Time flies, especially when I get various waves of data that needs analyzing, and also Thanksgiving, tests/papers from class, etc… Lots of tasks will carry over from November, BUT nevertheless, headway has been made.

Olys at dock

  • Clean, measure broodstock (after hatchery meeting)

Geoduck proteomics

  • Finish Results section
    • Correlation btwn various parameters, protein abundance
    • Try to use Structural Equation Model instead of linear regression model
    • QA %>1SD & %>2SD calculations
  • Get more methods info from Micah, Emma
  • Focus on DISCUSSION
  • Identify target journals
  • Determine figures for paper. Candidates:
    • Box/violin plots of protein abundances by subbasin, including 3 peptides separately. Like this, but with 4 panes for each protein:
    • NMDS plot (like what I already have) with overlaying circles by subbasin
    • 3-pane plot of pH, T, and DO (continuous)? Would be messy with all 8 traces from each site.
    • Correlation matrices for: variables identified in model (DO var, pH >1sd %, and growth)
  • Remaining things that Emma said I should do:
    • Linear Response Plot, as per Emma: Peak area on the y, amount of peptide (moles) on the x. Don’t know absolute quantity of experimental peptides, could make the x-axis relative quantity or something. Can generate plots like these in MSstats.
    • DIA error rate
    • SRM dilution curve
  • Idea for paper, but would take a while (probably a few days): screen all proteins in DIA Skyline data for a “menu” of targetable, detectable proteins (based on visual check of chromatograms). This could be published alongside the paper as a very usable resource.
  • More lit Research
    • Growth related to protein expression – but why only some proteins were diff. expressed?
    • Bioassays
    • Determine if abundance difference is “biologically relevant.” If I can find SRM abundance good (haven’t found good references yet), OR find citable % diff.

Prep for Oly genetics hatchery vs. wild analysis

  • re-Read Fischer et al. 2012
  • Get most current data set from Crystal
  • ID best program to use

Application things

  • Finish NDSEG, get letters
  • AA travel awards (?)


  • Submit MS paperwork to office & proposal online (pending approval from Rick, Jackie)
  • Learn how to tag notebook posts
  • Create README.md for proteomics data on Owl

In January:

  • Determine which Oly samples to sequence from last spring/winter
  • Meet with STATS resource to figure out how to analyze Oly larval survival – parse out survival data to 224um, to juvenile, by time for reps

On hold:

  • Record podcast pilot with Megan
  • Find local vendor that sells dry suits, try on to determine size, then purchase gear

Accomplishments from last month

  • Checked out Oly histology data from last year’s experiment
  • Cireculated MS proposal for approval from committee
  • Submitted NSA abstracts, travel grant app
  • Sampled experimental Olys for histology (time=0), got animals into treatments
  • Received collection permit @ Mud Bay, made contact with homeowners for access, collected first batch
  • Gave Oly shells to Heater @ Wood’s lab for Polydora inspection
  • Got the CoEnv travel grant for AA
  • Proteomics stuff:
    • Finalized SRM tech. rep filtering process
    • Tracked down tidal charts from each Geoduck Proteomics ouplant location; used to process/filter environmental data from Micah
    • Generated summary statstics on environmental data
    • Re-ran proteomics analyses on the peptide-level, where transitions within each peptide were summed. Lambda-transformed peptide abundance for normal distribution, then ran 2-way ANOVAs.
    • Learned how to run stepwise linear resgression models, ran on diff. expressed proteins with environmental summary stats.
    • Refined methods section of paper
    • Made headway on results section of paper
    • Drafted intro; will likely need to revise based on results
    • Finished parasite class things
    • Generated notebook for DIA data in Skyline
    • R-script for merging DIA results with annotations, extracting SRM targets from this dataframe
    • Generated peptide variability stats for paper
    • Discarded unecessary vials (the “just in case” vials, and autosampler vials prepped for mass spec). If I need to re-run samples, I’d prepare new autosamper vials from my digested peptides.

from LabNotebook http://ift.tt/2C7PsXW

December Goals (belated)
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