HTTP File Upload Implementation

http-file-upload-implementation

Summary

Design and deploy an HTTP/HTML form-based file upload interface for receiving mass spectrometry raw files (.raw, .mzML, .mzXML) into a web platform. This skill enables the data collection entry point for automated omics workflows by validating file formats, enforcing size/integrity constraints, persisting uploads to a repository, and tracking ingestion outcomes.

When to use

You are building the initial data ingestion step of a high-throughput MS platform and need to accept raw MS files from users or instruments via a web interface. Use this skill when you require automated validation of vendor-specific formats (Thermo .raw, mzML, mzXML), reliable persistence to a backend repository, and auditable upload status tracking (success/failure/pending).

When NOT to use

• Input files are already validated, processed, or in a downstream format (e.g., feature tables, mzTab); this skill targets raw ingestion only.
• Your platform is a desktop application; this skill assumes a web-based HTTP interface.
• You require real-time processing or streaming ingestion; this skill validates and persists static file uploads.

Inputs

• MS raw files (.raw, .mzML, .mzXML)
• HTML form submission with file payload
• User metadata (optional: experiment name, sample ID, instrument type)

Outputs

• Persisted raw MS file in repository
• Upload status record (success/failure/pending)
• Metadata log (timestamp, user, filename, file hash, ingestion outcome)
• HTTP response (success confirmation or validation error message)

How to apply

Implement a web-based uploader module using Python and Django that exposes an HTTP/HTML form endpoint for file selection and submission. Validate incoming uploads by checking file extension and format signature against supported MS raw file formats (.raw, .mzML, .mzXML) and enforce size and integrity constraints to reject malformed or oversized files. Configure a file storage backend (e.g., filesystem or cloud storage) to persist validated uploads to a repository accessible by downstream data management and processing layers. Integrate upload status tracking with logging to record success/failure/pending states and associate metadata (upload timestamp, user, filename, file hash) with each ingestion event. Test the uploader with representative MS raw files from different vendors to confirm correct storage and metadata recording.

Related tools

• Django (Web framework for implementing HTTP form endpoints and file upload handling)
• Python (Language for implementing file validation logic and upload workflow orchestration)
• JavaScript (Client-side form interaction and file preview/validation feedback in HTML interface)
• HTML (Markup for upload form UI and user interaction)
• Raw File Uploader (Reference uploader component integrated with MSConnect for ingesting MS raw files) — https://github.com/RTKlab-BYU/Raw_File_Uploader
• MSConnect (Platform that integrates and manages the upload entry point as part of the data collection step) — https://github.com/RTKlab-BYU/MSConnect

Evaluation signals

• Files matching supported MS formats (.raw, .mzML, .mzXML) are successfully written to the designated repository and are retrievable via the data management layer.
• Upload status log records all ingestion events with correct timestamps, user identifiers, filenames, and outcome states (success/failure/pending).
• Files failing validation (wrong format, oversized, corrupted) are rejected with informative error messages and not persisted to the repository.
• Metadata associations (e.g., file hash, integrity checksum) are correctly recorded and match the stored file content.
• Representative MS raw files from different vendors (Thermo, Waters, Sciex, etc.) can be ingested without loss of data or format corruption.

Limitations

• The uploader does not perform MS data analysis or processing; it only validates and persists raw files as an entry point.
• File format validation is extension and basic signature-based; deep format verification may require vendor-specific parsing libraries.
• Upload performance and storage capacity depend on backend infrastructure; large-scale, high-frequency uploads may require load balancing and distributed storage.
• The uploader itself does not include data processor or analysis software licenses; downstream processing requires integration with third-party tools.
• Security (authentication, authorization, encryption) is not explicitly covered in the workflow and must be implemented separately depending on deployment context.

Evidence

• [other] MSConnect works with the Raw File Uploader component to enable the data collection entry point, integrating this third-party tool to support automated high-throughput MS-based omics workflows.: "MSConnect works with the Raw File Uploader component to enable the data collection entry point, integrating this third-party tool to support automated high-throughput MS-based omics workflows"
• [other] Design the Raw File Uploader module using Python and Django to accept file uploads via HTTP/HTML form interface.: "Design the Raw File Uploader module using Python and Django to accept file uploads via HTTP/HTML form interface"
• [other] Implement file validation logic to verify raw MS file formats (e.g., .raw, .mzML, .mzXML) and enforce size/integrity constraints.: "Implement file validation logic to verify raw MS file formats (e.g., .raw, .mzML, .mzXML) and enforce size/integrity constraints"
• [other] Configure file storage backend to persist uploaded raw files in a designated repository accessible to the MSConnect data management layer.: "Configure file storage backend to persist uploaded raw files in a designated repository accessible to the MSConnect data management layer"
• [other] Integrate upload status tracking (success/failure/pending) and logging to report ingestion outcomes.: "Integrate upload status tracking (success/failure/pending) and logging to report ingestion outcomes"
• [readme] The platform is built with Python, Django, JavaScript, and HTML and works with Raw file uploader: "The platform is built with Python, Django, JavaScript, and HTML and works with Raw file uploader"
• [readme] MSConnect streamlines the entire process from data collection, data management, data processing and to data visualization by integrating and supporting various third-party tools: "MSConnect streamlines the entire process from data collection, data management, data processing and to data visualization by integrating and supporting various third-party tools"