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SLM (Sample Lifecycle Manager)

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SLM (Sample Lifecycle Manager) laboratory workflow and data management software.

SLM (Sample Lifecycle Manager) laboratory workflow and data management software.

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  • 1. SLM Sample Lifecycle Manager
  • 2.
      • BCF - Bio Computing Facility
      • UAGC - University of Arizona Genetics Core
      • ARL - Arizona Research Laboratories
  • 3.
      • UAGC provides molecular biology lab services to:
        • On campus UA researchers
        • University and non-profit scientists around the world
        • Corporate programs including:
          • National Geographic Genographic project
          • Family Tree DNA
      • UAGC processes > 100K samples per year
  • 4.
      • UAGC services:
        • Sample prep:
          • extraction, quantification, normalization, pcr, storage, plating
        • 454 sequencing
        • Ion Torrent sequencing
        • Sequenom genotyping and methylation analysis
        • Sanger sequencing
        • Fragment/str/microsatellite analysis
        • Taqman genotyping
        • Transgenic genotyping and cell line authentication
        • Real-Time PCR
        • Bioinformatics support and data analysis
  • 5.
      • UAGC environment:
        • High throughput automated laboratory
  • 6.
      • SLM Goal:
        • Integration of all UAGC services to provide complete and customized tissue-to-data workflow services for our customers.
      • SLM Scope:
        • Customer facing sample submission, data delivery, and collaboration.
        • Lab facing sample and workflow management.
        • Model every existing laboratory process, and allow 'easy' expansion for new processes.
  • 7.
      • SLM Features:
        • User, staff, and lab management
        • Sample submission
        • Sample and reagent volume and concentration tracking
        • Automated dilutions
        • 'Cherrypicking' reaction setup and plating
        • Robotic transfer builder
        • GLP status logging
        • Samplesheet creation
        • Result data delivery and collaboration
        • Billing reports
  • 8.
      • SLM First Generation
        • Actionscript/Flex front end, Python backend.
        • Traditional architecture with many user options.
  • 9.
      • Why it didn't work
        • Too many options to cover all possible workflows
        • Difficult for users to understand all options
        • Difficult for developers to manage/test all possible option configurations.
  • 10.
      • SLM Second Generation
        • Provide a modular architecture that makes it easier for developers to custom code exactly what's needed for a specific workflow.
  • 11.
      • Eager
        • A framework for developing workflow based web applications
      • Based on Django - a Python library for MVC web apps
      • Provides extensible workflow definitions
      • Provides common LIMS (laboratory information management system) models and methods:
        • Tube/plate management
        • Well label translations
        • Grid file parsing/writing
        • Volume/concentration tracking
        • Results data management
  • 12.  
  • 13.
      • HTML
      • Pros:
        • Modular using server-side templates
        • No special software required
        • Easily styled with CSS
        • Accessibility
      • Cons:
        • Browser compatibility issues
        • RIA requires lots of Javascript, which can have performance problems on older machines
  • 14.
      • Dojo Javascript Library
      • Pros:
        • Everything you could ever want is built-in
        • Modules and classes allow code organization
        • Automated build process for optimizing code
      • Cons:
        • Not as elegant or concise as JQuery
        • Sometimes the documentation is good (API), sometimes not so much (tutorials)....
  • 15.
      • Apache/mod_wsgi
      • Pros:
        • Available on every Linux box
        • OK documentation
        • Everyone knows how to configure it (or they should at least...)
      • Cons:
        • Doesn't have the performance and scalability of more modern web servers such as nginx
  • 16.
      • Python Pros
      • Elegant and concise (unlike this slide)
      • Great documentation
      • Tons of HIGH QUALITY libraries, with large std lib
      • Useful collection of built-in data types
      • Cool features:
        • lambdas, list comprehensions, generators, properties, decorators
      • Avoids pitfalls of other dynamic languages:
        • Namespaced
        • Everything is an object
        • Strong typing with no implicit or explicit casting
        • Runtime error on undefined variables
        • Error handling is consistent: runtime errors are always exceptions
  • 17.
      • Python Cons
      • Cons:
        • Some people think it's too slow... (BUT, writing C extensions is pretty easy)
        • Some people don't like blocks defined by indentation... (BUT, those people are crazy)
  • 18.
      • Django Pros
      • Large community
      • Great documentation
      • Reusable 'applications'
      • Simple request handlers
      • Form builder and validation, including CSRF protection
      • ORM (for simple models and tasks)
      • Command line scripts and shell interface
      • Designers can create and modify templates
      • Easy URL routing
      • Built-in management site (we don't use this feature)
  • 19.
      • Django Cons
      • ORM (for complex models and tasks)
      • They call their 'controller' the 'view'
      • Some nit-picky technical issues with templates
  • 20.
      • SQLite
      • Pros:
        • Single file database
        • No configuration required
      • Cons:
        • Can't handle concurrent writes
  • 21.
      • PostgreSQL
      • Pros:
        • Tons of features
        • Not owned by Oracle
        • Easy configuration
        • Works how you expect it to
      • Cons:
        • Connection overhead (can be mitigated by using connection pooling)
  • 22.
      • Application demo
  • 23.
      • Contact info:
        • Blog: www.limscoder.com
        • Twitter: @limscoder