BSM: Small but Powerful Biological Sequence Model for Genes and Proteins

## Abstract

Abstract Modeling biological sequences such as DNA, RNA, and proteins is crucial for understanding complex processes like gene regulation and protein synthesis. However, most current models either focus on a single type or treat multiple types of data separately, limiting their ability to capture cross-modal relationships. We propose that by learning the relationships between these modalities, the model can enhance its understanding of each type. To address this, we introduce BSM, a small but powerful mixed-modal biological sequence foundation model, trained on three types of data: RefSeq, Gene Related Sequences, and interleaved biological sequences from the web. These datasets capture the genetic flow, gene-protein relationships, and the natural co-occurrence of diverse biological data, respectively. By training on mixed-modal data, BSM significantly enhances learning efficiency and cross-modal representation, outperforming models trained solely on unimodal data. With only 110M parameters, BSM achieves performance comparable to much larger models across both single-modal and mixed-modal tasks, and uniquely demonstrates in-context learning capability for mixed-modal tasks, which is absent in existing models. Further scaling to 270M parameters demonstrates even greater performance gains, highlighting the potential of BSM as a significant advancement in multimodal biological sequence modeling.

## 1 Introduction

Biological sequences—such as DNA, RNA, and proteins—are fundamental to an organism’s functions, as they encode genetic information that determines structure, function, and regulatory mechanisms  (Watson & Crick, ; Nirenberg & Matthaei, ). Understanding these sequences is vital for unraveling the mysteries of biological evolution, deciphering disease mechanisms, and elucidating molecular interactions.

By applying machine learning algorithms to large-scale biological sequence data, one can capture evolutionary effects and extract complex patterns in gene transcription and protein translation. This not only enhances our understanding of gene regulation and protein function but also enables the prediction and generation of complex biological functions, significantly advancing our comprehension of biology and life processes (Nguyen et al., ).

Figure: Figure 1: Impact of mixed-modal data on model learning efficiency. After the first round of training, the model shows slow learning efficiency when using only Round 1 data (i.e., single-modal data). However, when trained on the newly introduced mixed-modal data, it achieves significantly lower validation loss for genes and proteins, indicating greatly improved learning efficiency. Similarly, the introduction of new web data after the second round further reduces validation loss.
Refer to caption: x1.png

Despite the rapid advancements in modeling biological sequences with machine learning, current efforts have primarily focused on creating unimodal models specialized for DNA, such as DNABert2 (Zhou et al., ), HyenaDNA (Nguyen et al., ), Caduceus (Schiff et al., ), NT (Dalla-Torre et al., ); RNA, including RNA-FM (Chen et al., ); or proteins, like ESM2 (Lin et al., ), ProTrans (Ahmed et al., ), ProGen2 (Nijkamp et al., ). However, complex biological processes such as gene regulation, CRISPR immunity, and genetic transposition involve interactions across multiple modalities.

Recently, several methods have focused on developing models capable of handling both gene and protein data. For example, Evo (Nguyen et al., ) is a 7B genomic foundation model pretrained on DNA sequences, which inherently contain the potential to express other modalities. It learns from large genomic regions to capture systems-wide interactions and enables the design of more sophisticated biological functions. LucaOne (He et al., ) is a 1.8B model that is trained on both gene and protein data separately, allowing the model to process and analyze both types of data concurrently. Although these models demonstrate excellent performance across various tasks, they achieve their capabilities primarily by increasing computational resources—LucaOne, for instance, utilizes 800 billion tokens for pretraining—and by scaling up model size to the billions to accommodate multiple modalities. This raises the question of whether smaller models can achieve similar capabilities, making advanced biological sequence modeling more accessible and practical.

Recent advancements in small language models (SLMs) have shown significant progress (Minaee et al., ). These models have demonstrated emerging capabilities and achieved performance levels comparable to much larger models. The secret behind this achievement lies in strategic training choices, such as using "textbook-quality" data. Capable SLMs are faster to run and easier to serve; meanwhile, ongoing improvements, as seen with the Phi series (Abdin et al., ), indicate that SLMs are still under-trained and have great potential for further improvement.

In this paper, we explore the construction of high-quality biological sequence data. According to the central dogma of molecular biology (Crick, ), which highlights the sequential flow of genetic information, DNA, RNA, and proteins are intrinsically interconnected. We propose that by learning the relationships among these three types of sequences, the model can enhance its understanding of each modality. In light of this, we introduce three types of mixed-modal data for pretraining: RefSeq, Gene Related Sequences, and interleaved biological sequences from the web. These datasets capture genetic flow, gene and protein relationships, and the natural co-occurrence of diverse biological data types, respectively. Unlike previous work, which trains models using unimodal data or combines different types in a simplistic manner (where each training sample consists of only one type), we explicitly train the model on this mixed-modal data to learn the relationships among them.

We emphasize that incorporating mixed-modal data facilitates a more comprehensive understanding of biological sequences and enables more effective acquisition of cross-modal representations by better learning the relationships between these modalities.
As illustrated in  Figure 1, our experiments reveal that relying on unimodal data to learn these capabilities results in slow learning efficiency and requires substantial data and model sizes. In contrast, training on mixed-modal data significantly lowers validation loss for genes and proteins, indicating greatly improved learning efficiency.

Based on these newly introduced types of high-quality mixed-modal data, we develop a small but powerful biological sequence foundation model, BSM, through a structured multi-round training approach. In this process, we conduct three rounds of training, progressively incorporating different types of mixed-modal data in the latter two rounds. By employing an annealing strategy, we optimize the data mix to ensure the best possible integration of these datasets. Our experiments demonstrate that BSM achieves performance comparable to that of billion-scale models on both single-modal and complex mixed-modal tasks. This proves the effectiveness of our method and its significant potential.

