Resources & Tools
Computational Resources and Databases for EPS Mutant Analysis
Genomic Analysis Pipeline
Complete workflow for whole-genome resequencing, alignment, variant calling, and mutation mapping.
Burrows-Wheeler Aligner for high-performance read mapping. Industry standard for aligning Illumina sequencing reads against large reference genomes using memory-efficient FM-index algorithms.
Function: Short read alignment to reference genome
Essential toolkit for manipulating next-generation sequencing data in SAM/BAM format. Provides sorting, indexing, and format conversion utilities for alignment files.
Function: Alignment sorting, indexing, and format conversion
Genome Analysis Toolkit from Broad Institute. Gold standard for variant discovery (SNPs and indels) using sophisticated Bayesian genotyping algorithms and base quality score recalibration.
Function: SNP/Indel calling and variant quality assessment
Rapid gene identification pipeline for mutant populations. Accelerates causal mutation discovery by comparing mutant and wild-type bulked segregant sequencing data through SNP index analysis.
Function: Bulked segregant analysis for mutation mapping
Specialized mutation detection software developed for EPS mutant library analysis. Optimized for Windows environment with WSL2 support, designed specifically for gene editing NGS data processing and variant annotation.
Function: EPS-specific mutation detection and analysis pipeline
Mutation Effect Prediction Models
Note: Lower effect values indicate more significant mutational impact on protein function.
DNA Language Models
Large-scale DNA foundation models for predicting mutation effects using 8,192 bp context windows.
Genome-scale DNA language model developed by Arc Institute. 7 billion parameter model trained on diverse prokaryotic and eukaryotic genomes for long-context understanding of genomic sequences and variant effects.
Parameters:
Evo2-7B | Context: 8kb
Plant-specific genome language model optimized for crop species. Specifically designed for plant genomic sequence analysis and mutation effect prediction in agricultural contexts.
Parameters:
PlantCAD2-Large-l48-d1536 | Context: 8kb
Protein Language Models
Type I: Sequence-Based Prediction
Predicts mutation effects based on protein sequence information and evolutionary patterns learned from large protein databases.
ESM2
Foundation protein language model for predicting mutational effects. Trained on UniRef50 database with 650 million parameters, providing reliable fitness predictions for single amino acid variants.
Parameters:
esm2_t33_650M_UR50D
ESM-1v
Ensemble of five independently trained models for robust mutation effect prediction. Uses ensemble averaging across multiple model weights to improve prediction reliability and reduce variance.
Ensemble Parameters:
esm1v_t33_650M_UR90S_1, esm1v_t33_650M_UR90S_2, esm1v_t33_650M_UR90S_3, esm1v_t33_650M_UR90S_4, esm1v_t33_650M_UR90S_5
esm1v_t33_650M_UR90S_1, esm1v_t33_650M_UR90S_2, esm1v_t33_650M_UR90S_3, esm1v_t33_650M_UR90S_4, esm1v_t33_650M_UR90S_5
Type II: Structure-Based Prediction
Predicts mutation effects by analyzing protein three-dimensional structure and spatial neighborhood information.
Structure-aware protein language model that incorporates 3D structural tokens alongside sequence tokens. Trained on AFDB (AlphaFold Database) and OMG/NCBI datasets for structure-informed mutation effect prediction.
Parameters:
SaProt_1.3B_AFDB_OMG_NCBI
Protein Structure-aware Sequence Network utilizing structural neighborhood information. Ensemble of nine models with varying k-NN graph parameters (k=10,20,30) and hidden dimensions (512,768,1280) for comprehensive mutation effect assessment.
Ensemble Parameters:
protssn_10_h512, k10_h768, k10_h1280
protssn_20_h512, k20_h768, k20_h1280
protssn_30_h512, k30_h768, k30_h1280
protssn_10_h512, k10_h768, k10_h1280
protssn_20_h512, k20_h768, k20_h1280
protssn_30_h512, k30_h768, k30_h1280
Protein Structure Prediction Models
State-of-the-art protein structure prediction model developed by Meta AI. Utilizes transformer architectures trained on millions of protein sequences to predict 3D structures with high accuracy.
Application: De novo protein structure prediction from amino acid sequences
DeepMind's revolutionary protein structure prediction system. AlphaFold3 extends capabilities to predict structures of complexes including proteins, DNA, RNA, and small molecules with unprecedented accuracy.
Application: Comprehensive biomolecular structure prediction including protein-ligand and protein-nucleic acid complexes
Public Databases
Essential public repositories for sequence data, genome annotations, and protein information.
NCBI
National Center for Biotechnology Information. Primary repository for DNA/protein sequences, literature, and genomic data.
MaizeGDB
Maize Genetics and Genomics Database. Comprehensive resource for maize genetic and genomic information.
