in infection times. Tests in simulations demonstrated that our method can make the inference of selection more reliable. We also applied our approach to SARS-CoV-2 data, where it excelled in smoothing and extending data from geographic regions or times with poor sampling. Overall, our method can aid in the reliable identification of mutations and variants with higher transmission rates.

evolutionary biology↗

bioRxiv2025

IFNBoost: An interpretable computational model for identifying IFNγ inducing peptides

MotivationInterferon-gamma (IFN{gamma}) is a pivotal cytokine that coordinates various aspects of the immune response, notably enhancing T-cell activation, clearing intracellular pathogens, and providing long-term immune protection. Identification of IFN{gamma}-inducing peptides is essential for the advancement of peptide-based vaccines and immunotherapies; however, the experimental determination of these peptides is hampered by the large number of potential peptide candidates present in pathogen proteins. ResultsIn this study, we present IFNBoost, a machine learning model developed to accurately predict IFN{gamma}-inducing peptides by leveraging existing immunological datasets, including both peptide sequences and associated metadata. IFNBoost demonstrates impressive performance metrics, achieving an accuracy of 0.819, an F1 score of 0.798, and a Matthews correlation coefficient (MCC) of 0.634. Evaluation against independent datasets demonstrates that IFNBoost surpasses all current models for predicting IFN{gamma}-inducing peptides, highlighting generalizability of the model. Our comprehensive analysis indicates that, in addition to peptide sequences, metadata features such as the source organism and host significantly enhance predictive accuracy. The predictions produced by IFNBoost have the potential to guide rational vaccine design, thereby improving vaccine efficacy via precise identification of peptides that elicit the desired cytokine responses. Availability and implementationTo improve the accessibility and utility of our model, we have developed a web application available at https://ifnboost.streamlit.app/.

bioinformatics↗

bioRxiv2022

Vaccinia virus vaccination is expected to elicit highly cross-reactive immunity to the 2022 monkeypox virus

Starting May 2022, a novel cluster of monkeypox virus infections was detected in humans. This has spread rapidly to non-endemic countries and sparked global concern. Vaccinia virus vaccines have demonstrated high efficacy against monkeypox viruses in the past and are considered an important outbreak control measure. Viruses observed in the current outbreak carry distinct genetic variation that have the potential to affect vaccine-induced immune recognition. Here, by investigating genetic variation with respect to orthologous immunogenic vaccinia-virus proteins, we report data that anticipates vaccine-induced immune responses to remain highly cross-reactive against the newly observed monkeypox viruses.

immunology↗

bioRxiv2019

Resolving genetic linkage reveals patterns of selection in HIV-1 evolution

Identifying the genetic drivers of adaptation is a necessary step in understanding the dynamics of rapidly evolving pathogens and cancer. However, signals of selection are obscured by the complex, stochastic nature of evolution. Pervasive effects of genetic linkage, including genetic hitchhiking and clonal interference between beneficial mutants, challenge our ability to distinguish the selective effect of individual mutations. Here we describe a method to infer selection from genetic time series data that systematically resolves the confounding effects of genetic linkage. We applied our method to investigate patterns of selection in intrahost human immunodeficiency virus (HIV)-1 evolution, including a case in an individual who develops broadly neutralizing antibodies (bnAbs). Most variants that arise are observed to have negligible effects on inferred selection at other sites, but a small minority of highly influential variants have strong and far-reaching effects. In particular, we found that accounting for linkage is crucial for estimating selection due to clonal interference between escape mutants and other variants that sweep rapidly through the population. We observed only modest selection for antibody escape, in contrast with strong selection for escape from CD8+ T cell responses. Weak selection for escape from antibody responses may facilitate bnAb development by diversifying the viral population. Our results provide a quantitative description of the evolution of HIV-1 in response to host immunity, including selection on the viral population that accompanies bnAb development. More broadly, our analysis argues for the importance of resolving linkage effects in studies of natural selection.

evolutionary biology↗