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Datasets:
genomic-benchmarks
/
mRNABench

Size:
1M<n<10M
Tags:
genomics
benchmark
rna
regression
License:
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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 4 new columns ({'u_oof_start', 'target_mrl_egfp_m1pseudo', 'kozak_quality', 'u_start'}) and 1 missing columns ({'target_mrl_designed'}).

This happened while the csv dataset builder was generating data using

gzip://mrl-sample-egfp-m1pseudo_train.csv::hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-egfp-m1pseudo/mrl-sample-egfp-m1pseudo_train.csv.gz, ['hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-designed/mrl-sample-designed_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-egfp-m1pseudo/mrl-sample-egfp-m1pseudo_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-egfp-pseudo/mrl-sample-egfp-pseudo_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-egfp-unmod/mrl-sample-egfp-unmod_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-mcherry/mrl-sample-mcherry_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-varying/mrl-sample-varying_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sugimoto/mrl-sugimoto_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/rnahl-human/rnahl-human_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/rnahl-mouse/rnahl-mouse_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/translation-efficiency-human/translation-efficiency-human_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/translation-efficiency-mouse/translation-efficiency-mouse_train.csv.gz']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              row_index: int64
              sequence: string
              cds: string
              splice: string
              target_mrl_egfp_m1pseudo: double
              u_start: int64
              u_oof_start: int64
              kozak_quality: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1339
              to
              {'id': Value('string'), 'row_index': Value('int64'), 'sequence': Value('string'), 'cds': Value('string'), 'splice': Value('string'), 'target_mrl_designed': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 4 new columns ({'u_oof_start', 'target_mrl_egfp_m1pseudo', 'kozak_quality', 'u_start'}) and 1 missing columns ({'target_mrl_designed'}).
              
              This happened while the csv dataset builder was generating data using
              
              gzip://mrl-sample-egfp-m1pseudo_train.csv::hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-egfp-m1pseudo/mrl-sample-egfp-m1pseudo_train.csv.gz, ['hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-designed/mrl-sample-designed_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-egfp-m1pseudo/mrl-sample-egfp-m1pseudo_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-egfp-pseudo/mrl-sample-egfp-pseudo_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-egfp-unmod/mrl-sample-egfp-unmod_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-mcherry/mrl-sample-mcherry_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sample-varying/mrl-sample-varying_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/mrl-sugimoto/mrl-sugimoto_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/rnahl-human/rnahl-human_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/rnahl-mouse/rnahl-mouse_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/translation-efficiency-human/translation-efficiency-human_train.csv.gz', 'hf://datasets/genomic-benchmarks/mRNABench@d2fd198c62746770a52a71617a684b26695b97fc/v1/translation-efficiency-mouse/translation-efficiency-mouse_train.csv.gz']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

