metadata
license: apache-2.0
Dataset Overview
The dataset consists of five tasks for cross-species modeling plant genomes at single-nucleotide resolution in plants. These tasks are:
- Translation Initiation Site (TIS) Prediction
- Translation Termination Site (TTS) Prediction
- Splice Donor Site Prediction
- Splice Acceptor Site Prediction
- Evolutionary Conservation Prediction
Tasks 1-4: Site Predictions
- Training Dataset: Generated from Arabidopsis chromosomes 1-4
- Validation Dataset: Generated from Arabidopsis chromosome 5
- Testing Datasets: Compiled from rice, sorghum, and maize
Task 5: Evolutionary Conservation Prediction
- Training Dataset: Generated from sorghum chromosomes 1-9
- Validation Dataset: Generated from sorghum chromosome 10
- Testing Datasets: Compiled in maize
This datasets facilitate robust cross-species nucleotide annotation.
Dataset sizes
TIS, TTS, Donor, Acceptor
| TIS | TTS | Donor | Acceptor | |||||
|---|---|---|---|---|---|---|---|---|
| # of positives | # of negatives | # of positives | # of negatives | # of positives | # of negatives | # of positives | # of negatives | |
| Training on Arabidopsis chromosome 1-4 | 24,711 | 173,880 | 25,112 | 220,452 | 96,752 | 483,268 | 97,224 | 536,179 |
| Validation on Arabidopsis chromosome 5 | 7,311 | 50,514 | 7,461 | 64,365 | 29,377 | 140,536 | 29,567 | 155,397 |
| Rice test | 2,974 | 1,400,115 | 2,974 | 3,718,029 | 21,963 | 3,764,549 | 21,963 | 4,151,774 |
| Sorghum test | 3,214 | 3,937,719 | 3,214 | 10,445,530 | 24,801 | 10,821,941 | 24,801 | 12,640,573 |
| Maize test | 3,098 | 11,265,574 | 3,098 | 29,535,973 | 24,399 | 34,516,038 | 24,399 | 40,025,899 |
Evolutionary conservation
| # of positives | # of negative | |
|---|---|---|
| Train | 429,043 | 429,043 |
| Validation | 19,030 | 19,030 |
| Test | 947,769 | 976,230 |
How to use
from datasets import load_dataset
import pandas as pd
repo_id = 'kuleshov-group/cross-species-single-nucleotide-annotation'
tis = load_dataset(repo_id, data_files={'train': 'TIS/train.tsv', 'valid': 'TIS/valid.tsv', 'test_rice':'TIS/test_rice.tsv', 'test_sorghum':'TIS/test_sorghum.tsv', 'test_maize':'TIS/test_maize.tsv'})
tis_train = tis['train']
# convert to pandas dataframe
tis_train_df = tis_train.to_pandas()