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

One of the most relevant succinct suffix array proposals in the literature is the Compressed Suffix Array (CSA) of Sadakane [ISAAC 2000] The CSA needs n(H0+O(log logσ)) bits of space, where n is the text size, σ is the alphabet size, and H0 the zero-order entropy of the text The number of occurrences of a pattern of length m can be computed in O(mlog n) time Most notably, the CSA does not need the text separately available to operate The CSA simulates a binary search over the suffix array, where the query is compared against text substrings These are extracted from the same CSA by following irregular access patterns over the structure Sadakane [SODA 2002] has proposed using backward searching on the CSA in similar fashion as the FM-index of Ferragina and Manzini [FOCS 2000] He has shown that the CSA can be searched in O(m) time whenever σ = O(polylog(n)).

In this paper we consider some other consequences of backward searching applied to CSA The most remarkable one is that we do not need, unlike all previous proposals, any complicated sub-linear structures based on the four-Russians technique (such as constant time rank and select queries on bit arrays) We show that sampling and compression are enough to achieve O(mlog n) query time using less space than the original structure It is also possible to trade structure space for search time Furthermore, the regular access pattern of backward searching permits an efficient secondary memory implementation, so that the search can be done with O(m logBn) disk accesses, being B the disk block size Finally, it permits a distributed implementation with optimal speedup and negligible communication effort.

/pdf/advantages-of-backward-searching-efficient-secondary-memory-1gh70ifs8h.pdf

Advantages of backward searching — efficient secondary memory and distributed implementation of compressed suffix arrays

We show space-economical algorithms for finding maximal unique matches (MUM's) between two strings which are important in large scale genome sequence alignment problems. Our algorithms require only O(n) bits (O(n/ log n) words) where n is the total length of the strings. We propose three algorithms for different inputs: When the input is only the strings, their compressed suffix array, or their compressed suffix tree. Their time complexities are O(n log n), O(n log?n) and O(n) respectively, where ? is any constant between 0 and 1. We also show an algorithm to construct the compressed suffix tree from the compressed suffix array using O(n log?n) time and O(n) bits space.

Space-Economical Algorithms for Finding Maximal Unique Matches

We propose new succinct representations of ordinal trees, which have been studied extensively. It is known that any $n$-node static tree can be represented in $2n + o(n)$ bits and a number of operations on the tree can be supported in constant time under the word-RAM model. However the data structures are complicated and difficult to dynamize. We propose a simple and flexible data structure, called the range min-max tree, that reduces the large number of relevant tree operations considered in the literature to a few primitives that are carried out in constant time on sufficiently small trees. The result is extended to trees of arbitrary size, achieving $2n + O(n /\polylog(n))$ bits of space. The redundancy is significantly lower than any previous proposal. Our data structure builds on the range min-max tree to achieve $2n+O(n/\log n)$ bits of space and $O(\log n)$ time for all the operations. We also propose an improved data structure using $2n+O(n\log\log n/\log n)$ bits and improving the time to the optimal $O(\log n/\log \log n)$ for most operations. Furthermore, we support sophisticated operations that allow attaching and detaching whole subtrees, in time $\Order(\log^{1+\epsilon} n / \log\log n)$. Our techniques are of independent interest. One allows representing dynamic bitmaps and sequences supporting rank/select and indels, within zero-order entropy bounds and optimal time $O(\log n / \log\log n)$ for all operations on bitmaps and polylog-sized alphabets, and $O(\log n \log \sigma / (\log\log n)^2)$ on larger alphabet sizes $\sigma$. This improves upon the best existing bounds for entropy-bounded storage of dynamic sequences, compressed full-text self-indexes, and compressed-space construction of the Burrows-Wheeler transform.

Fully-Functional Static and Dynamic Succinct Trees

https://www.cs.ucr.edu/~stelo/cpm/cpm05/cpm05_9_2_Fukagawa.pdf

Inferring a graph from path frequency

Recent research in compressing suffix arrays has resulted in two breakthrough indexing data structures, namely, compressed suffix arrays (CSA) [7] and FM-index [5]. Either of them makes it feasible to store a full-text index in the main memory even for a piece of text data with a few billion characters (such as human DNA). However, constructing such indexing data structures with limited working memory (i.e., without constructing suffix arrays) is not a trivial task. This paper addresses this problem. Currently, only CSA admits a space-efficient construction algorithm [15]. For a text T of length n over an alphabet Σ, this algorithm requires O(|Σ|nlogn) time and (2 H 0 + 1+e)n bits of working space, where H 0 is the 0-th order empirical entropy of T and e is any non-zero constant. This algorithm is good enough when the alphabet size | Σ| is small. It is not practical for text data containing protein, Chinese or Japanese, where the alphabet may include up to a few thousand characters.

Kunihiko Sadakane

Papers

Advantages of backward searching — efficient secondary memory and distributed implementation of compressed suffix arrays

Space-Economical Algorithms for Finding Maximal Unique Matches

Fully-Functional Static and Dynamic Succinct Trees

Inferring a graph from path frequency

Constructing compressed suffix arrays with large alphabets