Automating Large-scale In-silico Benchmarking for Genomic Foundation Models

## 1 Introduction

The central dogma of biology [] posits that genomes, including DNA and RNA, encode and transmit the genetic information essential for all living systems and underpin the translation of proteins. Despite decades of advancements in molecular biology, deciphering genomes remains a significant challenge [, , ]. Researchers have been striving for advanced and efficient genome analysis to better understand and synthesize RNA [] and DNA [] genomes. However, the efficiency and performance of conventional bioinformatics approaches [, ] have hardly kept pace with the rapid advancements in high-throughput sequencing technologies [, ]. The recent proliferation of foundation models [, , ] in the natural language processing domain has shown unprecedented potential for modeling complex ‘genomic languages’ []. These models are known as genomic foundation models (GFMs). Such GFMs are so versatile that they not only uncover genomic encoding patterns within DNA and RNA, but also support a diverse array of genomic tasks, such as RNA secondary structure prediction [], RNA function (e.g., translation efficiency) prediction [], and even RNA molecular design [, , ].

Despite these advancements, the broader adoption of GFMs for genomic research and related fields, including bioscience discovery and therapeutics design, is significantly hindered by the absence of standardized benchmarks. These benchmarks constitute the foundation for evaluating and comparing model performance, understanding model behavior, building confidence, and promoting the widespread application of GFMs. Unlike the deep learning community in computer vision and natural language processing, where benchmarking has a long-standing tradition, the genomic field faces unique challenges in establishing robust benchmarks due to the following challenges.

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Data Scarcity and Bias: A critical challenge is the lack of comprehensive and diverse datasets necessary for robust training and testing of GFMs. In practice, many genomic datasets are limited in scope and size, often exhibiting biases toward specific species or genome sequences. For instance, some GFMs are trained solely on evolutionary conserved sequences [, , ]. This scarcity of diverse datasets significantly hampers the models’ ability to generalize and perform effectively across a wide range of genomic contexts. This limitation not only restricts GFM training but also undermines their capacity to discover novel patterns and make accurate predictions in less-studied species.
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Metric Reliability: Another major concern affecting model reliability is the inconsistency of metrics used to benchmark performance. Different studies may employ varying metrics or implement the same metrics with minor differences []. This often leads to inconsistent results across studies. For example, Chen et al. [] and Fu et al. [] has reported significantly different results on the effectiveness of E2EFold [] because of the variations in evaluation metrics. Such inconsistencies can obscure true model performance and hinder the ability to draw reliable conclusions in genomic studies.
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Reproducibility: Ensuring reliable reproducibility of GFM experiments across different research environments remains a significant challenge. As reported by [], differences in computational environments, dataset splits, and even minor code implementation variations can lead to significant discrepancies in results. These inconsistencies hinder the validation and comparison of GFMs. Moreover, the absence of standardized benchmarking practices exacerbates these issues, underscoring the necessity of establishing protocols that can be consistently followed across studies and laboratories to ensure reliable and reproducible research outcomes. In addition, the inherent complexity of GFMs presents formidable roadblocks for domain scientists seeking to identify and implement best practices for GFM building and training tailored to their scientific inquiries. Together, these challenges significantly hamper the democratization of GFMs, limiting their accessibility and adoption across diverse research domains.
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Adaptive Benchmarking: As reported in recent studies [, ], predictive modeling performance can be significantly enhanced by jointly modeling various genomics, including DNA and RNA. While genomes and proteomes from diverse living systems may share similar patterns in bio-sequence modeling, there is a lack of holistic understanding of GFMs’ capabilities beyond their pre-training scenarios. For example, what is the capability of a GFM for structure prediction [, , , ] when the model was not pre-trained on the structure annotations of the target species? To address this, we developed a novel adaptive benchmarking protocol that enables comprehensive evaluations across a wide range of genomes and species. It is distinguished by its compatibility with diverse GFMs and benchmarks across different modalities of genomic data. Such adaptive benchmarking can facilitate findings from cross-genomic studies and provide valuable insights for future research.

To address these challenges, we develop a dedicated benchmarking toolkit, dubbed OmniGenBench, for GFM-oriented genomics, capable of benchmarking and leveraging GFMs in in-silico tasks.
This platform champions the following four key characteristics.

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We have collected and integrated $4$ large-scale benchmarks, $42$ millions of genome sequences from up to $75$ genomic datasets, into OmniGenBench to mitigate issues of data scarcity. We also perform data filtering for downstream tasks, e.g., structure predictions, that suffer from data leakage, reducing similar sequences and structures. This aids in addressing biases in the learning series and annotated data.
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To mitigate data and implementation bias that break metric reliability, we have integrated common metrics and developed automatic performance recording in benchmark evaluations to ensure consistently fair performance.
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To improve the reproducibility of benchmark experiments, OmniGenBench exploits the benchmarks compiled in a unified protocol, specifying detailed metadata and benchmark settings that follow the FAIR principles []. e.g., hyperparameters and dataset splits, that may lead to performance variance across models and datasets. On the other hand, the stark lack of biologist-friendly GFM-oriented genomics software like ViennaRNA(^1^11[https://www.tbi.univie.ac.at/RNA](https://www.tbi.univie.ac.at/RNA)), necessitates expertise in language modeling and bioinformatics to democratize GFMs and hinders the exploration of leveraging GFMs in genome modeling. The absence of standardized solutions may exacerbate conclusion inconsistencies in genome modeling studies, as custom-built solutions may introduce uncertainties in model reliability.
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Adaptive benchmarking is supported in OmniGenBench. It is distinguished by its compatibility with diverse GFMs and benchmarks across different modalities of genomic data. Such adaptive benchmarking can facilitate findings from cross-genomic studies and provide valuable insights for future research. For example, Yang & Li [] showed that RNA structure pre-training significantly improves model performance on DNA genomic benchmarks, indicating that structural information is vital even for DNA genomes.

## 2 OmniGenBench

Figure: Figure 1: (a) shows the available tools in the open-source software, including the AutoBench pipeline and genome embedding extraction. OmniGenBench also launches an online hub and leaderboard to support GFM development. (b) illustrates the diverse genomic tasks supported by OmniGenBench, enabling both benchmarking and fine-tuning. This allows even novices in GFM to implement and fine-tune models without writing custom code. OmniGenBench includes common task templates and offers built-in interfaces for implementing new tasks. In addition to the fine-tuning interfaces, OmniGenBench provides user-friendly tools for running inferences and deployments.
Refer to caption: x1.png

OmniGenBench is an open-source benchmarking software platform for GFMs, and its architecture is shown in Fig. 1. Fig. 1 (a) presents the available toolkit for genomics, including the AutoBench pipeline and genome embedding extraction, among other features. Fig. 1 (b) shows the diverse set of genomic tasks supported by OmniGenBench for both benchmarking and fine-tuning. Moreover, Fig. 1 (c) illustrates the user-friendly built-in interfaces in OmniGenBench for implementing and fine-tuning new models, as well as for running inferences and deployments. We delineate the components in the following sections.

OmniGenBench is an open-source benchmarking software platform for GFMs, and its architecture is shown in Fig. 1 Fig. 1.  Fig. 1 (a) presents the available tools for genomics, including the AutoBench pipeline and genome embedding extraction.  Fig. 1 (b) displays the genomic tasks that OmniGenBench supports for benchmarking and fine-tuning.  Fig. 1 (c) shows the user-friendly interfaces for implementing new models, as well as for running inferences and deployments. We describe the components in detail below.

### 2.1 AutoBench Pipeline

AutoBench is an automated benchmarking solution for genomics, involving the concepts of benchmark suite standardization, open-source GFM compatibility, and metric implementation. In AutoBench, four standardized benchmark suites can be evaluated with existing open-source GFMs, alleviating the data scarcity problem. Public and custom metric implementations are also supported, allowing users to benchmark models against specific performance requirements. The metrics in AutoBench are recommended and distributed as part of the benchmark suites to ensure metric reliability for GFMs. It prioritizes the standardization of benchmark suites and protocols to minimize benchmarking uncertainties.

#### Benchmark Suites

It has been recognized that comprehensive benchmark suites are crucial for language modeling evaluation. To be more specific,
genome languages are complicated and far from manipulation like natural languages, because the understanding of genetic information is challenging for GFMs. For example, single nucleotide variants (SNVs) [] and single nucleotide polymorphisms (SNPs) [] often lead to significant shifts in phenotypes, while the critical base differences in such long sequences are sparse [] and difficult for GFMs to perceive. Moreover, each genome of a species can essentially be regarded as a different language, which further complicates genome modeling.
To achieve robust evaluations, OmniGenBench has integrated four large-scale benchmarks that enable broad evaluations of in-silico tasks. These benchmarks comprise $42$ million genomic sequences and help alleviate the problem of data scarcity and bias, allowing for precise and generalizable performance assessments of GFMs.

The brief introductions of the available benchmarks for AutoBench in OmniGenBench are as follows:

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RNA Genomic Benchmark (RGB) []. RGB consists of $7$ challenging single-nucleotide (SN) level genome understanding tasks, curated or collected from published sources, as shown in Table 5. It aims to benchmark GFMs in SN-level modeling tasks such as predicting mRNA degradation rates and secondary structures. The sequences in RGB range from $107$ to $512$ bases, making them suitable for verifying RNA model efficacy. These downstream tasks in RGB assemble the first comprehensive RNA benchmark to assess the multi-species and SN-level modeling capabilities of GFMs. For detailed information on each dataset, such as their sources and sizes, please refer to Appendix A.1.
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Plant Genomic Benchmark (PGB). PGB(^2^22[https://huggingface.co/datasets/InstaDeepAI/plant-genomic-benchmark](https://huggingface.co/datasets/InstaDeepAI/plant-genomic-benchmark)) [] shown in Table 7, provides a large-scale DNA benchmark suite designed to evaluate GFMs specialized in plant biology. PGB involves $8$ types of DNA downstream subtasks, including a range of critical tasks such as promoter strength prediction and gene expression regression. There are $28$ datasets in total, with millions of DNA sequences in PGB, and the sequence lengths are up to $6,000$, which is quite long for most genomic FMs. Since the original evaluation protocol is not publicly available, we have re-implemented the auto-benchmark for all the subtasks from PGB in OmniGenBench.
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Genomic Understanding Evaluation (GUE) []. GUE is a multi-species genome classification benchmark that includes $36$ datasets across $9$ important genome analysis tasks, with input lengths ranging from $70$ to $10,000$. The benchmark covers a variety of species, including humans, fungi, viruses, and yeast, and explicitly defines evaluation metrics for each task, ensuring fair comparisons across different models. GUE consists of $7$ genome sequence classification problems and $28$ datasets, focusing on sequences with input lengths up to $1,000$, offering a robust testing ground for models handling longer genomic sequences.
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Genomic Benchmarks (GB) []. GB is an early collection of DNA genome datasets aimed at evaluating the genomic sequence classification performance of deep learning models. The benchmark includes $9$ datasets focusing on various regulatory elements, such as promoters, enhancers, and open chromatin regions, across different species like humans, mice, and roundworms. The downstream datasets aim to standardize comparisons, promote reproducibility, and drive innovation in genomic modeling.

