Software

Cell lineage and differentiation

Startle

Startle uses the maximum parsimony framework to find single-cell lineage trees from CRISPR-Cas9 based lineage tracing data using the star homoplasy model. [Code]

• Sashittal P.*, Schmidt H.*, Chan M. and Raphael B., Startle: a star homoplasy approach for CRISPR-Cas9 lineage tracing, BioRxiv 2023.

Cancer genome evolution

ConDoR

ConDoR is an algorithm to infer tumor phylogenies from targeted single-cell DNA sequencing data using the Constrained Dollo model. [Code]

• Sashittal P., Zhang H., Iacobuzio-Donahue C. A. and Raphael B., ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model, BioRxiv 2023.

Paction

Paction is a method to reconcile SNV and CNA clone proportions and trees for thesame tumor. The output is a set of tumor clones characterized by both SNVs and CNAs,the clone proportions and a comprehensive clone tree annotated with both SNV andCNA events. [Code]

• Sashittal P., Zaccaria S. and El-Kebir M., Parsimonious clone tree integration in cancer, Algorithms and Molecular Biology, 2022.

doubletD

doubletD is an efficient doublet detector designed for single-cell DNA sequencing (scDNA-seq) data. Incorporating doubletD in scDNA-seq analysis pipelines leads to more accurate cancer genotyping and tumor phylogeny inference. [Code]

• Weber, L.*, Sashittal P.*, and El-Kebir M., doubletD: detecting doublets in single-cell DNA sequencing data, Bioinformatics, 2021.

Infectious disease evolution and transmission

PRIMES

PRIMES is a computational to predict the sensitivity and specificity of a PCR assay to detect specific pathogen lineages of interest.[Code]

• Oh C.*, Sashittal P.*, Zhou A., Wang L., El-Kebir M. and Nguyen T. H., Design of SARS-CoV-2 variant-specific PCR assays considering regional and temporal characteristics, Applied and Environmental Microbiology, 2022.

CORSID

CORSID is an algorithm to simultaneously identify TRS sites, the core sequence and gene locations given an unannotated coronavirus genome sequence. [Code]

• Zhang C.*, Sashittal P.*, Xiang M., Zhang Y., Kazi A. and El-Kebir M., Accurate identification of transcription regulatory sequences and genes in Coronaviruses, Molecular Biology and Evolution, 2022.

Jumper

Jumper is a code that takes a bam file of RNA-seq reads and a Fasta file of the reference genome as input and reconstructs RNA transcripts and their abundances in the sample. [Code]

• Sashittal P., Zhang, C., Peng J. and El-Kebir M., Jumper enables discontinuous transcript assembly in Coronaviruses, Nature Communications, 2021.

TiTUS

TiTUS is a code that takes as input a timed pathogen phylogeny with leaves labelled by the host along with epidemiological data such as entry-removal times for the hosts and a contact map. It counts and uniformly samples from the set of feasbile interval vertex labelings of the timed phylogeny that satisfy the direct transmission constraint while supporting a weak transmission bottleneck. [Code]

• Sashittal, P. & El-Kebir, M., Sampling and Summarizing Transmission Trees with Multi-strain Infections, Bioinformatics, 2020. [Paper]

SharpTNI

SharpTNI is a tool for counting and sampling solutions from the space of parsimonious transmission networks under a weak transmission bottleneck constraint. This problem arises in phylodynamic and phylogeographic analyses of disease outbreaks. [Code]

• Sashittal, P. & El-Kebir, M., SharpTNI: Counting and Sampling Parsimonious Transmission Networks under a Weak Bottleneck, BMC Medical Genomics, 2020. [Paper]

Data-driven modeling of flow dynamics

lrDMD

Low-rank dynamic mode decomposition (lrDMD) solves for a rank-constrained linear representation of the dynamical system for given series of data snapshots. [Code]

• Sashittal, P. & Bodony, D., Reduced-Order Control using Low-Rank Dynamic Mode Decomposition, Theoretical and Computational Fluid Dynamics, 2019. [Paper]

KF-DMD

A data-driven method for optimal sensor placement and parameter optimization using transfer operator based reduced order models. [Code]

• Sashittal, P. & Bodony, D., Data-driven sensor placement for fluid flows, Theoretical and Computational Fluid Dynamics, 2021.