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2017_gray_informativeaa

To draw general conclusions about the effects of different amino acid substitutions, we analyzed 34,373 mutations in fourteen proteins whose effects were measured using large-scale mutagenesis approaches. Methionine was the most tolerated substitution while proline was the least tolerated. Histidine and asparagine best recapitulated the effects of other substitutions, even when the identity of the wild type amino acid was considered. Furthermore, highly disruptive substitutions like aspartic and glutamic acid had the most discriminatory power for detecting ligand interface positions. Our work highlights the utility of large-scale mutagenesis data, and our conclusions can help guide future mutagenesis studies.

2019_srcactivity

Data and code for Ahler et al 2019

2020_cava

Data and analyses associated with CAVA

2020_dots

Sequencing and statistical analysis for Rose, J.C., Popp, N.A., et al. 2020. Suppression of unwanted CRISPR/Cas9 editing by co-administration of catalytically inactivating truncated guide RNAs. Nat. Comm.

2022_srchsp90

2024_csr_tfo

2024_multistep

Data and analysis for Popp, NA, et al., 2024.

amyloidbeta2019

Analysis of amyloid ß large scale mutagenesis data by Gray et al. 2019

cyp2c19_2c9

Analysis pipeline for "Understanding the CYP family tree through deep mutational scanning: A joint analysis of CYP2C19 and 2C9 variant abundance"

enrich2

Tool for deep mutational scanning experiments.

enrich2-example

Example deep mutational scanning dataset for Enrich2

envision2017

We present Envision, an accurate predictor of protein variant molecular effect, trained using large-scale experimental mutagenesis data. All data and software in this study are freely available. The training data set and all code used to train the models and generate the figures presented in this manuscript are available here. Envision predictions, along with feature annotations, are available at https://envision.gs.washington.edu/.

lentilandingpad

simdms

Deep mutational scanning dataset simulator.

vampseq

We developed Variant Abundance by Massively Parallel Sequencing (VAMP-seq), which simultaneously measures the effects of thousands of missense variants on protein intracellular abundance, and applied it to study PTEN and TPMT, two clinically actionable genes. This repository houses the analysis scripts used for this study.

vcs_2019

Code and processed data for manuscript entitled, "High-throughput, Microscope-based Sorting to Dissect Cellular Heterogeneity"

vep-calibrations

vkor

Analysis pipeline for manuscript "Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact."