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--- |
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tags: |
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- DNA |
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- Genomics |
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- Plants |
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pretty_name: Plant Genomic Benchmark |
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license: cc-by-nc-sa-4.0 |
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--- |
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## Dataset Overview |
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This dataset features the 8 evaluation tasks presented in the AgroNT (A Foundational Large Language Model for Edible Plant |
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Genomes) paper. The tasks cover single output regression, multi output regression, binary classification, and multi-label classification which |
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aim to provide a comprehensive plant genomics benchmark. Additionally, we provide results from in silico saturation mutagenesis analysis of sequences |
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from the cassava genome, assessing the impact of >10 million mutations on gene expression levels and enhancer elements. See the ISM section |
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below for details regarding the data from this analysis. |
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| Name | # of Datasets(Species) | Task Type | Sequence Length (base pair) | |
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| -------- | ------- | -------- | ------- | |
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| Polyadenylation | 6 | Binary Classification | 400 | |
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| Splice Site | 2 | Binary Classification | 398 | |
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| LncRNA | 6 | Binary Classification | 101-6000 | |
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| Promoter Strength | 2 | Single Variable Regression | 170 | |
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| Terminator Strength | 2 | Single Variable Regression | 170 | |
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| Chromatin Accessibility | 7 | Multi-label Classification | 1000 | |
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| Gene Expression | 6 | Multi-Variable Regression | 6000 | |
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| Enhancer Region | 1 | Binary Classification | 1000 | |
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## Dataset Sizes |
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| Task Name | # Train Samples | # Validation Samples | # Test Samples | |
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| -------- | ------- | -------- | ------- | |
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|poly_a.arabidopsis_thaliana|170835|---|30384| |
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|poly_a.oryza_sativa_indica_group|98139|---|16776| |
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|poly_a.trifolium_pratense|111138|---|13746| |
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|poly_a.medicago_truncatula|47277|---|8850| |
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|poly_a.chlamydomonas_reinhardtii|90378|---|10542| |
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|poly_a.oryza_sativa_japonica_group|120621|---|20232| |
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|splicing.arabidopsis_thaliana_donor|2588034|---|377873| |
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|splicing.arabidopsis_thaliana_acceptor|1704844|---|250084| |
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|lncrna.m_esculenta|4934|---|360| |
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|lncrna.z_mays|8423|---|1629| |
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|lncrna.g_max|11430|---|490| |
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|lncrna.s_lycopersicum|7274|---|1072| |
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|lncrna.t_aestivum|11252|---|1810| |
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|lncrna.s_bicolor|8654|---|734| |
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|promoter_strength.leaf|58179|6825|7154| |
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|promoter_strength.protoplast|61051|7162|7595| |
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|terminator_strength.leaf|43294|5309|4806| |
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|terminator_strength.protoplast|43289|5309|4811| |
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|gene_exp.glycine_max|47136|4803|4803| |
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|gene_exp.oryza_sativa|31244|3702|3702| |
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|gene_exp.solanum_lycopersicum|27321|3827|3827| |
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|gene_exp.zea_mays|34493|4483|4483| |
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|gene_exp.arabidopsis_thaliana|25731|3401|3402| |
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|chromatin_access.oryza_sativa_MH63_RS2|5120000|14848|14848| |
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|chromatin_access.setaria_italica|5120000|19968|19968| |
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|chromatin_access.oryza_sativa_ZS97_RS2|5120000|14848|14848| |
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|chromatin_access.arabidopis_thaliana|5120000|9984|9984| |
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|chromatin_access.brachypodium_distachyon|5120000|14848|14848| |
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|chromatin_access.sorghum_bicolor|5120000|29952|29952| |
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|chromatin_access.zea_mays|6400000|79872|79872| |
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|pro_seq.m_esculenta|16852|1229|812| |
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*** It is important to note that fine-tuning for lncrna was carried out using all datasets in a single training. The reason for this is that the datasets are small and combining |
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them helped to improve learning. |
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## Example Usage |
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```python |
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from datasets import load_dataset |
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task_name='terminator_strength.protoplast' # one of the task names from the above table |
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dataset = load_dataset("InstaDeepAI/plant-genomic-benchmark",task_name=task_name) |
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``` |
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## In Silico Saturation Mutagensis |
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### File structure for: ISM_Tables/Mesculenta_305_v6_PROseq_ISM_LOG2FC.txt.gz |
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Intergenic enhancer regions based on Lozano et al. 2021 (https://pubmed.ncbi.nlm.nih.gov/34499719/) <br> |
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Genome version: Manihot esculenta reference genome v6.1 from Phytozome <br> |
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CHR: Chromosome <br> |
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POS: Physical position (bp) <br> |
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REF: Reference allele <br> |
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ALT: Alternative allele <br> |
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LOG2FC: Log fold change in Intergenic enhancer probability (log2(p_mutated_sequence / p_original_sequence)) <br> |
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### File structure for: ISM_Tables/Mesculenta_v6_GeneExpression_ISM_LOG2FC.txt.gz |
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Gene expression prediction based on: Wilson et al. 2016 (https://pubmed.ncbi.nlm.nih.gov/28116755/) <br> |
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Genome version: Manihot esculenta reference genome v6 from Ensembl 56 <br> |
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CHR: Chromosome <br> |
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POS: Physical position (bp) <br> |
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REF: Reference allele <br> |
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ALT: Alternative allele <br> |
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GENE: Gene ID <br> |
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STRAND: Gene strand <br> |
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TISSUE: Tissue type (Acronyms detailed in Figure 1 of Wilson et al.) <br> |
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LOG2FC: Gene expression log fold change (log2(gene_exp_mutated_sequence / gene_exp_original_sequence)) <br> |
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## Data source for Figures 2-8 |
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### File structure for: Figures/Figure[FIGURE_NUMBER]_panel[PANEL_LETTER].txt |
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Text files containing the data used to plot Figures 2 to 8 from Mendoza-Revilla & Trop et al., 2024. |
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The text files are named using the following format: Figure[FIGURE_NUMBER]_panel[PANEL_LETTER].txt |
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[FIGURE_NUMBER]: This is the number of the figure in the publication. For example, if the data corresponds to Figure 3, this part of the file name will be "Figure3". |
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[PANEL_LETTER]: This is the letter corresponding to a specific panel within the figure. Figures often contain multiple panels labeled with letters (e.g., a, b, c). For example, if the data corresponds to panel b of Figure 3, this part of the file name will be "panelb". |
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