To conclude, our work makes the following contributions: 1) We propose that explicitly learning the relationships between genes and proteins can enhance the model’s understanding of each modality. We introduce three types of mixed-modal data and strategically integrate them with unimodal data, ultimately resulting in our small but powerful BSM model. 2) We conduct extensive experiments demonstrating that BSM achieves performance comparable to billion-parameter models across various tasks, particularly excelling in mixed-modal tasks, and exhibits unique and strong few-shot learning capabilities with different modality combinations. 3) We conduct scaling experiments to show that BSM scales effectively; when increased to 270M parameters, it achieves better results, highlighting the potential of our approach.

## 2 Methods

### 2.1 Architecture

BSM employs a single-nucleotide tokenizer with a vocabulary that includes nucleotides, amino acids, and special tokens. It uses an autoregressive architecture to model biological sequences such as genes and proteins. By learning next-token prediction, the model reasons over sequences causally and captures statistical patterns and dependencies in the training data, enabling effective representation and generation of biological sequences. Furthermore, the autoregressive architecture’s sequential nature effectively handles long-range dependencies, which is crucial in biological sequences like DNA, RNA, and proteins, where long-context information can reveal critical functional relationships or structural interactions.

Figure: Figure 2: Overview of the pretraining data and training process of the BSM model. BSM utilizes three types of mixed-modal data: RefSeq, Gene Related Sequences, and interleaved biological sequences from the web for pretraining. It undergoes three rounds of training to enhance its ability to learn complex relationships among different types of biological data.
Refer to caption: x2.png

The BSM family includes models of two sizes, specifically BSM-110M and BSM-270M. BSM-110M is a decoder-only Transformer with 12 layers, each having 12 attention heads and a hidden dimension of 768, and BSM-270M features 20 layers, 16 attention heads, and a hidden dimension of 896. Both models utilize rotary position embedding (RoPE) (Su et al., ) with a base frequency hyperparameter of 100,000 and support a context length of 1024 tokens. To accelerate training, we employ flash-attention mechanisms (Dao, ).

### 2.2 Pretraining Data

High-quality biological data play a key role in developing effective models for biological sequences. In addition to using unimodal protein and gene data, we incorporate three types of mixed-modal data for continued pretraining, each containing valuable information that helps the model learn diverse dependencies and interactions in biological sequences, as well as critical functional relationships. These multi-modal datasets enhance the model’s learning efficiency and its ability to understand cross-modal relationships, enabling it to handle various biological data. In the following section, we introduce the data used in pretraining. Details of the data construction are listed in the Appendix B. Additionally, we perform data cleansing by removing instances of the downstream task test data from the pretraining data.

Single-modal Data
In the initial pretraining phase, we utilize single-modal datasets that exclusively contain nucleic acid (DNA or RNA) sequences or protein sequences, enabling the model to concentrate on understanding the essential structures, patterns, and unique characteristics of each modality. These datasets are sampled from the pretraining dataset of LucaOne (He et al., ), and we only use the sequence information from RefSeq (O’Leary et al., ), UniProt (Consortium, ) and ColabFold (Mirdita et al., ) without incorporating additional biological annotations, resulting in a data volume of 220 billion tokens. Single-modal data not only enhances the model’s ability to understand individual modality sequences but also establishes a solid foundation for continued pretraining on more complex multimodal data.

Mixed-modal Pair Data - NCBI RefSeq
To better learn the relationships among DNA, RNA, and proteins, we incorporate mixed-modal data from the NCBI RefSeq database (O’Leary et al., ). RefSeq provides a comprehensive and curated collection of annotated reference sequences that illustrate the flow of genetic information in the central dogma of molecular biology—DNA to RNA to protein. This resource is crucial for facilitating the understanding of gene-protein interactions and regulatory mechanisms, capturing essential details about transcription and translation processes.

We use data from 15 different species, including Bubalus Bubalis, Camelus Dromedarius, Human, and several others. Each species has its own unique DNA, RNA, and protein sequences, which are used to construct our gene-protein pairing dataset, resulting in a dataset of 9.2 billion tokens. This extensive data ensures a rich representation of genetic information, allowing the model to leverage diverse biological contexts for effective learning while recognizing the inherent links between DNA sequences and their corresponding proteins.

Mixed-modal Pair Data - Gene Related Sequences
To better understand the complex relationships between gene-gene and gene-protein, we incorporate data from the NCBI Gene database (Brown et al., ), which offers a detailed collection of gene-related sequences and annotations. The Gene Related Sequences data within this collection contains related sequences to the gene and provides links to the corresponding records in Entrez Nucleotide (Maglott et al., ),Entrez Protein (Ostell, ) or UniProtKB (Boutet et al., ).

We have constructed a dataset from the Gene Related Sequences data by sampling from multiple species, resulting in a diverse collection of 8.3 billion tokens. This dataset enables the model to capture complex dependencies, recognize patterns of gene regulation and protein expression, and enhances its understanding of gene and protein functions.

Mixed-modal Interleaved Data - Filtered Web Data
To simulate the natural co-occurrence of diverse biological data types and provide the model with a more realistic learning context, we integrate filtered web data from FineWeb-Edu (Penedo et al., ), which consists of high-quality web-crawled documents. We specifically filter this data to extract biological sequences within documents, using a special token, <sep>, to separate interleaved biological sequences, resulting in a final dataset of 33 million tokens.

By incorporating this curated dataset into our pretraining process, BSM can leverage a rich and diverse collection of arbitrarily interleaved biological sequences. This dataset features a greater number of interleaved sequences, enhancing the model’s ability to capture complex biological relationships and enabling it to understand and generate gene and protein sequences in any arbitrary context.

