An online database for behavioural phenotypes of the nematode Caenorhabditis elegans Tadas Jucikas Darwin College A thesis submitted on the 15th of August, 2013 for the Degree of Doctor of Philosophy I would like to dedicate this thesis to my loving grandmother Gabriel_e,devoted parents Janina and Pranciˇskusand caring siblings Kristina and Viktoras. I am humbled and truly grateful for your support. Abstract Advances in the fields of neurobiology, genomics, computer imaging and bioinfor- matics provide a favorable landscape to study genetic and molecular mechanisms of model organism behaviour. However, manual animal observation and char- acterization has been an intrinsically subjective and time consuming effort. It encouraged the development of computer aided automatic tracking systems ca- pable of recording animal behaviour and storing the data for further analysis. For the nematode C. elegans, an array of worm trackers have been built focus- ing on high spatial and temporal resolution or on optimizing for higher through- put and imaging multiple worms at once. Early worm trackers quantified animal locomotion and described diverse groups of neural functions like proprioception, synaptic transmission and neuromodulation. However, behavioural phenotypes have been described only for a small fraction of genes despite evidence that the majority of genes are needed for wild type fitness. Emergence of reverse genetics and RNA interference based screens have greatly outstripped the phenotyping capability of C. elegans research community. In addition, the algorithms and techniques used were not based on community standards and prevented pooling behavioural data collected in different research groups. To address the challenges of large scale behavioural phenotyping screens this thesis describes a platform that uses single worm trackers for high throughput analysis of behaviour experiments. The platform consists of an array of computers used for data collection and analysis, a MySQL database backbone and a set of MATLAB based tools for behaviour feature extraction and visualization. In addition to building a standalone high throughput analysis platform this work also proposes a standard for C. elegans neurobiology community and opens up a possibility of a centralized C. elegans behavioural experiment repository that would benefit from the work carried out by different research groups. The thesis also presents an online C. elegans behaviour database that is inte- grated with the data acquisition and analysis platform. The database hosts all of the behavioural experiment recordings and their analysis results. Users can access more than 10,000 experiments representing 329 strains, inspect their aggregate statistics and download the data for further analysis. Behavioural experiment videos can be viewed via the video sharing website YouTube and are embedded in the database pages. The online database address is http://wormbehavior.mrc- lmb.cam.ac.uk. This thesis also illustrates how the database could be used to carry out novel behavioural feature extraction and analysis by accessing strain feature data. C. elegans navigation behaviour was studied in greater detail with a focus on turns, reversals and their related properties. One of the novel reorientation phenotypes that have been identified manifests in a loss of ventral bias for two DEG/ENaC channel mutants asic-2 (ok289) and acd-5 (ok2657). This novel phenotype indi- cates functional similarity of these two genes. Similarly to that, other behaviours can be extracted and studied by downloading the feature data available via the online database. The platform presented in this thesis enables quantitative behavioural anal- ysis of C. elegans mutants and enables detection of previously uncharacterised locomotion phenotypes. It presents a dataset of more than 10,000 behaviour ex- periment recordings describing 329 C. elegans strains and is organized in a user friendly online database. It is to our knowledge the largest existing C. elegans behaviour dataset and it promises to be a rich resource for further exploratory analysis. The data and the methods presented in this thesis promote and inform further work in this area as well as encourage emergence of similar platforms in other model organisms. Declaration This thesis: • is my own work and contains nothing which is the outcome of work done in collaboration with others, except where specified in the text; • is not substantially the same as any that I have submitted for a degree or diploma or other qualification at any other university; and • does not exceed the prescribed limit of 60,000 words. Tadas Jucikas August, 2013 Acknowledgements The work for this thesis has been carried out at the Medical Research Council Laboratory of Molecular Biology (LMB), a research institute at the University of Cambridge. I would like to thank my supervisor Dr. William Schafer for giving me the opportunity to work on this challenging