BackgroundFor three decades, sequence logos are the de facto standard for the visualization of sequence motifs in biology and bioinformatics. Reasons for this success story are their simplicity and clarity. The number of inferred and published motifs grows with the number of data sets and motif extraction algorithms. Hence, it becomes more and more important to perceive differences between motifs. However, motif differences are hard to detect from individual sequence logos in case of multiple motifs for one transcription factor, highly similar binding motifs of different transcription factors, or multiple motifs for one protein domain.ResultsHere, we present DiffLogo, a freely available, extensible, and user-friendly R package for visualizing motif differences. DiffLogo is capable of showing differences between DNA motifs as well as protein motifs in a pair-wise manner resulting in publication-ready figures. In case of more than two motifs, DiffLogo is capable of visualizing pair-wise differences in a tabular form. Here, the motifs are ordered by similarity, and the difference logos are colored for clarity. We demonstrate the benefit of DiffLogo on CTCF motifs from different human cell lines, on E-box motifs of three basic helix-loop-helix transcription factors as examples for comparison of DNA motifs, and on F-box domains from three different families as example for comparison of protein motifs.Conclusions DiffLogo provides an intuitive visualization of motif differences. It enables the illustration and investigation of differences between highly similar motifs such as binding patterns of transcription factors for different cell types, treatments, and algorithmic approaches.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0767-x) contains supplementary material, which is available to authorized users.

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BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. ChIP-seq has become the major technology to uncover genomic regions containing those binding sites, but motifs predicted by traditional computational approaches using these data are distorted by a ubiquitous binding-affinity bias. Here, we present an approach for detecting and correcting this bias using inter-species information.ResultsWe find that the binding-affinity bias caused by the ChIP-seq experiment in the reference species is stronger than the indirect binding-affinity bias in orthologous regions from phylogenetically related species. We use this difference to develop a phylogenetic footprinting model that is capable of detecting and correcting the binding-affinity bias. We find that this model improves motif prediction and that the corrected motifs are typically softer than those predicted by traditional approaches.ConclusionsThese findings indicate that motifs published in databases and in the literature are artificially sharpened compared to the native motifs. These findings also indicate that our current understanding of transcriptional gene regulation might be blurred, but that it is possible to advance this understanding by taking into account inter-species information available today and even more in the future.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2682-6) contains supplementary material, which is available to authorized users.

BackgroundNew technologies for analyzing biological samples, like next generation sequencing, are producing a growing amount of data together with quality scores. Moreover, software tools (e.g., for mapping sequence reads), calculating transcription factor binding probabilities, estimating epigenetic modification enriched regions or determining single nucleotide polymorphism increase this amount of position-specific DNA-related data even further. Hence, requesting data becomes challenging and expensive and is often implemented using specialised hardware. In addition, picking specific data as fast as possible becomes increasingly important in many fields of science. The general problem of handling big data sets was addressed by developing specialized databases like HBase, HyperTable or Cassandra. However, these database solutions require also specialized or distributed hardware leading to expensive investments. To the best of our knowledge, there is no database capable of (i) storing billions of position-specific DNA-related records, (ii) performing fast and resource saving requests, and (iii) running on a single standard computer hardware.ResultsHere, we present DRUMS (Disk Repository with Update Management and Select option), satisfying demands (i)-(iii). It tackles the weaknesses of traditional databases while handling position-specific DNA-related data in an efficient manner. DRUMS is capable of storing up to billions of records. Moreover, it focuses on optimizing relating single lookups as range request, which are needed permanently for computations in bioinformatics. To validate the power of DRUMS, we compare it to the widely used MySQL database. The test setting considers two biological data sets. We use standard desktop hardware as test environment.ConclusionsDRUMS outperforms MySQL in writing and reading records by a factor of two up to a factor of 10000. Furthermore, it can work with significantly larger data sets. Our work focuses on mid-sized data sets up to several billion records without requiring cluster technology. Storing position-specific data is a general problem and the concept we present here is a generalized approach. Hence, it can be easily applied to other fields of bioinformatics.

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1495-1) contains supplementary material, which is available to authorized users.