17 research outputs found
Stitchprofiles.uio.no: analysis of partly melted DNA conformations using stitch profiles
In this study, we describe a web server that performs computations on DNA melting, thus predicting the localized separation of the two strands for sequences provided by the users. The output types are stitch profiles, melting curves, probability profiles, etc. Stitch profile diagrams visualize the ensemble of alternative conformations that DNA can adopt with different probabilities. For example, a stitch profile shows the possible loop openings in terms of their locations, sizes, probabilities and fluctuations at a given temperature. Sequences with lengths up to several tens or hundreds of kilobase pairs can be analysed. The tools are freely available at
Partly melted DNA conformations obtained with a probability peak finding method
Peaks in the probabilities of loops or bubbles, helical segments, and
unzipping ends in melting DNA are found in this article using a peak finding
method that maps the hierarchical structure of certain energy landscapes. The
peaks indicate the alternative conformations that coexist in equilibrium and
the range of their fluctuations. This yields a representation of the
conformational ensemble at a given temperature, which is illustrated in a
single diagram called a stitch profile. This article describes the methodology
and discusses stitch profiles vs. the ordinary probability profiles using the
phage lambda genome as an example.Comment: 11 pages, 9 figures; v3: major changes; v4: applications sectio
Segmentation of DNA sequences into twostate regions and melting fork regions
The accurate prediction and characterization of DNA melting domains by
computational tools could facilitate a broad range of biological applications.
However, no algorithm for melting domain prediction has been available until
now. The main challenges include the difficulty of mathematically mapping a
qualitative description of DNA melting domains to quantitative statistical
mechanics models, as well as the absence of 'gold standards' and a need for
generality. In this paper, we introduce a new approach to identify the twostate
regions and melting fork regions along a given DNA sequence. Compared with an
ad hoc segmentation used in one of our previous studies, the new algorithm is
based on boundary probability profiles, rather than standard melting maps. We
demonstrate that a more detailed characterization of the DNA melting domain map
can be obtained using our new method, and this approach is independent of the
choice of DNA melting model. We expect this work to drive our understanding of
DNA melting domains one step further.Comment: 17 pages, 8 figures; new introduction, added refs, minor change
The Genomic HyperBrowser: inferential genomics at the sequence level
The immense increase in the generation of genomic scale data poses an unmet
analytical challenge, due to a lack of established methodology with the
required flexibility and power. We propose a first principled approach to
statistical analysis of sequence-level genomic information. We provide a
growing collection of generic biological investigations that query pairwise
relations between tracks, represented as mathematical objects, along the
genome. The Genomic HyperBrowser implements the approach and is available at
http://hyperbrowser.uio.no
The Human Genomic Melting Map
In a living cell, the antiparallel double-stranded helix of DNA is a dynamically changing structure. The structure relates to interactions between and within the DNA strands, and the array of other macromolecules that constitutes functional chromatin. It is only through its changing conformations that DNA can organize and structure a large number of cellular functions. In particular, DNA must locally uncoil, or melt, and become single-stranded for DNA replication, repair, recombination, and transcription to occur. It has previously been shown that this melting occurs cooperatively, whereby several base pairs act in concert to generate melting bubbles, and in this way constitute a domain that behaves as a unit with respect to local DNA single-strandedness. We have applied a melting map calculation to the complete human genome, which provides information about the propensities of forming local bubbles determined from the whole sequence, and present a first report on its basic features, the extent of cooperativity, and correlations to various physical and biological features of the human genome. Globally, the melting map covaries very strongly with GC content. Most importantly, however, cooperativity of DNA denaturation causes this correlation to be weaker at resolutions fewer than 500 bps. This is also the resolution level at which most structural and biological processes occur, signifying the importance of the informational content inherent in the genomic melting map. The human DNA melting map may be further explored at http://meltmap.uio.no
A stitch in time: Efficient computation of genomic DNA melting bubbles
Background: It is of biological interest to make genome-wide predictions of
the locations of DNA melting bubbles using statistical mechanics models.
Computationally, this poses the challenge that a generic search through all
combinations of bubble starts and ends is quadratic.
Results: An efficient algorithm is described, which shows that the time
complexity of the task is O(NlogN) rather than quadratic. The algorithm
exploits that bubble lengths may be limited, but without a prior assumption of
a maximal bubble length. No approximations, such as windowing, have been
introduced to reduce the time complexity. More than just finding the bubbles,
the algorithm produces a stitch profile, which is a probabilistic graphical
model of bubbles and helical regions. The algorithm applies a probability peak
finding method based on a hierarchical analysis of the energy barriers in the
Poland-Scheraga model.
Conclusions: Exact and fast computation of genomic stitch profiles is thus
feasible. Sequences of several megabases have been computed, only limited by
computer memory. Possible applications are the genome-wide comparisons of
bubbles with promotors, TSS, viral integration sites, and other melting-related
regions.Comment: 16 pages, 10 figure
The differential disease regulome
Background
Transcription factors in disease-relevant pathways represent potential drug targets, by impacting a distinct set of pathways that may be modulated through gene regulation. The influence of transcription factors is typically studied on a per disease basis, and no current resources provide a global overview of the relations between transcription factors and disease. Furthermore, existing pipelines for related large-scale analysis are tailored for particular sources of input data, and there is a need for generic methodology for integrating complementary sources of genomic information.
Results
We here present a large-scale analysis of multiple diseases versus multiple transcription factors, with a global map of over-and under-representation of 446 transcription factors in 1010 diseases. This map, referred to as the differential disease regulome, provides a first global statistical overview of the complex interrelationships between diseases, genes and controlling elements. The map is visualized using the Google map engine, due to its very large size, and provides a range of detailed information in a dynamic presentation format.
The analysis is achieved through a novel methodology that performs a pairwise, genome-wide comparison on the cartesian product of two distinct sets of annotation tracks, e.g. all combinations of one disease and one TF.
The methodology was also used to extend with maps using alternative data sets related to transcription and disease, as well as data sets related to Gene Ontology classification and histone modifications. We provide a web-based interface that allows users to generate other custom maps, which could be based on precisely specified subsets of transcription factors and diseases, or, in general, on any categorical genome annotation tracks as they are improved or become available.
Conclusion
We have created a first resource that provides a global overview of the complex relations between transcription factors and disease. As the accuracy of the disease regulome depends mainly on the quality of the input data, forthcoming ChIP-seq based binding data for many TFs will provide improved maps. We further believe our approach to genome analysis could allow an advance from the current typical situation of one-time integrative efforts to reproducible and upgradable integrative analysis. The differential disease regulome and its associated methodology is available at http://hyperbrowser.uio.no