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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 8 new columns ({'cnag_id_1', 'gene_id_2', 'gene_id_1', 'p_value', 'cnag_id_2', 'coevo_gene_id_2', 'coevo_gene_id_1', 'coevolution_coefficient'}) and 3 missing columns ({'query_target', 'rank_score', 'ref_target'}).

This happened while the csv dataset builder was generating data using

hf://datasets/maomlab/CryptoCEN/CoEvo_network.tsv (at revision 216b539c2f8ad59c3202031d9413ee3eac54efb8)

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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              coevo_gene_id_1: string
              coevo_gene_id_2: string
              coevolution_coefficient: double
              p_value: double
              gene_id_1: string
              gene_id_2: string
              cnag_id_1: string
              cnag_id_2: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1257
              to
              {'ref_target': Value(dtype='string', id=None), 'query_target': Value(dtype='string', id=None), 'rank_score': Value(dtype='float64', id=None)}
              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 1316, in compute_config_parquet_and_info_response
                  parquet_operations, partial = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 909, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              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 8 new columns ({'cnag_id_1', 'gene_id_2', 'gene_id_1', 'p_value', 'cnag_id_2', 'coevo_gene_id_2', 'coevo_gene_id_1', 'coevolution_coefficient'}) and 3 missing columns ({'query_target', 'rank_score', 'ref_target'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/maomlab/CryptoCEN/CoEvo_network.tsv (at revision 216b539c2f8ad59c3202031d9413ee3eac54efb8)
              
              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.

