Palash Sashittal

PhyloStage infers multi-stage pathway-level cancer progression models from tumor phylogenies, aligning with known AML progression and stratifying NSCLC patients by tumor size.

This paper introduces CoEvoLink, a computational framework that predicts virus-host interactions by integrating sequence data with coevolutionary phylogenetic models, improving prediction accuracy over existing methods.

POTTR identifies recurrent mutation trajectories in cancer and development using posets, resolving uncertainty in phylogenies and discovering significant new patterns.

SCOPE explores the space of tumor phylogenies consistent with scWGS data, showing multiple phylogenies are possible and highlighting how data quality and evolutionary constraints affect uncertainty.

This study uses single-nucleus DNA sequencing to analyze pancreatic cancer evolution, revealing patterns of mutation and copy number variation across clinical scenarios.

The paper uses simulations to show that body temperature evolution is constrained by protein thermal performance, leading to predictable patterns of rapid initial change followed by slow adaptation.

The paper introduces Carta, an algorithm that infers cell differentiation maps from single-cell lineage tracing data, showing improved accuracy over existing methods in simulated and real data.

This paper introduces an algorithm to reconstruct longitudinal tumor phylogenies from single-cell sequencing data and demonstrates its ability to detect rare clones persisting through treatment.

The paper identifies clonal inflammatory microglia as a cause of neurodegeneration in LCH and ECD, showing their association with neuronal loss and suggesting CSF1R inhibitors as a therapeutic option.

Startle is a method for inferring CRISPR-Cas9 lineage trees using a star homoplasy model, showing improved accuracy on simulated and real datasets.

ConDoR improves tumor phylogeny inference by integrating copy-number constraints with SNV data, outperforming existing methods on simulated data and producing more plausible phylogenies in real datasets.

The paper introduces a zero-agnostic copy number transformation model and develops an algorithm for inferring copy number phylogenies, showing improved performance on simulated and real data.

Jumper improves transcript assembly for Nidovirales by addressing discontinuous transcription, outperforming existing methods and identifying non-canonical transcripts.

CORSID identifies transcription regulatory sequences and genes in unannotated coronavirus genomes without prior information.

This study identifies multiple SARS-CoV-2 strains within and across hosts using bulk RNA sequencing and consensus sequences, revealing within-host diversity, recombination, and geographic strain variation.

TiTUS samples and summarizes transmission trees for multi-strain infections using SATISFIABILITY, demonstrating accuracy on simulated and HIV data.

SharpTNI counts and samples parsimonious transmission networks under weak bottlenecks using timed phylogenies, accurately identifying co-transmissions in Ebola outbreaks.