Feedback from Evo

Perinelle Naïa, Thomas Lacroix, Hélène Chiapello, Guillaume Kon Kam King,
Arnaud Ferré, Guillaume Gautreau

► To cite this version:

Perinelle Naïa, Thomas Lacroix, Hélène Chiapello, Guillaume Kon Kam King, Arnaud Ferré, et al. Feedback from Evo. GT LEGO - Journées du GDR BIMMM, GDR BIMMM, Nov 2025, Nantes, France. hal-05388811

HAL Id: hal-05388811

https://hal.science/hal-05388811v1

Submitted on 29 Nov 2025

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Feedback from Evo

Naïa Périnelle, Thomas Lacroix, Hélène Chiapello, Guillaume Kon Kam King, Arnaud Ferré, Guillaume Gautreau.

25th of November, GT LEGO, GDR BIMMM

<figure><img src="image_3.png" /></figure>

RÉPUBLIQUE
FRANÇAISE

Liberté
Égalité
Fraternité

INRAO

MaiAGE
# Pangenomics inspired by linguistics

The pangenomic concept to measure genomic diversity and stability (core and accessory genomes)

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>Harold Heaps
(1922-2016)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>The Bible, King James Version, Complete</figcaption></figure>

Heaps' Law is an empirical law that describes the number of distinct words in a document as an increasing function of the document length.

$$F = p(N) = \kappa N^{\gamma}$$

* γ > 1 : supralinear tendency

* γ = 1 : linear tendency

* 0 < γ < 1 : sublinear tendency
> What does pangenome mean?

# of "pangenome" or "pan-genome" occurrences

From an approach to measure
genomic biodiversity...

<figure><img src="image_7.png" /></figure>

Tettelin *et al.*,
2008

<figure><img src="image_8.png" /></figure>

Garrison *et al.*, 2018
(VG)

... to methods for managing
graphs of ever-increasing
genomic sequences

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>
> Methods to handle ever-increasing genomic sequences

Pangenomics now deeply relies on pangenome graphs:

- pangenome graphs try to compile all the possible (or at least as much as possible) genomic variations sampled in a population in a graph (DBG, variation graphs..).

- But each day, genomic information becomes more and more abundant, 
  ⇒ so these graphs grew tremendously complex!

⇒ Efforts to optimize these graphs and make them computationally manageable open up a significant area of research

<figure><img src="image_11.png" /></figure>
> What to learn from Natural Language Processing ?

* Instead of building graphs of all combinations of words in sentences, research in linguistics, especially Natural Language Processing (NLP) now relies on **Large Language Models (LLMs) to capture the relationships between words (more generally lexical tokens) in the language**

* These models, composed of billions of parameters, are pre-trained on vast datasets and can capture **complex linguistic structures and semantics without explicitly mapping out all possible combinations.**

* These models are then **fine-tuned** to a wide range of downstream tasks (most of the time: chatbots)

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>
> Could pangenome meet AI?

* It is uncertain whether constructing increasingly larger pangenome graphs using sophisticated data structures and algorithms is the best approach to deciphering and understanding biology

* Instead, due to the open-ended nature of genetic diversity (much like language ?) constructing pangenomes as large foundational models could help model and capture unknown genomic relationships until now.

In other words, is pangenome also the concept of a Foundation Model ?

<figure><img src="image_14.png" /></figure>
<figure><img src="image_15.png" /></figure>

# « Attention Is All You Need »

<figure><img src="image_16.png" /></figure>

Given a prompt:

During my travel to the city of Barcelona, my favorite relative and I explored vibrant streets until our legs, the most tired body part, been gged for rest. We found a tiny café, shared local food, and laughed until the feeling of exhaustion on melted into joy. Some moments live forever in memory.

<figure><img src="image_17.png" /></figure>

Billions of trained
weights in the model

Tokens represented in a
multidimensional space
« embeddings »

<figure><img src="image_18.png" /></figure>

Ruder, 2016

https://www.ruder.io/

word-embeddings-1/

Encoder-only

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

Encoder-
Decoder

<figure><img src="image_21.png" /></figure>

Decoder-only

The sun dipped below the rooftops, casting golden light on our table, as music played softly and strangers smiled passing by.

Lors de mon voyage dans la ville de Barcelona, mon parent préféré et moi avons parcouru des rues animées jusqu'à ce que nos jambes, la partie du corps la plus fatiguée, réclame du repos. Nous avons trouvé un petit café, partagé des spécialités locales et ri jusqu'à ce que le sentiment d'épuisement se transforme en joie. Certains instants restent à jamais gravés en mémoire.

Translate into another language

* Passes through dozens of transformer layers.

* Each layer captures more and more information about the links between tokens

INRAO

Feedback from Evo

Predicts next tokens
# Self-supervised learning to build foundational models

Computationally
Intensive !!!

<figure><img src="image_22.png" /></figure>

## 1. Pre-training

## 2. Evaluation / refinement / specialisation

Expert guidance
highly required

<figure><img src="image_23.png" /></figure>

**INRAO**
Feedback from Evo

<figure><img src="image_24.png" /></figure>

## Two self-supervised learning strategies:

Causal
Language
Model
(e.g.: GPT)

Output

<figure><img src="image_25.png" /></figure>

Input:
Unidirectional (left -> right)

<figure><img src="image_26.png" /></figure>

Output

<figure><img src="image_27.png" /></figure>

Input
Bidirectional

Masked
Language
Model
(e.g.: BERT)

Both
lead to:

## Foundational models

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

Additional
data

* Reinforcement learning

* Fine-tuning on specific tasks
<figure><img src="image_30.png" /></figure>

# From NLP to genomics: Evo 1

## Towards genomic Language Models (gLMs)
<figure><img src="image_31.png" /></figure>

Large Language of Life Models (LLLM) as suggested by Eric Topol

LLM

1. English

<figure><img src="image_32.png" /></figure>

"THE QUICK BROWN FOX
JUMPS OVER THE LAZY DOG"

A
Aback
Abandon
...

• BioMedLM

• PubMedBERT

• BioBERT

gLM

2. DNA

<figure><img src="image_33.png" /></figure>

AGGACTGGACCT

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

• DNABERT ⇌ 2 ⇌ S

• Nucleotide Transformer

• Evo 1 ⇌ 1.5 ⇌ 2

pLM

3. Protein

<figure><img src="image_37.png" /></figure>

FYERATIMKHWE

<figure><img src="image_38.png" /></figure>

Ala

<figure><img src="image_39.png" /></figure>

His

<figure><img src="image_40.png" /></figure>

Gly

• ESM 1 ⇌ 2 ⇌ 3

• ProteinBERT

• ProtMamba

• ProtT5

gLM

4. Genome

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

dnaA
rpoB
recA
...

• gLM

• Bacformer

• Meriem Youssef PhD student
(ANR project
PanGAIMiX)

<figure><img src="image_43.png" /></figure>
<figure><img src="image_44.png" /></figure>

Large Language of Life Models (LLLM) as suggested by Eric Topol

LLM

1. English

<figure><img src="image_45.png" /></figure>

"THE QUICK BROWN FOX
JUMPS OVER THE LAZY DOG"

A
Aback
Abandon
...

• BioMedLM

• PubMedBERT

• BioBERT

gLM

2. DNA

<figure><img src="image_46.png" /></figure>

AGGACTGGACCT

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>

• DNABERT ⇌ 2 ⇌ S

• Nucleotide Transformer

• Evo 1 ⇌ 1.5 ⇌ 2

pLM

3. Protein

<figure><img src="image_50.png" /></figure>

FYERATIMKHWE

Ala

His

Gly

• ESM 1 ⇌ 2 ⇌ 3

• ProteinBERT

• ProtMamba

• ProtT5

gLM

4. Genome

<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>

dnaA
rpoB
recA
...