### 2.3 Pretraining Procedure

Three-round Training We pretrain BSM models from scratch in an end-to-end manner, which includes a three-round training process, as shown in Figure 2. It begins by establishing a foundational understanding of individual types of biological sequences (DNA, RNA, or proteins) using 100B single-modal tokens. The model then advances to incorporate multi-modal data, enhancing its ability to understand relationships and transitions between different biological data types, which is crucial for tasks involving mixed modalities. Specifically, in the second round, it utilizes an additional 120B single-modal data along with a certain amount of multi-modal pair data from RefSeq (24B) and Gene Related Sequences (12B). In the third round, it trains on a small amount of high-quality mixed-modal data, including pair data from RefSeq (1.3B) and Gene Related Sequences (1.3B) as well as web interleaved data (13.3B). By upsampling these high-quality but relatively small multi-modal datasets during continued pretraining, we significantly enhance the BSM model’s performance across a range of biological tasks, making it a powerful tool for decoding the complexities of molecular biology.

We set the learning rate to decay from 2e-5 to 1e-5 in the first round. In the second round, it decays from 1e-5 to 1e-7, and in the third round, it decays from 1e-6 to 0. The tokenizer is trained with a peak learning rate of 2e-5.

Simulated Annealing & Data Mixing To obtain a high-quality biological model, it is essential to carefully determine the proportion of different data sources in the pretraining data mix. Similar to Blakeney et al. () and Llama 3.1 (Dubey et al., ), we find that annealing allows us to evaluate the value of newly introduced mixed-modal datasets effectively and efficiently. We measure the value of these datasets by training the BSM-110M model over 1000 steps, during which the learning rate is linearly annealed from current stage’s learning rate to 0. In these experiments, we utilize annealing to assess various data mixing strategies and select the one with the lowest validation loss for the second and third rounds. Ultimately, we employ a ratio of 10:2:1 for single-modal data, RefSeq, and Gene Related Sequences in the second round, and a ratio of 1:1:10 for RefSeq, Gene Related Sequences, and web interleaved data in the third round. After determining the best mix, we train a larger model (BSM-270M) on this selected data mix.

## 3 Experiments

We evaluate BSM’s capabilities in understanding and generating biological sequences across a variety of tasks, including both mixed-modal and single-modal tasks. BSM demonstrates outstanding performance on multi-modal tasks, even surpassing many billion-scale models. We also assess BSM’s in-context learning (ICL) ability in a few-shot setting for mixed-modal tasks, demonstrating that BSM possesses this capability, which has not yet been observed in other biological sequence models. Additionally, we investigate the model’s supervised fine-tuning (SFT) and zero-shot performance on protein and gene-related tasks. Scaling experiments confirm that further increasing the model size continues to enhance its performance. We conduct an ablation study on the performance of models from different rounds to verify the value of multi-modal data. Finally, we also evaluate the model’s generative abilities using the perplexity metric. Details of evaluation datasets are listed in the Appendix C.

Implementation Details For tasks requiring SFT, we fine-tune the model using a learning rate of 1e-6 and a batch size of 16. For tasks that require two sequences as input, other baseline models lack the ability to simultaneously process both sequences, especially when it comes to handling gene and protein pairs. Instead, they use a dual-tower structure, employing two independent encoders to encode each sequence separately. In contrast, BSM directly connects the two sequences as input using a <sep> token, allowing the model to evaluate their relationship directly in a unified context. Implementation details are listed in the Appendix A.

### 3.1 Mixed-Modal Modeling & Few-Shot Evaluation

As shown in Figure 3, in mixed-modal tasks, such as RNA-protein interactions, BSM outperforms larger models like LucaOne. In the Central Dogma task, which focuses on DNA-protein associations, BSM achieves performance comparable to LucaOne. Additionally, in the few-shot learning setting without fine-tuning, BSM achieves performance close to SFT. Notably, BSM is the only existing biological sequence model capable of few-shot learning on mixed-modal data. These results highlight BSM’s ability to efficiently process and analyze mixed-modal biological sequence data, positioning it as a leading model in the field despite its smaller size.

Figure: Figure 3: Results on mixed-modal tasks and few-shot evaluation. In the RNA-protein mixed-modal task (ncRPI), BSM outperforms larger models like LucaOne. In the DNA-protein mixed-modal task (Central Dogma), BSM achieves performance comparable to LucaOne. In few-shot learning settings without fine-tuning, BSM performs similarly to SFT, making it the only biological sequence model capable of few-shot learning on mixed-modal data.
Refer to caption: x3.png

ncRNA-Protein Interactions (ncRPI) The ncRPI (Han & Zhang, ) task is a binary classification task aimed at predicting interactions between various non-coding RNAs (ncRNAs) and proteins, which is crucial for understanding cellular functions. Both BSM-110M and BSM-270M surpass the performance of billion-scale biological models, such as DNABert2 + ESM2-3B and LucaOne 1.8B. Notably, the results for BSM-270M are comparable to those of ncRPI-LGAT, a model specifically tailored for this task.

Central Dogma The Central Dogma task is a binary classification task curated by LucaOne, aimed at recognizing the intrinsic association between DNA sequences and their corresponding proteins based on the central dogma. Specifically, the DNA-protein pairs are constructed from the RefSeq database (O’Leary et al., ). To ensure data integrity, we removed 57 instances of test data that were present in our pretraining dataset. We conducted two experimental settings for this task: one involved SFT with 3,200 training samples, while the other utilized few-shot learning without fine-tuning the model.

In the SFT experiment, BSM-270M performs comparably to LucaOne despite using a much smaller model size. Additionally, BSM outperform DNABERT2 + ESM2-3B in performance.

