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

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

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Emerging infectious diseases, such as severe acute respiratory syndrome (SARS) and Zika virus disease, present a major threat to public health1–3. Despite intense research efforts, how, when and where new diseases appear are still a source of considerable uncertainty. A severe respiratory disease was recently reported in Wuhan, Hubei province, China. As of 25 January 2020, at least 1,975 cases had been reported since the first patient was hospitalized on 12 December 2019. Epidemiological investigations have suggested that the outbreak was associated with a seafood market in Wuhan. Here we study a single patient who was a worker at the market and who was admitted to the Central Hospital of Wuhan on 26 December 2019 while experiencing a severe respiratory syndrome that included fever, dizziness and a cough. Metagenomic RNA sequencing4 of a sample of bronchoalveolar lavage fluid from the patient identified a new RNA virus strain from the family Coronaviridae, which is designated here ‘WH-Human 1’ coronavirus (and has also been referred to as ‘2019-nCoV’). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that the virus was most closely related (89.1% nucleotide similarity) to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) that had previously been found in bats in China5. This outbreak highlights the ongoing ability of viral spill-over from animals to cause severe disease in humans. Phylogenetic and metagenomic analyses of the complete viral genome of a new coronavirus from the family Coronaviridae reveal that the virus is closely related to a group of SARS-like coronaviruses found in bats in China.

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A new coronavirus associated with human respiratory disease in China.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

An accurate estimation of the barrier height between two given secondary structures of DNA molecules is known to be a fundamental and difficult problem. In 1998 Morgan and Higgs proposed a heuristic algorithm based on the shortest path between the two structures, and in DNA 10, Kubota and Hagiya did an exact algorithm based on the flooding. The former runs in a practical time for sufficiently large length n of molecule and would always show a good performance if the barrier always appeared near the shortest path. The only but crucial drawback of the latter on the other hand is that it cannot run for a large n; we found an instance of even length n = 46 for which the run did not complete because of the memory. In this paper we formulate it as an optimization problem, and then propose a new heuristic algorithm based on the local search strategy. We use the Morgan and Higgs' heuristics as the engine to find a locally optimal solution, and based on the local search paradigm, we repeat this local search starting from the solution of the previous local search, with the hope that this sequence of improvements will eventually reach the optimum solution. We also discuss some techniques to improve the performance. We demonstrate that for size about 200, our algorithm runs in 5 seconds, and for many of the cases (13 cases out of 16) in which the Kubota and Hagiya's algorithm can complete, our algorithm exactly answers the optimum values.

A local search based barrier height estimation algorithm for DNA molecular transitions

We consider the orthogonal range search problem: given a point set P in d-dimensional space and an orthogonal query region Q, return some information on \(P\cap Q\). We focus on the counting query to count the number of points of P contained in Q, and the reporting query to enumerate all points of P in Q.

Practical Space-Efficient Data Structures for High-Dimensional Orthogonal Range Searching

Space efficient data structures for partial ordered sets or posets are well-researched field. It is known that a poset with n elements can be represented in n 2 / 4+ o ( n 2 ) bits [30] and can also be represented in (1 + ϵ ) n log n + 2 nk + o ( nk ) bits [19] where k is width of the poset. In this paper, we make the latter data structure occupy 2 n ( k − 1) + o ( nk ) bits by considering topological labeling on the elements of posets. Also considering the topological labeling, we propose a new data structure that calculates queries on transitive reduction graphs of posets faster though queries on transitive closure graphs are computed slower. Moreover, we propose an alternative data structure for topological labeled posets that calculates both of the queries faster though it uses 3 nk − 2 n + o ( nk ) bits of space. Additionally, we discuss the advantage of these data structures from the perspective of an application for BlockDAG, which is a more scalable version of Blockchain.

Space-Efficient Data Structure for Posets with Applications

We consider the range mode problem where given a sequence and a query range in it, we want to find items with maximum frequency in the range. We give time- and space- efficient algorithms for this problem. Our algorithms are efficient for small maximum frequency cases. We also consider a natural generalization of the problem: the range mode enumeration problem, for which there has been no known efficient algorithms. Our algorithms have query time complexities which is linear to the output size plus small terms.

Enumerating Range Modes

We present general techniques to speed up local search type algorithms for designing DNA sequences which satisfy thermodynamical constraints based on the minimum free energy (MFE) criteria. MFE based constraints are generally difficult to handle in local search type algorithms, since these algorithms typically require a large number of time-consuming calculations of MFE to find an improved solution. In this paper, we introduce general techniques to reduce such calculations of MFE. The ideas are based on the reuse of MFE computations and fast approximation of MFE, both of which fit the nature of local search type algorithms. In computational experiments, our techniques succeeded in speeding up typical local search type algorithms without degenerating the original performance of the algorithms.

Kunihiko Sadakane

Papers

A local search based barrier height estimation algorithm for DNA molecular transitions

Practical Space-Efficient Data Structures for High-Dimensional Orthogonal Range Searching

Space-Efficient Data Structure for Posets with Applications

Enumerating Range Modes

Speeding Up Local-Search Type Algorithms for Designing DNA Sequences under Thermodynamical Constraints