Initial results: new egg-laying mutants?

tl;dr: Using the data collected so far, we’ve already identified some mutants that look like they have different egg laying rates than the reference strain.  These could point to previously unknown roles for the mutated genes in nervous system function.  See the plot below for a summary of some of the results.

Following the dedicated work of so many worm watchers, we got classifications for all of the data that we initially uploaded to the site (thanks again!).  Since then, we’ve had a chance to look at some of the results.

Remember that the goal of this project is to find genes that play roles in behaviour.  The way we do that is by looking at mutant worms with known genetic changes to see if they behave differently.  If they do, then that’s a hint that the altered gene affects behaviour.  Many of the genes we are focussing on are likely to function in nerve cells so these results can provide new starting points for learning how brains work at the molecular level.

With a project like Worm Watch Lab, each video gets classified by multiple people.  We can compare different people’s annotations to help filter out mistakes and refine the timing of egg laying events.  Bertie Gyenes, a graduate student in my lab, has followed up on the work Vicky talked about in her blog post.

subspecial_illAt this point, we’ve just done some preliminary analysis of the average egg laying rate (how many eggs worms lay per video).  The plot summarises this data.  Each bar shows the range of egg laying rates observed for a given strain (the red crosses are individual outlying worms) and the dashed orange line shows the rate typically observed for the laboratory reference strain N2.  The plot only shows data for the mutants with the lowest and highest egg laying rates.  As you can see, many of the worms with low rates are called egl.  That’s because these genes were initially discovered by screening for mutants with abnormal egg laying.  These are expected hits.  What you can also see are worms with unusually high egg laying rates.  Many of these, including the current record holder in our dataset dnc-1 were not previously known to have abnormal egg laying rates, which means we already have hints of new discoveries in the data that’s been categorised so far!

If you’re interested in learning more about any of these genes, just go to http://wormbase.org/ and put the gene name into the search bar.  Sometimes you can learn interesting things about the genes.  For example, mutations in the human version of dnc-1 can lead to Charcot-Marie-Tooth disease.

We’re getting ready to upload some new videos to Worm Watch Lab soon.  Please consider contributing some more annotations for the next batch of data.  It will be exciting to see what else we’ll find.

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About André Brown

I'm a scientist with the Medical Research Council in the UK.

3 responses to “Initial results: new egg-laying mutants?”

  1. brianaharder says :

    Thanks for the update André! So great to hear that the preliminary analysis turned up some interesting finds.

    Will we be looking at an all new set of mutations in the new videos?

    • André Brown says :

      Most likely, the first new set of data will be from ‘N2’ animals. This is the reference (non-mutated) strain that we will compare the mutants too. (the line in the plot is just a literature estimate and doesn’t necessarily reflect the egg laying rate we get in these experiments). Soon, I hope we’ll have some results on a semi-automated way of detecting egg laying. It may not be perfect, but even if we could reliably eliminate half of the videos that definitely don’t have egg laying, that would be really useful. At that point, we will probably try to post the remaining (pre-filtered) mutant data.

      • brianaharder says :

        Getting an equal amount of information on the control strain makes a lot of sense.

        I’m glad to hear you guys are working on pre-filtering the videos, I’m a big fan of automatic classification working in harmony with citizen science. It’s very cool to be able to use cit science classifications to build an auto classifier that can reduce the amount of data that needs human eyes on it!

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