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--- |
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license: mit |
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tags: |
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- biology |
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- plants |
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- gene expression |
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pretty_name: Maize and Arabidopsis gene expression |
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--- |
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# Dataset Card for Maize and Arabidopsis gene expression |
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Plant Gene expression data used for benchmarking sequence to gene expression prediction ML models. |
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### Dataset Description |
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Species included are Maize and Arabidopsis thaliana. Dataset includes gene expression values for leaf and root tissues. |
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Within the `tasks` folder, datasets are broken down by `species-task-tissue`. Genomes in the `genomes` folders include the annotation and the GFF files associated with that specific genome. |
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All tasks are split by 80% train, 10% validation, and 10% test. |
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## Dataset Structure |
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``` |
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dataset |
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genomes/ |
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Arabidopsis_thaliana/ |
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annotation.fa |
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ath.gff |
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Zea_mays/ |
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annotation.fa |
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ath.gff |
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tasks/ |
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species-task-tissue/ |
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train.tsv |
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validate.tsv |
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test.tsv |
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``` |
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- **Curated by:** Taylor Ferebee, Travis Wrightsman, Jingjing Zhai, Aaron Gokaslan, Volodymyr Kuleshov, Edward S. Buckler |
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- **Repository:** [https://github.com/maize-genetics/expression-survey] |
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- **Paper:** PLExBench: A benchmarking suite for predicting gene expression in plants |
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- **License:** MIT |
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### Dataset Sources |
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|sample_name|species|genotype|library_layout|library_selection|reads_location|organ|age|condition|replicate|batch|reference| |
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|SRR505743|Arabidopsis_thaliana|Col-0|single-read|random|sra|root|seedling|controlled|1|1|SRP013631| |
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|SRR505744|Arabidopsis_thaliana|Col-0|single-read|random|sra|leaf|seedling|controlled|1|1|SRP013631| |
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|SRR953400|Arabidopsis_thaliana|Col-0|single-read|random|sra|leaf|seeding|controlled|1|1|PRJNA215448| |
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|SRR1005386|Arabidopsis_thaliana|Col-0|single-read|random|sra|leaf|seedling|controlled|1|1|PRJNA222364| |
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|SRR578947|Arabidopsis_thaliana|Col-0|single-read|random|sra|root|seedling|controlled|1|1|SRP013631| |
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|SRR578948|Arabidopsis_thaliana|Col-0|single-read|random|sra|root|seedling|controlled|1|1|SRP013631| |
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|ERR2096663|Zea_mays|B73|paired-end|polyA|sra|leaf|seedling|controlled|1|1|PRJEB22166| |
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|ERR2096664|Zea_mays|B73|paired-end|polyA|sra|leaf|seedling|controlled|1|1|PRJEB22166| |
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|ERR2096665|Zea_mays|B73|paired-end|polyA|sra|leaf|seedling|controlled|1|1|PRJEB22166| |
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|ERR2096666|Zea_mays|B73|paired-end|polyA|sra|leaf|seedling|controlled|1|1|PRJEB22166| |
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|ERR2096667|Zea_mays|B73|paired-end|polyA|sra|leaf|seedling|controlled|1|1|PRJEB22166| |
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|ERR3773807|Zea_mays|B73|paired-end|polyA|sra|root|seedling|controlled|1|1|PRJEB35943| |
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|ERR3773808|Zea_mays|B73|paired-end|polyA|sra|root|seedling|controlled|1|1|PRJEB35943| |
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|ERR986091|Zea_mays|B73|paired-end|random|sra|root|seedling|controlled|1|1|PRJEB10406| |
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### Curation Rationale |
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To choose experiments for leaf and root tissues, we focused on datasets that have been used in a recent study and can be found in |
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multiple databases. |
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#### Data Collection and Processing |
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In the max gene expression datasets, for each gene, we take the maximum transcript per million TPM value over experiments. Similarly, for the |
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absolute expression datasets, we take the mean TPM value over experiments. Finally, for the on-off ex- |
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pression, we assign 1 to a gene if it has a TPM value in one of the tissues. To create train-test-validation |
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splits, we use orthogroup guided splitting as introduced by Washburn et al. 2019. Then, we split the training test sets so |
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that we train on 80% of the orthogroups and test on 10%. Note that for each of the task-based datasets, we |
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keep the same train-test-validate split. |
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**BibTeX:** |
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## Dataset Card Authors |
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Taylor Ferebee ([email protected]) |
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## Dataset Card Contact |
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Taylor Ferebee ([email protected]), Cinta Romay, Edward S. Buckler |
