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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)
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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 |
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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