Few-shot Learning In the few-shot learning setting, we concatenate few-shot demonstrations with the test sample, each containing a DNA and protein sequence, as input for BSM. We then calculate the log probability for each token in the tested protein sequence. This allows us to obtain the overall generation probability for the tested protein sequence. We then set a threshold for this generation probability to classify whether the protein is linked to the tested DNA, and subsequently compute the prediction accuracy.

Despite not being fine-tuned, the BSM model demonstrates strong performance in identifying correct DNA-protein associations. The experiments show that increasing the number of demonstrations further enhances performance, with the few-shot learning results approaching those of SFT. This experiment highlights BSM’s in-context learning capability, particularly in mixed-modal tasks, a capability that other existing models do not possess.

Figure: Figure 4: Results on four protein tasks. BSM outperforms all baseline models in PPI and ProtLoc, achieving the best results. In ProtStab, its performance matches LucaOne. Additionally, in the zero-shot protein fitness prediction task, BSM shows comparable results to Evo-7B and Progen2-large.
Refer to caption: x4.png

### 3.2 Protein Modeling Evaluation

We evaluate BSM’s capabilities on four protein tasks, with results shown in Figure 4. Notably, we surpass all baseline models in both the PPI and ProtLoc tasks, achieving the best results. In the ProtStab task, we obtain results comparable to LucaOne. Additionally, in the zero-shot protein fitness prediction task, we achieve performance similar to Evo-7B and Progen2-large. These results highlight BSM’s capability in modeling protein sequences through a deep understanding of protein functions and activities, despite its smaller size.

Protein-Protein Interaction (PPI) The PPI task is pivotal for mapping out how proteins interact within biological systems. We use the DeepPPI (Sun et al., ) database that contains human protein interactions for binary classification. The models are fine-tuned on this dataset, and their performances are assessed based on prediction accuracy. Both BSM-110M and BSM-270M surpass protein-specific models like DeepPPI and ESM2-3B, as well as multimodal biological sequence models like LucaOne.

Prokaryotic Protein Subcellular Location (ProtLoc) ProtLoc (Xu et al., ) predicts the subcellular localization of prokaryotic proteins, classifying them into six compartments like the cell membrane and cytoplasm. It uses a strategy similar to DeepLocPro (Moreno et al., ), helping to understand protein functions based on their locations. In this task, both BSM-110M and BSM-270M achieve the best results.

Protein Stability (ProtStab) evaluates protein stability by correlating features with stability measurements from the TAPE dataset (Rao et al., ). This task is important for understanding protein activity and can assist in drug design and biotechnology applications. In this task, BSM outperforms ESM-2-3B and TAPE, attaining results comparable to LucaOne.

Zero-shot Protein Fitness Prediction This task evaluates models’ ability to predict the impact of mutations on protein function without task-specific fine-tuning. It uses Deep Mutational Scanning (DMS) datasets (Jacquier et al., ; Firnberg et al., ; Adkar et al., ; Tsuboyama et al., ; Kelsic et al., ), where a comprehensive set of mutations is introduced into protein-coding sequences to measure their effects on fitness. Fitness serves as a metric for how effectively a protein performs a specific function.

Following the implementation of Evo, the model predicts fitness scores based solely on its understanding of the protein sequence in a zero-shot setting. In the experiments, Evo-7B and NT-500M use gene sequences as input, while other protein models like ESM-2 650M and Progen2-large rely on protein sequences. In contrast, BSM utilizes both gene and protein sequences due to its mixed-modal modeling capability. Our experiments demonstrate that incorporating gene data enhances BSM-110M performance on this task, increasing the SRCC from 40.7% to 42.3%. This advancement not only establishes BSM’s broad applicability in computational biology but also showcases its forward-looking nature in improving protein modeling capabilities through the integration of genetic information. Ultimately, BSM-270M achieves performance comparable to Evo-7B and Progen2-large, although it does not surpass ESM-2 650M.

Figure: Figure 5: Results on two gene-related tasks. BSM outperformed Evo 7B in the zero-shot ncRNA fitness prediction task, accurately predicting the effects of mutations on ncRNA functionality without task-specific fine-tuning. It also performed well in the ncRNAFam multi-class classification task.
Refer to caption: x5.png

### 3.3 Gene Modeling Evaluation

We evaluate BSM’s capabilities on several critical genomic challenges, with results shown in Figure 5. BSM outperformed Evo 7B in the zero-shot ncRNA fitness prediction task, leveraging its understanding of genomic sequences to predict the effects of mutations on ncRNA functionality without task-specific fine-tuning. It also performed well in the ncRNAFam multi-class classification task. These collective achievements underscore the model’s comprehensive strength in genomic analysis and its potential to contribute significantly to molecular biology research.

Zero-shot ncRNA Fitness Prediction This task investigates the model’s ability to predict the functional implications of mutations in non-coding RNAs (ncRNAs), including tRNAs, rRNAs, and ribozymes (Kobori et al., ; Andreasson et al., ; Domingo et al., ; Guy et al., ). Understanding these roles is crucial for cellular processes like protein synthesis and gene regulation. We use ncRNA Deep Mutational Scanning (DMS) data for evaluation, which includes various mutations and their effects on ncRNA functionality. Following Evo, we adopt a zero-shot approach to assess whether the pretrained BSM can generalize its understanding of genomic sequences to accurately predict the impact of mutations on ncRNA fitness without task-specific fine-tuning. Experimental results show that both BSM-110M and BSM-270M achieve the best performance, surpassing other methods, including Evo-7B.

