Research preview · bioinformatics performance layer

Make scientific software faster.

AutoZyme is the optimization layer for research code. Like enzymes that accelerate reactions without changing their products, AutoZyme uses autonomous search to find safe speedups inside widely used scientific tools — shipped as drop-in patches with the same API, same outputs, and no workflow changes.

See the benchmarks → Submit an optimization Request a package
Preprint coming soon · arXiv:TBD First public releases: SeuratTurbo · R / ScanpyTurbo · Python · 59 accelerated tools across 9 scientific domains

By the numbers

Aggregated across all tracked benchmarks
277
Benchmarks
59
Active tool targets
161
Datasets
58
Methods
16.9×
Median speedup
5,633×
Peak speedup

How AutoZyme finds and compounds speedups

Agent discovery and community requests converge into one verification pipeline
Agent-discovered

AutoZyme searches for bottlenecks

The AutoZyme agent scans public bioinformatics and scientific-computing ecosystems for slow, memory-heavy, or widely used methods worth optimizing.

Community-requested

Researchers nominate pain points

Users request packages that are too slow, hit OOM, or waste repeated analysis time. Votes and reproducibility help prioritize the queue.

01

Baseline and gates

We freeze representative inputs, upstream baselines, output concordance metrics, and acceptance thresholds.

02

Iterate and verify

AutoZyme generates candidate changes, benchmarks them, rejects divergent outputs, and keeps only reproducible speedups.

03

Release and package

Accepted optimizations are published as drop-in AutoZyme packages or upstream-ready patches with reproducible benchmark evidence.

04

Return to Lab

The optimized result becomes the new public baseline in AutoZyme Lab, where contributors can try to push it further.

Lab is not only a submission portal. It is the public continuation point: every verified release can become a new frozen challenge baseline for the community to improve again.

Benchmarks at a glance

Released public subset; broader bioinformatics targets are staged as they pass gates
4 threads AMD Ryzen 9 7950X (16-core) · 128 GB DDR5 · Windows 11
NormalizeData
30×
1.000
FindVariableFeatures
14×
1.000
ScaleData
18×
1.000
RunPCA
73×
1.000
FindNeighbors
2.8×
1.000
RunUMAP
3.3×
0.997
FindAllMarkers
161×
1.000
SCTransform
15×
1.000
IntegrateLayers (CCA)
25×
1.000
Datasets
PBMC 68k PBMC 208k Heart 486k TMS SS2 110k Gastrulation 139k SPLiT-seq 156k ifnb 14k (2 batches) TMS SS2 110k (4 batches) Gastrulation 139k (3 batches)
Per-thread × per-dataset matrix on the Benchmarks page →

Install

Drop-in — no API changes to your existing pipelines
R · SeuratTurbo Seurat v5.x 
# Install from GitHub (CRAN release coming)
remotes::install_github("ElliotXie/seurat-turbo")

library(Seurat)
library(SeuratTurbo)   # activates patches

# Use Seurat exactly as you normally would —
# NormalizeData / RunPCA / FindClusters / etc.
# are transparently accelerated.
Requires R ≥ 4.0, Seurat ≥ 5.0
Python · ScanpyTurbo Scanpy v1.11.x 
# Install from GitHub (PyPI release coming)
pip install git+https://github.com/ElliotXie/scanpy-turbo.git

import scanpy as sc
import scanpy_turbo   # activates patches

# Use Scanpy exactly as you normally would —
# pp.normalize_total / tl.leiden / etc.
# are transparently accelerated.
Requires Python ≥ 3.10, Scanpy ≥ 1.11