#### Benchmark Standardization

The universal protocols for benchmark suites benchmarks has been absent in existing works, leading to performance biases caused by in-consistent implementations, such as hyperparameters. To tackle this problem, we propose the benchmark standardization, compiling suites with comprehensive components that guarantee identical benchmarking results, such as metadata, hyperparameter settings, custom code implementations, metrics specifications.

Benchmark metadata is essential for ensuring adherence to the FAIR principles []. We included primary keys in the metadata, including data sources, species information, genome specifications (e.g., DNA, RNA), and data scale measures. We also encourage to include custom keys in the future benchmarks to allow users to interpret such benchmark suites in depth. To increase benchamrking reproducibility, We freeze the hyperparameter settings in the standardized benchmark suites, because a slight change of hyperparameter may lead to significant variances [], such as batch sizes and optimizers.
We also notice that the some genomic tasks requires custom codes to complete the benchmark, e.g., codes to process the data and implement the model and tokenizers. Therefore, OmniGenBench can parse custom codes in the configuration corresponding to each task and override builtin behaviors. In OmniGenBench, we mirrored a diverse set of common metrics from scikit-learn(^3^33[https://scikit-learn.org](https://scikit-learn.org)) to supported diverse tasks. However, we are aware of some tasks that demand special unsupported metrics, OmniGenBench will load the load metric implementations in the standardized suites like the custom codes.
In OmniGenBench, we have implemented a diverse set of common performance metrics, such as F1 score, MCC and AUC. Apart from the built-in metrics, custom metrics can be included in benchmark suites to eliminate the performance variance caused by implementations.

The precompiled benchmark suites are distributed and evaluated according to OmniGenBench. This standardization not only enhances the reproducibility of benchmark results but also facilitates a more nuanced understanding of model strengths and limitations across diverse genomic tasks.

#### Genomic Foundation Models

The primary challenges stem from the heterogeneity of GFM architectures such as Transformers  [, ], Hyena [, , ] and Mamba  [, ].
These versatile implementations of GFMs require distinct environment and package requirements, as we as different interfaces for initialization, training and inference, leading to inefficient GFMs performance evaluation across benchmarks. Moreover, there have been attempts of genome tokenization methods [, , , ]. The tokenization of genome sequences encountered significant variances between different downstream tasks, such as k-mers [, ], Byte Pair Encoding (BPE) [, ], and Single Nucleotide Tokenizers (SNT) [, , ]. These tokenization methods feature different implementations and are tailored to be compatible with particular GFMs. In instances where tokenizers are incorrectly instantiated or utilized, this can lead to reports of unreliable performance metrics.

To standardize benchmarking across diverse GFMs with respect to specialized tokenizers, we have developed wrapper templates to unify interfaces and tokenizers, streamlining elastic benchmarking of open-source or customized GFMs. For example, OmniGenBench is capable of accommodating GFMs integrating RNA secondary structures modeling  [] with a simple model wrapper while existing benchmark tools have yet to achieve such flexibility. We have supported an array of open-source GFMs in OmniGenBench, detailed in Appendix B.3, and tutorials for adapting future GFMs will be released along with the open-source repository.

#### Adaptive Benchmarking

AutoBench parses the configuration(^4^44We show an example configuration of RGB at [https://github.com/yangheng95/OmniGenomeBench/tree/master/examples/RGB/RNA-mRNA/config.py](https://github.com/yangheng95/OmniGenomeBench/tree/master/examples/RGB/RNA-mRNA/config.py)) of standardized benchmark suites, automates the evaluation processes via unified interfaces, adaptive benchmarking benchmarking among diverse GFMs and suites is seamless in OmniGenBench, i.e., without any modification of the command.
The compiled suites offer standard benchmark processes among various GFMs and versatile data modalities from diverse species. We provide a tutorial for AutoBench in this page(^5^55[https://github.com/yangheng95/OmniGenomeBench/tree/master/examples/tutorials/AutoBench_Tutorial.ipynb](https://github.com/yangheng95/OmniGenomeBench/tree/master/examples/tutorials/AutoBench_Tutorial.ipynb)) .
The rationale of adaptive benchmarking in genomics is multifaceted. Firstly, given the vast diversity of genomic data across species, adaptive benchmarking can reveal a GFM’s potential for cross-species applications, a critical factor in comparative genomics and evolutionary studies. Finally, GFMs may exhibit surprising proficiency in tasks they weren’t explicitly trained for, potentially uncovering novel applications or insights into the relationship between different genomic tasks. Secondly, it allows researchers to gauge models’ ability to capture genomic knowledge rather than task-specific patterns, and it stress-tests GFMs under scenarios of underrepresented genomic sequences or tasks. This helps identify limitations or biases in the models that may not be apparent in their primary training domains. Finally, the adaptive benchmarking calls for standardized and universal platforms that accelerate the evolution of GFMs with largely decreased requirements of expertise on benchmarking.

### 2.2 Genomics Software

GFMs have been extensively used for proof-of-concept in-silico experiments, such as secondary structure and translation efficiency predictions, but have yet to be widely acknowledged in in-vivo scenarios. Existing GFM studies generally require significant expertise in both NLP and molecular biology, which hampers the widespread adoption of GFM-guided genome analysis. Therefore, OmniGenBench has been designed as open-source software dedicated to genome modeling, similar to ViennaRNA.

#### Genomic Toolkit

As open-source software for universal genomics, we have curated a range of features, including genome embedding extraction, genome data augmentation, and common genomic tasks such as RNA design. Additionally, users can utilize the user-friendly application interfaces (APIs) to complete genomic downstream tasks without any prior knowledge, such as training and testing a GFM for RNA sequence classification, enabling users to easily integrate OmniGenBench into their workflows. We provide some code examples(^6^66[https://github.com/yangheng95/OmniGenomeBench](https://github.com/yangheng95/OmniGenomeBench)) to demonstrate the toolkit’s usage, e.g., automated benchmarking, showcasing the utmost utility of OmniGenBench in genomic modeling.

#### Online Hub

Inspired by successful practices within the NLP community, we have developed an online hub designed to host and distribute a wide range of resources. This hub provides a centralized platform where users can easily access precompiled benchmark suites and fine-tuned models, enabling efficient testing and experimentation for GFMs. It simplifies the workflow for both novices and experts by offering ready-to-use benchmarks and models that can be downloaded or directly integrated into their research pipelines. This hub is community-driven, encouraging collaboration and innovation by allowing researchers from around the world to contribute their own benchmarks, models, and evaluation metrics.

#### Leaderboard

We have developed an open GFM leaderboard, showcasing detailed task-wise performance for DNA and RNA downstream tasks. Please refer to the current leaderboard in Appendix C. To mitigate benchmarking bias, performance fidelity and comparison fairness are ensured as the evaluation results are generated by AutoBench, eliminating manual interventions. The leaderboard helps researchers identify GFMs’ strengths and weaknesses on a task-by-task basis, aiding them in selecting the most appropriate models for their tasks. The leaderboard also accepts community contributions, such as new benchmark result submissions.

#### Design Principles

To improve the sustainability of OmniGenBench, it has been specifically engineered to simplify the complexities associated with genomic modeling, ensuring research reliability and enhancing prediction efficiency, adhering to the following design principles:

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Utility: The software provides user-friendly interfaces for hundreds of downstream tasks, such as RNA design. It allows the training and inference of genomic tasks to be conducted with minimal coding.
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Simplicity: Comprehensive tutorials are available to assist GFM novices, covering a range of topics from data curation and augmentation to GFM fine-tuning and inference.
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Diversity: A broad spectrum of benchmarks and GFMs is included to support the development of various task types and model architectures, fostering innovation and experimentation in genomic research.
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Extensibility: The software supports the benchmarking of custom tokenizers, GFMs, and metrics without the need to modify existing source code, offering flexibility and adaptability to meet specific research needs.
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Community: An interactive leaderboard showcases detailed performance evaluations of GFMs, promoting transparency and competitive development within the field. Additionally, a containerized evaluation environment is provided to minimize the impact of software variability on the reliability of benchmarking.

## 3 Benchmark Results

In this section, we present a comprehensive evaluation of GFMs using the OmniGenBench. We report the performance of open-source GFMs across four major genomic benchmark suites, i.e., RGB, PGB, GUE and GB, highlighting GFMs’ strengths and weaknesses across diverse genomic tasks and datasets. To mitigate potential class imbalance issues, we adopt the macro F1 score as the metric in classification tasks, replacing accuracy where necessary.

### 3.1 RNA Genomic Benchmark (RGB)

The RGB comprises seven challenging single-nucleotide level RNA modeling tasks, designed to evaluate models’ fine-grained capabilities in understanding RNA sequences, such as predicting RNA structures. These tasks include mRNA degradation rate prediction, single-nucleotide modification detection (SNMD), single-nucleotide modification regression (SNMR), and RNA secondary structure prediction tasks such as Archive2, Stralign, and bpRNA. Additionally, EternaV2 evaluates models on RNA design tasks.

Table 1 presents the performance of various GFMs on the RGB tasks. Overall, OmniGenome achieves the best performance across all tasks, highlighting its exceptional capability in RNA structure modeling. This superior performance can be attributed to OmniGenome’s integration of structural information into its modeling process, which is particularly beneficial for tasks requiring secondary structure prediction.

