its_a_scam
its_a_scam - It's a scam To Sell Another Single Cell Analysis
Method: modular tools for processing and per-cell quality control of
single cell genomic data, built on the
gcat C++ library
and described in
Prabakar & Meyer, bioRxiv (2026).
It works with common open-source tools to generate scRNA-seq and
scATAC-seq count matrices and computes quality metrics directly from
alignment files. Documentation is available at
its-a-scam.readthedocs.io.
BATMAN
BATMAN - Bayesian Inference of Activation of TCR by Mutant Antigens:
python package for predicting TCR activation by mutant peptides
based on their distances to the TCR's index peptide, described in
Banerjee et al., Cell Systems (2025).
BATMAN can be
used for within-TCR and cross-TCR predictions and in active learning
mode to design TCR activation assays.
copepodTCR
copepodTCR - COmbinatorial PEptide POoling Design for TCR
specificity: python package and webtool for the design of
combinatorial peptide pooling schemes for TCR specificity assays, described in
Kovaleva et al., bioRxiv (2025).
CopepodTCR guides the user through all stages of the experimental
design and interpretation, including pooling schemes, peptide mixing
layouts and results evaluation.
codePUB
Python tool to generate balanced constant-weight Gray codes for detecting
consecutive positive items in the efficient construction of
combinatorial pooling schemes, described in
He, Kovaleva et al., Bioinformatics (2025).
PhenotypeSimulator
R/CRAN framework for simulating genotype to phenotype relationships, described in
Meyer & Birney, Bioinformatics (2018).
It can
model multiple traits with multiple underlying genetic loci as well as complex
covariate and observational noise structure. This package has been designed
to work with many common genetic tools both for input and
output.
plinkQC
R/CRAN package for common genotype quality control steps, described in
Syed et al., Bioinformatics (2026).
It provides
an easily applicable workflow from per-individual and per-marker quality
control to the generation of the new, quality-controlled dataset.
The outcomes of each quality control step are summarised in a quality
control report.
LiMMBo
Python module for computationally efficient joint genetic analysis of
high-dimensional phenotypes, using linear mixed models with bootstrapping, described in
Meyer et al., bioRxiv (2018).