• gLM

• Bacformer

• Meriem Youssef PhD student
(ANR project
PanGAIMiX)

<figure><img src="image_53.png" /></figure>
<figure><img src="image_54.png" /></figure>

Evo (November 2024)

Science

JOURNALS

<figure><img src="image_55.png" /></figure>

RESEARCH ARTICLE

GENERATIVE GENOMICS

Sequence modeling and design from molecular to genome scale with Evo

Eric Nguyen¹,²†, Michael Poli³,⁴†‡, Matthew G. Durrant¹†, Brian Kang¹,²†, Dhruva Katrekar¹†,

David B. Li¹,²†, Liam J. Bartie¹, Armin W. Thomas⁵, Samuel H. King¹,², Garyk Brixi¹,⁶, Jeremy Sullivan¹,

Madelena Y. Ng⁷, Ashley Lewis⁸, Aaron Lou³, Stefano Ermon³,⁹, Stephen A. Baccus¹⁰,

Tina Hernandez-Boussard⁸, Christopher Ré³, Patrick D. Hsu¹,¹¹*, Brian L. Hie¹,⁵,¹²*

The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an organism's function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA codesign with a language model. Evo also learns how small mutations affect whole-organism fitness and generates megabase-scale sequences with plausible genomic architecture. These prediction and generation capabilities span molecular to genomic scales of complexity, advancing our understanding and control of biology.

<figure><img src="image_56.png" /></figure>

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<figure><img src="image_58.png" /></figure>

E

<figure><img src="image_59.png" /></figure>

Phylum

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<table><tr><td>Model size</td><td>7B</td></tr><tr><td>Context size (# of tokens/ bases)</td><td>single-nucleotide resolution: 131Kb or 8Kb</td></tr><tr><td>Architecture</td><td>Decoder only: Striped Hyena</td></tr><tr><td>Training dataset</td><td>Prokaryotic genomes / plasmids / phages</td></tr><tr><td>Training dataset size</td><td>315 billions</td></tr><tr><td>Training cost (estimation by myself at market price)</td><td>70k$</td></tr></table>

INRAO

Feedback from Evo

26/11/2025 / Guillaume Gautreau / MalAGE unit
<figure><img src="image_61.png" /></figure>

# Transformer

## Attention Is All You Need

<figure><img src="image_62.png" /></figure>
<figure><img src="image_63.png" /></figure>

Evo: not (exactly) a transformer

# Transformer

Attention Is All You Need *

* provided you are NOT trying to process **whole genomes** at base scale without **billionaire** funding since:

**Attention complexity is O(N²)**

<figure><img src="image_64.png" /></figure>
> Evo: not (exactly) a transformer

<figure><img src="image_65.png" /></figure>

<figure><img src="image_66.png" /></figure>
Michael Poli*¹, Stefano Massaroli*², Eric Nguyen¹*,
Daniel Y. Fu¹, Tri Dao¹, Stephen Baccus¹,
Yoshua Bengio², Stefano Ermon¹,†, Christopher Ré¹,†

<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /></figure>

Evo is based on Striped Hyena

<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /></figure>
Michael Poli*,¹, Stefano Massaroli*,², Eric Nguyen¹,*,
Daniel Y. Fu¹, Tri Dao¹, Stephen Baccus¹,
Yoshua Bengio², Stefano Ermon¹,†, Christopher Ré¹,†

<figure><img src="image_71.png" /></figure>

<figure><img src="image_72.png" /></figure>

Evo is based on Striped Hyena

<figure><img src="image_73.png" /></figure>

Complexity: $O(Nk)$

$N$: # of tokens in window

$k$: convolution kernel size

if $k \to N \Rightarrow$ Complexity: $O(N^2)$

Fast Fourier Transform: $O(N \log(N))$
<figure><img src="image_74.png" /></figure>

Likelihood landscape

<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /></figure>

A

LM likelihood of
protein sequence or
DNA coding sequence

Predict

Fitness

<figure><img src="image_77.png" /></figure>

C

LM likelihood of
ncRNA sequence

Predict

Fitness

<figure><img src="image_78.png" /></figure>

E

LM likelihood of
regulatory DNA
sequence

Predict

mRNA
expression

Protein
expression

<figure><img src="image_79.png" /></figure>

B

Zero-shot bacterial protein fitness prediction

<figure><img src="image_80.png" /></figure>

D

Zero-shot ncRNA fitness prediction

<figure><img src="image_81.png" /></figure>

F

Promoter activity prediction

<figure><img src="image_82.png" /></figure>

G

Protein expression
prediction
(Kosuri et al., 2013)

<figure><img src="image_83.png" /></figure>
> Much more applications

* Generative design of protein-RNA complexes

<figure><img src="image_84.png" /><figcaption>**Fig. 3.** *Fine-tuning on CRISPR-Cas sequences enables generative design of protein-RNA complexes.* (A) Design task: Generating sequences encoding CRISPR Cas defense complexes composed of protein and ncRNA components. (B) Fine tuning on 8-kb length genome sequences containing CRISPR Cas systems after its initial 8-kb pretraining phase. Special conditioning layers (“cas” (“cosin”, “cosil”)”) is preposed to the beginning of each sequence during fine tuning. (C) When promoting with the token for a given type of Cas protein, the most common Cas protein found in the resulting generated sequences corresponds to that token being proposed (Materials and methods). (D) Holigamns representing the distribution of percentage identity of a generated Cas protein sequence to any Cas protein sequence in the training dataset. Samples from a model trained only on CRISPR-Cas sequences (top) and samples from a model fine-tuned on CRISPR-Cas off the base (Evo model) (bottom). Both models were trained on CRISPR-Cas sequences using the same hyperparameters.</figcaption></figure>

(E) Associated core protein coding genes and ncRNA components found in type I CRISPR systems in the EvCo91 focus as determined by pCR9 and EvCRISPR ncRNA prediction algorithms. (F) Time course results for SpCas9 and EvCo91 leakage reactions after isolation with cationic gels and 1 mM DNA target at 20 °C. (G) Time course results for Cas9 and Cas10 leakage reactions after isolation with cationic gels and 1 mM DNA target at 20 °C. (H) Predicted secondary structure of the sRNA from the EvCo91-1 generation. Secondary structure differences between the EvCo91-sRNA and the SpCas9 sRNA are highlighted in red. (I) Alpha/43 (AF43) structure prediction of EvCo91. I is aligned to the crystal structure of SpCas9 (PO8: 4006). (J) Alpha/43 (AF43) structure prediction of the EvCo91-sRNA aligned to the crystal structure of SpCas9 (PO8: 4006). (K) Alpha/43 (AF43) structure prediction of the SpCas9 sRNA (79 nt scaffold = 20 nt spacer).