Non-coding RNA Family (ncRNAFam) The ncRNAFam task is a sophisticated multi-class classification challenge, requiring models to accurately categorize non-coding RNA (ncRNA) sequences into 88 distinct families (Noviello et al., ; Rossi et al., ). These ncRNAs, while not coding for proteins, play indispensable roles in gene expression regulation and other cellular processes. Our fine-tuned BSM model achieves a remarkable accuracy of 97.5%, slightly below LucaOne but surpassing DNABert2 110M. This achievement underscores BSM’s proficiency in discerning the subtleties of ncRNA sequences, showcasing its advanced capability to classify these crucial non-coding elements with high precision.

Figure: Figure 6: Effectiveness of scaling BSM. Validation loss curves for the BSM 110M and 270M models across three rounds of training illustrate that scaling enhances model capabilities.
Refer to caption: x6.png

### 3.4 Scaling Up BSM

To unlock the full potential of BSM, we investigate its scaling properties by increasing its parameters from 110M to 270M. As shown in Figure 6, BSM-270M exhibits lower validation loss across all three rounds of training, demonstrating a significant improvement compared to BSM-110M. Experiments across diverse biological tasks also indicate that a larger model enhances performance. This scaling shows that increasing model size can further enhance BSM’s capabilities, underscoring the effectiveness of our approach and the considerable value of incorporating mixed-modal data in advancing biological sequence modeling.

### 3.5 Ablation Study on Mixed-Modal Data

As shown in 1(a), we validate the value of three types of mixed-modal data by comparing the performance of models across different rounds. We conduct experiments on the ncRPI and PPI tasks, and the results demonstrate that Round 2, by incorporating RefSeq and Gene Related Sequence data, enhances performance on both tasks. In Round 3, the introduction of interleaved mixed-modal data from the web, even with limited data (33M tokens), further improves the model’s performance.

### 3.6 Perplexity Evaluation

Perplexity (PPL) is one of the most common metrics for evaluating the generation capabilities of language models. It is defined as the exponentiated average negative log-likelihood of a sequence, reflecting how well a model can predict the next word based on the preceding context. A lower perplexity score indicates a better ability of the model to accurately predict the next word.
We evaluate BSM’s generation capability using perplexity on our validation protein data. As illustrated in 1(b), BSM outperforms the larger model ProGPT2 700M, although it doesn’t surpass Progen2 2.7B. This demonstrates that using mixed-modal data for pretraining allows smaller models to effectively model and generate protein sequences.

**(a) Ablation study on mixed-modal data**
| Model | ncRPI | PPI |
| --- | --- | --- |
| Round1 | / | 0.9591 |
| Round2 | 0.9422 | 0.9722 |
| Round3 | 0.9494 | 0.9750 |

## 4 Related Work

### 4.1 Biological Sequence Model

Biological sequences are the cornerstone of molecular biology, encapsulating the genetic information that governs life processes. Traditional approaches to modeling these sequences often focus on modality-specific models, each tailored to a particular type of sequence—DNA, RNA, or protein. For instance, DNABert2 (Zhou et al., ), HyenaDNA (Nguyen et al., ), and Caduceus (Schiff et al., ) are designed for DNA sequences, while RNA-FM specializes in RNA. Models like ESM2 (Lin et al., ), ProGPT2 (Ferruz et al., ), and ProGen2 (Nijkamp et al., ) are specifically developed for protein sequences. While these models have made significant progress in their respective areas, they are limited in their ability to capture the complex relationships between different biological data types.

Recently, new methods have emerged that develop models capable of handling both gene and protein data, such as LucaOne (He et al., ) and Evo (Nguyen et al., ). These advancements demonstrate the potential of large-scale models to manage complex multi-modal biological data. For example, Evo suggests that genes can express other modalities, learning from large genomic regions to capture system-wide interactions. LucaOne integrates single-modality gene and protein data with extensive annotations through multi-task learning. However, these models often require significant computational resources. In contrast, our approach operates without task-specific supervision, explicitly leveraging mixed-modal data to improve efficiency and endow smaller models with cross-modal capabilities.

### 4.2 Small Language Model

The emergence of Small Language Models (SLMs) has gained significant attention as promising alternatives to resource-intensive Large Language Models (LLMs) (Minaee et al., ). Capable small LMs are faster to run and easier to deploy, making them attractive for various applications. Their success can be attributed to strategic training choices, such as the emphasis on high-quality data in models like Phi (Gunasekar et al., ), the introduction of weight reuse in Phi-2 (Javaheripi et al., ) to scale capabilities, and the use of knowledge distillation techniques in Gemma 2 (Team et al., ) to enhance performance. Furthermore, SLMs exhibit significant potential for improvement, as ongoing enhancements in model performance—such as those observed in the Phi series—suggest that these models remain under-trained and present ample opportunities for further development. Inspired by these works, we investigate the use of high-quality mixed-modal biological data for pretraining our BSM model, which optimizes performance within a smaller model to effectively process DNA, RNA, and protein data.

## 5 Conclusion

In this study, we have demonstrated that high-quality mixed-modal biological data is essential for enhancing both cross-modal and single-modal learning capabilities in our BSM models. The results indicate that protein-gene interleaving data has considerable potential to improve model performance, highlighting the importance of data quality in training effective biological models.

However, our research has certain limitations. We utilized only a partial dataset from RefSeq and Gene Related Sequence data, which lacks exploration of other valuable types of cross-modal data, such as gene-protein interactions data. Additionally, we mined only a relatively small dataset of interleaved biological sequences from the web, it still yielded continuous improvements in model performance. This suggests that there is substantial room for further investigation, and we believe that leveraging larger and more diverse datasets could enhance our model’s capabilities even further. Our future work will focus on exploring additional types of cross-modal data to fully realize the potential of mixed-modal approaches in biological sequence modeling and contribute to advancements in the field.