id
string
row_index
int64
sequence
string
cds
string
splice
string
target_mrl_designed
float64
mRNABench:mrl-sample-designed:v1:train:0000000
0
GGGACATCGTAGAGAGTCGTACTTACGCTTCTGTCCTCCCTGCTCAGGGTCCTGCAGCGTTGCCTCTGCTCAGCCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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6.16992
mRNABench:mrl-sample-designed:v1:train:0000001
1
GGGACATCGTAGAGAGTCGTACTTACCGACACCTTGATCTTGGACTTCCAGCTGCAGAACGCGTGCAGGGCGCTGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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6.496648
mRNABench:mrl-sample-designed:v1:train:0000002
2
GGGACATCGTAGAGAGTCGTACTTAAAGCAGGTCGCTGTAAACGGGTCGTCTGTACAATGGAAACCATATAAGAGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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3.899053
mRNABench:mrl-sample-designed:v1:train:0000003
3
GGGACATCGTAGAGAGTCGTACTTATAACCGATGACACGTTATCATTCGCTGGATATGCGCCAGGCCCGTAAAGTATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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4.05458
mRNABench:mrl-sample-designed:v1:train:0000004
4
GGGACATCGTAGAGAGTCGTACTTACCGCCCGCCCCGCAAATTGCCGGCCTTACCCCAACACCTGTGGGTCCCGGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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4.306363
mRNABench:mrl-sample-designed:v1:train:0000005
5
GGGACATCGTAGAGAGTCGTACTTAACTCGCAATTGCTCCCAGCAAGAAACCGTTGTCATACCTTGTTACTGCCAATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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6.989155
mRNABench:mrl-sample-designed:v1:train:0000006
6
GGGACATCGTAGAGAGTCGTACTTACCCAGCAAAATCAAGCTGTTTTCTTTTGAAAGTTCAAACTCATCAAGATTATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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7.170883
mRNABench:mrl-sample-designed:v1:train:0000007
7
GGGACATCGTAGAGAGTCGTACTTAGCGCGCCGGGCCGCCCGGGAAGCCTCCGTCCCCGCGGCGGCGGCGGCAACATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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4.184066
mRNABench:mrl-sample-designed:v1:train:0000008
8
GGGACATCGTAGAGAGTCGTACTTACCTCTCTTAACATGGAGCCGGCGGAAGGGGTGGTGTAGGGCCGGGCGATAATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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2.456267
mRNABench:mrl-sample-designed:v1:train:0000009
9
GGGACATCGTAGAGAGTCGTACTTAATCTCAGAACTTCTATCAAAGAAAGAAGACAACAACCGATTATTTGTTCAATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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7.361411
mRNABench:mrl-sample-designed:v1:train:0000010
10
GGGACATCGTAGAGAGTCGTACTTACAACGAACGCGATAATGAACAAACATAGGGTGCGAAGAAGCGTCATGACCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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6.259835
mRNABench:mrl-sample-designed:v1:train:0000011
11
GGGACATCGTAGAGAGTCGTACTTACCCTATCTCAGCGCCCTGCCGCCGCCGCCGCGGCCCAGCGAGCGGCCCTGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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5.007351
mRNABench:mrl-sample-designed:v1:train:0000012
12
GGGACATCGTAGAGAGTCGTACTTATCATCCTGCAGATCTGGAGCAGCATTCCAGGGCCTGCCCATCACCTCTTCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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5.852492
mRNABench:mrl-sample-designed:v1:train:0000013
13
GGGACATCGTAGAGAGTCGTACTTACCCTGCCCCTGCCCCTCCCAGCACCTGGCCCACACCCTGCAGCCCGCCCCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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5.735292
mRNABench:mrl-sample-designed:v1:train:0000014
14
GGGACATCGTAGAGAGTCGTACTTACCAGCTCCTGGAGTTTCCGAGGTTCGTGCGCGTCTGGTGGCGGCGGAGTGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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6.573215
mRNABench:mrl-sample-designed:v1:train:0000015
15
GGGACATCGTAGAGAGTCGTACTTACGATATGATGGTCCTTGCTGCAGGGATTTAAGTCTACTTGCTTTTACATCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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5.462677
mRNABench:mrl-sample-designed:v1:train:0000016
16
GGGACATCGTAGAGAGTCGTACTTACCCTCAACATGGATCCAGACACGAAAGTTCCTGATAACACACCAATCGAAATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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6.48016
mRNABench:mrl-sample-designed:v1:train:0000017
17
GGGACATCGTAGAGAGTCGTACTTATTTTTTTTAATAAAACTATGTTCATGTTTATCAAAATGTTTAGATTCAATATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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6.662383
mRNABench:mrl-sample-designed:v1:train:0000018
18
GGGACATCGTAGAGAGTCGTACTTAGTATTGGAGGGGACAGAATGACCTTCCGGCCTTGAGTCCCTGGGGAGCAGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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3.396181
mRNABench:mrl-sample-designed:v1:train:0000019
19
GGGACATCGTAGAGAGTCGTACTTACGCCCCACCCCATCCTCCTGCCTTCCCGAGCTCCGGTGTCCCCCGCTGCCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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4.555106
mRNABench:mrl-sample-designed:v1:train:0000020
20
GGGACATCGTAGAGAGTCGTACTTACACCGCCCCTTCTCGGCCCTCCTGCGTTTGCCCAGGGTCGGCCCGCAGTGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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5.655078
mRNABench:mrl-sample-designed:v1:train:0000021
21