* Generative design of transposable biological systems

<figure><img src="image_85.png" /><figcaption>**Fig. 5.** *Evo learns mutational effects on organismal fitness across diverse bacterial and phage genomes.* (A) For genome-scale prediction and generation tasks, we first pretrained Evo on sequences with 832 tokens and then extended its context window size in a second pretraining phase to sequences of 131,072 tokens. (B) We performed an silico, genome-wide mutagenesis screen in which we introduced premature stop codons at each coding sequence in a genome. We computed the language model (LM) likelihood of the mutated gene sequence plus some amount of additional genomic context (up to 66 kbs). We then took the likelihood of this likelihood to the likelihood of the unmutated sequence. We tested whether these likelihood ratios would be predictive of gene essentiality. (C) Volte and strip plots of the distribution of the strength of gene essentiality prediction across 58 studies (each dot corresponds to a different study), in which each study conducted a genome-wide essentiality screen in a bacterial (N = 56)</figcaption></figure>

or phage (N = 2) species. We measured predictive performance as the AUROC in which the LM likelihood ratio is used to predict a binary label (“essential” or “nonessential” (“Gene-only context”) indicates that the model is provided with only the gene sequence and no additional flanking genomic context. (“8k context”) and (“6k context”) indicate that the LM is provided with the gene sequence and flanking genomic context up to a total of 832 or 65,536 tokens, respectively. Evo has some predictive performance with gene-only context, has vastly improved performance from gene-only to 6k context, and some outer improvements from 8k to 6k context. (D) Histograms representing the distributions of the log of the likelihood ratios would be predictive of gene essentiality. (E) Volte and strip plots of the distribution of the strength of gene essentiality prediction across 58 studies (each dot corresponds to a different study), in which each study conducted a genome-wide essentiality screen in a bacterial (N = 56)

* Prediction of gene essentiality

<figure><img src="image_86.png" /><figcaption>**Fig. 4.** *First-tuning on (S200/S605) sequences enables generative design of transposable biological systems.* (A) (S200 and S605 MGEs) contain a single 19-kb insert and are based on the 19-kb right end terminus. They interact with the TopK to accomplish transcription. S605 MGEs additionally associate a TopK arm complex that performs DNA leakage. Our design task is to find the free TopK after its initial pretraining phase, on natural sequences containing 19-kb insert. (B) Examples of sequences that contain DNA, ncRNA, and protein components. (C) The free TopK arm after its initial pretraining phase, on natural sequences containing 19-kb insert. (D) Examples of sequences that contain DNA, ncRNA, and protein components. (E) Histograms representing the distribution of the percentage identity of the Ev-generated TopK and TopK proteins to their best matches in the first training set of natural TopK and TopK proteins. (F) Schematic of the in vitro assay for evaluating designed TopK activity on coded DNA. (G) Examples of topK proteins for the formation of the RE-E junction in the resulting circuitous product, and (j) insertion will produce a</figcaption></figure>

band from the pairing of two ncRNA substrates, both detectable by a single PCR. (E) Schematic of the Ev-generated (S200+4k) system. (G)Vs, containing an element generated by its direct DNA and protein components, are used to pair the top end with the bottom end. The top gel with SYBR Gold showing that the Ev-generated TopK functions in vitro on ncRNA substrates, requiring the catalytically active enzyme (T120) and with substrate, reduced activity on DNA substrate. (F) Examples of the Ev-generated (S605+4k) system. (G)Vs, containing memory repressors, are used to pair the top end with the bottom end. The top gel with SYBR Gold showing that the Ev-generated (S605+4k) TopK functions in vitro on the Ev-generated (S605+4k) system, requiring the catalytically active enzyme (T120) and with substrate, reduced activity on DNA substrates. (G) Examples of the Ev-generated (S605+4k) system, requiring the catalytically active enzyme (T120) and with substrate, reduced activity on DNA substrates. (H) Examples of the Ev-generated (S605+4k) system, requiring the catalytically active enzyme (T120) and with substrate, reduced activity on DNA substrates. (I) Examples of the Ev-generated (S605+4k) system, requiring the catalytically active enzyme (T120) and with substrate, reduced activity on DNA substrates.

* Evo generates megabase-scale sequences with plausible genomic architecture

<figure><img src="image_87.png" /><figcaption>**Fig. 6.** *Evo generates megabase-scale sequences with plausible genomic architectures.* (A) We pre-trained Evo with species-level tokens used during the second pretraining phase. (B) Before the pretraining phase, we use bacterial genes (primarily) and generate sequences of 600 nt in length. (B) Histograms depicting the distribution of coding density scores among 114,143 drops of sequences generated by Evo (Ev-generated) sequences from natural bacteria (“natural bacteria”) and 19,194 sequences from the four base pairs were sampled uniformly at random (“random sequences”). (C) Arrow plots, depicting the excretion of coding sequences on an example (S200+S605) sequence generated by Evo, derived from a natural genome, or sampled uniformly. Coding sequences are depicted as arrows in which the horizontal length of the arrow corresponds to the gene essentiality and the direction of the arrow indicates the strand. The top and bottom rows of arrows indicate the 5'-to-3' and 3'-to-5' strands, respectively, and the Ev-generated sequence was designated as the 5'-to-2' strand. Both</figcaption></figure>

Ev-generated and natural genomes exhibit operon-like structure in which clusters of colocalized genes are on the same strand. (D) and (E) An (S200+S605) generated sequence are represented as an arrow plot, as (C). Below the pretraining phase, we use bacterial genes (primarily) and generate sequences of 600 nt in length. (B) Histograms depicting the distribution of coding density scores among 114,143 drops of sequences generated by Evo (Ev-generated) sequences from natural bacteria (“natural bacteria”) and 19,194 sequences from the four base pairs were sampled uniformly at random (“random sequences”). (C) Arrow plots, depicting the excretion of coding sequences on an example (S200+S605) sequence generated by Evo, derived from a natural genome, or sampled uniformly. Coding sequences are depicted as arrows in which the horizontal length of the arrow corresponds to the gene essentiality and the direction of the arrow indicates the strand. The top and bottom rows of arrows indicate the 5'-to-3' and 3'-to-5' strands, respectively, and the Ev-generated sequence was designated as the 5'-to-2' strand. Both

<figure><img src="image_88.png" /></figure>
> Generated sequence : what about homology with real sequences?

<figure><img src="image_89.png" /></figure>

« **ESMFold structure predictions** of all protein-coding sequences from 100 to 1024 amino acids in length, as identified by Prodigal.

The structure predictions are aligned to natural proteins, which are then mapped to associated GO molecular function terms (Materials and Methods).

The largest GO categories are displayed as clusters, alongside a large cluster containing all other proteins. ATP, adenosine triphosphate. »

⇒ why something so sophisticated without just indicating the %id percent?
<figure><img src="image_90.png" /></figure>

# Feedback on Evo 1

## Evaluation of the credibility of the output
<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

# Methodology

<caption>Naïa Périnelle
(M2 student)</caption>

1. Sampling real sequences at random position from 28 genomes

2. Each sequence is split into:

* a prompt (context)

* a reference

3. A sequence is generated from the prompt using Evo with the same length as the reference

4. The generated sequence is aligned to the reference
(Gotoh global alignment, match: 1 ; open gap: -5 ; mismatch/gap extension: -1)

