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

Motif stem search (MSS) is a recent motif search problem to search motifs on large-alphabet inputs. A motif stem is an l-length string with some wildcards. The goal of the MSS problem is to find a set of stems that represents a superset of all (l, d) motifs present in the input sequences. The three main contributions of this paper are as follows: (1) We build motif stem representation more precisely by using regular expressions. (2) We give a new method for generating all possible motif stems. (3) We propose an efficient algorithm, called StemFinder, for solving the MSS problem. Compared with the previous algorithms, StemFinder runs much faster and first solves the (17, 8), (19, 9) and (21, 10) challenging instances on protein sequences; moreover, StemFinder reports fewer stems representing a smaller superset of all (l, d) motifs.

StemFinder: An efficient algorithm for searching motif stems over large alphabets

The notion of an I/O interface for optical digital (write-once) disks is introduced that is quite different from earlier research in this area. The purpose of an I/O interface is to allow existing operating systems and application programs that use magnetic disks to use optical disks instead, with minimum difficulty. The interface is especially geared to applications that are not update-intensive or that require access to previous versions of records. We define what it means for an I/O interface to be disk-efficient. We demonstrate a disk-efficient interface and show that its I/O performance in many cases is optimum, up to a constant factor, among all disk-efficient interfaces. The basis of the interface is a data structure we call offset trees, which stores information about intervals with dynamically changing coordinates. Since this complexity model is based on practical concerns, these theoretical results translate nicely into an efficient implementation.

Computational Complexity of an Optical Disk Interface (Extended Abstract)

We investigate the problem of smoothing multiplexed network traffic when either a streaming server transmits data to multiple clients or a storage server accesses data from multiple storage devices or other servers. We introduce efficient algorithms for lexicographically optimally smoothing the aggregate bandwidth requirements over a shared network link. Possible applications include improvement in the bandwidth utilization of network links and reduction in the energy consumption of server hosts. In the data transmission problem, we consider the case in which the clients have different buffer capacities and unlimited bandwidth constraints or unlimited buffer capacities and different bandwidth constraints. For the data access problem, we handle the general case of a shared buffer capacity and individual network bandwidth constraints. Previous approaches for the data access problem handled either the case of only a single stream or did not compute the lexicographically optimal schedule. By provably minimizing the variance of the required aggregate bandwidth, lexicographically optimal smoothing makes the maximum resource requirements within the network more predictable and increases the useful resource utilization. It also improves fairness in sharing a network link among multiple users and makes new requests from future clients more likely to be successfully admitted without the need for rescheduling previously accepted traffic. With appropriate hardware and system support, data traffic smoothing can also reduce the energy consumption of the host processor and the communication links. Overall, we expect that efficient resource management at the network edges will better meet quality of service requirements without restricting the scalability of the system.

Optimal lexicographic shaping of aggregate streaming data

This study explores an alternative way of storing text files to answer exact match queries faster. We decompose the original file into two parts as filter and payload. The filter part contains the most informative k bits of each byte, and the remaining bits of the bytes are concatenated in the order of appearance to generate the payload. We refer to this structure as k-bit filtered format. When an input pattern is to be searched on the k-bit filtered structure, the same decomposition is performed on the pattern. The k bits from each byte of the pattern form the pattern filter bit sequence, and the rest is the payload. The pattern filter is first scanned on the filter part of the file. At each match position detected in the filter part, the pattern payload is verified against the corresponding location in the payload part of the text. Thus, instead of searching an m-byte pattern on an n-byte text, first k·m bits are scanned on k·n bits, followed by a verification of (8-k)·m bits on the respective locations of the matching positions. Experiments conducted on natural language texts, plain ASCII DNA sequences and random byte sequences showed that the search performance with the proposed scheme is on average two times faster than the tested best exact pattern-matching algorithms. The highest gain is obtained on plain ASCII DNA sequences. We also developed an effective bitwise pattern-matching algorithm of possible independent interest within this study.

Jeffrey Scott Vitter

Papers

StemFinder: An efficient algorithm for searching motif stems over large alphabets

Computational Complexity of an Optical Disk Interface (Extended Abstract)

Optimal lexicographic shaping of aggregate streaming data

Fast Pattern-Matching via k-bit Filtering Based Text Decomposition

Geometric and Spatial Data Structures in External Memory