**Table 1: The performance of OmniGenBench and baseline models on the RGB, with results averaged based on five random seeds. “N.A.” means not available for predictive tasks.**
| Model | mRNA | SNMD | SNMR | Archive2 | Stralign | bpRNA | EternaV2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| RMSE | AUC | F1 | F1 | F1 | F1 | Accuracy |  |
| ViennaRNA | N.A. | N.A. | N.A. | $73.99$ | $74.09$ | $65.03$ | $33$ |
| MXFold2 | N.A. | N.A. | N.A. | $90.09$ | $97.01$ | $64.99$ | N.A. |
| Ufold | N.A. | N.A. | N.A. | $89.78$ | $95.76$ | $78.38$ | N.A. |
| DNABERT2 | $0.8158$ | $49.94$ | $15.86$ | $55.73$ | $64.09$ | $33.77$ | 0 0 |
| HyenaDNA | $0.8056$ | $53.32$ | $39.80$ | $71.18$ | $91.24$ | $57.43$ | 0 0 |
| Caduceus | $0.8026$ | $57.01$ | $39.59$ | $74.37$ | $92.28$ | $59.76$ | 0 0 |
| NT-V2 | $0.7826$ | $50.49$ | $26.01$ | $68.36$ | $83.18$ | $56.95$ | 0 0 |
| Agro-NT | $0.7830$ | $49.99$ | $26.38$ | $62.81$ | $72.54$ | $46.87$ | 0 0 |
| SpliceBERT | $0.7340$ | $58.11$ | $46.44$ | $79.89$ | $93.81$ | $71.59$ | $3$ |
| 3UTRBERT | $0.7772$ | $50.02$ | $24.01$ | $68.62$ | $88.55$ | $57.90$ | 0 0 |
| RNABERT | $0.8087$ | $51.32$ | $29.14$ | $24.66$ | $83.68$ | $47.96$ | 0 0 |
| RNA-MSM | $0.7321$ | $57.86$ | $45.22$ | $68.72$ | $91.15$ | $64.44$ | $2$ |
| RNA-FM | $0.7297$ | $59.02$ | $42.21$ | $82.55$ | $95.07$ | $78.16$ | $4$ |
| OmniGenome | $\mathbf{0.7121}$ | $\mathbf{64.13}$ | $\mathbf{52.44}$ | $\mathbf{91.89}$ | $\mathbf{98.21}$ | $\mathbf{83.18}$ | $84$ |

In particular, OmniGenome significantly outperforms other models on the mRNA degradation rate prediction task, achieving an RMSE of $0.7121$, compared to the second-best RMSE of $0.7297$ by RNA-FM. Similarly, for the SNMD task, OmniGenome achieves an AUC of $64.13$, surpassing the second-best score of $59.02$ by RNA-FM. These results indicate that OmniGenome effectively captures single-nucleotide level variations, which are crucial in RNA function and regulation. Furthermore, in the secondary structure prediction tasks (Archive2, Stralign, bpRNA), OmniGenome demonstrates superior performance, highlighting its proficiency in modeling RNA secondary structures. This can be attributed to OmniGenome’s incorporation of structural context during pretraining, which enhances its ability to understand and predict RNA folding patterns.
One limitation observed is that models not specifically designed for RNA tasks, such as DNABERT2 and HyenaDNA, perform poorly on RNA-specific tasks. This underscores the importance of tailoring GFMs to the specific characteristics of RNA sequences.

In summary, the RGB results highlight the critical role of structural modeling in RNA genomics and demonstrate the effectiveness of OmniGenome in capturing complex RNA features. Future GFMs may benefit from incorporating structural information to enhance performance on RNA-related tasks.

### 3.2 Plant Genomic Benchmark (PGB)

The PGB comprises DNA-based tasks focused on plant biology, and the detailed task descriptions can be found in the Appendix A. The sequences in PGB contain up to $6,000$ bases, presenting challenges for models in handling long genomic sequences.
Table 2 summarizes the performance of various GFMs on the PGB tasks. Resembling the results of RGB, OmniGenome achieves top-tier performance across most tasks, even though it was only trained on RNA. This suggests that OmniGenome generalizes well to DNA-based tasks, likely due to shared sequence motifs and structural similarities between RNA and DNA.

**Table 2: Performance of open-source GFMs on PGB, where the results are re-implemented based on our evaluation protocol. “PolyA” stands for Polyadenylation, “Chrom Acc” for Chromatin Accessibility, “Prom Str” for Promoter Strength, “Term Str” for Terminator Strength, “Splice” for Splice Site, “Gene Exp” for Gene Expression, and “Enh Reg” for Enhancer Region.**
| Model | PolyA | LncRNA | Chrom Acc | Prom Str | Term Str | Splice | Gene Exp | Enhancer |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| F1 | F1 | F1 | RMSE | RMSE | F1 | RMSE | F1 |  |
| DNABERT2 | $41.35$ | $72.55$ | $61.49$ | $0.99$ | $0.24$ | $45.34$ | $14.78$ | $36.40$ |
| HyenaDNA | $83.11$ | $58.21$ | $52.20$ | $0.88$ | $0.26$ | $90.28$ | $14.79$ | $66.17$ |
| Caduceus | $70.89$ | $68.40$ | $64.53$ | $0.91$ | $0.26$ | $78.51$ | $14.72$ | $60.83$ |
| NT-V2 | $71.26$ | $73.08$ | $65.71$ | $0.81$ | $0.27$ | $95.05$ | $14.79$ | $73.89$ |
| Agro-NT | $78.89$ | $67.24$ | $63.27$ | $0.94$ | $0.78$ | $88.45$ | $15.56$ | $62.83$ |
| SpliceBERT | $65.23$ | $71.88$ | $63.62$ | $0.75$ | $0.22$ | $96.45$ | $\mathbf{14.70}$ | $69.71$ |
| 3UTRBERT | $76.48$ | $70.75$ | $63.71$ | $1.04$ | $0.36$ | $94.44$ | $14.87$ | $71.67$ |
| RNA-BERT | $78.54$ | $61.99$ | $48.94$ | $1.81$ | $0.38$ | $94.45$ | $14.89$ | $57.61$ |
| RNA-MSM | $84.25$ | $67.49$ | $53.52$ | $1.28$ | $0.28$ | $95.49$ | $14.87$ | $61.45$ |
| RNA-FM | $84.94$ | $68.75$ | $54.92$ | $0.95$ | $0.27$ | $95.95$ | $14.83$ | $57.14$ |
| OmniGenome | $\mathbf{87.55}$ | $\mathbf{77.96}$ | $\mathbf{67.69}$ | $\mathbf{0.59}$ | $\mathbf{0.18}$ | $\mathbf{98.41}$ | $14.71$ | $\mathbf{79.77}$ |

In the PolyA task, OmniGenome achieves an F1 score of $87.55$, outperforming the second-best model, RNA-FM, which achieves $84.94$. Similarly, for the LncRNA task, OmniGenome attains an F1 score of $77.96$, significantly higher than the second-best score of $73.08$ by NT-V2.
OmniGenome excels in the Splice Site prediction task, achieving an F1 score of $98.41$, surpassing the second-best score of $96.45$ by SpliceBERT. This suggests that OmniGenome effectively captures sequence motifs important for splicing, which is crucial in gene expression regulation.
These results indicate that GFMs incorporating structural context, like OmniGenome, can generalize effectively across different genomic modalities (RNA and DNA) and species (plants). The strong performance of OmniGenome on DNA-based tasks suggests that structural modeling enhances the understanding of genomic sequences beyond the specific type of nucleic acid.
However, it’s also observed that some models specifically designed for DNA tasks, such as NT-V2 and SpliceBERT, perform competitively on certain tasks. This underscores the importance of task-specific pretraining and the potential benefits of integrating both sequence and structural information in GFMs.

In summary, the PGB results highlight the potential for cross-modal generalization in GFMs and the value of incorporating structural context to enhance performance on diverse genomic tasks.

### 3.3 Genomic Understanding Evaluation (GUE)

The GUE is a multi-species benchmark like RGB and PGB, but focuses on the non-plant genomes. The sequences in GUE range in length and complexity, providing a robust assessment of GFMs’ abilities to generalize across species and genomic tasks.
Table 3 presents the performance of various GFMs on the GUE tasks. While OmniGenome does not achieve the highest performance on all tasks, it consistently delivers competitive results, demonstrating strong cross-species generalization despite being primarily trained on RNA data.

**Table 3: Performance of open-source GFMs on GUE, where the results are re-implemented based on our evaluation protocol. The performance for each task is the average macro F1 score in all sub-datasets.**
| Model | Yeast EMP | Mouse TF-M | Virus CVC | Human TF-H | Human PD | Human CPD | Human SSP |
| --- | --- | --- | --- | --- | --- | --- | --- |
| F1 | F1 | F1 | F1 | F1 | F1 | F1 |  |
| DNABERT-2 | $75.85$ | $\mathbf{86.23}$ | $58.23$ | $81.80$ | $90.17$ | $82.57$ | $85.21$ |
| HyenaDNA | $73.08$ | $73.44$ | $27.59$ | $77.62$ | $91.19$ | $84.31$ | $83.34$ |
| Caduceus | $73.49$ | $78.18$ | $27.49$ | $79.56$ | $89.13$ | $85.09$ | $81.82$ |
| NT-V2 | $74.93$ | $78.10$ | $32.71$ | $79.12$ | $90.87$ | $84.70$ | $84.13$ |
| SpliceBERT | $77.66$ | $84.97$ | $47.17$ | $\mathbf{82.77}$ | $\mathbf{92.24}$ | $83.96$ | $\mathbf{93.81}$ |
| 3UTRBERT | $71.89$ | $71.46$ | $34.84$ | $74.85$ | $82.37$ | $\mathbf{90.51}$ | $81.95$ |
| RNA-BERT | $60.14$ | $59.83$ | $21.08$ | $67.48$ | $79.87$ | $76.25$ | $44.75$ |
| RNA-MSM | $64.99$ | $79.15$ | $51.81$ | $78.72$ | $91.28$ | $85.42$ | $84.24$ |
| RNA-FM | $74.41$ | $78.24$ | $52.22$ | $79.27$ | $92.18$ | $86.05$ | $84.76$ |
| OmniGenome | $\mathbf{78.51}$ | $84.72$ | $\mathbf{64.41}$ | $81.73$ | $90.04$ | $85.22$ | $90.39$ |

In the Yeast EMP task, OmniGenome achieves the highest F1 score of $78.51$, slightly outperforming SpliceBERT of $77.66$. For the Virus CVC task, OmniGenome also achieves the best performance with an F1 score of $74.72$, indicating its strong ability to model viral genomic sequences.
However, for tasks like Human TF-H and Human SSP, models like SpliceBERT and DNABERT2 achieve higher scores. This suggests that these models may be better optimized for human genomic sequences or specific tasks like splice site prediction.
The results on GUE highlight the challenges in developing GFMs that generalize across different species and genomic tasks. While OmniGenome demonstrates strong cross-species performance, there is variability depending on the specific task and species.
These findings suggest that combining the strengths of different GFMs or developing ensemble methods could be a fruitful direction for future research. Additionally, incorporating more diverse training data and task-specific fine-tuning may enhance the performance of GFMs across a broader range of tasks.

