HiCEnterprise: identifying long range chromosomal contacts in Hi-C data

Motivation Computational analysis of chromosomal contact data is currently gaining popularity with the rapid advance in experimental techniques providing access to a growing body of data. An important problem in this area is the identification of long range contacts between distinct chromatin regions. Such loops were shown to exist at different scales, either mediating relatively short range interactions between enhancers and promoters or providing interactions between much larger, distant chromosome domains. A proper statistical analysis as well as availability to a wide research community are crucial in a tool for this task. Results We present HiCEnterprise, a first freely available software tool for identification of long range chromatin contacts not only between small regions, but also between chromosomal domains. It implements four different statistical tests for identification of significant contacts for user defined regions or domains as well as necessary functions for input, output and visualization of chromosome contacts. Availability The software and the corresponding documentation are available at: github.com/regulomics/HiCEnterprise. Supplementary information Supplemental data are available in the online version of the article and at the website regulomics.mimuw.edu.pl/wp/hicenterprise.

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HiCEnterprise: Identifying long range chromosomal contacts in HiC data

10.7287/peerj.preprints.27753 ◽

2019 ◽

Author(s):

Hanna Kranas ◽

Irina Tuszyńska ◽

Bartek Wilczynski

Keyword(s):

Statistical Analysis ◽

Long Range ◽

Computational Analysis ◽

Software Tool ◽

Experimental Techniques ◽

Input Output ◽

Accurate Identification ◽

Rapid Advance ◽

Chromosome Domains ◽

Growing Body

Computational analysis of chromosomal capture data is currently gaining popularity with the rapid advance in experimental techniques providing access to growing body of data. An important problem in this area is the identification of long-range contacts be- tween distinct chromatin regions. Such loops were shown to exist at different scales, either mediating interactions between enhancers and promoters or providing much longer interactions between functionally interacting distant chromosome domains. A proper statistical analysis is crucial for accurate identification of such interactions from experi- mental data. We present HiCEnterprise, a software tool for identification of long-range chromatin contacts. It implements three different sta- tistical tests for identification of significant contacts at different scales as well as necessary functions for input, output and visualization of chromosome contact matrices.

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HiCEnterprise: Identifying long range chromosomal contacts in HiC data

10.7287/peerj.preprints.27753v1 ◽

2019 ◽

Author(s):

Hanna Kranas ◽

Irina Tuszyńska ◽

Bartek Wilczynski

Keyword(s):

Statistical Analysis ◽

Long Range ◽

Computational Analysis ◽

Software Tool ◽

Experimental Techniques ◽

Input Output ◽

Accurate Identification ◽

Rapid Advance ◽

Chromosome Domains ◽

Growing Body

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Boost-HiC: computational enhancement of long-range contacts in chromosomal contact maps

Bioinformatics ◽

10.1093/bioinformatics/bty1059 ◽

2019 ◽

Vol 35 (16) ◽

pp. 2724-2729 ◽

Cited By ~ 3

Author(s):

L Carron ◽

J B Morlot ◽

V Matthys ◽

A Lesne ◽

J Mozziconacci

Keyword(s):

Long Range ◽

Short Range ◽

Supplementary Information ◽

Supplementary Data ◽

Missing Information ◽

High Confidence ◽

Contact Maps ◽

Genome Wide ◽

Algorithmic Procedure

Abstract Motivation Genome-wide chromosomal contact maps are widely used to uncover the 3D organization of genomes. They rely on collecting millions of contacting pairs of genomic loci. Contacts at short range are usually well measured in experiments, while there is a lot of missing information about long-range contacts. Results We propose to use the sparse information contained in raw contact maps to infer high-confidence contact counts between all pairs of loci. Our algorithmic procedure, Boost-HiC, enables the detection of Hi-C patterns such as chromosomal compartments at a resolution that would be otherwise only attainable by sequencing a hundred times deeper the experimental Hi-C library. Boost-HiC can also be used to compare contact maps at an improved resolution. Availability and implementation Boost-HiC is available at https://github.com/LeopoldC/Boost-HiC. Supplementary information Supplementary data are available at Bioinformatics online.