project. Dr. Schafer has given me a tremendous amount of freedom and maintained utmost faith in me over the years for which I am truly grateful. I was given an opportunity to present my work in a number of conferences, participate in collaborations and visit other laboratories. The skills and knowledge that I gained while working in Dr. Schafer's group will benefit me for many years to come. A big thanks also goes to my second supervisor Dr. Sarah Teichmann. I have never been turned down when contacting Dr. Teichmann for advice and have always been welcomed with enthusiasm. It was extremely important to know that I could always turn up and discuss a challenging bottleneck that I was facing. I had a pleasure to work together with extremely motivating and inspiring individuals at Dr. Schafer's group. A special thanks goes to Andr´eBrown for discussing the progress of my work in great detail and always suggesting the most insightful observations about the methods that I was wrestling with or results that I tried to understand. I would also like to thank Buyun Zhao for helping me to grasp the beauty and complexity of C. elegans genetics as well as allowing me not to feel alone when turning up to the lab in a jacket and tie. A special thanks goes to Laura Grundy for painstakingly tracking thousands of animals and for being extremely understanding when changes and alterations were necessary. The patience that you displayed was colossal for which I am truly grateful. I thank Victoria Butler for tremendous amount of work that she did at the early stages of the project. The tracking protocol was defined with her help as well as the first recordings which allowed me to start tackling analysis algorithms. I would also like to thank Eviatar Yemini for his support and guidance when building the analysis pipeline. The contribution of Eviatar was central to the completion of this work. I will continue to be motivated by the ambition, competitiveness and never ending energy displayed by him over the years of our work together. The group would not have been as enjoyable without Robyn Branicky who excited everyone about important social activities and maintained the spirit of the group. Denise Walker has worked tirelessly to keep everything organized and put a huge amount of effort when the group had to move to the new LMB building. With her care and attention the tracker and computer equipment reached the new building without a single scratch. I would like to thank Marios Chatzigeorgiou for providing valuable advice while refining the pipeline. I also thank Barry Bentley for injecting fresh energy into the project and adding much needed attention to detail. I am very happy to leave the tracker system in his capable hands. I thank former members of Dr. Schafer group Ithai Rabinowitch, James Cregg, Yosinori Tanizava and Marina Ezurra. Their support at the early days of the project was very important. Ithai and James especially have poured their energy into the project and allowed it to gain momentum much faster. I would also like to thank the neighbouring Dr. Mario De Bono group and its members for continuing support. I am also grateful to Dr. Tobias Kl¨opper for guiding me through the Internet and database technology and providing encouragement during the most challenging parts of the PhD. My work at the LMB could not have progressed smoothly without extremely talented and caring support staff. I would like to thank the divisional admin- istrators - Caroline Winsor, Kate French, Alison Turnock and Ami Cull as well as postgraduate managers Liz Pryke and Amie Blake for taking care of me. I would like to thank the LMB IT office and the scientific computing department, especially Jake Grimmett for taking care of all of my requests, as outrageous as they were at times. In addition to everyone in the laboratory I have been extremely fortunate to spend time with remarkable individuals at the University of Cambridge. They have surrounded me in Fitzwilliam and Darwin Colleges as well as the soci- eties that I had a pleasure to be part of. Some of the friends that provided me with much needed company were Sasan Behravesh, Progromakis Parasoglou, Aaron A. Bloch, Rory Stark, William Eucker, Zhenwei Peng, Vladimir Orlov, Sohan Dasgupta, Art¯urasRatkus, Ignas Budvytis, Alex Tayler, Sebastian Pen- der, Jamie Blaza, Alex Hampton, Brent C. J. Clickard, Dwayne R. Menezes, Tomoyuki Kuwaharada, Aaron Ralby, Simon Brunner and many others. In addition to the friends in Cambridge I have received support from the ones further away as well. I would like to thank Arvydas Bloˇz_e, Andrew J. Knott, Peras Vaiˇcius,Rasa Balsyt_e,Aist_eAnusait_e-Daubarasand Darius Daubaras, Ry- tis Vitkauskas, Laura Dzelzyt_e, Jorge and Isabel Dorado and many others. I am truly grateful to Nikhil Sasidharan for his friendship and also for taking his time to proof read this thesis. I would also like to thank Mouna Chraibi for her support and faith in me during the most trying times.