Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Dataset Summary

This dataset is a processed, tabular representation of the NCBI RefSeq GFF file for the human reference genome GRCh38.p14. The original GFF (General Feature Format) data, GCF_000001405.40_GRCh38.p14_genomic.gff, has been converted to the Parquet format for efficient storage and fast querying using libraries like Pandas and Apache Arrow.

The data contains annotations for all genomic features (genes, transcripts, exons, CDS, etc.) on the primary sequences of the GRCh38 human genome assembly.

Data Structure

The dataset is stored as a single Parquet file and can be easily loaded into a Pandas DataFrame.

Column Name	Data Type	Description	Example Value
`seqid`	string	NCBI Accession ID for the sequence (chromosome). Needs mapping to `chrX` names.	`NC_000001.11`
`source`	string	Source of the feature (e.g., RefSeq, BestRefSeq).	`BestRefSeq`
`type`	string	Type of the genomic feature (e.g., `gene`, `transcript`, `exon`, `CDS`).	`gene`
`start`	int32	Feature start position (1-based, inclusive).	`11874`
`end`	int32	Feature end position (1-based, inclusive).	`14409`
`score`	string	Score (always `.`)	`.`
`strand`	string	Feature strand (`+` for forward, `-` for reverse).	`+`
`phase`	string	Phase for CDS features (0, 1, or 2).	`.`
`attributes`	string	Feature attributes, stored as a JSON string/dictionary.	`{"ID": "gene-DDX11L1", "Dbxref": ["GeneID:100287102", "HGNC:HGNC:37102"]...}`

Usage

1. Python (Pandas) Setup

To easily convert the seqid (NCBI Accession ID) to the standard chromosome name (chr1, chr2, etc.), you can use the following mapping dictionary extracted from the GRCh38.p14 assembly report.

ncid2chr = {
    'NC_000001.11': 'chr1', 'NC_000002.12': 'chr2', 'NC_000003.12': 'chr3',
    'NC_000004.12': 'chr4', 'NC_000005.10': 'chr5', 'NC_000006.12': 'chr6',
    'NC_000007.14': 'chr7', 'NC_000008.11': 'chr8', 'NC_000009.12': 'chr9',
    'NC_000010.11': 'chr10', 'NC_000011.10': 'chr11', 'NC_000012.12': 'chr12',
    'NC_000013.11': 'chr13', 'NC_000014.9': 'chr14', 'NC_000015.10': 'chr15',
    'NC_000016.10': 'chr16', 'NC_000017.11': 'chr17', 'NC_000018.10': 'chr18',
    'NC_000019.10': 'chr19', 'NC_000020.11': 'chr20', 'NC_000021.9': 'chr21',
    'NC_000022.11': 'chr22', 'NC_000023.11': 'chrX', 'NC_000024.10': 'chrY',
    'NC_012920.1': 'chrM'
}

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