### 3.4 Genomic Benchmarks (GB)

GB is a collection of DNA genome datasets aimed at evaluating the performance of models on sequence classification tasks involving regulatory elements such as promoters, enhancers, and open chromatin regions across different species including humans, mice, and roundworms.
Table 4 shows the performance of various GFMs. The tasks are denoted by their species and regulatory elements, and the acronyms are explained in Appendix A.4.

**Table 4: Performance of open-source GFMs on GB, where the results are re-implemented based on our evaluation protocol.. The performance (macro F1) for each task is the average macro F1 score across all sub-datasets.**
| Model | DEM | DOW | DRE | DME | HCE | HEE | HRE | HNP | HOR |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| F1 | F1 | F1 | F1 | F1 | F1 | F1 | F1 | F1 |  |
| DNABERT-2 | $92.67$ | $95.17$ | $43.77$ | $77.21$ | $\mathbf{75.58}$ | $80.66$ | $78.14$ | $85.80$ | $68.03$ |
| HyenaDNA | $88.21$ | $94.13$ | $70.11$ | $76.44$ | $70.38$ | $79.58$ | $96.33$ | $85.99$ | $67.03$ |
| Caduceus | $92.13$ | $94.74$ | $72.03$ | $75.61$ | $70.20$ | $76.47$ | $79.16$ | $84.36$ | $63.17$ |
| NT-V2 | $91.66$ | $94.32$ | $\mathbf{78.20}$ | $\mathbf{81.72}$ | $71.98$ | $79.85$ | $93.30$ | $85.30$ | $68.53$ |
| SpliceBERT | $\mathbf{94.72}$ | $\mathbf{96.42}$ | $72.29$ | $74.70$ | $73.50$ | $79.60$ | $95.23$ | $\mathbf{89.57}$ | $68.89$ |
| 3UTRBERT | $89.50$ | $90.22$ | $74.35$ | $80.14$ | $70.23$ | $76.33$ | $\mathbf{98.47}$ | $82.49$ | $66.78$ |
| RNA-BERT | $76.56$ | $62.17$ | $50.11$ | $60.79$ | $66.69$ | $63.29$ | $46.57$ | $73.80$ | $56.59$ |
| RNA-MSM | $79.38$ | $93.71$ | $54.13$ | $75.90$ | $69.79$ | $78.07$ | $94.87$ | $84.28$ | $63.93$ |
| RNA-FM | $91.53$ | $95.49$ | $74.77$ | $79.74$ | $71.62$ | $80.03$ | $95.72$ | $87.14$ | $\mathbf{69.38}$ |
| OmniGenBench | $94.16$ | $93.49$ | $77.17$ | $80.34$ | $73.51$ | $\mathbf{82.23}$ | $95.66$ | $87.87$ | $68.97$ |

In the DEM and DOW tasks, SpliceBERT achieves the highest F1 scores, with OmniGenome closely following in DEM and RNA-FM in DOW. For the DRE task, NT-V2 achieves the best performance with an F1 score of $78.20$, with OmniGenome performing closely.
In the HEE task, OmniGenome attains the highest F1 score of $82.23$, surpassing the second-best score of $80.03$ by RNA-FM. This indicates OmniGenome’s effectiveness in modeling human enhancer regions.
From a global perspective, these results demonstrate that while different GFMs excel in specific tasks, OmniGenome consistently performs well across various genomic benchmarks, highlighting its versatility. The performance variations across models suggest that task-specific features and training data significantly impact model efficacy.
A limitation observed is that GFMs primarily trained on RNA data, like RNA-BERT and RNA-MSM, lost on DNA-based tasks. This underscores the importance of training data relevance and the potential need for multimodal pretraining strategies.

In conclusion, the GB results emphasize the need for GFMs that can generalize across different genomic tasks and species. Integrating structural information, as done in OmniGenome, appears to enhance model performance on complex genomic tasks.

### 3.5 Overall Discussion

Our comprehensive evaluation across four genomic benchmarks reveals that OmniGenome consistently achieves top-tier performance, particularly excelling in tasks that involve structural modeling of RNA sequences. The integration of structural information in OmniGenome enhances its ability to capture complex sequence features, which is advantageous across diverse genomic tasks.
While OmniGenome demonstrates strong performance even on DNA-based tasks, models specifically tailored to certain tasks or species, such as SpliceBERT and DNABERT2, sometimes outperform OmniGenome in those specific contexts. This suggests that task-specific or species-specific pretraining can provide benefits, and there is potential for combining the strengths of different models.

The absence of results for certain models on some benchmarks (e.g., RNA-BERT, RNA-MSM, and RNA-FM on GUE) highlights the challenges in benchmarking GFMs across diverse datasets. Differences in model architectures, pretraining data, and tokenization strategies can impact a model’s applicability to specific tasks. Future work should focus on developing unified evaluation protocols and improving the interoperability of GFMs. An important consideration is the need for detailed descriptions of the models evaluated, including their architectures, pretraining data, and key features. This information is crucial for understanding the factors contributing to their performance and for reproducing results. We acknowledge that such details are essential and included in Appendix A.

Overall, our comprehensive benchmarking highlights the importance of integrating structural information into GFMs and suggests that models capable of capturing both sequence and structural features offer improved performance across a range of genomic tasks. This work provides valuable insights for the development of next-generation GFMs and underscores the need for continued efforts in benchmarking to drive advancements in genomic modeling.

## 4 Related Works

The GFM-oriented platforms, such as benchmarking and application toolkits, have been investigated but have yet to be revolutionised. For example, there are several benchmarking studies such as RNABench [], GenBench [], BEACON [], and DEGB [], and the application software like Kipoi(^7^77[https://kipoi.org](https://kipoi.org)) [].
Overall, these benchmarking tools do not prioritise the standardisation and automation of GFM benchmarking and generally focus on specific scenarios such as DNA benchmarking. On the other hand, there is no GFM-dedicated software which leverages the unprecedented capability of GFMs in the wide applications of in-silico genomics. Please find more details of the related works in Appendix 4.

### 4.1 Benchmark

Recognizing the critical role of benchmarking in genomic modeling, several tools have been developed to evaluate genomic models. Among these are RNABench [], GenBench [], BEACON [], and DEGB [].

RNABench focuses on a set of benchmarks, such as RNA secondary structure prediction, and lacks support for evaluating the latest pre-trained models. GenBench is a modular DNA benchmarking framework that provides a DNA evaluation solution but does not extend to RNA benchmarking, and it may not prioritize user-friendliness.
BEACON is a recent benchmarking tool aimed at RNA foundational models, offering some RNA evaluation datasets. However, it may lack benchmarking scalability and the complexity of its environment setup poses challenges for novices. DEGB serves as an evaluation benchmark for genomic embeddings, supporting both amino acids and nucleic acids. Its main limitation lies in the small scale of its evaluation benchmarks, and it does not support downstream applications of GFMs.
Classic genomic modeling tools like Kipoi [] have been developed to standardize access to trained models for genomic sequence analysis, offering a repository of models. However, Kipoi focuses on providing access to classic models, not GFMs, rather than benchmarking comprehensively.

There are some protein benchmarking tools, such as ProteinGym [], Flip [] and Peer [], to name a few.
ProteinGym is a large-scale benchmarking tool focused on protein fitness prediction and design. It provides over $250$ deep mutational scanning assays, offering a standardized dataset to evaluate machine learning models across millions of mutated protein sequences. ProteinGym is designed to assess both zero-shot and supervised models, particularly in predicting the effects of mutations and aiding protein engineering for applications like genetic disease, agriculture, and healthcare.
Flip provides a benchmark for predicting the protein sequence-function relationship, a critical aspect of protein engineering. It includes data for tasks such as adeno-associated virus stability, protein domain stability, and thermostability from multiple protein families. Flip is designed to evaluate model generalization under various conditions, such as low-resource or extrapolative scenarios. Its datasets are curated to assess the capacity of models to predict functional properties of proteins in real-world protein engineering tasks.
Peer is a comprehensive multi-task benchmark that offers $17$ tasks across five categories, including protein function prediction, localization, structure, and interaction predictions. It evaluates a wide range of machine learning methods, from traditional approaches to large pre-trained protein language models. Peers’ broad scope helps assess model performance in different protein-related tasks, contributing to advancements in protein sequence understanding and engineering.

Existing tools do not adequately address the challenges of comprehensive, large-scale evaluation of RNA and DNA GFMs. They often lack support for downstream applications and do not facilitate the ease of use or scalability necessary to catalyses the democratization and revolution of GFM research. This gap has motivated the development of a new benchmarking tool designed to cover a broad spectrum of foundational DNA and RNA models and provide an extensive benchmarking suite.

### 4.2 Genomic Foundation Models

In recent years, the modeling of biological sequences, including DNA, RNA, and proteins, has garnered significant attention. Protein modeling, exemplified by works such as AlphaFold [, , ] and ESM [], has advanced considerably over the past years, outpacing developments in DNA and RNA modeling.

In the domain of genomic sequence modeling, early efforts focused on adapting natural language processing architectures to handle genomic data. For instance, DNABERT [] repurposed the BERT [] architecture for genomic sequences, demonstrating preliminary success on in-silico genomic tasks. Building upon this, DNABERT2 [] introduced improvements by replacing k-mer tokenization with byte-pair encoding (BPE) tokenization, enhancing model performance across multiple species.

To explore the capabilities of large-scale foundation models (FMs), the Nucleotide Transformers V2 [], AgroNT [], and SegmentNT [] scaled models to billions of parameters. These models achieved promising results in understanding DNA genomes, with parameter counts reaching up to $2.5$ billion and $1$ billion, respectively. AgroNT, pre-trained on multi-species edible plant DNA sequences, however, did not transfer effectively to RNA sequence modeling in subsequent experiments. Addressing the challenge posed by the considerable length of genomic sequences, recent works have emphasized long-range sequence modeling and introduced auto-regressive FMs, such as HyenaDNA [] and Evo [].

In the context of RNA genomic modeling, several preliminary studies have emerged, including scBERT [], RNABERT [], RNA-FM [], RNA-MSM [], and RNAErnie []. These models, however, are typically trained on limited-scale databases due to the scarcity and expense of obtaining RNA sequences. Some FMs focus on specific RNA types, such as coding sequences (CDS)[], 5’ untranslated regions (5’UTR)[], 3’ untranslated regions (3’UTR)[], or precursor mRNA sequences[], which constrains their ability to capture the full diversity of RNA sequences. Uni-RNA [] has been reported to achieve strong performance owing to its large-scale model and extensive database. However, it is not open-sourced, precluding direct comparison in experiments.
ChatNT [] is a multimodal conversational agent designed to assist with tasks involving DNA, RNA, and protein sequences. It can handle diverse genomic and proteomic tasks, such as predicting sequence structures, simulating biological processes, or interacting with foundational models. ChatNT integrates advanced AI models to facilitate research in genomic data processing, enhancing accessibility and scalability in tasks across multiple biological modalities.