GGGACATCGTAGAGAGTCGTACTTATAATCTGAAAAAGAAGTAAGCGACATCGCAATTTAGAAAGAGTTGACTGTATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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6.96428
mRNABench:mrl-sample-designed:v1:train:0000022
22
GGGACATCGTAGAGAGTCGTACTTAACCAGCAGCAGGAACGGCTCCTGCTGTCTGTCCTTCCCCGTCATGTTGCCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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2.793641
mRNABench:mrl-sample-designed:v1:train:0000023
23
GGGACATCGTAGAGAGTCGTACTTACCTGTGTCAGCCCCGTCACCGCCGGGCGGCCCGCTCGGAGTCTGAGGGAGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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5.269903
mRNABench:mrl-sample-designed:v1:train:0000024
24
GGGACATCGTAGAGAGTCGTACTTAATACCAAAAAGACTACCACACAATCTAAGGAAATTGTCTCTGACTAGGTCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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7.196388
mRNABench:mrl-sample-designed:v1:train:0000025
25
GGGACATCGTAGAGAGTCGTACTTACCGGACAGGGCCAAAGTGCTAGGTGCGTGCGTGCGGCATACTTCATAACTATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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7.413468
mRNABench:mrl-sample-designed:v1:train:0000026
26
GGGACATCGTAGAGAGTCGTACTTACCGTCACTTTCCCCGGACAAAAATGGAGCACCCAAAAAAAAGCTCACGTCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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2.560987
mRNABench:mrl-sample-designed:v1:train:0000027
27
GGGACATCGTAGAGAGTCGTACTTATACGTCTTGTTGGATCGTTGTTATTTTTCATTGTATTATTAAGTTCAAATATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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8.00799
mRNABench:mrl-sample-designed:v1:train:0000028
28
GGGACATCGTAGAGAGTCGTACTTACATTTATTATTTGTTTGGATCAGATTCATATCAGTGTTACAAAAATTATGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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7.553622
mRNABench:mrl-sample-designed:v1:train:0000029
29
GGGACATCGTAGAGAGTCGTACTTACAGTGAAACTAATAATTGTGTTTTTTTTTTTTAAAATTATTATTATTGCTATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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7.849709
mRNABench:mrl-sample-designed:v1:train:0000030
30
GGGACATCGTAGAGAGTCGTACTTACCGGCAAAAGCCTGACCTTACACCTAGAATCCGCTATGGTGTGCGTTGCAATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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7.103933
mRNABench:mrl-sample-designed:v1:train:0000031
31
GGGACATCGTAGAGAGTCGTACTTACCTTGGCAATCAATGGGGTCTCTTGCACCAACCTCTCCCATGCCAGTGCCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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2.955713
mRNABench:mrl-sample-designed:v1:train:0000032
32
GGGACATCGTAGAGAGTCGTACTTACCCTCTTTGATTCTTCTAGCTGTTTCACTATTGGGCAACCAGACACCAGAATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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7.217179
mRNABench:mrl-sample-designed:v1:train:0000033
33
GGGACATCGTAGAGAGTCGTACTTACGCCGGGAGACGCGAACCTGCCGGACCACCGCGCGGGGACGACGGCGGCCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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6.153306
mRNABench:mrl-sample-designed:v1:train:0000034
34
GGGACATCGTAGAGAGTCGTACTTACCAGGGTCGTGCCGCCGGCGGGTCGGGCTGGGCAATGCCTCGCGGGCGCAATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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2.428527
mRNABench:mrl-sample-designed:v1:train:0000035
35
GGGACATCGTAGAGAGTCGTACTTATTTTTGATTTTGATTTGTTTCAGAGAGAAACAATTTATAAAAAAATAGCAATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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5.010381
mRNABench:mrl-sample-designed:v1:train:0000036
36
GGGACATCGTAGAGAGTCGTACTTACGCGTATTCCAGTCACACTTCGGGTCGGTCCACGGGAACACCCTGCCACGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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5.431468
mRNABench:mrl-sample-designed:v1:train:0000037
37
GGGACATCGTAGAGAGTCGTACTTACTGCAGCCTTGAAGTACTTCCAGACCGTGACTGACTATGGCAAGGACCTGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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5.008337
mRNABench:mrl-sample-designed:v1:train:0000038
38
GGGACATCGTAGAGAGTCGTACTTACCAGAGAGCCGCGCCCGCCGCGCGGAGCGCCCCTTCGCGTCCCCTGCACCATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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5.418044
mRNABench:mrl-sample-designed:v1:train:0000039
39
GGGACATCGTAGAGAGTCGTACTTACATGATCAGCCGTGGGAGCCACGGGAATCAATTGCTTCCAGTACGACTGGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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4.390392
mRNABench:mrl-sample-designed:v1:train:0000040
40
GGGACATCGTAGAGAGTCGTACTTACGACCTCGATGGGTGGGTGGATCGACTCGGATTAACTGATGACTCCATCGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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4.45087
mRNABench:mrl-sample-designed:v1:train:0000041
41
GGGACATCGTAGAGAGTCGTACTTACCATGACAAAGCGCGTTATAATAAGGCAGCACGGGGCCGATCTGTCCCCGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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5.503062
mRNABench:mrl-sample-designed:v1:train:0000042
42
GGGACATCGTAGAGAGTCGTACTTACGAATACTTCGGTGCGTCCACCACATTTTGGCTTGGCGGGGCGCCCTCCGATGGGCGAATTAAGTAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAA...
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End of preview.

