Fast local fragment chaining using sum-of-pair gap costs

<p>Abstract</p> <p>Background</p> <p>Fast seed-based alignment heuristics such as <monospace>BLAST</monospace> and <monospace>BLAT</monospace> have become indispensable tools in comparative genomics for all studies aiming at the evolutionary relations of proteins, genes, and non-coding RNAs. This is true in particular for the large mammalian genomes. The sensitivity and specificity of these tools, however, crucially depend on parameters such as seed sizes or maximum expectation values. In settings that require high sensitivity the amount of short local match fragments easily becomes intractable. Then, fragment chaining is a powerful leverage to quickly connect, score, and rank the fragments to improve the specificity.</p> <p>Results</p> <p>Here we present a fast and flexible fragment chainer that for the first time also supports a sum-of-pair gap cost model. This model has proven to achieve a higher accuracy and sensitivity in its own field of application. Due to a highly time-efficient index structure our method outperforms the only existing tool for fragment chaining under the linear gap cost model. It can easily be applied to the output generated by alignment tools such as <monospace>segemehl</monospace> or <monospace>BLAST</monospace>. As an example we consider homology-based searches for human and mouse snoRNAs demonstrating that a highly sensitive <monospace>BLAST</monospace> search with subsequent chaining is an attractive option. The sum-of-pair gap costs provide a substantial advantage is this context.</p> <p>Conclusions</p> <p>Chaining of short match fragments helps to quickly and accurately identify regions of homology that may not be found using local alignment heuristics alone. By providing both the linear and the sum-of-pair gap cost model, a wider range of application can be covered. The software clasp is available at <url>http://www.bioinf.uni-leipzig.de/Software/clasp/</url>.</p

Sensitive Long-Indel-Aware Alignment of Sequencing Reads

The tremdendous advances in high-throughput sequencing technologies have made population-scale sequencing as performed in the 1000 Genomes project and the Genome of the Netherlands project possible. Next-generation sequencing has allowed genom-wide discovery of variations beyond single-nucleotide polymorphisms (SNPs), in particular of structural variations (SVs) like deletions, insertions, duplications, translocations, inversions, and even more complex rearrangements. Here, we design a read aligner with special emphasis on the following properties: (1) high sensitivity, i.e. find all (reasonable) alignments; (2) ability to find (long) indels; (3) statistically sound alignment scores; and (4) runtime fast enough to be applied to whole genome data. We compare performance to BWA, bowtie2, stampy and find that our methods is especially advantageous on reads containing larger indels

Computational Molecular Biology

Computational Biology is a fairly new subject that arose in response to the computational problems posed by the analysis and the processing of biomolecular sequence and structure data. The field was initiated in the late 60's and early 70's largely by pioneers working in the life sciences. Physicists and mathematicians entered the field in the 70's and 80's, while Computer Science became involved with the new biological problems in the late 1980's. Computational problems have gained further importance in molecular biology through the various genome projects which produce enormous amounts of data. For this bibliography we focus on those areas of computational molecular biology that involve discrete algorithms or discrete optimization. We thus neglect several other areas of computational molecular biology, like most of the literature on the protein folding problem, as well as databases for molecular and genetic data, and genetic mapping algorithms. Due to the availability of review papers and a bibliography this bibliography

Improvements in the Accuracy of Pairwise Genomic Alignment

Pairwise sequence alignment is a fundamental problem in bioinformatics with wide applicability. This thesis presents three new algorithms for this well-studied problem. First, we present a new algorithm, RDA, which aligns sequences in small segments, rather than by individual bases. Then, we present two algorithms for aligning long genomic sequences: CAPE, a pairwise global aligner, and FEAST, a pairwise local aligner. RDA produces interesting alignments that can be substantially different in structure than traditional alignments. It is also better than traditional alignment at the task of homology detection. However, its main negative is a very slow run time. Further, although it produces alignments with different structure, it is not clear if the differences have a practical value in genomic research. Our main success comes from our local aligner, FEAST. We describe two main improvements: a new more descriptive model of evolution, and a new local extension algorithm that considers all possible evolutionary histories rather than only the most likely. Our new model of evolution provides for improved alignment accuracy, and substantially improved parameter training. In particular, we produce a new parameter set for aligning human and mouse sequences that properly describes regions of weak similarity and regions of strong similarity. The second result is our new extension algorithm. Depending on heuristic settings, our new algorithm can provide for more sensitivity than existing extension algorithms, more specificity, or a combination of the two. By comparing to CAPE, our global aligner, we find that the sensitivity increase provided by our local extension algorithm is so substantial that it outperforms CAPE on sequence with 0.9 or more expected substitutions per site. CAPE itself gives improved sensitivity for sequence with 0.7 or more expected substitutions per site, but at a great run time cost. FEAST and our local extension algorithm improves on this too, the run time is only slightly slower than existing local alignment algorithms and asymptotically the same

