Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals.

Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package.

Availability: http://maq.sourceforge.net 

Contact: [email protected]

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Fast and accurate short read alignment with Burrows–Wheeler transform

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.

/pdf/the-weka-data-mining-software-an-update-3zs9onav7o.pdf

The WEKA data mining software: an update

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Given a set $\D$ of $d$ patterns of total length $n$, the dictionary matching problem is to index $\D$ such that for any query text $T$, we can locate the occurrences of any pattern within $T$ efficiently. This problem can be solved in optimal $O(|T|+occ)$ time by the classical AC automaton (Aho and Corasick, 1975) where $occ$ denotes the number of occurrences. The space requirement is $O(n)$ words. In the \emph{approximate} dictionary matching problem with one error, we consider a substring of $T[i..j]$ an occurrence of $P$ whenever the edit distance between $T[i..j]$ and $P$ is at most one. For this problem, the best known indexes are by Cole et al. (2004), which requires $O(n+ d\log{d})$ words of space and reports all occurrences in $O(|T|\log{d}\log{\log{d}}+occ)$ time, and by Ferragina et al. (1999), which requires $O(n^{1+\epsilon})$ words of space and reports all occurrences in $O(|T|\log\log n + occ)$ time. Recently, there have been successes in compressing the dictionary matching index while keeping the query time optimal (Belazzougui, 2010, Hon et al., 2010). However, a compressed index for approximate dictionary matching problem is still open. In this paper, we propose the first such index which requires an optimal $nH_k+O(n)+o(n\log\sigma)$-bit index space, where $H_k$ denotes the $k$th-order empirical entropy of $\D$, and $\sigma$ is the size of alphabet set from which all the characters in $\D$ and $T$ are drawn. The query time of our index is $O(\sigma |T|\log^3{n}\log{\log{n}}+occ)$.

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Compressed Dictionary Matching with One Error

http://www.ittc.ku.edu/~jsv/Papers/HLS07.ApproximateStringMatching.pdf

Cache-oblivious index for approximate string matching

We present a new complexity-theoretic approach to incremental computation. We define complexity classes that capture the intuitive notion of incremental efficiency and study their relation to existing complexity classes. We show that all common logs p-complete problems for poly are incrplt-complete for poly. This suggests that non-redundant problems that seem inherently unparallelizable also seem hard to dynamize. We show that a form of transitive closure is complete under incremental reduction for nlgsp and give similar problems which are incrementally complete for the classes logsp and non-uniform nick. We show that under certain restrictions problems which have efficient dynamic solutions also have efficient parallel solutions. We also look at the time complexity of circuit value and network stability problems restricted to comparator gates. We show that the dynamic version of the comparator-circuit value problem and ``Lex-First Maximal Matching'''' problem is in logsp and that of the comparator-network stability problem and ``Man-Optimal Stable Marriage Problem'''' is in logsp given an oracle which operates in nlgsp. This shows that the dynamic versions of these problems are solvable quickly in parallel even though there are no known nick algorithms to solve them from scratch.

A Complexity-Theoretic Approach to Incremental Computation

The authors present new vector quantization algorithms. The new approach is to formulate a vector quantization problem as a 0-1 integer linear program. They first solve its relaxed linear program by linear programming techniques. Then they transform the linear program solution into a provably good solution for the vector quantization problem. These methods lead to the first known polynomial-time full-search vector quantization codebook design algorithm and tree pruning algorithm with provable worst-case performance guarantees. They also introduce the notion of pseudorandom pruned tree-structured vector quantizers. Initial experimental results on image compression are very encouraging. >

/pdf/nearly-optimal-vector-quantization-via-linear-programming-edccfhp354.pdf

Jeffrey Scott Vitter

Papers

Compressed Dictionary Matching with One Error

Cache-oblivious index for approximate string matching

A Complexity-Theoretic Approach to Incremental Computation

Nearly optimal vector quantization via linear programming

A parallel algorithm for recognizing unordered depth-first search