File size: 5,297 Bytes
10c0ee7 b64931f 5f5c6f4 b64931f 933a968 b64931f 5f5c6f4 b64931f 5f5c6f4 b64931f d4986ce b64931f 5f5c6f4 b64931f 5f5c6f4 b64931f 5294acf b64931f 331f7d8 b64931f 5f5c6f4 b64931f b452610 386c59b 699e931 b452610 386c59b b52785a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
tags:
- DNA
- Genomics
- Plants
pretty_name: Plant Genomic Benchmark
license: cc-by-nc-sa-4.0
---
## Dataset Overview
This dataset features the 8 evaluation tasks presented in the AgroNT (A Foundational Large Language Model for Edible Plant
Genomes) paper. The tasks cover single output regression, multi output regression, binary classification, and multi-label classification which
aim to provide a comprehensive plant genomics benchmark. Additionally, we provide results from in silico saturation mutagenesis analysis of sequences
from the cassava genome, assessing the impact of >10 million mutations on gene expression levels and enhancer elements. See the ISM section
below for details regarding the data from this analysis.
| Name | # of Datasets(Species) | Task Type | Sequence Length (base pair) |
| -------- | ------- | -------- | ------- |
| Polyadenylation | 6 | Binary Classification | 400 |
| Splice Site | 2 | Binary Classification | 398 |
| LncRNA | 6 | Binary Classification | 101-6000 |
| Promoter Strength | 2 | Single Variable Regression | 170 |
| Terminator Strength | 2 | Single Variable Regression | 170 |
| Chromatin Accessibility | 7 | Multi-label Classification | 1000 |
| Gene Expression | 6 | Multi-Variable Regression | 6000 |
| Enhancer Region | 1 | Binary Classification | 1000 |
## Dataset Sizes
| Task Name | # Train Samples | # Validation Samples | # Test Samples |
| -------- | ------- | -------- | ------- |
|poly_a.arabidopsis_thaliana|170835|---|30384|
|poly_a.oryza_sativa_indica_group|98139|---|16776|
|poly_a.trifolium_pratense|111138|---|13746|
|poly_a.medicago_truncatula|47277|---|8850|
|poly_a.chlamydomonas_reinhardtii|90378|---|10542|
|poly_a.oryza_sativa_japonica_group|120621|---|20232|
|splicing.arabidopsis_thaliana_donor|2588034|---|377873|
|splicing.arabidopsis_thaliana_acceptor|1704844|---|250084|
|lncrna.m_esculenta|4934|---|360|
|lncrna.z_mays|8423|---|1629|
|lncrna.g_max|11430|---|490|
|lncrna.s_lycopersicum|7274|---|1072|
|lncrna.t_aestivum|11252|---|1810|
|lncrna.s_bicolor|8654|---|734|
|promoter_strength.leaf|58179|6825|7154|
|promoter_strength.protoplast|61051|7162|7595|
|terminator_strength.leaf|43294|5309|4806|
|terminator_strength.protoplast|43289|5309|4811|
|gene_exp.glycine_max|47136|4803|4803|
|gene_exp.oryza_sativa|31244|3702|3702|
|gene_exp.solanum_lycopersicum|27321|3827|3827|
|gene_exp.zea_mays|34493|4483|4483|
|gene_exp.arabidopsis_thaliana|25731|3401|3402|
|chromatin_access.oryza_sativa_MH63_RS2|5120000|14848|14848|
|chromatin_access.setaria_italica|5120000|19968|19968|
|chromatin_access.oryza_sativa_ZS97_RS2|5120000|14848|14848|
|chromatin_access.arabidopis_thaliana|5120000|9984|9984|
|chromatin_access.brachypodium_distachyon|5120000|14848|14848|
|chromatin_access.sorghum_bicolor|5120000|29952|29952|
|chromatin_access.zea_mays|6400000|79872|79872|
|pro_seq.m_esculenta|16852|1229|812|
*** 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
them helped to improve learning.
## Example Usage
```python
from datasets import load_dataset
task_name='terminator_strength.protoplast' # one of the task names from the above table
dataset = load_dataset("InstaDeepAI/plant-genomic-benchmark",task_name=task_name)
```
## In Silico Saturation Mutagensis
### File structure for: ISM_Tables/Mesculenta_305_v6_PROseq_ISM_LOG2FC.txt.gz
Intergenic enhancer regions based on Lozano et al. 2021 (https://pubmed.ncbi.nlm.nih.gov/34499719/) <br>
Genome version: Manihot esculenta reference genome v6.1 from Phytozome <br>
CHR: Chromosome <br>
POS: Physical position (bp) <br>
REF: Reference allele <br>
ALT: Alternative allele <br>
LOG2FC: Log fold change in Intergenic enhancer probability (log2(p_mutated_sequence / p_original_sequence)) <br>
### File structure for: ISM_Tables/Mesculenta_v6_GeneExpression_ISM_LOG2FC.txt.gz
Gene expression prediction based on: Wilson et al. 2016 (https://pubmed.ncbi.nlm.nih.gov/28116755/) <br>
Genome version: Manihot esculenta reference genome v6 from Ensembl 56 <br>
CHR: Chromosome <br>
POS: Physical position (bp) <br>
REF: Reference allele <br>
ALT: Alternative allele <br>
GENE: Gene ID <br>
STRAND: Gene strand <br>
TISSUE: Tissue type (Acronyms detailed in Figure 1 of Wilson et al.) <br>
LOG2FC: Gene expression log fold change (log2(gene_exp_mutated_sequence / gene_exp_original_sequence)) <br>
## Data source for Figures 2-8
### File structure for: Figures/Figure[FIGURE_NUMBER]_panel[PANEL_LETTER].txt
Text files containing the data used to plot Figures 2 to 8 from Mendoza-Revilla & Trop et al., 2024.
The text files are named using the following format: Figure[FIGURE_NUMBER]_panel[PANEL_LETTER].txt
[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".
[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".
|