lehner-lab Goto Github PK

abetadms

Analysis scripts for processing Abeta deep mutational scanning (DMS) data

alfred_method

Scripts for the analysis described in "Systematic discovery of germline cancer predisposition genes through the identification of somatic second hits", Park et al. 2018

archstabms

Source code for analyses and to reproduce all figures in the following publication: The genetic architecture of protein stability (Faure AJ et al., 2023)

biophysical_ambiguity

Codes to reproduce the analysis and figures for the project "Biochemical ambiguities prevent accurate genetic prediction"

canya

A hybrid neural network to predict nucleation propensity

chromatin_noise_paper

Custom pipeline and scripts for the analysis described in "Systematic Analysis of the Determinants of Gene Expression Noise in Embryonic Stem Cells", Faure et al. 2017

combinatorialcores

Source code for analyses and figure reproduction in "Genetics, energetics and allostery during a billion years of hydrophobic protein core evolution", Escobedo et. al 2024

concentration_epistasis_ci

Codes and data for the published paper (Nat.Comms 2019) 'Changes in gene expression predictably shift and switch genetic interactions'

concentration_epistasis_grn

This is codes for 'Changes in gene expression alter mutational effects and genetic interactions in an underlying biochemical parameter-dependent way.'

constitutive_exons

Scripts needed to reproduce Baeza-Centurion et al. 2020

deep_indel_mutagenesis

dimsum

An error model and pipeline for analyzing deep mutational scanning (DMS) data and diagnosing common experimental pathologies

dimsumms

Analysis scripts to reproduce the figures and results from the computational analyses described in the paper Faure and Schmiedel et al. "DiMSum: An error model and pipeline for analyzing deep mutational scanning (DMS) data and diagnosing common experimental pathologies", 2020

dms2structure

Scripts for "Determining protein structures using deep mutagenesis", Schmiedel & Lehner, Nature Genetics, 2019

domainome

doubledeepms

Source code for fitting thermodynamic models (MoCHI), downstream analyses and to reproduce all figures in the following publication: Mapping the energetic and allosteric landscapes of protein binding domains (Faure AJ, Domingo J & Schmiedel JM et al., 2022)

fas-indel-library

fitness-landscapes-dataviz

Visualisation of fitness landscapes

fos_jun_trans_epistasis

Scripts for "The genetic landscape of a physical interaction", Diss & Lehner 2018

fuzzy_specificity

Code for "A complete map of specificity encoding for a partially fuzzy protein interaction" by Taraneh Zarin and Ben Lehner

harmoniouscombinations

Datasets and supplementary data for New, A.M., Lehner, B. Harmonious genetic combinations rewire regulatory networks and flip gene essentiality. Nat Commun 10, 3657 (2019) doi:10.1038/s41467-019-11523-z Code authored by Aaron New

higher-order_epistasis_trna

Scripts needed to reproduce Domingo et al. 2018

krasddpcams

Source code for computational analyses and to reproduce all figures in the following publication: The energetic and allosteric landscape for KRAS inhibition (Weng C, Faure AJ & Lehner B, 2022)

mean-noise-fitness-landscapes

Scripts to reproduce analysis of Schmiedel et al. "Empirical noise-mean fitness landscapes and the evolution of gene expression" bioRxiv, 2018

microscopycode-dhar_et_al

Custom code for microscopy data analysis described in "Single cell functional genomics reveals the importance of mitochondria in cell-to-cell phenotypic variation", Dhar et al.

microscopydata_dhar_et_al

Data of microcolony growth rate of WT and deletion strains obtained through high-throughput microscopy assay in Dhar et al., "Single cell functional genomics reveals the importance of mitochondria in cell-to-cell variation in proliferation, drug resistance and mutation outcome" - https://www.biorxiv.org/content/early/2018/06/13/346361

mochi

Neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis and allostery from deep mutational scanning data

mochims

Source code for analyses and to reproduce all figures in the following publication: MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis and allostery from deep mutational scanning data (Faure AJ & Lehner B, 2024)

pdzextms

Source code for analyses and to reproduce all figures in the following publication: The effects of PDZ domain extensions on energies, energetic couplings and allostery (Hidalgo-Carcedo C & Faure AJ et al., 2023)

rdgvassociation

Script to (1) perform component extraction via ICA & VAE, (2) perform network analysis, and (3) replicate paper figures from manuscript. Pre-print on bioRxiv: https://www.biorxiv.org/content/10.1101/2021.11.14.468508v1.