634 research outputs found
Wavelet analysis on symbolic sequences and two-fold de Bruijn sequences
The concept of symbolic sequences play important role in study of complex
systems. In the work we are interested in ultrametric structure of the set of
cyclic sequences naturally arising in theory of dynamical systems. Aimed at
construction of analytic and numerical methods for investigation of clusters we
introduce operator language on the space of symbolic sequences and propose an
approach based on wavelet analysis for study of the cluster hierarchy. The
analytic power of the approach is demonstrated by derivation of a formula for
counting of {\it two-fold de Bruijn sequences}, the extension of the notion of
de Bruijn sequences. Possible advantages of the developed description is also
discussed in context of applied
Optimal Assembly for High Throughput Shotgun Sequencing
We present a framework for the design of optimal assembly algorithms for
shotgun sequencing under the criterion of complete reconstruction. We derive a
lower bound on the read length and the coverage depth required for
reconstruction in terms of the repeat statistics of the genome. Building on
earlier works, we design a de Brujin graph based assembly algorithm which can
achieve very close to the lower bound for repeat statistics of a wide range of
sequenced genomes, including the GAGE datasets. The results are based on a set
of necessary and sufficient conditions on the DNA sequence and the reads for
reconstruction. The conditions can be viewed as the shotgun sequencing analogue
of Ukkonen-Pevzner's necessary and sufficient conditions for Sequencing by
Hybridization.Comment: 26 pages, 18 figure
Focus: A Graph Approach for Data-Mining and Domain-Specific Assembly of Next Generation Sequencing Data
Next Generation Sequencing (NGS) has emerged as a key technology leading to revolutionary breakthroughs in numerous biomedical research areas. These technologies produce millions to billions of short DNA reads that represent a small fraction of the original target DNA sequence. These short reads contain little information individually but are produced at a high coverage of the original sequence such that many reads overlap. Overlap relationships allow for the reads to be linearly ordered and merged by computational programs called assemblers into long stretches of contiguous sequence called contigs that can be used for research applications. Although the assembly of the reads produced by NGS remains a difficult task, it is the process of extracting useful knowledge from these relatively short sequences that has become one of the most exciting and challenging problems in Bioinformatics.
The assembly of short reads is an aggregative process where critical information is lost as reads are merged into contigs. In addition, the assembly process is treated as a black box, with generic assembler tools that do not adapt to input data set characteristics. Finally, as NGS data throughput continues to increase, there is an increasing need for smart parallel assembler implementations. In this dissertation, a new assembly approach called Focus is proposed. Unlike previous assemblers, Focus relies on a novel hybrid graph constructed from multiple graphs at different levels of granularity to represent the assembly problem, facilitating information capture and dynamic adjustment to input data set characteristics. This work is composed of four specific aims: 1) The implementation of a robust assembly and analysis tool built on the hybrid graph platform 2) The development and application of graph mining to extract biologically relevant features in NGS data sets 3) The integration of domain specific knowledge to improve the assembly and analysis process. 4) The construction of smart parallel computing approaches, including the application of energy-aware computing for NGS assembly and knowledge integration to improve algorithm performance.
In conclusion, this dissertation presents a complete parallel assembler called Focus that is capable of extracting biologically relevant features directly from its hybrid assembly graph
New Algorithms and Lower Bounds for Sequential-Access Data Compression
This thesis concerns sequential-access data compression, i.e., by algorithms
that read the input one or more times from beginning to end. In one chapter we
consider adaptive prefix coding, for which we must read the input character by
character, outputting each character's self-delimiting codeword before reading
the next one. We show how to encode and decode each character in constant
worst-case time while producing an encoding whose length is worst-case optimal.
In another chapter we consider one-pass compression with memory bounded in
terms of the alphabet size and context length, and prove a nearly tight
tradeoff between the amount of memory we can use and the quality of the
compression we can achieve. In a third chapter we consider compression in the
read/write streams model, which allows us passes and memory both
polylogarithmic in the size of the input. We first show how to achieve
universal compression using only one pass over one stream. We then show that
one stream is not sufficient for achieving good grammar-based compression.
Finally, we show that two streams are necessary and sufficient for achieving
entropy-only bounds.Comment: draft of PhD thesi
- …