Efficient methods for read mapping

DNA sequencing is the mainstay of biological and medical research. Modern sequencing machines can read millions of DNA fragments, sampling the underlying genomes at high-throughput. Mapping the resulting reads to a reference genome is typically the first step in sequencing data analysis. The problem has many variants as the reads can be short or long with a low or high error rate for different sequencing technologies, and the reference can be a single genome or a graph representation of multiple genomes. Therefore, it is crucial to develop efficient computational methods for these different problem classes. Moreover, continually declining sequencing costs and increasing throughput pose challenges to the previously developed methods and tools that cannot handle the growing volume of sequencing data. This dissertation seeks to advance the state-of-the-art in the established field of read mapping by proposing more efficient and scalable read mapping methods as well as tackling emerging new problem areas. Specifically, we design ultra-fast methods to map two types of reads: short reads for high-throughput chromatin profiling and nanopore raw reads for targeted sequencing in real-time. In tune with the characteristics of these types of reads, our methods can scale to larger sequencing data sets or map more reads correctly compared with the state-of-the-art mapping software. Furthermore, we propose two algorithms for aligning sequences to graphs, which is the foundation of mapping reads to graph-based reference genomes. One algorithm improves the time complexity of existing sequence to graph alignment algorithms for linear or affine gap penalty. The other algorithm provides good empirical performance in the case of the edit distance metric. Finally, we mathematically formulate the problem of validating paired-end read constraints when mapping sequences to graphs, and propose an exact algorithm that is also fast enough for practical use.Ph.D

