BackgroundDNA fingerprinting is a technique for comparing DNA patterns that has applications in a wide variety of contexts. Several commercial and freely-available tools can be used to analyze DNA fingerprint gel images; however, commercial tools are expensive and usually difficult to use; and, free tools support the basic functionality for DNA fingerprint analysis, but lack some instrumental features to obtain accurate results.ResultsIn this paper, we present GelJ, a feather-weight, user-friendly, platform-independent, open-source and free tool for analyzing DNA fingerprint gel images. Some of the outstanding features of GelJ are mechanisms for accurate lane- and band-detection, several options for computing migration models, a number of band- and curve-based similarity methods, different techniques for generating dendrograms, comparison of banding patterns from different experiments, and database support.ConclusionsGelJ is an easy to use tool for analyzing DNA fingerprint gel images. It combines the best characteristics of both free and commercial tools: GelJ is light and simple to use (as free programs), but it also includes the necessary features to obtain precise results (as commercial programs). In addition, GelJ incorporates new functionality that is not supported by any other tool.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0703-0) contains supplementary material, which is available to authorized users.

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DNA fingerprinting is a genetic typing technique that allows the analysis of the genomic relatedness between samples, and the comparison of DNA patterns. This technique has multiple applications in different fields (medical diagnosis, forensic science, parentage testing, food industry, agriculture and many others). An important task in molecular epidemiology of infectious diseases is the analysis and comparison of pulsed-field gel electrophoresis (PFGE) patterns. This is applied to determine the clonal diversity of bacteria in the follow-up of outbreaks or for tracking specific clones of special relevance. The resulting images produced by DNA fingerprinting are sometimes difficult to interpret, and multiple tools have been developed to simplify this task. In this article, we present a survey of tools for analysing DNA fingerprints. In particular, we compare 33 tools using a set of predefined criteria. The comparison was carried out by hands-on experiences-whenever possible-and inspecting the documentation of the tools. As no system is preferred in all the possible scenarios, we have created a spreadsheet that can be customized by researchers to determine the best system for their needs.

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Background Deep learning techniques have been successfully applied to bioimaging problems; however, these methods are highly data demanding. An approach to deal with the lack of data and avoid overfitting is the application of data augmentation , a technique that generates new training samples from the original dataset by applying different kinds of transformations. Several tools exist to apply data augmentation in the context of image classification, but it does not exist a similar tool for the problems of localization, detection, semantic segmentation or instance segmentation that works not only with 2 dimensional images but also with multi-dimensional images (such as stacks or videos). Results In this paper, we present a generic strategy that can be applied to automatically augment a dataset of images, or multi-dimensional images, devoted to classification, localization, detection, semantic segmentation or instance segmentation. The augmentation method presented in this paper has been implemented in the open-source package CLoDSA. To prove the benefits of using CLoDSA, we have employed this library to improve the accuracy of models for Malaria parasite classification, stomata detection, and automatic segmentation of neural structures. Conclusions CLoDSA is the first, at least up to the best of our knowledge, image augmentation library for object classification, localization, detection, semantic segmentation, and instance segmentation that works not only with 2 dimensional images but also with multi-dimensional images.

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