Searching large genomic data sets for local alignments poses a computational challenge. A particular obstacle is the handling of repetitive sequences that appear in various contexts and incur a high runtime cost. For practical homology search, it is important to develop a specific but sensitive filter. Good filters reduce the search space before alignment without missing significant matches. We introduce DREAM-Stellar, a parallelized, updated version of the pairwise local aligner Stellar. The new aligner, DREAM-Stellar, is composed of four steps: preprocessing the queries and references, building a data structure for distributing the queries, computing in parallel the results and finally combining them. For distributing the queries we use the IBF data structure and a new prefilter for local alignments. We present our comparison of five local aligners on simulated and real genomic data and conclude that heuristic tools like BLAST miss a large percentage of significant local alignments or "drown" them in millions of less significant matches. This new version of Stellar is up to 900 times faster on 32 parallel threads than its single-threaded predecessor and can find all alignments between a pair of genomes in minutes. With that, the runtime of DREAM-Stellar is on par with tools like BLAST etc. DREAM-Stellar is very practical and fast on very long sequences which makes it a suitable new tool for finding local alignments between genomic sequences under the edit distance model. The software is freely available for Linux and Mac OS X at https://github.com/seqan/dream-stellar

Motivation:Pairwise sequence alignment is undoubtedly a central tool in many bioinformatics analyses. In this paper, we present a generically accelerated module for pairwise sequence alignments applicable for a broad range of applications. In our module, we unified the standard dynamic programming kernel used for pairwise sequence alignments and extended it with a generalized inter-sequence vectorization layout, such that many alignments can be computed simultaneously by exploiting SIMD (single instruction multiple data) instructions of modern processors. We then extended the module by adding two layers of thread-level parallelization, where we (a) distribute many independent alignments on multiple threads and (b) inherently parallelize a single alignment computation using a work stealing approach producing a dynamic wavefront progressing along the minor diagonal. Results:We evaluated our alignment vectorization and parallelization on different processors, including the newest Intel® Xeon® (Skylake) and Intel® Xeon PhiTM (KNL) processors, and use cases. The instruction set AVX512-BW (Byte and Word), available on Skylake processors, can genuinely improve the performance of vectorized alignments. We could run single alignments 1600 times faster on the Xeon PhiTM and 1400 times faster on the Xeon® than executing them with our previous sequential alignment module. Availability and implementation:The module is programmed in C++ using the SeqAn (Reinert et al., 2017) library and distributed with version 2.4 under the BSD license. We support SSE4, AVX2, AVX512 instructions and included UME: SIMD, a SIMD-instruction wrapper library, to extend our module for further instruction sets. We thoroughly test all alignment components with all major C++ compilers on various platforms. Supplementary information:Supplementary data are available at Bioinformatics online.

Let w ∈ℕ and U = {0, 1, … , 2^w-1} be a bounded universe of w-bit integers. We present a dynamic data structure for predecessor searching in U. Our structure needs O(loglog ) time for queries and O(loglog ) expected time for updates, where is the difference between the query element and its nearest neighbor in the structure. Our data structure requires linear space. This improves a result by Bose et al. [CGTA, 46(2), pp. 181–189]. The structure can be applied for answering approximate nearest neighbor queries in low dimensions and for dominance queries on a grid.

The unidirectional FM index was introduced by Ferragina and Manzini in 2000 and allows to search a pattern in the index in one direction. The bidirectional FM index 2FM was introduced by Lam et al. in 2009. It allows to search for a pattern by extending an infix of the pattern arbitrarily to the left or right. If $$\sigma $$ is the size of the alphabet then the method of Lam et al. can conduct one step in time $$\mathcal {O}\sigma $$ while needing space $$\mathcal {O}\sigma \cdot n$$ using constant time rank queries on bit vectors. Schnattinger and colleagues improved this time to $$\mathcal {O}\log \sigma $$ while using $$\mathcal {O}\log \sigma \cdot n$$ bits of space for both, the FM and 2FM index. This is achieved by the use of binary wavelet trees. In this paper we introduce a new, practical method for conducting an exact search in a uni- and bidirectional FM index in $$\mathcal {O}1$$ time per step while using $$\mathcal {O}\log \sigma \cdot n + o\log \sigma \cdot \sigma \cdot n$$ bits of space. This is done by replacing the binary wavelet tree by a new data structure, the Enhanced Prefixsum Rank dictionary EPR-dictionary. We implemented this method in the SeqAn C++ library and experimentally validated our theoretical results. In addition we compared our implementation with other freely available implementations of bidirectional indices and show that we are between $$\approx 2.2-4.2$$ times faster. This will have a large impact for many bioinformatics applications that rely on practical implementations of 2FM indices e.g. for read mapping. To our knowledge this is the first implementation of a constant time method for a search step in 2FM indices.

Background: The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome (Venter et al., 2001) would not have been possible without advanced assembly algorithms and the development of practical BWT based read mappers have been instrumental for NGS analysis. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there was a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use. We previously addressed this by introducing the SeqAn library of efficient data types and algorithms in 2008 ( Doring et al., 2008). Results: The SeqAn library has matured considerably since its first publication 9 years ago. In this article we review its status as an established resource for programmers in the field of sequence analysis and its contributions to many analysis tools. Conclusions: We anticipate that SeqAn will continue to be a valuable resource, especially since it started to actively support various hardware acceleration techniques in a systematic manner.

We introduce a new method for conducting an exact search in a uni- and bidirectional FM index in $\mathcal{O}(1)$ time per step while using $\mathcal{O}(\log \sigma \cdot n) + o(\log \sigma \cdot \sigma \cdot n)$ bits of space. This is done by replacing the wavelet tree by a new data structure, the \emph{Enhanced Prefixsum Rank dictionary} (EPR-dictionary). To our knowledge this is the first constant time method for a search step in 2FM indices and a space improvement for FM indices. We implemented this method in the SeqAn C++ library and experimentally validated our theoretical results. In addition we compared our implementation with other freely available implementations of bidirectional indices and show that we are between $\approx 2.7-4.6$ times faster. This will have a large impact for many bioinformatics applications that rely on practical implementations of (2)FM indices e.g. for read mapping.

IP-packet routing is one of the most practical solved problems in computer science with millions of routed packets per second. The algorithmic problem behind it is the so-called predecessor problem. Assuming a universe U , in this problem one can query a set S ⊆ U for the predecessor of an element q within the set S, i.e. max{x ∈ S | x < q}. If the set S can be manipulated, the problem is called the dynamic predecessor problem, otherwise it is called the static predecessor problem. In this thesis I give an overview of several different data structures for the w-bit word RAM on bounded integer universes, i.e. U = {0, . . . , 2 − 1}. Those data structures are based on the van-Emde-Boas-Tree [BKZ76]. For each data structure I work out the details of the algorithms by giving pseudo-code and for some of them I present new techniques, which give an easier and clearer presentation of the data structure and the algorithms. Last but not least, I answer an open problem concerning the space usage stated by Bose et al [Bos+13]. Eidesstattliche Erklärung Ich versichere hiermit an Eides statt, dass diese Arbeit von niemand anderem als meiner Person verfasst worden ist. Alle verwendeten Hilfsmittel wie Berichte, Bücher, Internetseiten oder Ähnliches sind im Literaturverzeichnis angegeben, Zitate aus fremden Arbeiten sind als solche kenntlich gemacht. Die Arbeit wurde bisher in gleicher oder ähnlicher Form keiner anderen Prüfungskommission vorgelegt und auch nicht veröffentlicht.