The mapping task and its various applications in next-generation sequencing

The aim of this thesis is the development and benchmarking of computational methods for the analysis of high-throughput data from tiling arrays and next-generation sequencing. Tiling arrays have been a mainstay of genome-wide transcriptomics, e.g., in the identification of functional elements in the human genome. Due to limitations of existing methods for the data analysis of this data, a novel statistical approach is presented that identifies expressed segments as significant differences from the background distribution and thus avoids dataset-specific parameters. This method detects differentially expressed segments in biological data with significantly lower false discovery rates and equivalent sensitivities compared to commonly used methods. In addition, it is also clearly superior in the recovery of exon-intron structures. Moreover, the search for local accumulations of expressed segments in tiling array data has led to the identification of very large expressed regions that may constitute a new class of macroRNAs. This thesis proceeds with next-generation sequencing for which various protocols have been devised to study genomic, transcriptomic, and epigenomic features. One of the first crucial steps in most NGS data analyses is the mapping of sequencing reads to a reference genome. This work introduces algorithmic methods to solve the mapping tasks for three major NGS protocols: DNA-seq, RNA-seq, and MethylC-seq. All methods have been thoroughly benchmarked and integrated into the segemehl mapping suite. First, mapping of DNA-seq data is facilitated by the core mapping algorithm of segemehl. Since the initial publication, it has been continuously updated and expanded. Here, extensive and reproducible benchmarks are presented that compare segemehl to state-of-the-art read aligners on various data sets. The results indicate that it is not only more sensitive in finding the optimal alignment with respect to the unit edit distance but also very specific compared to most commonly used alternative read mappers. These advantages are observable for both real and simulated reads, are largely independent of the read length and sequencing technology, but come at the cost of higher running time and memory consumption. Second, the split-read extension of segemehl, presented by Hoffmann, enables the mapping of RNA-seq data, a computationally more difficult form of the mapping task due to the occurrence of splicing. Here, the novel tool lack is presented, which aims to recover missed RNA-seq read alignments using de novo splice junction information. It performs very well in benchmarks and may thus be a beneficial extension to RNA-seq analysis pipelines. Third, a novel method is introduced that facilitates the mapping of bisulfite-treated sequencing data. This protocol is considered the gold standard in genome-wide studies of DNA methylation, one of the major epigenetic modifications in animals and plants. The treatment of DNA with sodium bisulfite selectively converts unmethylated cytosines to uracils, while methylated ones remain unchanged. The bisulfite extension developed here performs seed searches on a collapsed alphabet followed by bisulfite-sensitive dynamic programming alignments. Thus, it is insensitive to bisulfite-related mismatches and does not rely on post-processing, in contrast to other methods. In comparison to state-of-the-art tools, this method achieves significantly higher sensitivities and performs time-competitive in mapping millions of sequencing reads to vertebrate genomes. Remarkably, the increase in sensitivity does not come at the cost of decreased specificity and thus may finally result in a better performance in calling the methylation rate. Lastly, the potential of mapping strategies for de novo genome assemblies is demonstrated with the introduction of a new guided assembly procedure. It incorporates mapping as major component and uses the additional information (e.g., annotation) as guide. With this method, the complete mitochondrial genome of Eulimnogammarus verrucosus has been successfully assembled even though the sequencing library has been heavily dominated by nuclear DNA. In summary, this thesis introduces algorithmic methods that significantly improve the analysis of tiling array, DNA-seq, RNA-seq, and MethylC-seq data, and proposes standards for benchmarking NGS read aligners. Moreover, it presents a new guided assembly procedure that has been successfully applied in the de novo assembly of a crustacean mitogenome.Diese Arbeit befasst sich mit der Entwicklung und dem Benchmarken von Verfahren zur Analyse von Daten aus Hochdurchsatz-Technologien, wie Tiling Arrays oder Hochdurchsatz-Sequenzierung. Tiling Arrays bildeten lange Zeit die Grundlage für die genomweite Untersuchung des Transkriptoms und kamen beispielsweise bei der Identifizierung funktioneller Elemente im menschlichen Genom zum Einsatz. In dieser Arbeit wird ein neues statistisches Verfahren zur Auswertung von Tiling Array-Daten vorgestellt. Darin werden Segmente als exprimiert klassifiziert, wenn sich deren Signale signifikant von der Hintergrundverteilung unterscheiden. Dadurch werden keine auf den Datensatz abgestimmten Parameterwerte benötigt. Die hier vorgestellte Methode erkennt differentiell exprimierte Segmente in biologischen Daten bei gleicher Sensitivität mit geringerer Falsch-Positiv-Rate im Vergleich zu den derzeit hauptsächlich eingesetzten Verfahren. Zudem ist die Methode bei der Erkennung von Exon-Intron Grenzen präziser. Die Suche nach Anhäufungen exprimierter Segmente hat darüber hinaus zur Entdeckung von sehr langen Regionen geführt, welche möglicherweise eine neue Klasse von macroRNAs darstellen. Nach dem Exkurs zu Tiling Arrays konzentriert sich diese Arbeit nun auf die Hochdurchsatz-Sequenzierung, für die bereits verschiedene Sequenzierungsprotokolle zur Untersuchungen des Genoms, Transkriptoms und Epigenoms etabliert sind. Einer der ersten und entscheidenden Schritte in der Analyse von Sequenzierungsdaten stellt in den meisten Fällen das Mappen dar, bei dem kurze Sequenzen (Reads) auf ein großes Referenzgenom aligniert werden. Die vorliegende Arbeit stellt algorithmische Methoden vor, welche das Mapping-Problem für drei wichtige Sequenzierungsprotokolle (DNA-Seq, RNA-Seq und MethylC-Seq) lösen. Alle Methoden wurden ausführlichen Benchmarks unterzogen und sind in der segemehl-Suite integriert. Als Erstes wird hier der Kern-Algorithmus von segemehl vorgestellt, welcher das Mappen von DNA-Sequenzierungsdaten ermöglicht. Seit der ersten Veröffentlichung wurde dieser kontinuierlich optimiert und erweitert. In dieser Arbeit werden umfangreiche und auf Reproduzierbarkeit bedachte Benchmarks präsentiert, in denen segemehl auf zahlreichen Datensätzen mit bekannten Mapping-Programmen verglichen wird. Die Ergebnisse zeigen, dass segemehl nicht nur sensitiver im Auffinden von optimalen Alignments bezüglich der Editierdistanz sondern auch sehr spezifisch im Vergleich zu anderen Methoden ist. Diese Vorteile sind in realen und simulierten Daten unabhängig von der Sequenzierungstechnologie oder der Länge der Reads erkennbar, gehen aber zu Lasten einer längeren Laufzeit und eines höheren Speicherverbrauchs. Als Zweites wird das Mappen von RNA-Sequenzierungsdaten untersucht, welches bereits von der Split-Read-Erweiterung von segemehl unterstützt wird. Aufgrund von Spleißen ist diese Form des Mapping-Problems rechnerisch aufwendiger. In dieser Arbeit wird das neue Programm lack vorgestellt, welches darauf abzielt, fehlende Read-Alignments mit Hilfe von de novo Spleiß-Information zu finden. Es erzielt hervorragende Ergebnisse und stellt somit eine sinnvolle Ergänzung zu Analyse-Pipelines für RNA-Sequenzierungsdaten dar. Als Drittes wird eine neue Methode zum Mappen von Bisulfit-behandelte Sequenzierungsdaten vorgestellt. Dieses Protokoll gilt als Goldstandard in der genomweiten Untersuchung der DNA-Methylierung, einer der wichtigsten epigenetischen Modifikationen in Tieren und Pflanzen. Dabei wird die DNA vor der Sequenzierung mit Natriumbisulfit behandelt, welches selektiv nicht methylierte Cytosine zu Uracilen konvertiert, während Methylcytosine davon unberührt bleiben. Die hier vorgestellte Bisulfit-Erweiterung führt die Seed-Suche auf einem reduziertem Alphabet durch und verifiziert die erhaltenen Treffer mit einem auf dynamischer Programmierung basierenden Bisulfit-sensitiven Alignment-Algorithmus. Das verwendete Verfahren ist somit unempfindlich gegenüber Bisulfit-Konvertierungen und erfordert im Gegensatz zu anderen Verfahren keine weitere Nachverarbeitung. Im Vergleich zu aktuell eingesetzten Programmen ist die Methode sensitiver und benötigt eine vergleichbare Laufzeit beim Mappen von Millionen von Reads auf große Genome. Bemerkenswerterweise wird die erhöhte Sensitivität bei gleichbleibend guter Spezifizität erreicht. Dadurch könnte diese Methode somit auch bessere Ergebnisse bei der präzisen Bestimmung der Methylierungsraten erreichen. Schließlich wird noch das Potential von Mapping-Strategien für Assemblierungen mit der Einführung eines neuen, Kristallisation-genanntes Verfahren zur unterstützten Assemblierung aufgezeigt. Es enthält Mapping als Hauptbestandteil und nutzt Zusatzinformation (z.B. Annotationen) als Unterstützung. Dieses Verfahren ermöglichte die erfolgreiche Assemblierung des kompletten mitochondrialen Genoms von Eulimnogammarus verrucosus trotz einer vorwiegend aus nukleärer DNA bestehenden genomischen Bibliothek. Zusammenfassend stellt diese Arbeit algorithmische Methoden vor, welche die Analysen von Tiling Array, DNA-Seq, RNA-Seq und MethylC-Seq Daten signifikant verbessern. Es werden zudem Standards für den Vergleich von Programmen zum Mappen von Daten der Hochdurchsatz-Sequenzierung vorgeschlagen. Darüber hinaus wird ein neues Verfahren zur unterstützten Genom-Assemblierung vorgestellt, welches erfolgreich bei der de novo-Assemblierung eines mitochondrialen Krustentier-Genoms eingesetzt wurde

