1,550 research outputs found
Training-free Measures Based on Algorithmic Probability Identify High Nucleosome Occupancy in DNA Sequences
We introduce and study a set of training-free methods of
information-theoretic and algorithmic complexity nature applied to DNA
sequences to identify their potential capabilities to determine nucleosomal
binding sites. We test our measures on well-studied genomic sequences of
different sizes drawn from different sources. The measures reveal the known in
vivo versus in vitro predictive discrepancies and uncover their potential to
pinpoint (high) nucleosome occupancy. We explore different possible signals
within and beyond the nucleosome length and find that complexity indices are
informative of nucleosome occupancy. We compare against the gold standard
(Kaplan model) and find similar and complementary results with the main
difference that our sequence complexity approach. For example, for high
occupancy, complexity-based scores outperform the Kaplan model for predicting
binding representing a significant advancement in predicting the highest
nucleosome occupancy following a training-free approach.Comment: 8 pages main text (4 figures), 12 total with Supplementary (1 figure
Reference Based Genome Compression
DNA sequencing technology has advanced to a point where storage is becoming
the central bottleneck in the acquisition and mining of more data. Large
amounts of data are vital for genomics research, and generic compression tools,
while viable, cannot offer the same savings as approaches tuned to inherent
biological properties. We propose an algorithm to compress a target genome
given a known reference genome. The proposed algorithm first generates a
mapping from the reference to the target genome, and then compresses this
mapping with an entropy coder. As an illustration of the performance: applying
our algorithm to James Watson's genome with hg18 as a reference, we are able to
reduce the 2991 megabyte (MB) genome down to 6.99 MB, while Gzip compresses it
to 834.8 MB.Comment: 5 pages; Submitted to the IEEE Information Theory Workshop (ITW) 201
Large-scale compression of genomic sequence databases with the Burrows-Wheeler transform
Motivation
The Burrows-Wheeler transform (BWT) is the foundation of many algorithms for
compression and indexing of text data, but the cost of computing the BWT of
very large string collections has prevented these techniques from being widely
applied to the large sets of sequences often encountered as the outcome of DNA
sequencing experiments. In previous work, we presented a novel algorithm that
allows the BWT of human genome scale data to be computed on very moderate
hardware, thus enabling us to investigate the BWT as a tool for the compression
of such datasets.
Results
We first used simulated reads to explore the relationship between the level
of compression and the error rate, the length of the reads and the level of
sampling of the underlying genome and compare choices of second-stage
compression algorithm.
We demonstrate that compression may be greatly improved by a particular
reordering of the sequences in the collection and give a novel `implicit
sorting' strategy that enables these benefits to be realised without the
overhead of sorting the reads. With these techniques, a 45x coverage of real
human genome sequence data compresses losslessly to under 0.5 bits per base,
allowing the 135.3Gbp of sequence to fit into only 8.2Gbytes of space (trimming
a small proportion of low-quality bases from the reads improves the compression
still further).
This is more than 4 times smaller than the size achieved by a standard
BWT-based compressor (bzip2) on the untrimmed reads, but an important further
advantage of our approach is that it facilitates the building of compressed
full text indexes such as the FM-index on large-scale DNA sequence collections.Comment: Version here is as submitted to Bioinformatics and is same as the
previously archived version. This submission registers the fact that the
advanced access version is now available at
http://bioinformatics.oxfordjournals.org/content/early/2012/05/02/bioinformatics.bts173.abstract
. Bioinformatics should be considered as the original place of publication of
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Structure induction by lossless graph compression
This work is motivated by the necessity to automate the discovery of
structure in vast and evergrowing collection of relational data commonly
represented as graphs, for example genomic networks. A novel algorithm, dubbed
Graphitour, for structure induction by lossless graph compression is presented
and illustrated by a clear and broadly known case of nested structure in a DNA
molecule. This work extends to graphs some well established approaches to
grammatical inference previously applied only to strings. The bottom-up graph
compression problem is related to the maximum cardinality (non-bipartite)
maximum cardinality matching problem. The algorithm accepts a variety of graph
types including directed graphs and graphs with labeled nodes and arcs. The
resulting structure could be used for representation and classification of
graphs.Comment: 10 pages, 7 figures, 2 tables published in Proceedings of the Data
Compression Conference, 200
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