## Appendix A Implementation Details

We use hyperparameters listed in Table 2 for pretraining.

**Table 2: Hyperparameters for BSM-110M and BSM-270M models**
| Hyperparameter | BSM-110M | BSM-270M |
| --- | --- | --- |
| Number of params (M) | 110 | 270 |
| Number of layers | 12 | 20 |
| Number of heads | 12 | 16 |
| Head dimensions | 768 | 896 |
| Context length | 1024 | 1024 |
| Batch size | 4096 | 1024 |
| Learning rate(R1/R2/R3) | 2e-5/1e-5/1e-6 | 2e-5/1e-5/1e-6 |
| Total steps | 32,500 | 124,000 |

## Appendix B Details of Pretraining data Construction

Mixed-modal Pair Data - NCBI RefSeq We use 15 species data from the RefSeq dataset, which are listed in Table 3.
We construct pair data using any combination of DNA, RNA, and protein, with a shuffled order.

**Table 3: Fifteen different species in the RefSeq dataset used for pretraining.**
| No. | Species | No. | Species | No. | Species |
| --- | --- | --- | --- | --- | --- |
| 1 | Bubalus Bubalis | 6 | Peromyscus Californicus | 11 | Mucor Saturninus |
| 2 | Camelus Dromedarius | 7 | Fusarium Annulatum | 12 | Penicillium Chermesinum |
| 3 | Human | 8 | Melampsora | 13 | Sporopachydermia Quercuum |
| 4 | Macaca Assamensis | 9 | Metschnikowia | 14 | Tranzscheliella Williamsii |
| 5 | Macaca Nigra | 10 | Mucor Saturninus | 15 | Xylariales |

Mixed-modal Pair Data - Gene Related Sequences
We randomly extract 8.3 billion tokens from multiple species to construct our datasets, employing the same methodology for pairing DNA, RNA, and protein sequences in RefSeq datasets, where the order is randomly selected.

Mixed-modal Interleaved Data - Filtered Web Data
We filter documents from the Fineweb-Edu dataset that contain three or more biological sequences and use the extracted sequences to construct our dataset. This data ensures that the model is exposed to a richer variety of biological sequence data, simulating the natural co-occurrence of sequences in real bioinformatics environments.

## Appendix C Evaluation Data Details

ncRNA-Protein Interactions (ncRPI) The ncRPI dataset is specifically designed to evaluate the model’s ability to predict interactions between non-coding RNAs (ncRNAs) and proteins. It consists of three sub-datasets: NPInter2.0, NPInter2.0_lncRNA, and RPI7317, which contain ncRNA-protein interaction pairs. Specifically, the NPInter2.0 dataset contains 10,412 experimentally validated ncRNA-protein interaction pairs, involving 4,636 ncRNAs and 449 proteins; the NPInter2.0_lncRNA dataset includes 4,158 lncRNA-protein interaction pairs, involving 990 lncRNAs and 27 proteins; and the RPI7317 dataset contains 7,317 lncRNA-protein interaction pairs, involving 1,874 lncRNAs and 118 proteins. Negative samples (i.e., non-interaction pairs) are generated by randomly pairing ncRNAs and proteins from these datasets, creating an equal number of negative samples to positive samples.

Central Dogma
The central dogma dataset is meticulously designed to assess the model’s ability to recognize the intrinsic links between DNA sequences and their corresponding proteins. It consists of 8,533 accurate DNA-protein pairs selected from 13 species in the NCBI-RefSeq database. Each DNA sequence is extended to include an additional 100 nucleotides at both the 5’ and 3’ contexts, preserving intron sequences within the data. To evaluate the model’s discrimination capabilities, negative samples are generated by introducing substitutions, insertions, or deletions within the DNA sequences, or by altering amino acids in the protein sequences to ensure that the resulting DNA sequences cannot be accurately translated into their respective proteins. All samples are randomly allocated to the training, validation, and testing sets in a ratio of 4:3:25.

Protein-Protein Interaction (PPI) The PPI dataset is a valuable resource for assessing protein-protein interactions (PPIs), sourced from the DeepPPI database, which includes a vast array of unique protein pairs. These datasets are crucial for unraveling the complex molecular communication mechanisms within cells. Specifically, the dataset comprises 59,766 training samples, 7,430 validation samples, and 7,425 test samples, collectively forming a binary classification task aimed at predicting whether a pair of protein sequences will interact. Each sample consists of a pair of protein sequences labeled as 1 (indicating interaction) or 0 (indicating no interaction). To ensure the accuracy and validity of the assessment, the dataset is meticulously divided to guarantee consistency in data distribution across the training, validation, and test sets.

Prokaryotic Protein Subcellular Location (ProtLoc) The ProtLoc dataset is a specialized dataset designed for predicting the subcellular localization of prokaryotic proteins. It comprises a curated selection of protein sequences from the UniProt and PSORTdb databases, with each sequence annotated for its specific subcellular location within the cell, such as the cytoplasm, cytoplasmic membrane, periplasmic space, outer membrane, cell wall and surface, and extracellular space, which are the six primary regions. This dataset is extensively used in the field of bioinformatics to train and evaluate machine learning models for the accurate prediction of protein subcellular localization. The dataset is divided into 9,915 training samples, 1,991 validation samples, and 1,131 test samples to support the training and assessment of models.

