Biology subjects

Roddur, M. S.

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

3 recordsLinked to original sources

bioRxiv2026

STEQ: A statistically consistent quartet distance based species tree estimation method

Accurate estimation of large-scale species trees from multilocus data in the presence of gene tree discordance remains a major challenge in phylogenomics. Although maximum likelihood, Bayesian, and statistically consistent summary methods can infer species trees with high accuracy, most of these methods are slow and not scalable to large number of taxa and genes. One of the promising ways for enabling large-scale phylogeny estimation is distance based estimation methods. Here, we present STEQ, a new statistically consistent, fast, and accurate distance based method to estimate species trees from a collection of gene trees. We used a quartet based distance metric which is statistically consistent under the multi-species coalescent (MSC) model. The running time of STEQ scales as [O] (kn2 log n), for n taxa and k genes, which is asymptotically faster than the leading summary based methods such as ASTRAL. We evaluated the performance of STEQ in comparison with ASTRAL and wQFM-TREE - two of the most popular and accurate coalescent-based methods. Experimental findings on a collection of simulated and empirical datasets suggest that STEQ enables significantly faster inference of species trees while maintaining competitive accuracy with the best current methods. STEQ is publicly available at https://github.com/prottoysaha99/STEQ.

bioinformatics↗

bioRxiv2024

Characterizing the Solution Space of Migration Histories of Metastatic Cancers with MACH2

Understanding the migration history of cancer cells is essential for advancing metastasis research and developing therapies. Existing migration history inference methods often rely on parsimony criteria such as minimizing migrations, comigrations, and seeding locations. Importantly, existing methods either yield a single optimal solution or are probabilistic algorithms without guarantees on optimality nor comprehensiveness of the returned solution space. As such, current methods are unable to capture the full extent of uncertainty inherent to the data. To address these limitations, we introduce MACH2, a method that systematically enumerates all plausible migration histories by exactly solving the PO_SCPLOWARSIMONIOUSC_SCPLOW MO_SCPLOWIGRATIONC_SCPLOW HO_SCPLOWISTORYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWWITHC_SCPLOW TO_SCPLOWREEC_SCPLOW RO_SCPLOWEFINEMENTC_SCPLOW (PMH-TR). In addition to the migration and comigration criteria, MACH2 employs a novel parsimony criterion that minimizes the number of clones unobserved in their inferred location of origin. MACH2 allows one to specify both the order and the set of criteria to include during optimization, allowing users to adapt the model to specific analysis needs. MACH2 also includes a summary graph to identify high-confidence migrations. Finally, we introduce MACH2-viz, an interactive webtool for visualizing and exploring MACH2 solution spaces. Using simulated tumors with known ground truth, we show that MACH2, especially the version that prioritizes the new unobserved clone criterion, outperforms existing methods. On real data, MACH2 detects extensive uncertainty in non-small cell lung, ovarian, and prostate cancers, and infers migration histories consistent with orthogonal experimental data.

cancer biology↗

bioRxiv2023

TRIBAL: Tree Inference of B cell Clonal Lineages

B cells are a critical component of the adaptive immune system, responsible for producing antibodies that help protect the body from infections and foreign substances. Single cell RNA-sequencing (scRNA-seq) has allowed for both profiling of B cell receptor (BCR) sequences and gene expression. However, understanding the adaptive and evolutionary mechanisms of B cells in response to specific stimuli remains a significant challenge in the field of immunology. We introduce a new method, TRIBAL, which aims to infer the evolutionary history of clonally related B cells from scRNA-seq data. The key insight of TRIBAL is that inclusion of isotype data into the B cell lineage inference problem is valuable for reducing phylogenetic uncertainty that arises when only considering the receptor sequences. Consequently, the TRIBAL inferred B cell lineage trees jointly capture the somatic mutations introduced to the B cell receptor during affinity maturation and isotype transitions during class switch recombination. In addition, TRIBAL infers isotype transition probabilities that are valuable for gaining insight into the dynamics of class switching. Via in silico experiments, we demonstrate that TRIBAL infers isotype transition probabilities with the ability to distinguish between direct versus sequential switching in a B cell population. This results in more accurate B cell lineage trees and corresponding ancestral sequence and class switch reconstruction compared to competing methods. Using real-world scRNA-seq datasets, we show that TRIBAL recapitulates expected biological trends in a model affinity maturation system. Furthermore, the B cell lineage trees inferred by TRIBAL were equally plausible for the BCR sequences as those inferred by competing methods but yielded lower entropic partitions for the isotypes of the sequenced B cell. Thus, our method holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies. AvailabilityTRIBAL is available at https://github.com/elkebir-group/tribal

immunology↗