mRNABench, curated

mRNABench, as published in quality-curated genomic benchmarks - one format, fixed row order, a permanent ID on every row. 11 datasets, 33 files, 1,413,237 rows, one gzipped CSV per split.

Getting the data

Two packages are the way in: genomic-benchmarks-data for people, genomic-benchmarks-data4agents for agents, the same functions either way. They resolve the URL, check the checksum, and carry each dataset's QC results, which this repository does not.

pip install genomic-benchmarks-data

from genomic_benchmarks_data import load_dataset

df = load_dataset("mRNABench", "mrl-sample-designed", "train")

A number measured on these files belongs on the leaderboard, which is what makes two of them comparable, and which prints what QC found about a dataset above every table.

Datasets

dataset task species train val test
mrl-sample-designed Regression Homo sapiens 70011 15003 15003
mrl-sample-egfp-m1pseudo Regression Homo sapiens 222927 47770 47771
mrl-sample-egfp-pseudo Regression Homo sapiens 222927 47770 47771
mrl-sample-egfp-unmod Regression Homo sapiens 222927 47770 47771
mrl-sample-mcherry Regression Homo sapiens 134257 28770 28770
mrl-sample-varying Regression Homo sapiens 74571 15979 15980
mrl-sugimoto Regression Homo sapiens 8186 1753 1754
rnahl-human Regression Homo sapiens 9057 1942 1940
rnahl-mouse Regression Mus musculus 9606 2058 2059
translation-efficiency-human Regression Homo sapiens 7196 1543 1542
translation-efficiency-mouse Regression Mus musculus 7597 1628 1628

Files are v1/<dataset>/<dataset>_<split>.csv.gz, where v1 is the version of the data. A dataset whose sequences change appears under a new prefix rather than being rewritten here.

Licence and provenance

dataset licence
mrl-sample-designed No restrictions (GEO deposition)
mrl-sample-egfp-m1pseudo No restrictions (GEO deposition)
mrl-sample-egfp-pseudo No restrictions (GEO deposition)
mrl-sample-egfp-unmod No restrictions (GEO deposition)
mrl-sample-mcherry No restrictions (GEO deposition)
mrl-sample-varying No restrictions (GEO deposition)
mrl-sugimoto CC BY 4.0
rnahl-human CC BY 4.0
rnahl-mouse CC BY 4.0
translation-efficiency-human No restrictions (author statement)
translation-efficiency-mouse No restrictions (author statement)

Every row has an identity

Each file carries two columns the upstream release does not:

  • id - <collection>:<dataset>:<version>:<split>:<row index>, the index zero-padded to 7 digits.
  • row_index - the row's 0-based position in this file.

Row order is fixed within a major version, so an id names the same sequence for as long as that version exists - which is what makes a reported number traceable to what it was computed on. The corpus metadata carries the SHA-256 of every split file, so a copy can be checked against the audited one.

Citation

Cite the collection's own publication (https://doi.org/10.1101/2025.07.05.662870) for the data, and Automated quality control for genomic sequence benchmarks reveals pervasive single-feature separability and train-test leakage for the curation and the audit.
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