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AMINONET– a tool to construct and visualize amino acid networks, and to calculate topological parameters

Journal of Applied Crystallography ◽

10.1107/s002188981000110x ◽

2010 ◽

Vol 43 (2) ◽

pp. 367-369 ◽

Cited By ~ 8

Author(s):

Md. Aftabuddin ◽

Sudip Kundu

Keyword(s):

Amino Acid ◽

Long Range ◽

Short Range ◽

Software Tool ◽

Contact Networks ◽

Topological Parameters ◽

Structure File ◽

Protein Contact Networks

AMINONETis a Java-based software tool to construct different protein contact networks (unweighted and weighted; long range, short range and any range; hydrophobic, hydrophilic, charged or all-amino-acid networks). The networks thus constructed can be visualized. The software will also help in the calculation of the values of the different topological parameters of the constructed networks. The user can either provide a PDB ID or upload a structure file in PDB format as input. If necessary, the user can also do the same for a large number of proteins, uploading a batch file as input (details described in the document available online).

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MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs

Bioinformatics ◽

10.1093/bioinformatics/btaa045 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2690-2696

Author(s):

Jarkko Toivonen ◽

Pratyush K Das ◽

Jussi Taipale ◽

Esko Ukkonen

Keyword(s):

Markov Models ◽

Expectation Maximization Algorithm ◽

Software Tool ◽

Specific Weight ◽

Training Data ◽

Supplementary Information ◽

Markov Modeling ◽

Binding Motifs ◽

The Difference ◽

Probability Matrices

Abstract Motivation Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. Results We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average. Availability and implementation Software implementation is available from https://github.com/jttoivon/moder2. Supplementary information Supplementary data are available at Bioinformatics online.

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Structural basis of long-range to short-range synaptic transition in NHEJ

Nature ◽

10.1038/s41586-021-03458-7 ◽

2021 ◽

Author(s):

Siyu Chen ◽

Linda Lee ◽

Tasmin Naila ◽

Susan Fishbain ◽

Annie Wang ◽

...

Keyword(s):

Long Range ◽

Short Range ◽

Structural Basis

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Comparison of short-range order in irradiated dysprosium titanates

npj Materials Degradation ◽

10.1038/s41529-021-00165-6 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Roman Sherrod ◽

Eric C. O’Quinn ◽

Igor M. Gussev ◽

Cale Overstreet ◽

Joerg Neuefeind ◽

...

Keyword(s):

Long Range ◽

Short Range ◽

Structural Model ◽

Ion Irradiation ◽

Function Analysis ◽

Ion Beam ◽

Structural Response ◽

Heavy Ion ◽

Range Order ◽

Radiation Resistant

AbstractThe structural response of Dy2TiO5 oxide under swift heavy ion irradiation (2.2 GeV Au ions) was studied over a range of structural length scales utilizing neutron total scattering experiments. Refinement of diffraction data confirms that the long-range orthorhombic structure is susceptible to ion beam-induced amorphization with limited crystalline fraction remaining after irradiation to 8 × 1012 ions/cm2. In contrast, the local atomic arrangement, examined through pair distribution function analysis, shows only subtle changes after irradiation and is still described best by the original orthorhombic structural model. A comparison to Dy2Ti2O7 pyrochlore oxide under the same irradiation conditions reveals a different behavior: while the dysprosium titanate pyrochlore is more radiation resistant over the long-range with smaller degree of amorphization as compared to Dy2TiO5, the former involves more local atomic rearrangements, best described by a pyrochlore-to-weberite-type transformation. These results highlight the importance of short-range and medium-range order analysis for a comprehensive description of radiation behavior.

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Parallelized calculation of permutation tests

Bioinformatics ◽

10.1093/bioinformatics/btaa1007 ◽

2020 ◽

Author(s):

Markus Ekvall ◽

Michael Höhle ◽

Lukas Käll

Keyword(s):

Dynamic Programming ◽

Sample Size ◽

Permutation Test ◽

Statistical Tests ◽

Permutation Tests ◽

Supplementary Information ◽

Attractive Alternative ◽

Test Statistic ◽

Sample Distribution ◽

Running Time

Abstract Motivation Permutation tests offer a straightforward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naïve implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive. Results Parallelization of the Green algorithm was found possible by non-trivial rearrangement of the structure of the algorithm. A speed-up—by orders of magnitude—is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g. the widely used asymptotic Mann-Whitney U-test. Availabilityand implementation In Python 3 code from the GitHub repository https://github.com/statisticalbiotechnology/parallelPermutationTest under an Apache 2.0 license. Supplementary information Supplementary data are available at Bioinformatics online.

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