72,263 research outputs found
In search of lost introns
Many fundamental questions concerning the emergence and subsequent evolution
of eukaryotic exon-intron organization are still unsettled. Genome-scale
comparative studies, which can shed light on crucial aspects of eukaryotic
evolution, require adequate computational tools.
We describe novel computational methods for studying spliceosomal intron
evolution. Our goal is to give a reliable characterization of the dynamics of
intron evolution. Our algorithmic innovations address the identification of
orthologous introns, and the likelihood-based analysis of intron data. We
discuss a compression method for the evaluation of the likelihood function,
which is noteworthy for phylogenetic likelihood problems in general. We prove
that after preprocessing time, subsequent evaluations take time almost surely in the Yule-Harding random model of -taxon
phylogenies, where is the input sequence length.
We illustrate the practicality of our methods by compiling and analyzing a
data set involving 18 eukaryotes, more than in any other study to date. The
study yields the surprising result that ancestral eukaryotes were fairly
intron-rich. For example, the bilaterian ancestor is estimated to have had more
than 90% as many introns as vertebrates do now
Indexing large genome collections on a PC
Motivation: The availability of thousands of invidual genomes of one species
should boost rapid progress in personalized medicine or understanding of the
interaction between genotype and phenotype, to name a few applications. A key
operation useful in such analyses is aligning sequencing reads against a
collection of genomes, which is costly with the use of existing algorithms due
to their large memory requirements.
Results: We present MuGI, Multiple Genome Index, which reports all
occurrences of a given pattern, in exact and approximate matching model,
against a collection of thousand(s) genomes. Its unique feature is the small
index size fitting in a standard computer with 16--32\,GB, or even 8\,GB, of
RAM, for the 1000GP collection of 1092 diploid human genomes. The solution is
also fast. For example, the exact matching queries are handled in average time
of 39\,s and with up to 3 mismatches in 373\,s on the test PC with
the index size of 13.4\,GB. For a smaller index, occupying 7.4\,GB in memory,
the respective times grow to 76\,s and 917\,s.
Availability: Software and Suuplementary material:
\url{http://sun.aei.polsl.pl/mugi}
Universal Lossless Compression with Unknown Alphabets - The Average Case
Universal compression of patterns of sequences generated by independently
identically distributed (i.i.d.) sources with unknown, possibly large,
alphabets is investigated. A pattern is a sequence of indices that contains all
consecutive indices in increasing order of first occurrence. If the alphabet of
a source that generated a sequence is unknown, the inevitable cost of coding
the unknown alphabet symbols can be exploited to create the pattern of the
sequence. This pattern can in turn be compressed by itself. It is shown that if
the alphabet size is essentially small, then the average minimax and
maximin redundancies as well as the redundancy of every code for almost every
source, when compressing a pattern, consist of at least 0.5 log(n/k^3) bits per
each unknown probability parameter, and if all alphabet letters are likely to
occur, there exist codes whose redundancy is at most 0.5 log(n/k^2) bits per
each unknown probability parameter, where n is the length of the data
sequences. Otherwise, if the alphabet is large, these redundancies are
essentially at least O(n^{-2/3}) bits per symbol, and there exist codes that
achieve redundancy of essentially O(n^{-1/2}) bits per symbol. Two sub-optimal
low-complexity sequential algorithms for compression of patterns are presented
and their description lengths analyzed, also pointing out that the pattern
average universal description length can decrease below the underlying i.i.d.\
entropy for large enough alphabets.Comment: Revised for IEEE Transactions on Information Theor
Universal Indexes for Highly Repetitive Document Collections
Indexing highly repetitive collections has become a relevant problem with the
emergence of large repositories of versioned documents, among other
applications. These collections may reach huge sizes, but are formed mostly of
documents that are near-copies of others. Traditional techniques for indexing
these collections fail to properly exploit their regularities in order to
reduce space.
We introduce new techniques for compressing inverted indexes that exploit
this near-copy regularity. They are based on run-length, Lempel-Ziv, or grammar
compression of the differential inverted lists, instead of the usual practice
of gap-encoding them. We show that, in this highly repetitive setting, our
compression methods significantly reduce the space obtained with classical
techniques, at the price of moderate slowdowns. Moreover, our best methods are
universal, that is, they do not need to know the versioning structure of the
collection, nor that a clear versioning structure even exists.
We also introduce compressed self-indexes in the comparison. These are
designed for general strings (not only natural language texts) and represent
the text collection plus the index structure (not an inverted index) in
integrated form. We show that these techniques can compress much further, using
a small fraction of the space required by our new inverted indexes. Yet, they
are orders of magnitude slower.Comment: This research has received funding from the European Union's Horizon
2020 research and innovation programme under the Marie Sk{\l}odowska-Curie
Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094
New Family of Robust 2D Topological Insulators in van der Waals Heterostructures
We predict a new family of robust two-dimensional (2D) topological insulators
in van der Waals heterostructures comprising graphene and chalcogenides BiTeX
(X=Cl, Br and I). The layered structures of both constituent materials produce
a naturally smooth interface that is conducive to proximity induced new
topological states. First principles calculations reveal intrinsic
topologically nontrivial bulk energy gaps as large as 70-80 meV, which can be
further enhanced up to 120 meV by compression. The strong spin-orbit coupling
in BiTeX has a significant influence on the graphene Dirac states, resulting in
the topologically nontrivial band structure, which is confirmed by calculated
nontrivial Z2 index and an explicit demonstration of metallic edge states. Such
heterostructures offer an unique Dirac transport system that combines the 2D
Dirac states from graphene and 1D Dirac edge states from the topological
insulator, and it offers new ideas for innovative device designs
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