## 5 Conclusion

We propose OmniGenBench in this work to address the challenges of GFMs in benchmarking and application. OmniGenBench tackles the crux lies in the evaluation of modeling the complex ’genomic language’ of DNA and RNA by integrating four large-scale benchmarks and $42$ million genome sequences from $75$ datasets, to support the evaluation of $10+$ open-source GFMs. Moreover, OmniGenBench is an open-source software that simplifies the pipelines of genomic modeling, ensuring in-silico genomic research reliability and efficiency, and promoting community collaboration through a dedicated leaderboard.

## Acknowledgment

This work was supported in part by the UKRI Future Leaders Fellowship under Grant MR/S017062/1 and MR/X011135/1; in part by NSFC under Grant 62376056 and 62076056; in part by the Royal Society under Grant IES/R2/212077; in part by the EPSRC under Grant 2404317; in part by the Kan Tong Po Fellowship (KTP\R1\231017); and in part by the Amazon Research Award and Alan Turing Fellowship.

## Appendix A Benchmark Details

### A.1 RNA Genomic Benchmark

The detailed task descriptions for each nucleic acid and species, including the number of examples, classes, evaluation metric, and sequence length, are outlined in Table 5. Each task is carefully curated to reflect the complexity and variety inherent in genomic data, providing a robust framework for assessing the nuanced capabilities of state-of-the-art RNA FMs. RGB contains $6$ SN-level tasks that are curated or collected from published articles. The purpose of RGB is to benchmark genomic FMs in challenging SN-level modeling tasks such as the detection and repair of SN mutations, mRNA sequence degradation rates, and RNA secondary structure prediction. Due to the lack of a plant RNA benchmark dataset, RGB includes the modeling of RNA sequences from a variety of species, e.g., plant and human. The sequence length in RGB ranges from $107$ to $512$, which is sufficient for most RNA understanding tasks. In summary, these multi-species and SN-level tasks in RGB serve as the first comprehensive benchmark utilized to assess the RNA sequence modeling capabilities of OmniGenBench and its baseline models. The brief introduction of the datasets in RGB is as follows:

- •
Single-Nucleotide Mutation Detection (SNMD): We developed a plant RNA dataset synthesizing the single-nucleotide mutations. Focused on identifying potential single nucleotide changes, this task is essential for detecting mutations linked to genetic disorders. The SNMD dataset introduces up to $10$ random mutations in the original sequences, regardless of variation ratios. Cross-entropy is utilized as the loss function for this binary token classification task.
- •
Single-Nucleotide Mutation Repair (SNMR): This task challenges the model to suggest corrective actions at the single nucleotide level, aiding in gene therapy approaches. The SNMR dataset mirrors the SNMD dataset, with cross-entropy as the loss function, indicating a token 4-way (i.e., A, U, C, G) classification task.
- •
mRNA Degrade Rate Prediction (mRNA): Estimating the decay rate of nucleotides in mRNA sequences, this task is vital for deciphering gene expression and regulation. The dataset originates from the Kaggle COVID-19 vaccine design competition(^8^88[https://www.kaggle.com/competitions/stanford-covid-vaccine](https://www.kaggle.com/competitions/stanford-covid-vaccine)), focusing solely on sequence-based degradation rate prediction and excluding RNA structures. It’s a token regression task using MSE as the loss function, with the dataset re-split into training, validation, and testing sets for evaluation.
- •
RNA Secondary Structure Prediction (bpRNA & Archive2 & RNAStralign): Aiming to predict RNA folding into secondary structures, this task is fundamental to RNA functionality and interactions. We evaluated OmniGenBench on four datasets, bpRNA [] (TR0, VL0, TS0 sets), ArchiveII [], RNAStralign [] and Rfam []. Following existing works, we have excluded sequences over $512$ bases and complex structures, simplifying to three symbols: ‘(’, ‘.’, ‘)’Ṙesults may not directly compare with other studies due to these modifications. Cross-entropy serves as the loss function.

**Table 5: The brief statistics of subtasks in the RGB. These benchmark datasets are held out or not included in the pretraining database. The numbers of examples in training, validation and testing sets are separated by “/”. ^∗ indicates the datasets are used for zero-shot performance evaluation only.**
| Task | Task Type | # of examples | # of classes | Metric | Sequence length | Source |
| --- | --- | --- | --- | --- | --- | --- |
| SNMD | Token classification | $8,000/1,000/1,000$ | $2$ | AUC | $200$ | This work |
| SNMR | Token classification | $8,000/1,000/1,000$ | $4$ | macro F1 | $200$ | This work |
| mRNA | Token regression | $1,735/193/192$ | — | RMSE | $107$ | [Kaggle](https://www.kaggle.com/competitions/stanford-covid-vaccine) |
| bpRNA | Token classification | $10,814/1,300/1,305$ | $3$ | macro F1 | $\leq 512$ | [] |
| AchiveII | Token classification | $2278/285/285$ | $3$ | macro F1 | $\leq 500$ | [] |
| RNAStrAlign | Token classification | $17483/2186/2185$ | $3$ | macro F1 | $\leq 500$ | [] |

Please find the appendix for the input and output examples of each subtask in RGB. The detailed task descriptions for each nucleic acid and species, including the number of examples, classes, evaluation metric, and sequence length, are outlined in Table 5. Each task is carefully curated to reflect the complexity and variety inherent in genomic data, providing a robust framework for assessing the nuanced capabilities of state-of-the-art RNA FMs.

Table 6 show the virtual examples of different datasets in RGB. Please refer to our supplementary materials to find the datasets for more details.

**Table 6: The virtual input and output examples in the four benchmarks. The “$\dots$” represents the sequences that are omitted for better presentation and the red color indicates the wrong prediction in classification tasks. In the mRNA dataset, all single nucleotides have three values to predict. Note that “T” and “U” can be regarded as the same symbol in RNA sequences and depend on different datasets.**
| Genome Type | Dataset | Column | Examples |
| --- | --- | --- | --- |
| RNA | SNMD | Input Sequence | G A G T A $\dots$ T T G A G |
| True Label | 0  0  1  0  0 $\dots$ 0  0  1  0  0 |  |  |
| Prediction | 0  0  0  0  0 $\dots$ 0  0  1  0  0 |  |  |
| SNMR | Input Sequence | T A C G A  $\dots$ C T G A T |  |
| True Label | T A C A A $\dots$ G T A A T |  |  |
| Prediction | T A C A A $\dots$ C T G A T |  |  |
| mRNA | Input Sequence | G G $\dots$ A C |  |
| True Label | [0.1,0.3,0.2] [0.8,0.4,0.1]$\dots$[0.9,0.4,0.3] [0.5,0.2,0.6] |  |  |
| Prediction | [0.1,0.3,0.2] [0.8,0.4,0.1]$\dots$[0.9,0.4,0.3] [0.5,0.2,0.6] |  |  |
| bpRNA | Input Sequence | G G C G A $\dots$ C U U U U |  |
| True Label | (   (   (   $\cdot$   $\cdot$ $\dots$ $\cdot$   $\cdot$   )   )   ) |  |  |
| Prediction | (   (   (   (   $\cdot$ $\dots$ $\cdot$   )   )   )   ) |  |  |
| DNA | Classification | Input Sequence | A T C G A $\dots$ T A G |
| True Label | 1 |  |  |
| Prediction | 0 |  |  |
| Regression | Input Sequence | G C C A T $\dots$ G C T |  |
| True Label | 2.56 |  |  |
| Prediction | 2.45 |  |  |
| Chrom Acc (Multi-label) | Input Sequence | A T C G $\dots$ C T G |  |
| True Label | [1, 0, 1, 1, 0, 1, 1, 0, 1] |  |  |
| Prediction | [1, 1, 1, 1, 0, 1, 1, 0, 1] |  |  |

### A.2 Plant Genomic Benchmark

PGB [] provides a comprehensive suite of datasets designed to evaluate and improve the predictive capabilities of GFMs in plant biology. This benchmark, as shown in Table 7, encompasses a range of critical genomic tasks, including binary classification, single and multi-variable regression, and multi-label classification, addressing various aspects of plant genomics such as RNA processing, gene expression, and chromatin accessibility. By integrating diverse genomic tasks, the PGB aims to facilitate advanced research and development in plant genomics, offering a robust platform for the assessment and enhancement of model performance across different plant species. To obtain a detailed description of PGB, please refer to Agro-NT [].

**Table 7: The genomic tasks in the Plant Genomic Benchmark. This table briefly enumerates each task by name, the number of datasets available, the type of classification or regression analysis required, the range of sequence lengths, and the total number of samples in each dataset. Please find the dataset details of PGB in Agro-NT.**
| Task | # of datasets | Task Type | Total # of examples | # of classes | Metric | Sequence length |
| --- | --- | --- | --- | --- | --- | --- |
| Polyadenylation | $6$ | Sequence classification | $738,918$ | $2$ | macro F1 | $400$ |
| Splice site | $2$ | Sequence classification | $4,920,835$ | $2$ | macro F1 | $398$ |
| LncRNA | $2$ | Sequence classification | $58,062$ | $6$ | macro F1 | $101-6000$ |
| Promoter strength | $2$ | Sequence regression | $147,966$ | — | RMSE | $170$ |
| Terminator strength | $2$ | Sequence regression | $106,818$ | — | RMSE | $170$ |
| Chromatin accessibility | $7$ | Multi-label classification | $5,149,696$ | $9-19$ | macro F1 | $1,000$ |
| Gene expression | $6$ | Multi-variable regression | $206,358$ | — | RMSE | $6,000$ |
| Enhancer region | $1$ | Sequence classification | $18,893$ | $2$ | macro F1 | $1,000$ |

### A.3 Genomic Understanding Evaluation

GUE [] serves as a DNA genomic benchmark, encompassing 36 datasets across nine crucial genome analysis tasks applicable to a variety of species. Similar to PGB and GB, it is used for evaluating the generalizability of OmniGenBench on DNA genome benchmarking. To thoroughly assess the capabilities of genome foundation models across sequences of varying lengths, tasks have been chosen with input lengths spanning from $70$ to $10,000$. The brief statistics for each dataset included in the GUE benchmark are displayed in Table 8, and the task descriptions are available in Zhang et al. []. Due to resource limitations, we do not include large-scale FMs in this benchmark, e.g., Agro-NT. Besides, we run the evaluation on a subset of GUE, where for each task we randomly select at most 10k samples from the original splits, e.g., training, testing and validation (if any) sets.