<table><thead><tr><th>Phylum</th><th>p_nbr</th><th>Species</th><th>s_nbr</th><th>Accession number</th></tr></thead><tbody><tr><td>Acidobacteriota</td><td>3175</td><td>Geothrix sp903857495</td><td>50</td><td>* GCA_903857495.1</td></tr><tr><td>Actinomycetota</td><td>44996</td><td>Mycobacterium tuberculosis</td><td>7337</td><td>* GCF_000195955.2</td></tr><tr><td>Bacillota</td><td>82709</td><td>Staphylococcus aureus</td><td>16021</td><td>* GCF_001027105.1</td></tr><tr><td>Bacillota.A</td><td>80317</td><td>Clostridioides difficile</td><td>2991</td><td>* GCF_001077535.1</td></tr><tr><td>Bacillota.B</td><td>1092</td><td>Avidehalobacter sp022797295</td><td>133</td><td>* GCA_022797295.1</td></tr><tr><td>Bacillota.C</td><td>2749</td><td>Phascolarctobacterium faecium</td><td>115</td><td>* GCF_003269275.1</td></tr><tr><td>Bacillota.l</td><td>10814</td><td>Faecalibaculum rodentium</td><td>403</td><td>* GCF_001564455.1</td></tr><tr><td>Bacteroidota</td><td>76591</td><td>UBA7173 sp001689485</td><td>1211</td><td>* GCA_001689485.1</td></tr><tr><td>Campylobacterota</td><td>11105</td><td>Campylobacter.D jejuni</td><td>2873</td><td>* GCF_001457695.1</td></tr><tr><td>Chloroflexota</td><td>4762</td><td>UBA9611 sp002746355</td><td>30</td><td>GCA_950054275.1</td></tr><tr><td>Cyanobacteriota</td><td>5634</td><td>MGBC122484 sp910586855</td><td>146</td><td>* GCA_910586855.1</td></tr><tr><td>Desulfobacterota</td><td>4840</td><td>Taurinovorans muris</td><td>498</td><td>* GCF_025232395.1</td></tr><tr><td>Myxococcota</td><td>1378</td><td>Corallococcus exiguus</td><td>19</td><td>* GCF_009909105.1</td></tr><tr><td>Nitrospira</td><td>1001</td><td>Nitrospira_A sp900170025</td><td>23</td><td>* GCF_900170025.1</td></tr><tr><td>Patescibacteria</td><td>8106</td><td>Nanosyncoccus sp948851665</td><td>87</td><td>GCA_949464065.1</td></tr><tr><td>Pseudomonadota</td><td>214930</td><td>Echerichia coli</td><td>38926</td><td>* GCF_003697165.2</td></tr><tr><td>Spirochaetota</td><td>4402</td><td>Leptospira interrogans</td><td>391</td><td>* GCF_900156205.1</td></tr><tr><td>Verrucomicrobiota</td><td>6636</td><td>Akkermansia muciniphila</td><td>1061</td><td>* GCF_000020225.1</td></tr><tr><td>Pseudomonadota</td><td>214930</td><td>Halomonas elongata</td><td>6</td><td>* GCF_000196875.2</td></tr><tr><td>Bacteroidota</td><td>76591</td><td>Prevotella copri</td><td>29</td><td>* GCF_025151535.1</td></tr><tr><td>Bacillota.A</td><td>80317</td><td>Faecalibacterium prausnitzii</td><td>124</td><td>* GCF_003324185.1</td></tr><tr><td>Bacillota.A</td><td>80317</td><td>Ruminococcus.B gnavus</td><td>194</td><td>* GCF_008121495.1</td></tr><tr><td>Bacillota</td><td>82709</td><td>Leuconostoc mesenteroides</td><td>239</td><td>* GCF_000014445.1</td></tr><tr><td>Bacillota</td><td>82709</td><td>Lactobacillus delbrueckii</td><td>342</td><td>GCF_006740305.1</td></tr><tr><td>Bacteroidota</td><td>76591</td><td>Flavobacterium psychrophilum</td><td>258</td><td>* GCF_002217405.1</td></tr><tr><td>Bacillota</td><td>82709</td><td>Streptococcus salivarius</td><td>113</td><td>* GCF_000785515.1</td></tr><tr><td>Halobacteriota</td><td>2941</td><td>Methanosarcina mazei</td><td>85</td><td>* GCF_000970205.1</td></tr><tr><td>Micrarchaeota</td><td>1171</td><td>Micrarchaeum_A acidiphilum_A</td><td>29</td><td>* GCF_016806735.1</td></tr></tbody></table>

<figure><img src="image_93.png" /></figure>
<figure><img src="image_94.png" /><figcaption>Naïa Périnelle
(M2 student)</figcaption></figure>

Species Average
Nucleotide Identity
in Prokaryotes

<figure><img src="image_95.png" /><figcaption># Results Evo 1 131k on E. coli</figcaption></figure>

Grouped Identity Boxplot for s_Escherichia coli (p_Pseudomonadota) - Accession: GCF_003697165.2

<figure><img src="image_96.png" /></figure>

<figure><img src="image_97.png" /></figure>

INRAE

Feedback from Evo

26/11/2025 / Guillaume Gautreau / MalAGE unit
<figure><img src="image_98.png" /><figcaption>Naïa Périnelle
(M2 student)</figcaption></figure>

Species Average
Nucleotide Identity
in Prokaryotes

# Results Evo 1 131k on « unnamed cyanobacteria »

Grouped Identity Boxplot for s_MGBC122484 sp910586855 (p_Cyanobacteriota) - Accession: GCA_910586855.1

<figure><img src="image_99.png" /></figure>
<figure><img src="image_100.png" /><figcaption>Naïa Périnelle
(M2 student)</figcaption></figure>

<figure><img src="image_101.png" /></figure>

# Impact of temperature

SOFTMAX WITHOUT TEMPERATURE (T=1)

$$ \frac{e^{z_i}}{\sum_j e^{z_j}} $$

<figure><img src="image_102.png" /></figure>

SOFTMAX WITH TEMPERATURE

$$ \frac{e^{z_i/T}}{\sum_j e^{z_j/T}} $$

<figure><img src="image_103.png" /></figure>

LESS ENTROPY → INCREASE IN ENTROPY (WITH INCREASE IN T) → MORE ENTROPY

s_Escherichia coli (p_Pseudomonadota)

context size: 1000

sequence length: 100

<figure><img src="image_104.png" /></figure>

$$ + \text{top}_p=1, \text{top}_k=4 $$

<figure><img src="image_105.png" /></figure>
<figure><img src="image_106.png" /><figcaption>Naïa Périnelle
(M2 student)</figcaption></figure>

Execution times

On RTX A6000 (LabIA LISN Univ. Paris-Saclay)

<figure><img src="image_107.png" /></figure>
> Credibility seems to be improved in Evo 1.5

Just published last week (sorry i didn't try yet ;))
> Evo 1.5

* Same architecture but extending the pretraining dataset of Evo 1 (8k) +50%

* Still on prokaryote/phage

* from 315 billion tokens (75,000 iterations) to 470 billion tokens (112,000 iterations)

* Only an 8Kb version

nature

Article

Semantic design of functional de novo genes from a genomic language model

https://doi.org/10.1038/s41586-025-09749-7

Received: 10 December 2024

Accepted: 13 October 2025

Published online: 19 November 2025

Open access

Check for updates

Aditi T. Merchant¹,⁵, Samuel H. King¹,⁵, Eric Nguyen¹,² & Brian L. Hie³,⁴,⁵,⁶

Generative genomic models can design increasingly complex biological systems¹. However, controlling these models to generate novel sequences with desired functions remains challenging. Here, we show that Evo, a genomic language model, can leverage genomic context to perform function-guided design that accesses novel regions of sequence space. By learning semantic relationships across prokaryotic genes², Evo enables a genomic 'autocomplete' in which a DNA prompt encoding genomic context for a function of interest guides the generation of novel sequences enriched for related functions, which we refer to as 'semantic design'. We validate this approach by experimentally testing the activity of generated anti-CRISPR proteins and type II and III toxin-antitoxin systems, including de novo genes with no significant sequence similarity to natural proteins. In-context design of proteins and non-coding RNAs with Evo achieves robust activity and high experimental success rates even in the absence of structural priors, known evolutionary conservation or task-specific fine-tuning. We then use Evo to complete millions of prompts to produce SynGenome, a database containing over 120 billion base pairs of artificial intelligence-generated genomic sequences that enables semantic design across many functions. More broadly, these results demonstrate that generative genomics with biological language models can extend beyond natural sequences.