Protein Stability (ProtStab) The ProtStab dataset is specifically designed for evaluating protein stability, sourced from the TAPE project, and includes stability measurement data for a variety of protein types, including natural proteins, mutants, and de novo designed proteins. The dataset comprises 53,614 training samples, 2,512 validation samples, and 12,851 test samples, providing comprehensive support for the training and evaluation of models. Each protein sample in the dataset is accompanied by a continuous numerical label indicating its stability measurement. When constructing and evaluating protein stability prediction models, Spearman’s Rank Correlation Coefficient (SRCC) is used as the evaluation metric to measure the performance of the model.

Zero-shot Protein Fitness Prediction
The Zero-shot Protein Fitness Prediction dataset is utilized for evaluating a model’s ability to predict the impact of mutations on protein function without any task-specific fine-tuning or supervision. This dataset encompasses multiple Deep Mutational Scanning (DMS) studies, which include exhaustive mutation scans of protein-coding sequences, along with experimentally measured fitness scores that quantify the protein’s ability to perform specific functions. The dataset comprises samples from both E. coli and human proteins, with the number of variants depending on the specific DMS study. Each sample consists of a protein sequence, the introduced nucleotide mutations, and the corresponding fitness score. The sequences are preprocessed to fit the input requirements of machine learning models. The performance of models on this dataset is assessed using the Spearman’s Rank Correlation Coefficient (SRCC), which measures the strength and direction of association between the model’s predictions and the experimental fitness measurements.

Zero-shot ncRNA Fitness Prediction
The Zero-shot ncRNA Fitness Prediction dataset aims to evaluate a model’s ability to predict the impact of mutations on the function of non-coding RNA (ncRNA) without any specific task fine-tuning or supervision. This dataset originates from multiple Deep Mutational Scanning (DMS) studies that conduct exhaustive mutation scans on ncRNA sequences and measure the effects of these mutations on fitness through experimental means. Non-coding RNA plays a crucial role in many biological processes, including gene expression regulation, signal transduction, and cellular differentiation. The dataset includes samples from DMS studies, with each sample comprising the wild-type ncRNA sequence, the introduced mutations, and the corresponding fitness scores. The sequences are preprocessed to meet the input requirements of machine learning models. The performance of the model on this dataset is assessed using the Spearman’s Rank Correlation Coefficient (SRCC), which measures the correlation between the model’s predictions and the experimentally determined fitness scores.

Non-coding RNA Family (ncRNAFam) The ncRNAFam dataset is specifically designed for evaluating models that classify non-coding RNA (ncRNA) sequences into their respective families. Non-coding RNAs play a crucial role in many key biological processes, including the regulation of gene expression, signal transduction, and cellular differentiation. This dataset encompasses a variety of ncRNA sequence types, such as long non-coding RNAs and small nucleolar RNAs, each with its unique biological functions. It comprises 105,864 training samples, 17,324 validation samples, and 25,342 test samples, all of which support the training and evaluation of classification models. The dataset is roughly divided into 80% for training, 10% for validation, and 10% for testing. Each ncRNA sequence in the dataset is labeled according to its family classification, and the accuracy metric is used to assess the performance of the models.

**Table 4: Evaluation data details.**
| Task | Task Type | Input Type | Train/Valid/ Test Size |
| --- | --- | --- | --- |
| ncRPI | Binary-Class(2) | RNA-Protein | 16,658/-/4,166 |
| Central Dogma | Binary-Class(2) | DNA-Protein | 3,200/2,400/19,943 |
| PPI | Binary-Class(2) | Protein-Protein | 59,766/7,430/7,425 |
| ProtLoc | Multi-Class(6) | Protein | 9,915/1,991/1,131 |
| ProtStab | Regression | Protein | 53,614/2,512/12,851 |
| ncRNAFam | Multi-Class(88) | RNA | 105,864/17,324/25,342 |

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: Could the species you used for training also have an effect on downstream performance. Existing models usually train on human+mammal data, occasionally also incorporating data from invertebrates, bacteria, fungi, etc. I think the BSM models were trained on a mix of mammal and fungal DNA from RefSeq, was there any logic to the species selected? Is it possible that any differences in performance are (at least partly) due to the species selected? This may be a useful thing to point out for curation (i.e. we get better perf because we picked better species), but currently I can't actually tell what the affect of adding in each specie is nor how much each specie is represetned in the training data.  NOTE:  the assembly accession information for each specie accessed is missing from the paper, please make sure this is available somewhere since these assemblies tend to be updated periodically by NCBI. 

How exactly were data leaks prevented? I understand that data from the downstream tasks was removed from pre-training but how exactly was this done and how are you sure that no leakage occurred by e.g. a very similar sequence, or having a gene whose transcribed protein is in the test set?
soundness: 2
strengths: I like the general concept, there are myriad types of data that can be used for biological sequence modeling, and I'd like to believe that being smart about data/modality selection can lead to increased performance with fewer parameters/FLOPs by leveraging multiple types of information at once.
summary: The authors of this work present BSM, small biological sequence models that ostensibly achieve competitive performance with existing larger models through the virtue of combining: 
1. Multiple types of data modalities (e.g DNA, RNA, and Protein sequences)
2. "Curating" the data used resulting in a smaller but higher quality dataset.

