Biology subjects

Eslahchi, C.

Publications and source records attributed to Eslahchi, C..

8 recordsLinked to original sources

bioRxiv2026

Ancestra: A lineage-explicit simulator for benchmarking B-cell receptor repertoire and lineage inference methods

AO_SCPLOWBSTRACTC_SCPLOWHigh-throughput B-cell receptor sequencing has transformed the analysis of adaptive immunity, but benchmarking clonal grouping and lineage reconstruction methods remains limited by the absence of datasets with known evolutionary histories. Here we present Ancestra, a lineage-explicit simulator of B-cell receptor heavy-chain affinity maturation. Ancestra models stochastic V(D)J recombination, context-dependent somatic hypermutation, affinity-based selection and clonal expansion while recording complete parent-child relationships and mutation events. The framework generates BCR heavy-chain sequence datasets together with their corresponding ground-truth lineage trees, enabling direct benchmarking of lineage-aware analytical methods. Across simulations, Ancestra recapitulates key properties of human repertoires, including complementarity-determining region 3 length distributions, amino-acid usage patterns, junctional mutation patterns consistent with IMGT criteria and heterogeneous branching topologies. Simulated lineages also reveal multi-label lineage trees, in which identical nucleotide sequences can arise independently along distinct evolutionary paths. Ancestra provides a practical foundation for rigorous benchmarking of lineage-aware immune repertoire analysis.

bioinformatics↗

bioRxiv2026

UnivAIRRse: A Unified Framework for Organizing and Comparing Adaptive Immune Receptor Repertoire Simulators

Adaptive immune receptor repertoire sequencing (AIRR-seq) enables large-scale profiling of B- and T-cell receptor diversity and has become a cornerstone of modern computational immunology. However, AIRR-seq provides only a partial and lossy molecular snapshot of immune dynamics, lacking explicit ground truth for clonal ancestry, lineage trajectories, antigen specificity, and longitudinal immune evolution. This limitation complicates benchmarking, method validation, and mechanistic interpretation of repertoire analysis pipelines. Here, we introduce UnivAIRRse, a unified hierarchical framework that organizes AIRR simulators within a shared conceptual coordinate system spanning five operational levels, from observed sequence data to the theoretical generative potential of the adaptive immune system. By explicitly distinguishing sequence-, clonal-, specificity-, repertoire-, and generative-level representations, UnivAIRRse enables systematic comparison of simulator assumptions, biological scope, abstraction level, and application focus. To our knowledge, this is the first review to formalize such a unified structure across biological, computational, and functional layers of AIRR simulation. Using this framework, we review how simulation supports benchmarking, strengthens computational inference, and enables multi-scale investigation of immune repertoire formation and evolution. We identify persistent limitations in existing simulators, including incomplete biological context, limited modularity, restricted interoperability, and overreliance on AIRR-seq as a molecular proxy for complex spatiotemporal immune processes. To operationalize this framework, we provide an interactive web-based AIRR Simulation Landscape Explorer (publicly available at https://www.imgt.org/AIRR-Simulator/) that enables dynamic filtering and comparison of simulators across biological scope, abstraction level, output fidelity, and application focus. Finally, we outline emerging directions toward digital-twin-ready immune simulation, emphasizing modular architectures, longitudinal multi-omic integration, uncertainty quantification, and dynamic model updating. By providing a coherent conceptual and operational coordinate system, UnivAIRRse establishes a foundation for reproducible, interpretable, and clinically actionable modeling of adaptive immune repertoires, bridging current simulation practices with the next generation of predictive and personalized immunological modeling. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/706510v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@7a5a95org.highwire.dtl.DTLVardef@d127f1org.highwire.dtl.DTLVardef@19545c9org.highwire.dtl.DTLVardef@118cc74_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

ComplexMatrixComb: Predicting Drug Combination IC50 Doses via Complex Numbers and Matrix Factorization

Determining precise drug concentrations to inhibit cancer cell growth is a critical but resource-intensive challenge, especially for combinations requiring many dose pairs. Existing computational methods often predict synergy or classify interactions but rarely estimate exact concentration pairs for a defined inhibitory effect. We introduce ComplexMatrixComb, a matrix factorization framework predicting drug combination dosages achieving IC50 for specific cancer cell lines. It encodes drug concentrations as real and imaginary components of complex numbers, modeling joint dose-response dynamics and enabling accurate IC50 estimation. Across ONeil, NCI-ALMANAC, and AZ-Dream datasets, ComplexMatrixComb outperformed traditional machine learning in regression and classification, showing robustness to drug order and adaptability to varied designs. Integration with models such as ComboFM and ComboLTR showed predicted concentrations closely matched ground-truth doses. Experimental validation of five drug pair-cell line predictions using MTT assays confirmed 50% growth inhibition. By reducing reliance on exhaustive screens, ComplexMatrixComb offers a scalable, data-driven tool to accelerate preclinical research and support personalized oncology strategies.

bioinformatics↗

bioRxiv2025

Evaluating microbial network inference methods: Moving beyond synthetic data with reproducibility-driven benchmarks

Microbial network inference is an essential approach for revealing complex interactions within microbial communities. However, the lack of experimentally validated gold standards presents a significant obstacle in evaluating the accuracy and biological relevance of inferred networks. This study delivers a comprehensive comparative assessment of six widely used microbial network inference algorithms--gCoda, OIPCQ, S-E(glasso), S-E(mb), SPRING, and SparCC--using four diverse real-world microbiome datasets alongside multiple types of generated data, including synthetic, noisy, and bootstrap-derived datasets. Our evaluation framework extends beyond conventional synthetic benchmarking by emphasizing reproducibility-focused assessments grounded in biologically realistic perturbations. We show that bootstrap resampling and low-level noisy datasets ([≤]10% perturbation) effectively preserve key statistical properties of real microbiome data, such as diversity indices, abundance distributions, and sparsity patterns. Conversely, synthetic datasets generated via the widely used SPIEC-EASI method exhibit substantial divergence from real data across these metrics. We find that while SparCC demonstrates superior robustness across varying data conditions, other methods tend to produce inflated performance metrics when evaluated on unrealistic synthetic networks. Notably, several algorithms fail to distinguish between structured and random networks, highlighting issues of structural insensitivity and the limitations of overreliance on synthetic benchmarks. To address these challenges, we propose a reproducibility-centered benchmarking framework that prioritizes real-data-derived perturbations and mandates rigorous statistical validation of synthetic datasets before their use. This work provides critical insights and practical guidance for the microbiome research community, aiming to foster more reliable and ecologically meaningful microbial network inference in the absence of a true ground truth.

bioinformatics↗

bioRxiv2022

PhiDsc: Protein functional mutation Identification by 3D Structure Comparison

Selective pressures that trigger cancer formation and progression shape the mutational landscape of somatic mutations in cancer. Given the limits within which cells are regulated, a growing tumor has access to only a finite number of pathways that it can alter. As a result, tumors arising from different cells of origin often harbor identical genetic alterations. Recent expansive sequencing efforts have identified recurrent hotspot mutated residues in individual genes. Here, we introduce PhiDsc, a novel statistical method developed based on the hypothesis that, functional mutations in a recurrently aberrant gene family can guide the identification of mutated residues in the familys individual genes, with potential functional relevance. PhiDsc combines 3D structural alignment of related proteins with recurrence data for their mutated residues, to calculate the probability of randomness of the proposed mutation. The application of this approach to the RAS and RHO protein families returned known mutational hotspots as well as previously unrecognized mutated residues with potentially altering effect on protein stability and function. These mutations were located in, or in proximity to, active domains and were indicated as protein-altering according to six in silico predictors. PhiDsc is freely available at https://github.com/hobzy987/PhiDSC-DALI.

bioinformatics↗

bioRxiv2021

NN-RNALoc: neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profiles

Localization of messenger RNAs (mRNA) as a widely observed phenomenon is considered as an efficient way to target proteins to a specific region of a cell and is also known as a strategy for gene regulation. The importance of correct intracellular RNA placement in the development of embryonic and neural dendrites has long been demonstrated in former studies. Improper localization of RNA in the cell, which has been shown to occur due to a variety of reasons, including mutations in trans-regulatory elements, is also associated with the occurrence of some neuromuscular diseases as well as cancer. We propose NN-RNALoc, a neural network-based model to predict the cellular location of mRNAs. The features extracted from mRNA sequences along with the information gathered from their proteins are fed to this prediction model. We introduce a novel distance-based sub-sequence profile for representation of RNA sequences which is more memory and time efficient and comparying to the k-mer frequencies, can possibly better encode sequences when the distance k increases. The performance of NN-RNALoc on the following benchmark datsets CeFra-seq and RNALocate, is compared to the results achieved by two powerful prediction models that were proposed in former studies named as mRNALoc and RNATracker The results reveal that the employment of protein-protein interaction information, which plays a crucial role in many biological functions, together with the novel distance-based sub-sequence profiles of mRNA sequences, leads to a more accurate prediction model. Besides, NN-RNALoc significantly reduces the required computing time compared to previous studies. Source code and data used in this study are available at: https://github.com/NeginBabaiha/NN-RNALoc

bioinformatics↗

bioRxiv2021

The Expression Pattern of miR-17, -24, -124 and -145 as Diagnostic Factor for Metastatic Gastric Cancer; a Lesson from Gastric Cancer Stem cells

BackgroundDistant metastasis of Gastric Cancer (GC) causes more than 700 000 deaths worldwide. Cancer Stem Cells (CSCs) are a subpopulation of cancer cells responsible for aggressiveness and chemoresistance in clinical settings. MicroRNAs (miRNAs) emerge as important players in regulating self-renewal and metastasis in CSCs. Understanding the role of miRNAs in CSCs offer a potential diagnostic tool for GC patients. This study is aimed to identify miRNAs that target both stemness and metastasis in gastric cancer stem cells (GCSCs) and differentially expressed in metastatic GC patients as diagnostic biomarkers for GC metastasis. MethodsWe investigate the gene expression profile of patients using the GEO database and Rstudio software. To obtain the regulatory networks and miRNAs, the STRING and miRwalk database used. The gastric cancer tissues were obtained from Iranian National Tumor Bank (INTB) to validate the results. ResultsOur results indicated three important regulatory cores affecting the immune systems regulation, tumor progress, and metastasis. Based on the bioinformatics results, four miRNAs miR-17-5p, miR-24-3p, miR-124-3p, and miR-145-5p, were selected, and their expression pattern was evaluated in 10 patients metastatic tumors compared to 10 nonmetastatic tumors by real-time PCR. The expression level of mir-17, -24, and -124 was upregulated about 8, 10, 60 folds, respectively, and miR-145 was down-regulated 4.5 folds in metastatic tumors compared to nonmetastatic tumors. Conclusionthe high expression level of miR-17, -24, -124, and low level of miR-145 in GC patients samples could be a potential biomarker for the presence of GCSCs and the diagnosis of metastasis.

cancer biology↗

bioRxiv2018

PSL-Recommender: Protein Subcellular Localization Prediction using Recommender System

Identifying a proteins subcellular location is of great interest for understanding its function and behavior within the cell. In the last decade, many computational approaches have been proposed as a surrogate for expensive and inefficient wet-lab methods that are used for protein subcellular localization. Yet, there is still much room for improving the prediction accuracy of these methods.\n\nPSL-Recommender (Protein subcellular location recommender) is a method that employs neighborhood regularized logistic matrix factorization to build a recommender system for protein subcellular localization. The effectiveness of PSL-Recommender method is benchmarked on one human and three animals datasets. The results indicate that the PSL-Recommender significantly outperforms state-of-the-art methods, improving the previous best method up to 31% in F1 - mean, up to 28% in ACC, and up to 47% in AVG. The source of datasets and codes are available at: https://github.com/RJamali/PSL-Recommender

bioinformatics↗