Bioinformatic analysis of Entamoeba histolytica SINE1 elements.

BACKGROUND: Invasive amoebiasis, caused by infection with the human parasite Entamoeba histolytica remains a major cause of morbidity and mortality in some less-developed countries. Genetically E. histolytica exhibits a number of unusual features including having approximately 20% of its genome comprised of repetitive elements. These include a number of families of SINEs - non-autonomous elements which can, however, move with the help of partner LINEs. In many eukaryotes SINE mobility has had a profound effect on gene expression; in this study we concentrated on one such element - EhSINE1, looking in particular for evidence of recent transposition. RESULTS: EhSINE1s were detected in the newly reassembled E. histolytica genome by searching with a Hidden Markov Model developed to encapsulate the key features of this element; 393 were detected. Examination of their sequences revealed that some had an internal structure showing one to four 26-27 nt repeats. Members of the different classes differ in a number of ways and in particular those with two internal repeats show the properties expected of fairly recently transposed SINEs - they are the most homogeneous in length and sequence, they have the longest (i.e. the least decayed) target site duplications and are the most likely to show evidence (in a cDNA library) of active transcription. Furthermore we were able to identify 15 EhSINE1s (6 pairs and one triplet) which appeared to be identical or very nearly so but inserted into different sites in the genome; these provide good evidence that if mobility has now ceased it has only done so very recently. CONCLUSIONS: Of the many families of repetitive elements present in the genome of E. histolytica we have examined in detail just one - EhSINE1. We have shown that there is evidence for waves of transposition at different points in the past and no evidence that mobility has entirely ceased. There are many aspects of the biology of this parasite which are not understood, in particular why it is pathogenic while the closely related species E. dispar is not, the great genetic diversity found amongst patient isolates and the fact, which may be related, that only a small proportion of those infected develop clinical invasive amoebiasis. Mobile genetic elements, with their ability to alter gene expression may well be important in unravelling these puzzles.

EpiCollect+: linking smartphones to web applications for complex data collection projects.

Previously, we have described the development of the generic mobile phone data gathering tool, EpiCollect, and an associated web application, providing two-way communication between multiple data gatherers and a project database. This software only allows data collection on the phone using a single questionnaire form that is tailored to the needs of the user (including a single GPS point and photo per entry), whereas many applications require a more complex structure, allowing users to link a series of forms in a linear or branching hierarchy, along with the addition of any number of media types accessible from smartphones and/or tablet devices (e.g., GPS, photos, videos, sound clips and barcode scanning). A much enhanced version of EpiCollect has been developed (EpiCollect+). The individual data collection forms in EpiCollect+ provide more design complexity than the single form used in EpiCollect, and the software allows the generation of complex data collection projects through the ability to link many forms together in a linear (or branching) hierarchy. Furthermore, EpiCollect+ allows the collection of multiple media types as well as standard text fields, increased data validation and form logic. The entire process of setting up a complex mobile phone data collection project to the specification of a user (project and form definitions) can be undertaken at the EpiCollect+ website using a simple 'drag and drop' procedure, with visualisation of the data gathered using Google Maps and charts at the project website. EpiCollect+ is suitable for situations where multiple users transmit complex data by mobile phone (or other Android devices) to a single project web database and is already being used for a range of field projects, particularly public health projects in sub-Saharan Africa. However, many uses can be envisaged from education, ecology and epidemiology to citizen science.

EpiCollect: linking smartphones to web applications for epidemiology, ecology and community data collection.

BACKGROUND: Epidemiologists and ecologists often collect data in the field and, on returning to their laboratory, enter their data into a database for further analysis. The recent introduction of mobile phones that utilise the open source Android operating system, and which include (among other features) both GPS and Google Maps, provide new opportunities for developing mobile phone applications, which in conjunction with web applications, allow two-way communication between field workers and their project databases. METHODOLOGY: Here we describe a generic framework, consisting of mobile phone software, EpiCollect, and a web application located within www.spatialepidemiology.net. Data collected by multiple field workers can be submitted by phone, together with GPS data, to a common web database and can be displayed and analysed, along with previously collected data, using Google Maps (or Google Earth). Similarly, data from the web database can be requested and displayed on the mobile phone, again using Google Maps. Data filtering options allow the display of data submitted by the individual field workers or, for example, those data within certain values of a measured variable or a time period. CONCLUSIONS: Data collection frameworks utilising mobile phones with data submission to and from central databases are widely applicable and can give a field worker similar display and analysis tools on their mobile phone that they would have if viewing the data in their laboratory via the web. We demonstrate their utility for epidemiological data collection and display, and briefly discuss their application in ecological and community data collection. Furthermore, such frameworks offer great potential for recruiting 'citizen scientists' to contribute data easily to central databases through their mobile phone.