C

<figure><img src="image_108.png" /></figure>

<figure><img src="image_109.png" /></figure>

Dataset

D

<figure><img src="image_110.png" /></figure>

<figure><img src="image_111.png" /></figure>

E

<figure><img src="image_112.png" /></figure>

Kingdom

Archaea (N = 4,416)

Bacteria (N = 80,789)

<figure><img src="image_113.png" /></figure>

Phylum

Pseudomonadota (N = 21,693)

Bacillota A (N = 11,264)

Bacteroidota (N = 11,000)

Actinomycetota (N = 8,813)

Bacillota (N = 5,292)

Patescibacteria (N = 3,374)

Chloroflexota (N = 1,910)

Cyanobacteriota (N = 1,830)

Verrucomicrobiota (N = 1,722)

Planctomycetota (N = 1,664)

Other phylum (N = 16,643)
> Evo 1.5

* Same architecture but extending the pretraining dataset of Evo 1 (8k) +50%

* Still on prokaryote/phage

* from 315 billion tokens (75,000 iterations) to 470 billion tokens (112,000 iterations)

* Only an 8Kb version

nature

Article

Semantic design of functional de novo genes from a genomic language model

https://doi.org/10.1038/s41586-025-09749-7

Received: 10 December 2024

Accepted: 13 October 2025

Published online: 19 November 2025

Open access

Check for updates

Aditi T. Merchant¹,⁵, Samuel H. King¹,⁵, Eric Nguyen¹,⁵ & Brian L. Hie¹,⁵,⁶,⁷

Generative genomic models can design increasingly complex biological systems¹. However, controlling these models to generate novel sequences with desired functions remains challenging. Here, we show that Evo, a genomic language model, can leverage genomic context to perform function-guided design that accesses novel regions of sequence space. By learning semantic relationships across prokaryotic genes², Evo enables a genomic 'autocomplete'³ in which a DNA prompt encoding genomic context for a function of interest guides the generation of novel sequences enriched for related functions, which we refer to as 'semantic design'. We validate this approach by experimentally testing the activity of generated anti-CRISPR proteins and type II and III toxin-antitoxin systems, including de novo genes with no significant sequence similarity to natural proteins. In-context design of proteins and non-coding RNAs with Evo achieves robust activity and high experimental success rates even in the absence of structural priors, known evolutionary conservation or task-specific fine-tuning. We then use Evo to complete millions of prompts to produce SynGenome, a database containing over 120 billion base pairs of artificial intelligence-generated genomic sequences that enables semantic design across many functions. More broadly, these results demonstrate that generative genomics with biological language models can extend beyond natural sequences.

C

Number of nucleotides

<figure><img src="image_114.png" /></figure>

Dataset

D

Number of genomes

<figure><img src="image_115.png" /></figure>

E

<figure><img src="image_116.png" /></figure>

Kingdom

<figure><img src="image_117.png" /></figure>

<figure><img src="image_118.png" /></figure>

Phylum

<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /></figure>

<figure><img src="image_121.png" /></figure>

<figure><img src="image_122.png" /></figure>

INRAO

Feedback from Evo

26/11/2025 / Guillaume Gautreau / MalAGE unit
> Evo 1.5

* Same architecture but extending pretraining dataset of Evo 1 (8

• Still on prokaryote/phage

* from 315 billion tokens (75,000 iterations) to 470 billion tokens (112,000 iterations)

• Only an 8Kb version

<figure><img src="image_123.png" /></figure>

among the
top cases
in the
distribution
(60%AAI≈
70%ANI)

<figure><img src="image_124.png" /></figure>

<figure><img src="image_125.png" /></figure>

Percentage of gene used as prompt

<figure><img src="image_126.png" /></figure>

Percentage of gene used as prompt

<figure><img src="image_127.png" /></figure>

Percentage of gene used as prompt
> Evo 1.5

* Same architecture but extending the pretraining dataset of Evo 1 (8k) +50%

* Still on prokaryote/phage

* from 315 billion tokens (75,000 iterations) to 470 billion tokens (112,000 iterations)

* Only an 8Kb version

nature

Article

Semantic design of functional de novo genes from a genomic language model

https://doi.org/10.1038/s41586-025-09749-7

Received: 10 December 2024

Accepted: 13 October 2025

Published online: 19 November 2025

Open access

Check for updates

Aditi T. Merchant¹,⁵, Samuel H. King¹,⁵, Eric Nguyen¹,² & Brian L. Hie³,⁴,⁵,⁶

Generative genomic models can design increasingly complex biological systems¹. However, controlling these models to generate novel sequences with desired functions remains challenging. Here, we show that Evo, a genomic language model, can leverage genomic context to perform function-guided design that accesses novel regions of sequence space. By learning semantic relationships across prokaryotic genes², Evo enables a genomic 'autocomplete' in which a DNA prompt encoding genomic context for a function of interest guides the generation of novel sequences enriched for related functions, which we refer to as 'semantic design'. We validate this approach by experimentally testing the activity of generated anti-CRISPR proteins and type II and III toxin-antitoxin systems, including de novo genes with no significant sequence similarity to natural proteins. In-context design of proteins and non-coding RNAs with Evo achieves robust activity and high experimental success rates even in the absence of structural priors, known evolutionary conservation or task-specific fine-tuning. We then use Evo to complete millions of prompts to produce SynGenome, a database containing over 120 billion base pairs of artificial intelligence-generated genomic sequences that enables semantic design across many functions. More broadly, these results demonstrate that generative genomics with biological language models can extend beyond natural sequences.

<figure><img src="image_128.png" /></figure>

<figure><img src="image_129.png" /></figure>

<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /></figure>

<figure><img src="image_132.png" /></figure>

<figure><img src="image_133.png" /></figure>

INRAO

Feedback from Evo

26/11/2025 / Guillaume Gautreau / MalAGE unit
> Experimental validation of Evo 1.5 generative output: toxin-antitoxin

<figure><img src="image_134.png" /><figcaption>(2) Screen toxins via growth inhibition assay</figcaption></figure>

<figure><img src="image_135.png" /><figcaption>(3) Generate conjugate antitoxins</figcaption></figure>

<figure><img src="image_136.png" /><figcaption>(4) Screen antitoxins against toxins via growth rescue assay</figcaption></figure>

<figure><img src="image_137.png" /></figure>
> Experimental validation of Evo 1.5 generative output: toxin-antitoxin

(1) Generate bacterial toxins

<figure><img src="image_138.png" /></figure>

(2) Screen toxins via growth inhibition assay

<figure><img src="image_139.png" /></figure>

pLDDT: 0.87

BLAST match: RelE/
ParE family toxin

<figure><img src="image_140.png" /></figure>

EvoRelE1

BLAST
match

Sequence ID: 71%

e-value: 3 × 10<sup>-39</sup>

TM-score: 0.98

(3) Generate conjugate antitoxins

<figure><img src="image_141.png" /></figure>

(4) Screen antitoxins against toxins via growth rescue assay

<figure><img src="image_142.png" /></figure>

<figure><img src="image_143.png" /></figure>
> Experimental validation of Evo 1.5 generative output: toxin-antitoxin

