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]

/pdf/fast-and-accurate-short-read-alignment-with-burrows-wheeler-43uqhgr33c.pdf

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.

/pdf/a-new-coronavirus-associated-with-human-respiratory-disease-4bxsuj7unz.pdf

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

In this paper we give an algorithmfor estimatingthe entropyofa string using the suffix array. Our algorithm is based on a new combinatorial property of the longest common prefix array of a string.

/pdf/entropy-estimation-with-suffix-arrays-5e2g43kdmd.pdf

Entropy estimation with suffix arrays

We design succinct encodings of {\it series-parallel, block-cactus} and {\it 3-leaf power} graphs while supporting the basic navigational queries such as degree, adjacency and neighborhood {\it optimally} in the RAM model with logarithmic word size. One salient feature of our representation is that it can achieve optimal space even though the exact space lower bound for these graph classes is not known. For these graph classes, we provide succinct data structures with optimal query support for the first time in the literature. For series-parallel multigraphs, our work also extends the works of Uno et al. (Disc. Math. Alg. and Appl., 2013) and Blelloch and Farzan (CPM, 2010) to produce optimal bounds.

Succinct Data Structures for Series-Parallel, Block-Cactus and 3-Leaf Power Graphs

ZDDs Zero-suppressed Binary Decision Diagrams [Minato 93] have been proposed to store set families compactly. Though more compact than other representations, they still use large amount of memory to support dynamic operations such as taking union and intersection of set families. DenseZDDs and Hybrid DenseZDDs [Denzumi et al. 2014] have been proposed to compress the size of static and dynamic ZDDs, respectively. There exist however no implementations of Hybrid DenseZDDs and their practical performance is unknown. This paper engineers a practical implementation of Hybrid DenseZDDs. Because of our new compression algorithm, our new Hybrid DenseZDDs run in reasonable time using little working memory. Experimental results on the frequent itemset mining problem show that our algorithm uses 33i¾ź% of memory compared with a standard ZDD at the cost of 40i¾ź% increase in running time.

Engineering Hybrid DenseZDDs

2011: John Z. Sun, Massachusetts Institute of Technology, "Scalar Quantization For Relative Error" (coauthored with Vivek Goyal). 2010: Navid Abedini, Texas A&M University, "A SAT-Based Scheme to Determine Optimal Fix-free Codes" (coauthored with Sunil Khatri and Serap Savari). 2009: Pavol Hanus, Technische Universitat Munchen, "Source Coding Scheme for Multiple Sequence Alignments" (coauthored with Janis Dingel, Georg Chalkidis, Joachim Hagenauer).

The Capocelli Prize

Scale-free networks naturally model wide area networks like the WWW. We consider the problem of fast exploration of huge scale-free networks using small memory space. Although there are many search algorithms for exploring an unknown graph, they require much space or time. For example, the depth first search requires some memory for all the nodes in the worst case, and the average number of steps in the random walk is O(n 3 ), where n is the size of the graph. Under assumptions reflecting WWW applications, we propose a new exploration paradigm called forest search particularly designed for scale-free networks, and theoretically evaluate its space complexity. We also demonstrate its superiority over random walk based search algorithms by conducting simulations.

Kunihiko Sadakane

Papers

Entropy estimation with suffix arrays

Succinct Data Structures for Series-Parallel, Block-Cactus and 3-Leaf Power Graphs

Engineering Hybrid DenseZDDs

The Capocelli Prize

Forest search : A paradigm for faster exploration of scale-free networks