ADAPT Model

Two-stage CNN from the Broad Institute for predicting Cas13 guide RNA activity.

Overview

ADAPT (Activity-informed Design with All-inclusive Patrolling of Targets) is a model developed at the Broad Institute for predicting Cas13 guide RNA activity. It was designed specifically for diagnostic guide design and trained on large-scale Cas13a (LwaCas13a) activity screens against diverse viral sequences.

Metsky HC, Welch NL, Pillai PP, Haradhvala NJ, Rumker L, Mantena S, Zhang YB, Yang DK, Ackerman CM, Weller J, Blainey PC, Myhrvold C, Mitzenmacher M, Sabeti PC (2022). “Designing sensitive viral diagnostics with machine learning” Nature Biotechnology.

Paper GitHub

SPACER automatically selects the ADAPT model when AI activity prediction is enabled for Cas13 analyses.

Info

ADAPT is the ML model for Cas13. For Cas12 guides, SPACER uses the EasyDesign model instead.

Model Architecture

Like EasyDesign, ADAPT uses a two-stage CNN cascade:

Stage 1 (Classification): Predicts whether a guide is active or inactive
Stage 2 (Regression): For guides classified as active, predicts a continuous activity score

Input Encoding

Parameter	Value
Guide length	28 nt
PAM	Not required (Cas13 is PAM-independent)
Flanking context	10 nt upstream + 10 nt downstream
Encoding	One-hot with 8 channels per position (4 target + 4 guide)

Scoring Integration

When AI activity prediction is enabled for a Cas13 analysis, SPACER runs ADAPT on each candidate guide. The predicted activity score is incorporated into the composite score via the ML adjustment component.

Warning

When ADAPT is active, the spacer length is constrained to 28 nt to match the model's training input. This is set automatically when you enable AI activity prediction for Cas13.

Performance