**Table 8: Statistics of tasks in the GUE, these details can be found in Section B.2. from Zhang et al. [].**
| Task | Metric | Datasets | Training | Validation | Testing |
| --- | --- | --- | --- | --- | --- |
| Core Promoter Detection | macro F1 | tata | $4,904$ | $613$ | $613$ |
| notata | $42,452$ | $5,307$ | $5,307$ |  |  |
| all | $47,356$ | $5,920$ | $5,920$ |  |  |
| Promoter Detection | macro F1 | tata | $4,904$ | $613$ | $613$ |
| notata | $42,452$ | $5,307$ | $5,307$ |  |  |
| all | $47,356$ | $5,920$ | $5,920$ |  |  |
| Transcription Factor Prediction (Human) | macro F1 | wgEncodeEH000552 | $32,378$ | $1,000$ | $1,000$ |
| wgEncodeEH000606 | $30,672$ | $1,000$ | $1,000$ |  |  |
| wgEncodeEH001546 | $19,000$ | $1,000$ | $1,000$ |  |  |
| wgEncodeEH001776 | $27,497$ | $1,000$ | $1,000$ |  |  |
| wgEncodeEH002829 | $19,000$ | $1,000$ | $1,000$ |  |  |
| Splice Site Prediction | macro F1 | reconstructed | $36,496$ | $4,562$ | $4,562$ |
| Transcription Factor Prediction (Mouse) | macro F1 | Ch12Nrf2\iggrab | $6,478$ | $810$ | $810$ |
| Ch12Zrf384hpa004051\iggrab | $5,395$ | $674$ | $674$ |  |  |
| MelJun\iggrab | $2,620$ | $328$ | $328$ |  |  |
| MelMafkDm2p5dStd | $1,904$ | $239$ | $239$ |  |  |
| MelNelf\iggrab | $15,064$ | $1,883$ | $1,883$ |  |  |
| Epigenetic Marks Prediction | macro F1 | H3 | $11,971$ | $1,497$ | $1,497$ |
| H3K14ac | $26,438$ | $3,305$ | $3,305$ |  |  |
| H3K36me3 | $29,704$ | $3,488$ | $3,488$ |  |  |
| H3K4me1 | $25,341$ | $3,168$ | $3,168$ |  |  |
| H3K4me2 | $24,545$ | $3,069$ | $3,069$ |  |  |
| H3K4me3 | $29,439$ | $3,680$ | $3,680$ |  |  |
| H3K79me3 | $23,069$ | $2,884$ | $2,884$ |  |  |
| H3K9ac | $22,224$ | $2,779$ | $2,779$ |  |  |
| H4 | $11,679$ | $1,461$ | $1,461$ |  |  |
| H4ac | $27,275$ | $3,410$ | $3,410$ |  |  |
| Covid Variant Classification | macro F1 | Covid | $77,669$ | $7,000$ | $7,000$ |
| Enhancer Promoter Interaction | macro F1 | GM12878 | $10,000$ | $2,000$ | $2,000$ |
| HeLa-S3 | $10,000$ | $2,000$ | $2,000$ |  |  |
| HUVEC | $10,000$ | $2,000$ | $2,000$ |  |  |
| IMR90 | $10,000$ | $2,000$ | $2,000$ |  |  |
| K562 | $10,000$ | $2,000$ | $2,000$ |  |  |
| NHEK | $10,000$ | $2,000$ | $2,000$ |  |  |
| Species Classification | macro F1 | fungi | $8,000$ | $1,000$ | $1,000$ |
| virus | $4,000$ | $500$ | $500$ |  |  |

### A.4 Genomic Benchmarks

GB is also a DNA-oriented FM benchmark suite, which can be used for generalizability evaluation of OmniGenome. It contains a well-curated collection of datasets designed for the classification of genomic sequences, focusing on regulatory elements across multiple model organisms. This collection facilitates robust comparative analysis and development of genomic FMs. The task names in the original repository are complex, we abbreviate the names as follows:

- •
DEM corresponds to ”Demo Coding vs Intergenomic Seqs”
- •
DOW is for ”Demo Human or Worm”
- •
DRE represents ”Drosophila Enhancers Stark”
- •
HCE is short for ”Human Enhancers Cohn”
- •
HEE denotes ”Human Enhancers Ensembl”
- •
HRE abbreviates ”Human Ensembl Regulatory”
- •
HNP shortens ”Human Nontata Promoters”
- •
HOR is an abbreviation for ”Human Ocr Ensembl”
- •
DME simplifies ”Dummy Mouse Enhancers Ensembl”

The brief statistics for each dataset included in the GUE benchmark are displayed in Table 8.
Similar to GUE, we run the evaluation on a subset of GB, where for each task we randomly select at most 10k samples from the original splits, e.g., training, testing and validation (if any) sets.

**Table 9: The brief statistics of datasets reported in the genomic benchmark [].**
| Task | # of Sequences | # of Classes | Class Ratio | Median Length | Standard Deviation |
| --- | --- | --- | --- | --- | --- |
| DME | $1,210$ | $2$ | $1.0$ | $2,381$ | $984.4$ |
| DEM | $100,000$ | $2$ | $1.0$ | $200$ | $0.0$ |
| DOW | $100,000$ | $2$ | $1.0$ | $200$ | $0.0$ |
| DRE | $6,914$ | $2$ | $1.0$ | $2,142$ | $285.5$ |
| HCE | $27,791$ | $2$ | $1.0$ | $500$ | $0.0$ |
| HEE | $154,842$ | $2$ | $1.0$ | $269$ | $122.6$ |
| HRE | $289,061$ | $3$ | $1.2$ | $401$ | $184.3$ |
| HNP | $36,131$ | $2$ | $1.2$ | $251$ | $0.0$ |
| HOR | $174,456$ | $2$ | $1.0$ | $315$ | $108.1$ |

## Appendix B Data Filtering in Benchmarking

The pertaining involves RNA sequences and structures prediction, we take the data and annotation leakage problem seriously.

- •
To avoid structure annotation leakage of downstream benchmarks, the secondary structure predictors for all FMs were randomly initialized for fair comparisons, which means the pre-trained structure predictor of OmniGenBench was not used in benchmarks, except for zero-shot SSP experiments. Please find the source codes for details.
- •
To reduce sequence leakage caused by evolutionary conservative sequences across multiple species, we use the ch-hit-est tool to calculate the sequence similarity between sequences from the OneKP database and downstream tasks. We adopt the similarity threshold of $80\%$ for ch-hit-est  [] to eliminate sequences whose homogeneous sequences appeared in the OneKP database. Subsequently, we exploit the blastn [] tool to query potentially leaked sequences in downstream benchmark datasets and further alleviate the data leakage problem. The e-value has been set to $1$ for rigorous sequence filtering.

### B.1 Experiment Settings

In this experiment, we carefully selected a set of key hyperparameters to optimize model performance. Below are the main hyperparameter settings along with detailed explanations:

- •
Dropout: To prevent the model from overfitting during training, we set the Dropout value to 0, meaning that no random neuron dropout is applied during training. This choice was made based on our consideration of model stability and generalization ability.
- •
Learning Rate: We set the learning rate to 2e-5, which is a relatively small value to ensure stable convergence, especially in complex training tasks. A smaller learning rate helps to avoid drastic fluctuations during the training process, leading to more precise optimization.
- •
Weight Decay: We applied a weight decay of 0.01 to control model complexity and prevent overfitting. Weight decay is a regularization technique that effectively constrains the growth of model parameters, maintaining the model’s generalization capability.
- •
Adam Optimizer: We used the Adam optimizer with its parameters set to $\beta_{1}=0.9$ and $\beta_{2}=0.999$. The Adam optimizer combines the benefits of momentum and adaptive learning rates, accelerating convergence and adapting to different gradient changes, thereby improving the efficiency and effectiveness of model training.
- •
Learning Rate Scheduler: We opted for a linear decay learning rate scheduler, allowing the learning rate to gradually decrease during training. This strategy helps the model make smaller adjustments as it approaches the optimal solution, ensuring a better convergence outcome.
- •
Batch Size: The batch size was set to 8. This relatively small batch size helps to efficiently train the model within limited memory resources, particularly when handling large-scale data, enabling a balance between model performance and computational resource usage.
- •
# of Epochs: We set the number of training epochs to 20. This setting ensures that the model can fully learn the features within the data while avoiding the negative effects of overtraining.
- •
Early Stopping: We implemented an early stopping mechanism, terminating the training early if the validation performance does not improve for 5 consecutive epochs. This mechanism effectively prevents model overfitting and saves training time.

It is important to note that for different tasks, some hyperparameter settings may be adjusted. To obtain accurate experimental results, please refer to the detailed parameter configurations in the compiled dataset specific to each task.

### B.2 Development Environment

The benchmark experiments based on OmniGenBench were conducted on a dedicated Linux computation node, equipped with $2$ Nvidia RTX $4090$ GPUs. For distributed model training, we employed version $4.44.0$ of the Transformers library alongside version $0.28.3$ of the Accelerate library. Our implementation framework of choice for OmniGenBench was PyTorch, specifically version $2.1.0$. The ViennaRNA version is $2.6.4$ in our experiments. While some existing code was adapted for the modules within OmniGenBench, the majority of the codebase, such as genomic sequences preprocessing, model pre-training, objective functions, and experiments, was meticulously crafted from scratch.

### B.3 Evaluation Baselines

To comprehensively evaluate the performance of the existing GFMs across the integrated benchmarks, i.e., RGB, PGB, GUE and GB, we have obtained the results of existing GFMs based on OmniGenBench.

