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Computational biology project incorporating functional genomics and machine learning
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Exploring the Differential Effects of Sequencing Resolution on Semi-Automated Genome Annotations

A project from Summer 2018 that compared the utility of newer ChIP methods ChIP-exo and ChIP-nexus on its effects on Semi-Automated Genome Annotation (SAGA) method, Segway's, ability to generate annotations from this data of higher signal to noise ratio and near single base pair resolution.


  1. Data Cleaning (data_preprocessing/)
    • Download raw data from ENCODE project and NCBI SRA (
    • Convert to bedgraph and sort data based on threshold cut off (
    • QC with PhantomPeakQualTools + peak calling with MACS2 to generate bedgraphs (MACS2/ & PhantomPeakQualTools/)
    • Store data in genomedata archive (
  2. Run Segway (segway/)
    • Training then identification rounds ( &
    • Set minibatch training of 10 round on 1% of the genome
    • Try with 5 different resolutions: 100bp, 1bp, 2bp, 30bp, 50bp
  3. Analyze Results
    • Recolour segway annotations with 10 different colours for visualization in genome browser (segway/)
    • Run stable marriage Hungarian algorithm on annotations (stablemarriage/)
    • Graph heatmaps and bipartite graphs in R. Account for negative/NaNs by adding pseudocount (LOD/2) to all data points prior to normalizing (pseudocount/)
    • All graph generating functions in datavisualization.R file, generated some in R notebook (can find in lab notebook)
  4. Miscellaneous
    • Script to clean up on the cluster (segway/
    • Alternate attempt at finding the LOD via the genomedata archives that were already generated (segway/
    • Get average counts from bedgraph file (data_preprocessing/
    • Optional conversion from bigwig to wiggle format (data_preprocessing/


Lab Notebook on (Mordor Server) Final Presentation

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