By being careful about the types and mixes of data used for pre-training, the authors are able to achieve performance competitive with much larger (e.g. 10x) larger models.
weaknesses: 1. It is not clear what the exact effect of each phase (data mix) is. 
    2. The BSM model is split up into three phases:
        3. Phase 1 trained on 100B single modal tokens (e.g each sequence is only nucleic acids or only protein sequences). 
        4. Phase 2 is further trained on 146B tokens for 246B tokens seen in total
        5. Phase 3 is then further trained on $\sim$ 16.9B more tokens for 262.2B tokens seen in total. 
    3. It is unclear to this reviewer what perfromance gains are due to the increased token budget vs from the different types of data included. For example, in Table 4, the authors attempt to show that increasing rounds (and therefore increasing token budgets) lead to a increase in downstream performance, but it's unclear if this gain is from the additional tokens or from the curration of the tokens. At the very least, a control where a BSM model is trained for the same number of tokens, but only with the tokens from phase 1 for example may help show that the performance increase is due to the incorporation of different types of data and not just due to training for longer. 
    4. The authors kind of show something like this in Figure 1, but it's unclear to me exactly what the validation set is (is it data from that phase? Some fixed set of data sampling from phase 3? I don't think I ever found a detailed description in the paper) so I'm not sure if the authors are showing that incoporating the other phase data actually makes the loss decrease faster or if they're showing some sort of domain shift (e.g. if val is from phase 3 but they keep training on phase 2, we might expect the validation loss to not decrease as fast). Figure 6 suffers from a similar issue where I'm unsure of what the validation data and so I'm unsure how to interpret the changes in loss per phase. 
    5. Taken together, all of these concerns lead me to doubt what the effect of the different data mixes actually is, which seems to be a key claim of the paper.  
2. The Authors do not compare to the smaller models (e.g. the small ESM models) where applicable. ESM has available 650M, 150M, and even smaller models. On all the protein tasks, the authors only report ESM-3B, except for zs-Protein Fitness Prediction where they use ESM-650M. Unless I'm missing something, reporting their 110M and 270M compared to ESM-150M would help showcase how ther training setup and architecture compares to a model similar in size. Note: going down to ESM-650M only for that one task is odd, was this a placeholder or something and 3B was to be shown in the final submission or was there some other reason for only showing 650M?
3. There is some details missing on the setup of all the models evalauted. What sequence lengths are used, for models that are truncated (e.g. max of 1024 tokens), how are long sequences processed? What was the fine-tuning procedure for each of the models? How much does this type of processing affect different model performance? Architectural choices like these likely have some effect on the performance of each model observed, but it's currently unclear how much these types of decisions contributed to the observed performance and so it's difficult to disambiguate what the true effect of the data selection is. 
4. More details are needed for each evaluation task. Appendix C and Table 4 are a good start, but there's details such as the number of instances of each class, the species the task was taken from, etc. Most of the tasks only report accuracy or SRCC, but it's unclear if this is an adequate summary statistic for each task given this missing information.

---

confidence: 3
contribution: 2
initial_rating: 1
presentation: 1
questions: See Weakness
soundness: 2
strengths: A compact mixed-modal biological sequence foundation model is introduced and assessed across various downstream tasks.
summary: This paper presents BSM, a compact mixed-modal biological sequence foundation model, trained using three types of data: RefSeq, Gene Related Sequences, and interleaved biological sequences from the web. The model's performance is assessed by comparing it to other existing larger models (e.g., LucaOne, ESM2, Evo, etc.) across various downstream tasks.
weaknesses: The clarity and precision of the paper could be greatly enhanced by making it more concise. For example:

•	The paper contains numerous repetitive phrases, sentences, and references that could be removed to improve readability.

•	Data and experimental details would be better summarized in tables for clarity and ease of comparison.

•	Section 4 is largely redundant.

•	Many figures are of poor quality, with barely readable legends.

Drawing conclusions from the experiments presented in the paper is challenging due to the lack of important details, including:

•	There are no details about the head used in the experiments in conjunction with the foundation models (FMs). These details are crucial and need to be carefully set for a head-to-head comparison with other FMs.

•	Additionally, for other FMs, there could be inverse scaling, where larger models do not consistently outperform smaller ones on all tasks (see McKenzie, et al. (2023). Inverse scaling: When bigger isn’t better. arXiv preprint arXiv:2306.09479). Therefore, it is difficult to conclude that the proposed BSM model performs the best, despite being the smallest, solely due to the introduction of additional data for training.

•	The differences in performance across models are mostly subtle in many experiments. Statistical analysis would be helpful here.

---

confidence: 3
contribution: 2
initial_rating: 5
presentation: 3
questions: - How do you choose the data mixing ratio? Is it heuristically decided? How the model performance varies as mixing ratio changes?
soundness: 2
strengths: - Modeling biological sequence and integrating heterogeneous types of sequences are important in building foundational model for biological sequences.
- Extensive experiments are conducted to demonstrate the superiority of BSM
summary: This paper proposes to model the multiple biological sequences (DNA, RNA, and Protein) into a single model for cross-modal and uni-modal biological sequence tasks.
Authors propose to utilize small language model for the task, which outperforming larger models in various tasks.
weaknesses: - The main motivation of this paper is to use the small language model (SLM) for biological sequences. But why do we need SLM for biological sequence analysis? SLM is usually practical in real-life for implementing language model for on device or resource limited settings. However, in biological sequence analysis, scientists may already have intensive computational resources. It would be great if the authors can justify why we need SLM for biological sequence analysis.
- It may be convincing, if the SLM performs better than larger model due to the limited availability of data. However, as shown in various experiments, larger models perform better than smaller models. For example, Figure 3 (b) LucaOne performs better than BSM models, and BSM-270M model performs better than BSM-110M model.
- Experimental results
    - I think we need more ablation studies on multi modal data to further demonstrate the necessity of multi-modal pre-training approach.

Meta-review: This paper introduces BSM, a small mixed-modal biological sequence foundation model that seeks to enhance cross-modal representation and task performance. The authors argue that leveraging cross-modal data enables smaller models to achieve performance comparable to or exceeding larger models, emphasizing data quality over sheer model size. BSM is evaluated on various single-modal and mixed-modal tasks, where it outperforms several billion-parameter models despite having only 110M–270M parameters.

Decision: Reject