bioRxiv ScienceSearch

Biology subjects

Wilczynski, B.

Publications and source records attributed to Wilczynski, B..

3 recordsLinked to original sources

BPscore: an effective metric for meaningful comparisons of structural chromosome segmentations

Studying the 3D structure of chromosomes is an emerging field flourishing in recent years because of rapid development of experimental approaches for studying chromosomal contacts. This has led to numerous studies providing results of segmentation of chromosome sequences of different species into so called Topologically Associating Domains (TADs). As the number of such studies grows steadily and many of them make claims about the perceived differences between TAD structures observed in different conditions, there is a growing need for good measures of similarity (or dissimilarity) between such segmentations. We provide here a BP score, which is a relatively simple distance metric based on the bipartite matching between two segmentations. In this paper, we provide the rationale behind choosing specifically this function and show its results on several different datasets, both simulated and experimental. We show that not only the BP score is a proper metric satisfying the triangle inequality, but that it is providing good granularity of scores for typical situations occuring between different TAD segmentations. We also introduce local variant of the BP metric and show that in actual comparisons between experimental datasets, the local BP score is correlating with the observed changes in gene expression and genome methylation. In summary, we consider the BP score a good foundation for analysing the dynamics of chromosome structures. The methodology we present in this work could be used by many researchers in their ongoing analyses making it a popular and useful tool.\n\nAuthor summaryMany researchers are interested in the chromosomal structure, its function and dynamics. Over the recent years, chromosome conformation capture (3C) methods have become the main source of experimental data on the subject and the Topologically Associating Domains (TADs) have become the de-facto standard unit of chromosomal structure. Many methods have been developed for TAD calling and the 3C experiments have been done in multiple conditions giving us a multitude of chromosomal segmentations describing the most atomic differences in chromosomal structure between conditions. Until now, such segmentations were compared mostly by very rough measures, such as the Jaccard coefficient or TAD overlaps or very general metrics like the variation of information coefficient. This has limited the researchers in the analysis of differential TAD segmentations, and practically prevented any proper analysis of TAD dynamics between conditions. Our approach has the potential to facilitate such analyses by providing researchers with mathematically sound metric that is designed specifically for the purpose and tested on both simulated and experimental data. Additionally, we provide a local variant of our measure that is a natural derivative of the BP score that can indicate which parts of the chromosomes are undergoing the most significant structural reorganizations.

bioinformatics

Bio.Ontology - Python tools for enrichment analysis and visualization of ontologies

MotivationFunctional annotation and enrichment analysis based on ontologies has become one of the standard methods of analysis of experimental results. Over the past decade, many methods have been proposed for statistical quantification of enrichment of different functional terms and many implementations of these methods are available. As the popularity of these methods grows, the need for tools facilitating their automation increases.\n\nResultsWe present a complete Python library for statistical enrichment analysis of gene sets and gene rankings compatible with most available biological ontologies. It allows the user to perform all necessary steps: reading the ontologies and gene annotations in multiple formats; performing enrichment analysis using various methods and visualizing the results as readable reports. Importantly, our library includes methods for correcting for multiple hypotheses testing including computation of False Discovery Rates.\n\nAvailabilityThe library is compatible with recent versions of python interpreter ([≥] 2.6 or [≥] 3.3) and is available on github at: https://github.com/regulomics/biopython together with an API documentation and a tutorial. The sample galaxy installation can be found at http://regulomics.mimuw.edu.pl/wp/GO/.\n\nContactbartek@mimuw.edu.pl

bioinformatics

DiffTAD: Detecting Differential contact frequency in Topologically Associating Domains Hi-C experiments between conditions

MotivationIn recent years, the interest in analyzing chromosome conformation by Hi-C and related techniques has grown. It has been shown that contact frequency matrices obtained by these methods correlate with other methods of measurement of activity such as transcriptomics and histone modification assays. This brings a question of testing for differential contact frequency between experiments to the field.\n\nResultsIn this work, we provide a freely available software that implements two statistical methods for testing the significance of differential contact frequency in topological domains between two experiments. One method follows an empirical, permutation based approach to computing p-values, while the other is a parametric test based on the Poisson-Binomial distribution.\n\nAvailabilityThe software is freely available on the GNU General Public License at https://bitbucket.org/rzaborowski/differential-analysis\n\nContact[r.zaborowski|bartek]@mimuw.edu.pl\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics