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.

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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

We develop two new probabilistic methods that allow us, for several different dynamic processes, to analyze the maximum data-structure size encountered during a sequence of insertions and deletions, such as in data structures like priority queues, dictionaries, linear lists, and symbol tables, and in sweepline structures for geometry and VLSI applications. The notion of the ``maximum'''' is basic to issues of resource preallocation. We apply our methods to several models of insertion and deletion, including combinatorial models of file histories (which include the data structures mentioned above) and probabilistic models. Included in our study is a non-Markovian process (algorithm) for processing sweepline information in an efficient way, called ``hashing with lazy deletion'''' (HwLD). We derive expressions for the expected maximum data-structure size that are asymptotically exact, that is, correct up to lower-order terms; in several cases of interest the expected value of the maximum size is asymptotically equal to the maximum expected size. This solves several open problems, including longstanding fundamental questions in queueing theory. Both of our approaches are robust and rely on novel applications of techniques from the analysis of algorithms. At a high level, our first method isolates the primary contribution to the maximum and bounds the lesser effects. In our second technique we relate the continuous-time probabilistic model to its discrete analogue --- the maximum slot occupancy in hashing.

General Methods for the Analysis of the Maximum Size of Data Structures

In modern I/O architectures, multiple disk drives are attached to each I/O bus. Under I/O-intensive workloads, the disk latency for a request can be overlapped with the disk latency and data transfers of requests to other disks, potentidly resulting in an aggregate I/O throughput at nearly bus bandwidth. This paper reports on a performance impairment that results from a previously unknown form of convoy behavior in disk I/O, which we call munds. In rounds, independent requests to distinct disks convoy, so that each disk services one request before any disk services its next re quest. We analyze log tiles to describe read performance of multiple Seagate Wren-7 disks that share a SCSI bus under a heavy workload, demonstrating the rounds behavior and quantifying its performance impact.

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Round-like behavior in multiple disks on a bus

http://ieeexplore.ieee.org/iel2/490/5567/00213368.pdf

Analysis of arithmetic coding for

Direct chaining is a popular and efficient class of hashing algorithms. In this paper we study optimum algorithms among direct chaining methods, under the restrictions that the records in the hash table are not moved after they are inserted, that for each chain the relative ordering of the records in the chain does not change after more insertions, and that only one link field is used per table slot. The varied-insertion coalesced hashing method (VICH), which is proposed and analyzed in [CV84], is conjectured to be optimum among all direct chaining algorithms in this class. We give strong evidence in favor of the conjecture by showing that VICH is optimum under fairly general conditions.

/pdf/optimum-algorithms-for-a-model-of-direct-chaining-4fkizw8349.pdf

Optimum Algorithms for a Model of Direct Chaining

In this paper, we focus upon the important problem of indexing and searching highly repetitive DNA sequence collections. Given a collection $G$ of t sequences $S_i$ of length n each, we can represent G succinctly in $2n\mathcal{H_k}(T) + O(n^\prime \log\log\;n) + o(qn^\prime) + o(tn)$ bits using $O(tn^2 + qn^\prime)$ time, where $\mathcal{H_k}(T)$ is the k-order empirical entropy of the sequence $T \in G$ that is used as the reference sequence, $n^\prime$ is the total number of variations between T and the sequences in G, and q is a small fixed constant. We can restore any length len substring $S[sp, \dots, sp + len - 1]$ of $S \in G$ in $O(n_{s}^{\prime} + len(\log\;n)^2/ \log\;n)$ time and report all positions where P occurs in G in $O(m\cdot t + occ\cdot t \cdot (\log\;n)^2/ \log\; \log\;n)$ time. In addition, we propose a dynamic programming method to find the variations between T and the sequences in G in a space-efficient way, with which we can build succinct structures to enable efficient search. For highly repetitive sequences, experimental results on the tested data demonstrate that the proposed method has significant advantages in space usage and retrieval time over the current state-of-the-art methods. The source code is available online.

Jeffrey Scott Vitter

Papers

General Methods for the Analysis of the Maximum Size of Data Structures

Round-like behavior in multiple disks on a bus

Analysis of arithmetic coding for

Optimum Algorithms for a Model of Direct Chaining

Efficient compression and indexing for highly repetitive DNA sequence collections.