**Table 10: The brief statistics of RNA and DNA FM baselines. Please note that the pertaining data scales cannot be directly compared because the measurements are different in various publications. The detailed introduction of these FMs can be found in original publications.**
| Model | Tokenization | # of Params. | Pre-training Data Scale | Pre-training Data Source | Species | Sequence Type |
| --- | --- | --- | --- | --- | --- | --- |
| DNABERT-2 | BPE | $117$M | $32.49$B Tokens | The 1000 Genomes Project | Human + $135$ Species | DNA |
| NT-V2-$100$M | k-mers | $96$M | $300$B Tokens | The 1000 Genomes Project, etc. | Human + $850$ Species | DNA |
| HyenaDNA-Large | SNT | $47$M | $3.2$B Tokens | Genome Reference Consortium | Human | DNA |
| Caduceus | SNT | $1.9$M | $35$B Tokens | Genome Reference Consortium | Human | DNA |
| Agro-NT-$1$B | k-mers | $985$M | $472.5$B Tokens | Ensembl Plants Database | $48$ Edible Plants | DNA |
| SpliceBERT | SNT | $19$M | $2$M Sequences | UCSC Genome Browser | Multi-Vertebrates | precursor-mRNA |
| RNA-BERT | SNT | $0.5$M | $4,069$ RNA Families | The RNA Families Database | Multi-Species | ncRNA |
| RNA-MSM | SNT | $96$M | $4,069$ RNA Families | The RNA Families Database | Multi-Species | ncRNA |
| RNA-FM | SNT | $96$M | $23$M Sequences | RNAcentral Database | Multi-Species | ncRNA |
| 3UTRBERT | k-mers | $86$M | $20,362$ Sequences | The GENCODE Project | Human | mRNA $3$’UTR |
| OmniGenome | SNT | $186$M | $54.2$B Tokens | The OneKP Initiative | $1124$ Plant Species | mRNA, CDS, UTR |

Please note that it is assumed that the structure annotation from ViennaRNA is always available for structure-contextualized modeling to enhance OmniGenome. In SSP tasks, we can also use the ViennaRNA’s structure annotations as contexts to improve downstream SSP performance.
Please refer to Appendix B.3 for brief introductions of these FMs.

We can compare OmniGenBench with the following RNA and DNA FMs shown in Table 10 as baselines to help evaluate the performance of OmniGenBench.
We are aware that some FMs are also developed for RNA, such as Uni-RNA [], 5UTR-LM [], etc. However, we cannot compare OmniGenBench with them because their source codes are very hard to work with in our efforts or are not publicly available.
To help understand the baseline FMs, we briefly summaries the FM in the following sections. Please find the method and experiment details of these FMs in the original publications.

- •
ViennaRNA []. ViennaRNA is a comprehensive genomic analysis tool that includes a diverse set of interfaces, such as RNAFold(^9^99[https://www.tbi.univie.ac.at/RNA/RNAfold.1.html](https://www.tbi.univie.ac.at/RNA/RNAfold.1.html)) and RNAInverse(^10^1010[https://www.tbi.univie.ac.at/RNA/RNAinverse.1.html](https://www.tbi.univie.ac.at/RNA/RNAinverse.1.html)) design. ViennaRNA serves as the baseline for RNA structure prediction and RNA design in our experiments.
- •
DNABERT2 []. DNABERT2 is one of the latest DNA FMs which improves the performance of DNABERT. The main modification of DNABERT2 is the tokenization method, which was changed to BPE from k-mers.
- •
HyenaDNA []. HyenaDNA is an autoregressive FM optimized for long-range genome data processing. HyenaDNA is based on the Hyena convolution architecture and capable of handling sequences up to $1$M bases in length.
- •
Caduceus []. Caduceus(^11^1111[https://huggingface.co/kuleshov-group/caduceus-ps_seqlen-131k_d_model-256_n_layer-16](https://huggingface.co/kuleshov-group/caduceus-ps_seqlen-131k_d_model-256_n_layer-16)) is an advanced DNA language model built on the MambaDNA architecture, designed to address challenges in genomic sequence modeling, such as long-range token interactions and reverse complementarity (RC).
- •
Nucleotide Transformer (NT) V2 []. The NT FMs were trained on DNA data, including the human reference genome and multi-species DNA sequences. They aim to capture the complex patterns within nucleotide sequences for various genome modeling applications.
- •
Agricultural Nucleotide Transformer (Agro-NT) []. Agro-NT is a large-scale DNA FM ($1$B parameters) akin to the Nucleotide Transformers but with a focus on plant DNA.
- •
SpliceBERT []. It was trained on $2$M precursor messenger RNA (pre-mRNA) and specialised in RNA splicing of pre-mRNA sequences.
- •
3UTRBERT []. This model was trained on $20$k 3’UTRs for 3’UTR-mediated gene regulation tasks. It uses k-mers tokenization instead of SNT.
RNA-BERT []. RNA-BERT is a BERT-style model pre-trained on a large corpus of non-coding RNA sequences. It uses masked language modeling (MLM) as its primary training objective. The model is designed to predict RNA structural alignments and can be fine-tuned for various RNA sequence classification and regression tasks
- •
RNA-MSM [] RNA-MSM is an unsupervised RNA language model based on multiple sequence alignment (MSA). It is the first model of its kind to produce embeddings and attention maps that directly correlate with RNA secondary structure and solvent accessibility. RNA-MSM is particularly effective for tasks involving evolutionary relationships in RNA sequences.
- •
RNA-FM [] RNA-FM is a BERT-based RNA foundation model trained on a vast dataset of non-coding RNA sequences. The model excels in predicting RNA structure and function by leveraging masked language modeling (MLM) during pre-training. RNA-FM’s training data is sourced from the RNAcentral database, providing it with extensive knowledge across diverse RNA species.
- •
OmniGenBench. OmniGenBench is the RNA genome FM that advocates the importance of sequence-structure alignment. Moreover, it is the first FM which addressed the in-silico RNA design task.
- •
OmniGenome: A FM dedicated to RNA genome modeling. This model leverages the computation-based structure to enhance the genome modeling ability and archives impressive performance on both RNA and DNA genomes.

## Appendix C Public Leaderboard

The public leaderboard has been launched with the manuscript, and the current layout of the leaderboard is illustrated in Fig. 2. We have included the results of open-source GFMs among four benchmark suites, and new results can be expected from the community. We are still working to include the performance of recent GFMs, and refine the leaderboard interface with better integrity.

Figure: Figure 2: The current webpage interface of the public leaderboard.
Refer to caption: extracted/5895782/figs/leaderboard.png

## Appendix D Limitations

The GFM benchmarking may not reflect the accurate performance in biology reality, we attribute the limitations of benchmarking to two major aspects:

- •
Lack of in-vivo Data: One of the critical limitations of GFMs lies in the absence of in-vivo verified genome data. While GFMs perform well in in-silico environments, where computational models and simulations are used to predict biological processes, these models are rarely validated against in-vivo data, which refers to experimental data obtained from living organisms. This presents a significant challenge for accurately translating model predictions to real-world biological applications. To be more specific, the complexity of biological systems, including interactions within cells, tissues, and organisms, often introduces variables that are not fully captured in computational simulations. For example, gene regulation, environmental factors, and cellular responses to genetic modifications may behave differently in living organisms than predicted by models trained on in-silico data. As a result, GFMs might not fully capture the biological complexity, leading to discrepancies between predicted and actual outcomes.
- •
Model Scale Constraints: The second major limitation is the model scales in benchmarking. As GFMs become larger and more sophisticated, their performance improves, but this scaling comes at a significant cost. Training as well as benchmarking large-scale GFMs requires immense computational resources, including high-performance GPUs or TPUs, massive memory allocation, and extensive storage for datasets. The cost of acquiring and maintaining this infrastructure can be prohibitive for many research institutions or companies, limiting access to cutting-edge GFMs.

## Appendix E Ethic Statement

The development of GFMs presents various ethical challenges that must be carefully considered. As we push the boundaries of what is possible with large-scale GFMs, such as Evo, it is crucial to establish a responsible framework for their development and application. GFMs enable advanced capabilities like generating and predicting DNA sequences at a whole-genome scale, which opens the door to significant breakthroughs in fields such as genetic engineering and therapeutic development. However, these same capabilities pose risks related to bio-security, inequality, and environmental disruption.

Safety and Ethical Implications: GFMs like OmniGenome could be misused by malicious actors for harmful purposes, such as creating synthetic organisms that could threaten bio-safety. It is essential to establish strict guidelines on access and use, including the development of safety guardrails, access controls, and audits to monitor queries and research outcomes.

Health and Social Inequity: While the open-source nature of GFMs promotes transparency and accessibility, there are concerns that the benefits of these tools may disproportionately favor well-resourced organizations, such as pharmaceutical companies, which could lead to further inequalities in global health. Intellectual property considerations also arise, as companies using open-source tools might monopolize treatments or set prohibitive costs, exacerbating health disparities.

Environmental Impact: The enhanced capabilities for genetic manipulation that GFMs enable could disrupt natural ecosystems, leading to potential loss of biodiversity or the emergence of harmful species. Additionally, the computational demands of training large models have environmental costs, such as increased carbon footprints, that must be weighed against the benefits of the scientific advancements.

In response to these concerns, we are committed to promoting ethical guidelines, transparency, and the responsible use of GFMs. We will collaborate with the community to continually refine these guidelines as the field evolves.

## Appendix F Social Impact

The societal impact of GFMs is substantial, with applications ranging from personalized medicine to environmental management. These models have the potential to revolutionize fields such as healthcare and agriculture by providing deeper insights into genetic data, enabling the discovery of new biomarkers, and assisting in the development of more effective therapies. In healthcare, GFMs can drive advancements in precision medicine, allowing for personalized treatments based on individual genetic profiles, which could drastically improve patient outcomes for conditions such as cancer or rare genetic disorders. In agriculture, GFMs can contribute to sustainable practices by improving crop yields and resistance to disease. However, careful consideration must be given to the ecological balance, as genetic modifications could have unforeseen consequences on ecosystems. As GFMs continue to evolve, their responsible development and deployment will be crucial to ensuring that their societal impact is positive and equitable.

However, there are also risks associated with the unequal access to these powerful tools. Entities with more resources and technical expertise may benefit disproportionately from GFMs, accelerating their research and economic returns while leaving lower-resourced institutions and countries at a disadvantage. To mitigate this, it is critical to ensure that access to GFMs is democratized through open-source initiatives, global collaboration, and capacity-building efforts in low-resource settings.

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 3
presentation: 1
questions: Does OmniGenome utilize structural information in its input?
soundness: 2
strengths: 1. The authors address the problem of evaluating genomic foundation models, which is an important problem.
2. They appear to have developed a comprehensive, well-documented framework for benchmarking.
3. They convincingly demonstrated that current language models trained on DNA sequences do not learn representations of RNA sequences as effectively as those trained purely on RNA sequences.
4. They provide strong evidence that supervising models on annotations of RNA structure provides these models with important information.
summary: This paper introduces a meta-benchmark for transfer learning that integrates tasks from four existing benchmark sets. They revise metrics used in these benchmarks to account for class imbalance. The authors implemented their evaluation protocol in a Python package they claim is easily extendable and flag this as their primary contribution.

