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Q-consensus: Read Quality Scores for Consensus Sequences

How do you interpret read Q-scores when you have several reads for the same sequence? Q-consensus is introduced to tackle that question. Q-consensus derives from the concept that more information is gained about a template sequence, the more reads that are provided; both when reads agree and disagree on a called base.

For more information, see draft_v0.3.pdf for a motivation for and derivation of the model.

Note: Q-consensus is a work in progress. See "To-do" section below.

Contents

  • draft_v0.3.pdf: The derivation of the Q-consensus model [Draft]
  • q-consensus.py: A Python implementation of the Q-consensus model
  • read-depth.ipynb: Testing how the model predicts Q-scores to increase when multiple reads agree on a consensus.

To-do

  • Handle gaps in alignment (and final refinement of model)
  • Optimization of speed
  • Set up as python module
  • Third party validation