feat(logging): improve error visibility and logging across application

[bencap]

This pull request introduces several improvements to error handling and logging throughout the codebase, especially for network/resource acquisition and alignment/mapping operations. It provides more detailed exception messages, standardized error reporting, and improved debugging information, making failures easier to diagnose and trace. Additionally, it introduces a custom database connection class with a longer timeout for UTA queries to reduce failures on long-running operations.

Error handling and logging improvements:

• Added detailed logging with exception tracebacks to all major failure points, including BLAT parsing, external resource checks (UTA, SeqRepo, Gene Normalizer), and transcript/protein accession lookups, using _logger.exception and improved error messages for better traceability. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

• Standardized error message formatting in ScoresetMapping and MappedScore objects to include both the exception type and message, improving output clarity for downstream consumers. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Resource/network acquisition robustness:

• Enhanced the request_with_backoff utility to log transient failures and server errors at each retry, and to raise a custom ResourceAcquisitionError (with the last exception as the cause) after exhausting all retries. [1] [2] [3] [4] [5]

Database connection improvements:

• Introduced a _TimeoutUtaDatabase class that increases the command timeout for UTA database queries from 60 seconds to 5 minutes, reducing failures on long-running range queries. Updated the main lookup code to use this subclass. [1] [2]

Exception propagation and critical error logging:

• Ensured exceptions are re-raised after logging and emitting error information, preventing silent failures and allowing for proper upstream handling. Also, improved critical error logging for resource acquisition failures. [1] [2]

These changes collectively make the codebase more robust, easier to debug, and more transparent in the face of errors and operational issues.