Dissecting multiple sequence alignment methods : the analysis, design and development of generic multiple sequence alignment components in SeqAn

Multiple sequence alignments are an indispensable tool in bioinformatics. Many applications rely on accurate multiple alignments, including protein structure prediction, phylogeny and the modeling of binding sites. In this thesis we dissected and analyzed the crucial algorithms and data structures required to construct such a multiple alignment. Based upon that dissection, we present a novel graph-based multiple sequence alignment program and a new method for multi-read alignments occurring in assembly projects. The advantage of the graph-based alignment is that a single vertex can represent a single character, a large segment or even an abstract entity such as a gene. This gives rise to the opportunity to apply the consistencybased progressive alignment paradigm to alignments of genomic sequences. The proposed multi-read alignment method outperforms similar methods in terms of alignment quality and it is apparently one of the first methods that can readily be used for insert sequencing. An important aspect of this thesis was the design, the development and the integration of the essential multiple sequence alignment components in the SeqAn library. SeqAn is a software library for sequence analysis that provides the core algorithmic components required to analyze large-scale sequence data. SeqAn aims at bridging the current gap between algorithm theory and available practical implementations in bioinformatics. Hence, we always describe in conjunction to the theoretical development of the methods, the actual implementation of the data structures and algorithms in order to strengthen the use of SeqAn as an experimental platform for rapidly developing and testing applications. All presented methods are part of the open source SeqAn library that can be downloaded from our website, www.seqan.de