ref_target
string
query_target
string
rank_score
float64
CNAG_00001-t26_1
CNAG_00001-t26_1
0.921313
CNAG_00001-t26_1
CNAG_07497-t26_1
0.919076
CNAG_00001-t26_1
CNAG_07790-t26_1
0.912885
CNAG_00001-t26_1
CNAG_06523-t26_1
0.912885
CNAG_00001-t26_1
CNAG_06938-t26_1
0.909417
CNAG_00001-t26_1
CNAG_07276-t26_1
0.900338
CNAG_00001-t26_1
CNAG_07586-t26_1
0.89779
CNAG_00001-t26_1
CNAG_07395-t26_1
0.897249
CNAG_00001-t26_1
CNAG_07584-t26_1
0.889555
CNAG_00001-t26_1
CNAG_07791-t26_1
0.885788
CNAG_00001-t26_1
CNAG_06522-t26_1
0.885788
CNAG_00001-t26_1
CNAG_07919-t26_1
0.885788
CNAG_00001-t26_1
CNAG_07621-t26_1
0.728656
CNAG_00001-t26_1
CNAG_05376-t26_1
0.027042
CNAG_00001-t26_1
CNAG_07739-t26_1
0.027042
CNAG_00001-t26_1
CNAG_04697-t26_1
0.027042
CNAG_00001-t26_1
CNAG_00730-t26_1
0.014598
CNAG_00001-t26_1
CNAG_06913-t26_1
0.010203
CNAG_00001-t26_1
CNAG_03017-t26_1
0.010203
CNAG_00272-t26_1
CNAG_00272-t26_1
0.999155
CNAG_00272-t26_1
CNAG_02440-t26_1
0.62403
CNAG_00272-t26_1
CNAG_01037-t26_1
0.527353
CNAG_00272-t26_1
CNAG_06150-t26_1
0.494536
CNAG_00272-t26_1
CNAG_04318-t26_1
0.437091
CNAG_00272-t26_1
CNAG_04450-t26_1
0.396718
CNAG_00272-t26_1
CNAG_03381-t26_1
0.310276
CNAG_00528-t26_1
CNAG_00528-t26_1
0.986629
CNAG_00528-t26_1
CNAG_03908-t26_1
0.761782
CNAG_00528-t26_1
CNAG_03908-t26_1
0.717308
CNAG_00528-t26_1
CNAG_01733-t26_1
0.741948
CNAG_00528-t26_1
CNAG_01733-t26_1
0.494536
CNAG_00528-t26_1
CNAG_02153-t26_1
0.736591
CNAG_00528-t26_1
CNAG_02153-t26_1
0.726456
CNAG_00528-t26_1
CNAG_02153-t26_1
0.702685
CNAG_00528-t26_1
CNAG_02153-t26_1
0.652196
CNAG_00528-t26_1
CNAG_01262-t26_1
0.730824
CNAG_00528-t26_1
CNAG_01262-t26_1
0.453606
CNAG_00528-t26_1
CNAG_00775-t26_1
0.730824
CNAG_00528-t26_1
CNAG_01867-t26_1
0.726456
CNAG_00528-t26_1
CNAG_01867-t26_1
0.506525
CNAG_00528-t26_1
CNAG_01867-t26_1
0.15283
CNAG_00528-t26_1
CNAG_01630-t26_1
0.724223
CNAG_00528-t26_1
CNAG_01630-t26_1
0.678883
CNAG_00528-t26_1
CNAG_03584-t26_1
0.719638
CNAG_00528-t26_1
CNAG_00073-t26_1
0.712623
CNAG_00528-t26_1
CNAG_00073-t26_1
0.601817
CNAG_00528-t26_1
CNAG_05795-t26_1
0.707687
CNAG_00528-t26_1
CNAG_05795-t26_1
0.64334
CNAG_00528-t26_1
CNAG_03297-t26_1
0.707687
CNAG_00528-t26_1
CNAG_03297-t26_1
0.589731
CNAG_00528-t26_1
CNAG_03124-t26_1
0.707687
CNAG_00528-t26_1
CNAG_03124-t26_1
0.647787
CNAG_00528-t26_1
CNAG_03124-t26_1
0.494536
CNAG_00528-t26_1
CNAG_03124-t26_1
0.310276
CNAG_00528-t26_1
CNAG_01600-t26_1
0.694264
CNAG_00528-t26_1
CNAG_05428-t26_2
0.697239
CNAG_00528-t26_1
CNAG_05428-t26_2
0.583402
CNAG_00528-t26_1
CNAG_05428-t26_1
0.697239
CNAG_00528-t26_1
CNAG_05428-t26_1
0.583402
CNAG_00528-t26_1
CNAG_02982-t26_1
0.694264
CNAG_00528-t26_1
CNAG_02982-t26_1
0.638463
CNAG_00528-t26_1
CNAG_02982-t26_1
0.453606
CNAG_00528-t26_1
CNAG_02982-t26_1
0.192272
CNAG_00528-t26_1
CNAG_01439-t26_1
0.684891
CNAG_00528-t26_1
CNAG_01439-t26_1
0.613264
CNAG_00528-t26_1
CNAG_04074-t26_1
0.681975
CNAG_00528-t26_1
CNAG_04074-t26_1
0.396718
CNAG_00528-t26_1
CNAG_06107-t26_1
0.678883
CNAG_00528-t26_1
CNAG_06107-t26_1
0.607725
CNAG_00528-t26_1
CNAG_06107-t26_1
0.601817
CNAG_00528-t26_1
CNAG_01432-t26_2
0.67534
CNAG_00528-t26_1
CNAG_01432-t26_2
0.562468
CNAG_00528-t26_1
CNAG_01432-t26_2
0.55465
CNAG_00528-t26_1
CNAG_01432-t26_2
0.453606
CNAG_00528-t26_1
CNAG_05465-t26_1
0.671665
CNAG_00528-t26_1
CNAG_05465-t26_1
0.517311
CNAG_00528-t26_1
CNAG_05465-t26_1
0.273092
CNAG_00528-t26_1
CNAG_05219-t26_1
0.671665
CNAG_00528-t26_1
CNAG_01432-t26_1
0.67534
CNAG_00528-t26_1
CNAG_01432-t26_1
0.569662
CNAG_00528-t26_1
CNAG_01432-t26_1
0.55465
CNAG_00528-t26_1
CNAG_01432-t26_1
0.546101
CNAG_00528-t26_1
CNAG_00516-t26_1
0.667952
CNAG_00528-t26_1
CNAG_07440-t26_1
0.660355
CNAG_00528-t26_1
CNAG_07440-t26_1
0.536894
CNAG_00528-t26_1
CNAG_07440-t26_1
0.15283
CNAG_00528-t26_1
CNAG_07756-t26_1
0.660355
CNAG_00528-t26_1
CNAG_01561-t26_1
0.656422
CNAG_00528-t26_1
CNAG_01561-t26_1
0.652196
CNAG_00528-t26_1
CNAG_01561-t26_1
0.15283
CNAG_00528-t26_1
CNAG_05294-t26_1
0.656422
CNAG_00528-t26_1
CNAG_05294-t26_1
0.618811
CNAG_00528-t26_1
CNAG_00693-t26_2
0.652196
CNAG_00528-t26_1
CNAG_00693-t26_2
0.546101
CNAG_00528-t26_1
CNAG_00693-t26_1
0.652196
CNAG_00528-t26_1
CNAG_00693-t26_1
0.546101
CNAG_00528-t26_1
CNAG_03554-t26_1
0.633385
CNAG_00528-t26_1
CNAG_03554-t26_1
0.437091
CNAG_00528-t26_1
CNAG_05101-t26_1
0.633385
CNAG_00528-t26_1
CNAG_05101-t26_1
0.506525
End of preview.

CryptoCEN: A Co-expression network for Cryptococcus neoformans

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.

MJ O'Meara, JR Rapala, CB Nichols, C Alexandre, B Billmyre, JL Steenwyk, A Alspaugh, TR O'Meara CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair Code available at https://github.com/maomlab/CalCEN/tree/master/vignettes/CryptoCEN

h99_transcript_annotations.tsv

  • Cryptococcus neoforman H99 (NCBI Taxon:235443) annotated protein features collected from FungiDB Release 49

top_coexp_hits.tsv

  • top 50 CrypoCEN associations for each gene

top_coexp_hits_0.05.tsv

  • top CrypoCEN associations for each gene filtered by score > 0.95 and at most 50 per gene

Data/estimated_expression_meta.tsv

  • Metadata for RNAseq estimated expression runs

Data/estimated_expression.tsv

  • gene by RNA-seq run estimated expression

Data/sac_complex_interactions.tsv

  • C. neoformans genes that are orthologous to S. cerevisiae genes who's proteins are involved in a protein complex

Networks/CryptoCEN_network.tsv

  • Co-expression network

Networks/BlastP_network.tsv

  • Protein sequence similarity network

Network/CoEvo_network.tsv

  • Co-evolution network
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