Orthogonally, the paper presents benchmarking results for several candidate foundation models for genomics. In addition to DNA language models, they include models trained on RNA. The authors highlight the strong performance of the RNA language model OmniGenome.
weaknesses: 1. The paper would be stronger if the evaluations were more comprehensive. The authors could have included newer benchmarks—like BEND (ICLR 2024) or those included in the recent preprint by Tang and Koo (https://doi.org/10.1101/2024.02.29.582810) —that address important limitations of the previous benchmarks.
2. It would also benefit from including additional language model baselines, like PlantCaduceus or GPN.
3. It would also be worthwhile to evaluate models other than language models, since these have been shown to outperform language models on transfer learning tasks.
4. I am not convinced of their claim that “OmniGenome generalizes well to DNA-based tasks, likely due to shared sequence motifs and structural similarities between RNA and DNA.” I do not think this explanation is true for noncoding elements. It is more likely that their benchmarking needs to be more thorough. From their comparisons, it is also unclear whether OmniGenome generalizes well—only that it generalizes comparably to language models trained on DNA.

---

confidence: 3
contribution: 1
initial_rating: 3
presentation: 2
questions: There are two repeated paragraphs in lines 154-167, as well as some other typos in the manuscript.
soundness: 2
strengths: 1. There are interesting ideas in this work that could be expanded and delved into further. For example, the authors attempt to create a unified benchmark for DNA and RNA tasks, which raises the potentially interesting topic of studying the cross-modality transferability of GFMs. This aspect could be expanded and investigated more deeply to provide valuable insights.
2. Having a unified platform like this can enhance reproducibility in GFM development and evaluation.
3. The platform includes user-friendly features such as an API, an online hub, and a leaderboard.
summary: In this paper, the authors present an integrated platform, GFMBench, for benchmarking genomic foundation models. The authors synthesized and standardized four existing benchmarks and created a unified software framework to run GFMs and calculate evaluation metrics in a streamlined manner. In general, I consider this work lacks novelty and does not contribute sufficiently to the field, which is my primary reason for recommending rejection. While there are engineering efforts to aggregate and standardize existing benchmarks, the contribution does not extend much beyond that.

One could argue that such work helps facilitate the benchmarking process, and I appreciate the effort the authors put into building the software. However, I do not think this type of contribution is suitable for a venue like ICLR. I acknowledge that others might hold a different opinion, so I am setting my confidence level to 3.
weaknesses: 1. As mentioned in the summary, this work generally lacks novelty.
2. One of the four main contributions claimed by the authors, Data scarcity and bias, is vaguely stated and not particularly valid. First, the discussion seems to conflate training data and evaluation data. This work addresses only the evaluation side, so the writing should focus on that. Second, since the data sources are four well-established benchmark datasets, there is no additional contribution to resolving the data scarcity issue, as no new dataset is introduced.
3. The authors did not discuss or justify the choice of the four benchmark datasets. There is no comprehensive review of existing benchmarks, which I think is conventional for such work. For example, the benchmarks from NT (https://www.biorxiv.org/content/10.1101/2023.01.11.523679v4), BEND (https://openreview.net/forum?id=uKB4cFNQFg), and Tang et al. (https://www.biorxiv.org/content/10.1101/2024.02.29.582810v2.full.pdf) are well-established and recognized benchmarks in the field but none were mentioned in the manuscript.
4. The manuscript contains many claims that lack supporting evidence and concrete examples. For instance: 
a. The authors mention metric variability but do not show empirical evidence. How would using the original metrics from each benchmark affect the results? 
b. The authors claim that this work helps test models on underrepresented sequences and tasks, but there is no further explanation or evidence on why or how this work addresses this issue.
5. The writing could be improved for conciseness and clarity. And there are some overly repetitive statements throughout the article.

---

confidence: 3
contribution: 3
initial_rating: 5
presentation: 4
questions: A list of questions, minor issues and suggestions is included here:


- The abstract mentions "the absence of open-source software for diverse genomics".
This doesn't sound true to me. Genomics is especially rich in open-source
tools (e.g. see the collection of tools on the Galaxy platform 
"The Galaxy platform for accessible, reproducible and collaborative biomedical analyses").

- On page 2 "lack of comprehensive and diverse datasets necessary for robust training and testing of GFMs.".
It would be great to support these claims with related work or references.

The first paragraph of page 4 is repeated twice.

- In Table 7 caption: "in Agro-NT".
Please include a citation to a tool, work, dataset or reference when mentioning
it for the first time. This happens also later in the text, e.g. "ViennaRNA", 
"Archive2", "Stralign", etc.

- In Table 5 caption: "These benchmark datasets are held out or not included in the pretraining database".
It is unclear to me what this means. Would be thankful if the authors 
could clarify what this means.

- What experiment is Appendix C referring to? Please clarify what these
hyperparameters are for.

- On page 6, "We provide a tutorial for AutoBench in ...".
To make the paper more consistent, it would be better to include the
tutorial URL in a footnote too.

- I understand that AutoBench is a standardisation suite for benchmarking, but
I believe that the paper could benefit from a more detailed explanation of it
and its contribution. For instance, how is it an "automated" benchmarking solution?

- Genome data augmentation is mentioned as a contribution, however,
no technical details are provided on how this is achieved. This is indeed
a subject of interest, and it would be great to have more
details.

- While I think that this is not a trivial issue, the paper does not mention
how are long sequences treated with the models. Is this because this is not an issue
for the model's evaluation? Otherwise, how is this managed? Is this handled or
should be handled in the GFMBench framework?

- Why does "GFMBench" appear as a model in Table 4?

- There are some spelling errors, e.g. "pertaining" in Line 1080.
Please double-check for spelling errors.

- "blastn" in Line 1066 is usually written in uppercase.
soundness: 3
strengths: Generally, the paper is well-written in good English and easy to follow.
The motivation for the paper is clear and the problem is well defined.
Overall, the authors make a significant contribution to the field of genomics and
foundational genomics models. Providing a unifying benchmark for evaluation
is a significant contribution and will very much contribute positively to the
development of the progress of GFMs. The authors will release a webpage with a
leaderboard for model comparison, which is really positive. The authors
will also release the code for GFMBench.
summary: This paper introduces GFMBench, a framework for genomic foundation model (GFM)
evaluation. The proposed benchmark covers a diverse set of genomic tasks and datasets, and
provides methods for standardised metrics and evaluation, thus 
enabling fair comparison of different GFMs. The papers include an evaluation of
multiple GFMs on the benchmark of standardised metrics, which allows the 
comparison of the models. GFMBench is released as open-source software and
it includes an online leaderboard.
weaknesses: There are some issues regarding the clarity of the contribution of 
the paper and the reported results. The paper also presents some claims
that are not backed up or briefly supported. The issues are listed below:

Major issues:

- Data scarcity is mentioned to be an issue for genomics datasets in the paper.
While it is true that diversity is a problem in the access to genomic data it is 
important to note that there are indeed many large-scale datasets available.
The main concern regarding this issue is that the authors claim that
GFMBench tackles this problem, however, as far as I'm concerned, the benchmark
does not contribute any novel data, but it is rather a compilation of
existing datasets. The claim that GFMBench mitigates "issues of data scarcity"
does not seem truly accurate.

- On page 5 it is claimed that "GFMBench" tackles the problem of 
"benchmark standardization". While I understand the contribution of common
metrics and implementation, I don't see how this is achieved in the
"hyperparameter settings". How is standardization achieved in this regard?

- In Line 486, it is said that "OmniGenome consistently performs well across various genomic benchmarks",
however, Table 4 results do not seem to match this claim (correct me if I'm wrong).

- Through the paper, the authors praise the performance of "OmniGenome" GFM,
as it shows good performance, especially on RGB and PGB tasks.
Is OmniGenome also a contribution to the paper? If so, it would be great
to have more details on it. If not, it would be great to provide more insights
into the other models and why they perform better or worse in some tasks. 
Similarly, since the contribution of the paper is a benchmark, it would be 
more significant to provide some insight into the difficulty of the tasks
and why performance varies across them, instead of commenting only on OmniGenome.
I acknowledge that the paper highlights repeated times that the major performance
of OmniGenome is due to the fact that it is trained on "secondary structure" tasks,
However, this fact is repeated multiple times in the paper while no comment
is provided on the other models or tasks at hand and why "secondary structure"
improves performance on the tasks.

---

confidence: 4
contribution: 1
initial_rating: 1
presentation: 2
questions: What specific technical innovations in benchmarking does GFMBench introduce?

What novel metrics or evaluation approaches are introduced?

What specific reproducibility issues does it solve that other frameworks don't?

Why were these 4 benchmarks and then GFMs selected among others?

In Table 4, why is GFMBench listed as a model?
soundness: 2
strengths: The paper integrates 4 large genomics benchmarks from other groups into a unified framework. They also create a way to run a pipeline with these benchmarks with GFMs.
summary: This paper introduces GFMBench, a framework for standardizing and automating benchmarking of Genomic Foundation Models (GFMs), which integrates four large-scale benchmarks containing 42 million genomic sequences across 75 datasets. The framework addresses several challenges in genomic modeling by providing unified protocols, consistent metrics, and automated evaluation pipelines that work across both DNA and RNA tasks from multiple species. GFMBench is released as open-source software with user-friendly interfaces, built-in tools for genomic tasks, and includes a public leaderboard and online hub for sharing benchmark results and resources with the research community.
weaknesses: The paper exhibits several significant limitations. First, the technical innovation appears limited in scope. While the work integrates multiple existing benchmarks into a unified framework, it does not create new benchmarking methodologies or metrics. The automation and standardization approaches described largely follow established machine learning practices, without introducing fundamental methodological advances in benchmarking or evaluation techniques.

The work's focus is predominantly engineering-oriented rather than research-driven. The main contributions center on developing practical tools and interfaces for the genomics community. While valuable from a practical perspective, the emphasis on standardization over novel scientific insights raises questions about its suitability for a top-tier machine learning conference. Furthermore, similar benchmark aggregation platforms already exist in other machine learning domains, making this approach less innovative.

Meta-review: This paper is about developing a better benchmark for transfer learning tasks. The construct a benchmark that is a composition of existing benchmarks sets and implement it in python. Reviewers noted the value of benchmarking models so that the community can assess performance and choose among models. There were concerns that some of the claims did not rest firmly on the evidence presented and that the novelty was not clearly described in relation to existing literature. The authors offered a rebuttal acknowledging that some of the concerns would be addressed in the subsequent research work. At this time the concerns about the claims and novelty place this work below the bar for acceptance.

Decision: Reject