(1) Generate bacterial toxins

<figure><img src="image_144.png" /></figure>

(2) Screen toxins via growth inhibition assay

<figure><img src="image_145.png" /></figure>

pLDDT: 0.87

BLAST match: RelE/
ParE family toxin

<figure><img src="image_146.png" /></figure>

<figure><img src="image_147.png" /></figure>

EvoRelE1

<figure><img src="image_148.png" /></figure>

BLAST
match

Sequence ID: 71%

e-value: 3 × 10<sup>-39</sup>

TM-score: 0.98

(3) Generate conjugate antitoxins

<figure><img src="image_149.png" /></figure>

(4) Screen antitoxins against toxins via growth rescue assay

<figure><img src="image_150.png" /></figure>

<figure><img src="image_151.png" /></figure>

<figure><img src="image_152.png" /></figure>

1
> Evo generates novel functional anti-CRISPR proteins

<figure><img src="image_153.png" /></figure>

<figure><img src="image_154.png" /></figure>
> Evo generates novel functional anti-CRISPR proteins

<figure><img src="image_155.png" /></figure>
> Evo generates novel functional anti-CRISPR proteins

<figure><img src="image_156.png" /></figure>

<figure><img src="image_157.png" /></figure>
> Evo generates novel functional anti-CRISPR proteins

<figure><img src="image_158.png" /></figure>

<figure><img src="image_159.png" /><figcaption>EvoAcr4</figcaption></figure>

<figure><img src="image_160.png" /></figure>

pLDDT: 0.70

<figure><img src="image_161.png" /><figcaption>BLAST match:
AcrIIA2</figcaption></figure>

Sequence ID: 58%
TM-score: 0.70
e-value: 6 × 10⁻³²

<figure><img src="image_162.png" /><figcaption>EvoAcr5</figcaption></figure>

pLDDT: 0.50

<figure><img src="image_163.png" /><figcaption>BLAST match:
AcrIIA4</figcaption></figure>

Sequence ID: 31%
TM-score: 0.33
e-value: 2 × 10⁻¹²
<figure><img src="image_164.png" /></figure>

# Evo2: Genome modeling and design
across all domains of life with Evo 2

When computational biology changes its scale
> Evo2 (preprint released in February)

* As Evo1, built to predict the next token at single-nucleotide resolution

* Uses an improved version 2 of Striped Hyena

* Train on all available kinds of DNA (9.3 trillion DNA base pairs)

* Context size goes to megabase scale

* Training cost ~5-10 M$ (GPU + electricity only) ! 150×Alphafold2

* Developed in direct collaboration with NVIDIA/Amazon

<figure><img src="image_165.png" /></figure>

<table><thead><tr><th></th><th>Evo1</th><th>Evo 1.5</th><th>Evo 2</th></tr></thead><tbody><tr><td>Publication</td><td>November 2024</td><td>November 2025</td><td>pre-print</td></tr><tr><td>Model size</td><td>7B</td><td>7B</td><td>1B, 7B, 40B</td></tr><tr><td>Context size (# of tokens/ bases)</td><td>8 Kb or 131 Kb</td><td>8 Kb</td><td>1 Mb</td></tr><tr><td>Architecture</td><td>Striped Hyena</td><td>Striped Hyena</td><td>Striped Hyena 2</td></tr><tr><td>Training dataset</td><td>Prokaryotic genomes / plasmids / phages</td><td>Updated Evo1 database</td><td>Evo1 + All NCBI Eukaryotes + Metagenome (MGnify)</td></tr><tr><td>Training dataset size</td><td>315 billions</td><td>470 billions</td><td>9300 billions</td></tr><tr><td>Training cost (estimation by myself at market price)</td><td>70k$</td><td>105k$</td><td>6M$</td></tr></tbody></table>
> Global UMAP of sequence diversity

* Depict the great prokaryotic diversity

* The clustering doesn't exactly match the expected phylogeny especially for prokaryotes (could be a UMAP artifact)

<figure><img src="image_166.png" /></figure>

Bacterial phylum

* Pseudomonadota

* Bacillota

* Bacteroidota

* Actinomycetota

* Other/Unknown Bacteria

Eukaryotic phylum

* Ascomycota

* Arthropoda

* Chordata

* Streptophyta

* Other/Unknown Eukaryota

Archaeal phylum

* Thermoproteota

* Halobacteriota

* Thermoplasmatota

* Nanoarchaeota

* Other/Unknown Archaea
> Striped Hyena 2 improve computation on large context

<figure><img src="image_167.png" /></figure>

<figure><img src="image_168.png" /></figure>

<figure><img src="image_169.png" /></figure>
<figure><img src="image_170.png" /></figure>

So many application layers

<figure><img src="image_171.png" /></figure>

Application
layer

**Prediction**
Gene/IncRNA essentiality

<figure><img src="image_172.png" /></figure>

**Prediction**
Genetic variant effects

<figure><img src="image_173.png" /></figure>

**Design**
Genome-scale
generation

<figure><img src="image_174.png" /></figure>

Cellular/
Organismal

**Prediction**
Protein mutational
scanning

<figure><img src="image_175.png" /></figure>

**Prediction**
Secondary
structure feature ID

<figure><img src="image_176.png" /></figure>

**Design**
Protein complex
generation

<figure><img src="image_177.png" /></figure>

Protein

**Prediction**
ncRNA mutational scanning

<figure><img src="image_178.png" /></figure>

**Prediction**
RNA stability/decay

<figure><img src="image_179.png" /></figure>

**Prediction**
Exon classification
Splicing variant effects

<figure><img src="image_180.png" /></figure>

RNA

**Prediction**
Regulatory element
feature ID

<figure><img src="image_181.png" /></figure>

**Design**
Chromatin accessibility
controlled generation

<figure><img src="image_182.png" /></figure>

Epigenome
> Applications: zero-shot prediction of variant effects

Evo 2
Likelihood landscape

<figure><img src="image_183.png" /></figure>

<figure><img src="image_184.png" /></figure>

<figure><img src="image_185.png" /></figure>
> Generative applications: DNA design up to bacterial genome scale

<figure><img src="image_186.png" /></figure>

* Synthetic design: generate proteins, enzymes, operons, pathways, systems

* Promoter engineering: generate inducible / tunable promoters

* Anonymization: generate synthetic genomes preserving signal

* Benchmarking: generate realistic synthetic datasets, diversity normalization

* Pangenome compression/normalization: generate normalized representations

* Assembly gap filling (MAGs)

* Data augmentation but are there actually working genomes?

