Biology subjects

Weerakoon, M.

Publications and source records attributed to Weerakoon, M..

4 recordsLinked to original sources

bioRxiv2026

Cellector: A tool to detect foreign genotype cells in scRNAseq data with applications in leukemia and microchimerism.

The existence of rare, genetically distinct cells can occur in various samples such as transplant patients, naturally occurring microchimerism between maternal and fetal tissues, and cancer samples with sufficient mutational burden. Computational methods for detecting these foreign cells are vital to studying these biological conditions. An application that is of particular interest is that of leukemia patients post hematopoietic cell transplant (HCT). In many leukemias, a primary therapy is HCT, after which, the primary genotype of the bone marrow and blood cells should be of donor origin. If cells exist that are of the patients genotype and the cell type lineage of the particular leukemia, this is known as measurable residual disease (MRD). If the MRD is high enough, this may represent a relapse of the patients leukemia. Furthermore, accurately estimating the MRD is important for driving clinical decision making for these patients. Here we present Cellector, a computational method for identifying rare foreign genotype cells in single cell RNAseq (scRNAseq) datasets. We show cellector accurately detects microchimeric cells down to an exceedingly low percentage of these cells present (0.05% or lower).

bioinformatics↗

bioRxiv2025

Souporcell3: Robust Demultiplexing for High-Donor Single-Cell RNA-seq Datasets

MotivationAccurate demultiplexing of pooled single-cell RNA-seq (scRNAseq) data is critical for large-scale studies. However, existing methods like vireo, while effective up to [~]16 donors, often struggle with poor clustering due to local optima as donor numbers rise. In high-donor scenarios, overlapping genotypes, a dense genotype space, and increased doublet formation make demultiplexing challenging, requiring methods that are robust to sparse, high-dimensional data and maintain reliable accuracy even as sample complexity grows. ResultsWe present an enhanced version of souporcell capable of demultiplexing up to 64 donors. The method uses 10x merge for initialization, K-Harmonic Means for robust clustering, and iterative refinement with reinitialization of low-quality clusters and locking of high-quality ones. Compared to vireo, vireo with overclustering, and the original souporcell, our approach completely eliminates duplicate clusters and achieves consistently high Adjusted Rand Index (ARI) scores across various doublet rates, demonstrating improved accuracy and scalability. AvailabilitySouporcell3 source code and documentation are released on GitHub: https://github.com/wheaton5/souporcell

bioinformatics↗

bioRxiv2024

LCSKPOA: Enabling banded semi-global partial order alignments via efficient and accurate backbone generation through extended lcsk++

BackgroundMost multiple sequence alignment and string-graph alignment algorithms focus on global alignment, but many applications exist for semi-global and local string-graph alignment. Long reads require enormous amounts of memory and runtime to fill out large dynamic programming tables. Effective algorithms for finding the backbone and thus defining a band of an alignment such as the longest common subsequence with kmer matches (LCSk++) exist but do not work with graphs. This study introduces an adaptation of the Longest Common Subsequence with kmer matches (LCSk++) algorithm tailored for graph structures, particularly focusing on Partial Order Alignment (POA) graphs. POA graphs, which are directed acyclic graphs, represent multiple sequence alignments and effectively capture the relationships between sequences. Current state of the art methods like ABPOA and SPOA, while improving POA, primarily focus on global alignment and thus are limited in local and semi-global banding scenarios. Our approach addresses these limitations by extending the LCSk++ algorithm to accommodate the complexities of graph-based alignment. ResultsOur extended LCSk++ algorithm integrates dynamic programming and graph traversal techniques to detect conserved regions within POA graphs, termed the LCSk++ backbone. This backbone enables precise banding of the POA matrix for local and semi-global alignment, significantly enhancing the construction of consensus sequences. Compared to unbanded semi-global POA, our method demonstrates substantial memory savings (up to 98%) and significant run-time reductions (up to 37-fold), particularly for long sequences. The method maintains high alignment scores and proves effective across various string lengths and datasets, including synthetic and PacBio HiFi reads. Parallel processing further enhances runtime efficiency, achieving up to 150x speed improvements on conventional PCs. ConclusionThe extended LCSk++ algorithm for graph structures offers a substantial advancement in sequence alignment technology. It effectively reduces memory consumption and optimizes run times without compromising alignment quality, thus providing a robust solution for local and semi-global alignment in POA graphs. This method enhances the utility of POA in critical applications such as multiple sequence alignment for phylogeny construction and graph-based reference alignment.

genomics↗

bioRxiv2024

TopoQual polishes circular consensus sequencing data and accurately predicts quality scores

SummaryPacific Biosciences (PacBio) circular consensus sequencing (CCS) aka high fidelity (HiFi) technology has revolutionized modern genomics by producing long (10+kb) and highly accurate reads by sequencing circularized DNA molecules multiple times and combining them into a consensus sequence. Currently the accuracy and quality value estimation is more than sufficient for genome assembly and germline variant calling, but the estimated quality scores are not accurate enough for confident somatic variant calling on single reads. Here we introduce TopoQual, a tool utilizing partial order alignments (POA), topologically parallel bases, and deep learning to polish consensus sequences and more accurately predict base qualities. We correct ~31.9% of errors in PacBio consensus sequences and validate base qualities up to q59 which is one error in 0.9 million bases enabling accurate somatic variant calling with HiFi data. Availability and implementationThe source code and installation instructions as well as validation dataset used are freely available at https://github.com/lorewar2/TopoQual

bioinformatics↗