Lane 8 - Fingerprint
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DNA 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.
In 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.
GelJ 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.
DNA fingerprinting is a technique for comparing DNA patterns that allows the analysis of the genomic relatedness among different samples, as well as to type and classify them. There are multiple DNA fingerprinting techniques, and the choice of which of them we must use depends on their applications (medical diagnosis, forensic science, parentage testing, food industry, agriculture, and many others) .
The GelJ main window (see Fig. 1) consists of 4 graphical entities. The GelJ menu provides the functionality to manage studies. The experiment panel contains the experiments of the current study, and allows the user to incorporate experiments to the active study using the following options: analyze a gel-image, duplicate an experiment of the study, import an experiment from another study, or import an experiment from a file (the latter allows the user to share experiments across computers using the export functionality included in GelJ; this is an important point since this feature allows the reproducibility of results). The comparison panel contains the comparisons carried out in the current study. Finally, the main panel shows the lanes associated with a selected experiment (or comparison), and supplies the functionality to attach information to each lane. Studies, experiments, and comparisons persist in GelJ using an embedded JavaDB database (the structure of the GelJ database is provided as a Additional file 1).
Main window of GelJ and dendrogram displayed by GelJ. Top-left panel of GelJ: experiments and functionality to manage experiments of the study. Bottom-left panel of GelJ: comparisons and functionality to manage comparisons of the study. Top-right panel of GelJ: lanes of the selected experiment (or comparison) and associated functionality. Bottom-right panel of GelJ: image of the gel associated with the selected experiment
Step 4. Band detection. GelJ automatically detects the bands of a gel-image. The method implemented to automatically detect bands follows the same intuitive idea explained for lane-segmentation: given a lane, the band-areas appear lighter than the empty background-areas between bands. Hence, band-positions of a lane are located by constructing the horizontal projection profile (also known as densitometric curve or histogram) of the lane, and subsequently finding the local maxima of such a profile (see Fig. 3).
Some of the local peaks from the densitometric curves come from noise (see Fig. 3), and they are excluded by using a minimum height criterion: the value of a local peak must be higher than a fixed-minimum to be considered as the location of a band. The optimum height-threshold is not the same for all the gel-images, and the user of GelJ can manually fix this value. Additionally, the optimum height-threshold can also vary from region to region of the same gel-image; GelJ deals with this issue by means of a lane-threshold: the user can adjust the height-threshold for each lane of a gel-image. The different thresholds can be adjusted by means of sliders that are synchronized with the image (i.e. when the value of a slider is changed, the selected bands on the image are automatically changed and showed to the user).
Once that the user has finished picking the bands of a gel, the molecular weights of the bands of each lane are automatically computed. Those weights are obtained taking into account the migration model and the shift of lanes that were previously computed in the normalization stage, this process is explained in . Finally, the list of molecular weights and the densitometric curve associated with each lane are stored for comparing such a lane with other lanes normalized with the same reference marker.
Finishing the experiment. Once the user has finished the analysis of an image (i.e. the four above steps have been completed), a new experiment is stored in the GelJ database and added to the main panel of the GelJ interface (see Fig. 1). Such an experiment will contain information like name, date or the image that was used to create the experiment; and, it will have associated a number of lanes, that correspond to the lanes of the analyzed image. By default, the user can add some fixed information (e.g. genus, species, strain number or country) to each lane; additionally, the user can also create on-the-fly new information fields (e.g. age or laboratory) to be added to the lanes of the experiment.
The main goal of DNA fingerprinting is the comparison of samples through the inspection of band patterns. This is usually a three-step process: selection of lanes to compare, computation of similarity matrices, and construction of dendrograms (a tree representing the relatedness among lanes ). GelJ provides a wizard that guides the user in the comparison of lanes by configuring several parameters related to compared lanes, similarity matrices, dendrograms, and the final output.
Similarity matrices. Given a list of n lanes, L, the similarity matrix of L is an nn matrix where the element of row i and column j encodes the similarity between the i-th and j-th lanes of L. There are two approaches to calculate the similarity between lanes: band-based and curve-based .
In the curve-based approach, the similarity is determined using a correlation coefficient computed from the densitometric curves of the lanes. GelJ supplies several curve-based methods for computing the similarity among lanes: Pearson correlation, Cosine coefficient, Euclidean distance, and Manhattan distance.
In addition to dendrograms, GelJ includes another mechanism to inspect the similarity among lanes. Namely, the user can request GelJ to find all the lanes that are similar to a given lane (the user can adjust several parameters in this search, like the method to compute the similarity or the minimum similarity percentage). This similarity-search can be carried out across all the studies available in the database. An example of this similarity search is provided in Fig. 4.
In the rest of this section, we provide a comparison of the features included in GelJ with the functionality supported by the most-complete tools employed to analyze gel-images. A survey of this kind of tool was presented in . From the 33 tools studied in that survey, 15 of them provide the 5 stages to compare samples from gel-images (i.e. pre-processing, lane segmentation, normalization, band detection, and fingerprint comparison); in turn, among the 15 tools, there were 5 commercial tools (GelComparII , GelQuant Pro , ImageQuant , Phoretix 1D-Pro , and TotalLab ) that excelled the rest of them. We contrast GelJ with these 5 commercial programs and also with the 2 best free tools (GelClust  and PyElph ).
Band detection. In this step, all the programs provide similar functionality (automatic band detection, height threshold, manual picking, and densitometric-curve display). Moreover, GelJ and the commercial tools provide additional features that simplify the band-detection task: synchronization of the histogram with the gel-image (bands can be added from the histogram), and undo/redo functionality for picking bands. GelJ is the only tool that offers the lane-by-lane threshold functionality to detect bands automatically.
In this paper, we have presented GelJ, a Java application designed for analysing DNA fingerprint gel images. GelJ is a user-friendly, platform-independent, open-source, and free tool that combines the simple design of free programs with instrumental features for the analysis of gel-images that are only available on commercial tools (e.g. mechanisms for accurate lane-detection, band- and curve-based methods for computing similarity among lanes, tools for searching similar samples across gels, or database support). Besides, it includes new features that are not avail in any other tool; for instance, lane-by-lane threshold for band detection or the adjustment of brightness and contrast for individual lanes.
Similarity and Clustering methods available in GelJ. In the AdditionalFile2.pdf document, we provide a brief explanation of the different methods available in GelJ to compute similarity among lanes and construct dendrograms. An explanation about the tolerance value for band matching is also given in this document. Additionally, we include several images to visually observe the differences among the different methods. (PDF 588 KB)
Recent advances in sequencing have improved the potential to characterize genetic variation on a genome-wide scale. Furthermore, this can now be done in species that have little or no pre-existing genomic resources. Current Illumina sequencing instruments are capable of producing up to 3 billion paired-end sequences on a single flow cell ( _systems.ilmn). To put this into context, it would provide enough data for greater than 100 times coverage of the perennial ryegrass genome (estimated to be 2.69 Gb; ) with 100 bp reads. This would still make complete genome re-sequencing costly on a large scale, and unnecessary for many applications. Fortunately, strategies exist to reduce the complexity of genomes to levels that can be sequenced more affordably in studies involving large numbers. These include using probe capture techniques , transcriptome re-sequencing , and complexity reduction techniques using restriction enzymes . After complexity reduction, samples can be individually tagged to allow multiplexing on a single lane of a flow cell. Cheap barcoding systems have already been developed that enable large-scale multiplexing , . The level of multiplexing that can be achieved is only limited by the complexity of the sample and the coverage required, given a specific sequencing throughput. 59ce067264