* Any other idea?
> The claim: first working gLM-generated genome

One of the easiest case scenario but still :

* Phage ΦX174 (Microviridae)

* Target E. coli cells

* Abundant in databases

* Very small: ~5Kb

<figure><img src="image_187.png" /></figure>

* Zero-shot inference doesn't work
  ⇔ Supervised fine-tuning (SFT) strategy on Microviridae genomes with soft prompting to introduce special token to 'control' identity during inference

<figure><img src="image_188.png" /></figure>

pre-print on BioRxiv (09/2025) :

Generative design of novel bacteriophages
with genome language models

Samuel H. King<fnref n="1" />,<fnref n="2" />, Claudia L. Driscoll<fnref n="1" />,<fnref n="3" />, David B. Li<fnref n="1" />,<fnref n="2" />, Daniel Guo<fnref n="1" />,<fnref n="4" />, Aditi T. Merchant<fnref n="1" />,<fnref n="2" />, Garyk Brixi<fnref n="1" />,<fnref n="5" />, Max E. Wilkinson<fnref n="6" />, and Brian L. Hie<fnref n="1" />,<fnref n="3" />,<fnref n="7" />,\*

<figure><img src="image_189.png" /></figure>

<footnote n="1">¹Arc Institute, Palo Alto, CA, USA</footnote>

<footnote n="2">²Department of Bioengineering, Stanford University, Stanford, CA, USA</footnote>

<footnote n="3">³Department of Chemical Engineering, Stanford University, Stanford, CA, USA</footnote>

<footnote n="4">⁴Department of Computer Science, Stanford University, Stanford, CA, USA</footnote>

<footnote n="5">⁵Department of Genetics, Stanford University, Stanford, CA, USA</footnote>

<footnote n="6">⁶Structural Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA</footnote>

<footnote n="7">⁷Stanford Data Science, Stanford University, Stanford, CA, USA</footnote>
> The claim: first working Al-generated genome

Evo-generated phages actually lyze E. coli genomes!

<figure><img src="image_190.png" /></figure>
> The claim: first working Al-generated genome

Evo-generated phages actually lyze E. coli genomes!

<figure><img src="image_191.png" /></figure>

...but even if modifications were introduced that were not so trivial, still it generates something **not totally new** compared to the actual WT phage...

<figure><img src="image_192.png" /></figure>
## Towards an interpretable model: Sparse Auto-Encoder (SAE)

<figure><img src="image_193.png" /></figure>

+ sparsity
penalty
in the loss
function

Feature #34M/31164353

**Golden Gate Bridge** feature example

The feature activates strongly on English
descriptions and associated concepts

in the Presidio at the end (that's the
huge park right next to the Golden Gate
bridge), perfect. But not all people

repainted, roughly, every dozen years."
"while across the country in san fran
cisco, the golden gate bridge was

it is a suspension bridge and has similar
coloring, it is often compared to the
Golden Gate Bridge in San Francisco, US

They also activate in multiple other languages
on the same concepts

ゴールデン・ゲート・ブリッジ、金門橋は、アメリカ西海岸のサンフランシスコ湾と太平洋が接続するゴールデンゲート海

골든게이트 교 또는 금문교 는 미국 캘리포니아주 골든게이트 해협에 위치한 웹수교이다. 골든게이트 교는 캘리포니아주 샌프란시

мост золотые ворота — висячий мост
через пролив золотые ворота. он со
единяет город сан-фран

And on relevant images as well

<figure><img src="image_194.png" /></figure>

<figure><img src="image_195.png" /></figure>
# Towards an interpretable model: Sparse Auto-Encoder (SAE)

Features observed :

* Phage-associated elements

* ORFs and intergenic regions

* tRNAs and rRNAs

* α-helices and β-sheets

* Frameshift mutations

* Transcription-factor binding motifs

* Exon–intron signals (human genomes used to annotate the woolly mammoth genome)

<figure><img src="image_196.png" /></figure>

<figure><img src="image_197.png" /></figure>

<figure><img src="image_198.png" /></figure>

<figure><img src="image_199.png" /></figure>

<figure><img src="image_200.png" /></figure>
Conclusion

* Openly available, but requires several Nvidia H100 GPU (~20k$) to run Evo2 7B/40B (Running Evo 1/1.5 is easier)
  → User-friendly interface available (but limited to small prompts): https://arcinstitute.org/tools/evo/evo-designer

* Independent work on relevant tasks required ! (e.g.: Evo2HiC, published last week)

* Ethical considerations to anticipate (biosecurity, misuses)

• Bioterrorism (human pathogenic viruses weren't included in Evo2 but what about fine tuning)

• Fake dataset pollution (SynGenome database of ‘generated genomes’), science integrity issues...

* Environmental concern if pre-training get again larger and larger (Evo 3 ?)

* Partially interpretable via Sparse AutoEncoders (explored in the Evo2 article)

* Multimodality : gLM X pPLM (gLM2, LucasOne)

* LLM x gLM : « Talk to your genome » (hard prompting strategies)

* ChatNT (NT X LLaMA, published Nature Machine Intelligence)

• BioReason (Evo2 x Wen3 CoT/RAG LLM, preprint on Biorxiv)

* DNA Retrieval Augmented Generation / Retrieval Augmented Fine Tuning

<figure><img src="image_201.png" /></figure>

<figure><img src="image_202.png" /></figure>

BioReason

Incentivizing Multimodal Biological
Reasoning within a DNA-LLM Model

Adibvafa Fallahpour<sup>1,2,3,5*</sup>, Andrew Magnuson<sup>1,2*</sup>, Purav Gupta<sup>1,2*</sup>, Shihao Ma<sup>1,2,3</sup>, Jack Naimer<sup>1,2,3</sup>, Arnav Shah<sup>1,2,3</sup>, Haonan Duan<sup>1,2</sup>, Omar Ibrahim<sup>3</sup>, Hani Goodarzi<sup>4,6*</sup>, Chris J. Maddison<sup>1,2,7*</sup>, Bo Wang<sup>1,2,3†</sup>

¹ University of Toronto

<figure><img src="image_203.png" /></figure>

² Vector Institute

<figure><img src="image_204.png" /></figure>

³ University Health Network <UHN>

⁴ Arc Institute

<figure><img src="image_205.png" /></figure>

⁵ Cohere

<figure><img src="image_206.png" /></figure>

⁶ University of California, San Francisco UC>SF

⁷ Google DeepMind DeepMind

<figure><img src="image_207.png" /></figure>

*Equal Contribution. †Equal Advising.

<figure><img src="image_208.png" /></figure>

<figure><img src="image_209.png" /></figure>

<figure><img src="image_210.png" /></figure>

<figure><img src="image_211.png" /></figure>
> Acknowledgements

<figure><img src="image_212.png" /></figure>

MaiAGE

<figure><img src="image_213.png" /><figcaption>Naïa Périnelle,
former M2 student,
now PhD student at INRAE IRHS</figcaption></figure>

<figure><img src="image_214.png" /><figcaption>Thomas Lacroix, IE
StatInfOmics team</figcaption></figure>

<figure><img src="image_215.png" /><figcaption>Hélène Chiapello, IR
StatInfOmics and Migale team</figcaption></figure>

<figure><img src="image_216.png" /><figcaption>Mahendra Mariadassous,
StatInfOmics team leader</figcaption></figure>

<figure><img src="image_217.png" /><figcaption>Guillaume Kon Kam King, CR
StatInfOmics team</figcaption></figure>

<figure><img src="image_218.png" /><figcaption>Arnaud Ferré, CR
Bibliome team</figcaption></figure>

Internship funding:

université
PARIS-SACLAY

**GRADUATE SCHOOL**
Informatique
et Sciences
du Numérique

<figure><img src="image_219.png" /></figure>

INRAO

Feedback from Evo

26/11/2025 / Guillaume Gautreau / MaiAGE unit

+ inspiring chat with

GPU providers:

- Evo 1: LabIA (LISN)

- Evo 2: Jean Zay (IDRIS)

L1SN
LABORATOIRE INTERDISCIPLINAIRE
DES SCIENCES DU NUMÉRIQUE

<figure><img src="image_220.png" /></figure>
## Applications: zero-shot prediction of variant effects

