DNA Patterns

Abstract: 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.

Abstract: This paper presents PyElph, a software tool which automatically extracts data from gel images, computes the molecular weights of the analyzed molecules or fragments, compares DNA patterns which result from experiments with molecular genetic markers and, also, generates phylogenetic trees computed by five clustering methods, using the information extracted from the analyzed gel image. The software can be successfully used for population genetics, phylogenetics, taxonomic studies and other applications which require gel image analysis. Researchers and students working in molecular biology and genetics would benefit greatly from the proposed software because it is free, open source, easy to use, has a friendly Graphical User Interface and does not depend on specific image acquisition devices like other commercial programs with similar functionalities do. PyElph software tool is entirely implemented in Python which is a very popular programming language among the bioinformatics community. It provides a very friendly Graphical User Interface which was designed in six steps that gradually lead to the results. The user is guided through the following steps: image loading and preparation, lane detection, band detection, molecular weights computation based on a molecular weight marker, band matching and finally, the computation and visualization of phylogenetic trees. A strong point of the software is the visualization component for the processed data. The Graphical User Interface provides operations for image manipulation and highlights lanes, bands and band matching in the analyzed gel image. All the data and images generated in each step can be saved. The software has been tested on several DNA patterns obtained from experiments with different genetic markers. Examples of genetic markers which can be analyzed using PyElph are RFLP (Restriction Fragment Length Polymorphism), AFLP (Amplified Fragment Length Polymorphism), RAPD (Random Amplification of Polymorphic DNA) and STR (Short Tandem Repeat). The similarity between the DNA sequences is computed and used to generate phylogenetic trees which are very useful for population genetics studies and taxonomic classification. PyElph decreases the effort and time spent processing data from gel images by providing an automatic step-by-step gel image analysis system with a friendly Graphical User Interface. The proposed free software tool is suitable for researchers and students which do not have access to expensive commercial software and image acquisition devices.

Abstract: DNA methylation is a major epigenetic modification that is strongly involved in the physiological control of genome expression. DNA methylation patterns are largely modified in cancer cells and can therefore be used to distinguish cancer cells from normal tissues. This review describes the main technologies available for the detection and the discovery of aberrantly methylated DNA patterns. It also presents the different sources of biological samples suitable for DNA methylation studies. We discuss the interest and perspectives on the use of DNA methylation measurements for cancer diagnosis through examples of methylated genes commonly documented in the literature. The discussion leads to our consideration for why DNA methylation is not commonly used in clinical practice through an examination of the main requirements that constitute a reliable biomarker. Finally, we describe the main DNA methylation inhibitors currently used in clinical trials and those that exhibit promising results.

Abstract: Motivation: The discovery of sparse amino acid patterns that match repeatedly in a set of protein sequences is an important problem in computational biology. Statistically significant patterns, that is, patterns that occur more frequently than expected, may identify regions that have been preserved by evolution and which may therefore play a key functional or structural role. Sparseness can be important because a handful of non-contiguous residues may play a key role, while others, in between, may be changed without significant loss of function or structure. Similar arguments may be applied to conserved DNA patterns. Available sparse pattern discovery algorithms are either inefficient or impose limitations on the type of patterns that can be discovered. Results: This paper introduces a deterministic pattern discovery algorithm, called Splash, which can find sparse amino or nucleic acid patterns matching identically or similarly in a set of protein or DNA sequences. Sparse patterns of any length, up to the size of the input sequence, can be discovered without significant loss in performances. Splash is extremely efficient and embarrassingly parallel by nature. Large databases, such as a complete genome or the non-redundant SWISS-PROT database can be processed in a few hours on a typical workstation. Alternatively, a protein family or superfamily, with low overall homology, can be analyzed to discover common functional or structural signatures. Some examples of biologically interesting motifs discovered by Splash are reported for the histone I and for the G-Protein Coupled Receptor families. Due to its efficiency, Splash can be used to systematically and exhaustively identify conserved regions in protein family sets. These can then be used to build accurate and sensitive PSSM or HMM models for sequence analysis. Availability: Splash is available to non-commercial research centers upon request, conditional on the signing of a test field agreement.