### Evo 2
Likelihood landscape

<figure><img src="image_221.png" /></figure>

<figure><img src="image_222.png" /></figure>

<figure><img src="image_223.png" /></figure>

### Correlation to Deep Mutation Scanning (DMS) assays
(https://proteingym.org/)

<figure><img src="image_224.png" /></figure>

<figure><img src="image_225.png" /></figure>

<table><thead><tr><th>Variant</th><th>Mutation</th><th>Counts (input)</th><th>Counts (selected)</th><th>Functional score</th></tr></thead><tbody><tr><td>Blue</td><td>A60P</td><td>3</td><td>1</td><td>0.33</td></tr><tr><td>Green</td><td>WT</td><td>3</td><td>3</td><td>1</td></tr><tr><td>Purple</td><td>S36T</td><td>3</td><td>5</td><td>1.67</td></tr></tbody></table>

### Zero-shot bacterial protein fitness prediction

<figure><img src="image_226.png" /></figure>
> An example using natural language:

* «Complète le paragraphe sans faire de recherche, juste sur la base de ton modèle »

> **Article 49**

Modifié par LOI constitutionnelle n°2008-724 du 23 juillet... - art. 24

Le Premier ministre, après délibération du Conseil des ministres, engage devant l'Assemblée nationale la responsabilité du Gouvernement sur son programme ou éventuellement sur une déclaration de politique générale.

L'Assemblée nationale met en cause la responsabilité du Gouvernement par le vote d'une motio......

<figure><img src="image_227.png" /></figure>
<figure><img src="image_228.png" /></figure>

## An example using natural language:

* «Complète le paragraphe sans faire de recherche, juste sur la base de ton modèle »

> **Article 49**

Modifié par LOI constitutionnelle n°2008-724 du 23 juillet... - art. 24

Le Premier ministre, après délibération du Conseil des ministres, engage devant l'Assemblée nationale la responsabilité du Gouvernement sur son programme ou éventuellement sur une déclaration de politique générale.

L'Assemblée nationale met en cause la responsabilité du Gouvernement par le vote d'une motion...

> **Article 49**

Modifié par LOI constitutionnelle n°2008-724 du 23 juillet... - art. 24

Le Premier ministre, après délibération du Conseil des ministres, engage devant l'Assemblée nationale la responsabilité du Gouvernement sur son programme ou éventuellement sur une déclaration de politique générale.

L'Assemblée nationale met en cause la responsabilité du Gouvernement par le vote d'une motion de censure. Une telle motion n'est recevable que si elle est signée par un dixième au moins des membres de l'Assemblée nationale ; le vote ne peut intervenir que quarante-huit heures après son dépôt. Seuls sont comptés les votes favorables à la motion, qui n'est adoptée qu'à la majorité des membres composant l'Assemblée nationale. Un député ne peut être signataire de plus de trois motions de censure au cours d'une même session ordinaire et d'une au cours d'une session extraordinaire. L'adoption de la motion entraîne la démission du Gouvernement.

<figure><img src="image_229.png" /></figure>
<figure><img src="image_230.png" /></figure>

## An example using natural language:

* «Complète le paragraphe sans faire de recherche, juste sur la base de ton modèle»

> **Article 49**

Modifié par LOI constitutionnelle n°2008-724 du 23 juillet... - art. 24

Le Premier ministre, après délibération du Conseil des ministres, engage devant l'Assemblée nationale la responsabilité du Gouvernement sur son programme ou éventuellement sur une déclaration de politique générale.

L'Assemblée nationale met en cause la responsabilité du Gouvernement par le vote d'une motion...

> **Article 49**

Modifié par LOI constitutionnelle n°2008-724 du 23 juillet... - art. 24

Le Premier ministre, après délibération du Conseil des ministres, engage devant l'Assemblée nationale la responsabilité du Gouvernement sur son programme ou éventuellement sur une déclaration de politique générale.

L'Assemblée nationale met en cause la responsabilité du Gouvernement par le vote d'une motion de censure. Une telle motion n'est recevable que si elle est signée par un dixième au moins des membres de l'Assemblée nationale ; le vote ne peut intervenir que quarante-huit heures après son dépôt. Seuls sont comptés les votes favorables à la motion, qui n'est adoptée qu'à la majorité des membres composant l'Assemblée nationale. Un député ne peut être signataire de plus de trois motions de censure au cours d'une même session ordinaire et d'une au cours d'une session extraordinaire. L'adoption de la motion entraîne la démission du Gouvernement.

> **Article 49**

Modifié par LOI constitutionnelle n°2008-724 du 23 juillet... - art. 24

Le Premier ministre, après délibération du Conseil des ministres, engage devant l'Assemblée nationale la responsabilité du Gouvernement sur son programme ou éventuellement sur une déclaration de politique générale.

L'Assemblée nationale met en cause la responsabilité du Gouvernement par le vote d'une motion de censure. Une telle motion n'est recevable que si elle est signée par un dixième au moins des membres de l'Assemblée nationale. Le vote ne peut avoir lieu que quarante-huit heures après son dépôt. Seuls sont recensés les votes favorables à la motion de censure qui ne peut être adoptée qu'à la majorité des membres composant l'Assemblée. Sauf dans le cas prévu à l'alinéa ci-dessous, un député ne peut être signataire de plus de trois motions de censure au cours d'une même session ordinaire et de plus d'une au cours d'une même session extraordinaire.
<figure><img src="image_231.png" /></figure>

## An example using natural language:

* «Complète le paragraphe sans faire de recherche, juste sur la base de ton modèle»

> **Article 49**

Modifié par LOI constitutionnelle n°2008-724 du 23 juillet... - art. 24

Le Premier ministre, après délibération du Conseil des ministres, engage devant l'Assemblée nationale la responsabilité du Gouvernement sur son programme ou éventuellement sur une déclaration de politique générale.

L'Assemblée nationale met en cause la responsabilité du Gouvernement par le vote d'une motion...

> **Article 49**

Modifié par LOI constitutionnelle n°2008-724 du 23 juillet... - art. 24

Le Premier ministre, après délibération du Conseil des ministres, engage devant l'Assemblée nationale la responsabilité du Gouvernement sur son programme ou éventuellement sur une déclaration de politique générale.

L'Assemblée nationale met en cause la responsabilité du Gouvernement par le vote d'une motion de censure. Une telle motion n'est recevable que si elle est signée par un dixième au moins des membres de l'Assemblée nationale ; le vote ne peut intervenir que quarante-huit heures après son dépôt. Seuls sont comptés les votes favorables à la motion, qui n'est adoptée qu'à la majorité des membres composant l'Assemblée nationale. Un député ne peut être signataire de plus de trois motions de censure au cours d'une même session ordinaire et d'une au cours d'une session extraordinaire. L'adoption de la motion entraîne la démission du Gouvernement.

> **Article 49**

Modifié par LOI constitutionnelle n°2008-724 du 23 juillet... - art. 24

Le Premier ministre, après délibération du Conseil des ministres, engage devant l'Assemblée nationale la responsabilité du Gouvernement sur son programme ou éventuellement sur une déclaration de politique générale.

L'Assemblée nationale met en cause la responsabilité du Gouvernement par le vote d'une motion de censure. Une telle motion n'est recevable que si elle est signée par un dixième au moins des membres de l'Assemblée nationale. Le vote ne peut avoir lieu que quarante-huit heures après son dépôt. Seuls sont recensés les votes favorables à la motion de censure qui ne peut être adoptée qu'à la majorité des membres composant l'Assemblée. Sauf dans le cas prévu à l'alinéa ci-dessous, un député ne peut être signataire de plus de trois motions de censure au cours d'une même session ordinaire et de plus d'une au cours d'une même session extraordinaire.