IMPROVING BWA-MEM WITH GPU PARALLEL COMPUTING

Due to the many advances made in designing algorithms, especially the ones used in bioinformatics, it is becoming harder and harder to improve their efficiencies. Therefore, hardware acceleration using General-Purpose computing on Graphics Processing Unit has become a popular choice. BWA-MEM is an important part of the BWA software package for sequence mapping. Because of its high speed and accuracy, we choose to parallelize the popular short DNA sequence mapper. BWA has been a prevalent single node tool in genome alignment, and it has been widely studied for acceleration for a long time since the first version of the BWA package came out. This thesis presents the Big Data GPGPU distributed BWA-MEM, a tool that combines GPGPU acceleration and distributed computing. The four hardware parallelization techniques used are CPU multi-threading, GPU paralleled, CPU distributed, and GPU distributed. The GPGPU distributed software typically outperforms other parallelization versions. The alignment is performed on a distributed network, and each node in the network executes a separate GPGPU paralleled version of the software. We parallelize the chain2aln function in three levels. In Level 1, the function ksw\_extend2, an algorithm based on Smith-Waterman, is parallelized to handle extension on one side of the seed. In Level 2, the function chain2aln is parallelized to handle chain extension, where all seeds within the same chain are extended. In Level 3, part of the function mem\_align1\_core is parallelized for extending multiple chains. Due to the program's complexity, the parallelization work was limited at the GPU version of ksw\_extend2 parallelization Level 3. However, we have successfully combined Spark with BWA-MEM and ksw\_extend2 at parallelization Level 1, which has shown that the proposed framework is possible. The paralleled Level 3 GPU version of ksw\_extend2 demonstrated noticeable speed improvement with the test data set

Third-generation RNA-sequencing analysis : graph alignment and transcript assembly with long reads

The information contained in the genome of an organism, its DNA, is expressed through transcription of its genes to RNA, in quantities determined by many internal and external factors. As such, studying the gene expression can give valuable information for e.g. clinical diagnostics. A common analysis workflow of RNA-sequencing (RNA-seq) data consists of mapping the sequencing reads to a reference genome, followed by the transcript assembly and quantification based on these alignments. The advent of second-generation sequencing revolutionized the field by reducing the sequencing costs by 50,000-fold. Now another revolution is imminent with the third-generation sequencing platforms producing an order of magnitude higher read lengths. However, higher error rate, higher cost and lower throughput compared to the second-generation sequencing bring their own challenges. To compensate for the low throughput and high cost, hybrid approaches using both short second-generation and long third-generation reads have gathered recent interest. The first part of this thesis focuses on the analysis of short-read RNA-seq data. As short-read mapping is an already well-researched field, we focus on giving a literature review of the topic. For transcript assembly we propose a novel (at the time of the publication) approach of using minimum-cost flows to solve the problem of covering a graph created from the read alignments with a set of paths with the minimum cost, under some cost model. Various network-flow-based solutions were proposed in parallel to, as well as after, ours. The second part, where the main contributions of this thesis lie, focuses on the analysis of long-read RNA-seq data. The driving point of our research has been the Minimum Path Cover with Subpath Constraints (MPC-SC) model, where transcript assembly is modeled as a minimum path cover problem, with the addition that each of the chains of exons (subpath constraints) created from the long reads must be completely contained in a solution path. In addition to implementing this concept, we experimentally studied different approaches on how to find the exon chains in practice. The evaluated approaches included aligning the long reads to a graph created from short read alignments instead of the reference genome, which led to our final contribution: extending a co-linear chaining algorithm from between two sequences to between a sequence and a directed acyclic graph.Transkriptiossa organismin geenien mallin mukaan luodaan RNA-molekyyleja. Lukuisat tekijät, sekä solun sisäiset että ulkoiset, määrittävät mitä geenejä transkriptoidaan, ja missä määrin. Tämän prosessin tutkiminen antaa arvokasta tietoa esimerkiksi lääketieteelliseen diagnostiikkaan. Yksi yleisistä RNA-sekvensointidatan analyysitavoista koostuu kolmesta osasta: lukujaksojen (read sequences) linjaus referenssigenomiin, transkriptien kokoaminen, ja transkriptien ekspressiotasojen määrittäminen. Toisen sukupolven sekvensointiteknologian kehityksen myötä sekvensoinnin hinta laski huomattavasti, mikä salli RNA-sekvensointidatan käytön yhä useampaan tarkoitukseen. Nyt kolmannen sukupolven sekvensointiteknologiat tarjoavat kertaluokkaa pidempiä lukujaksoja, mikä laajentaa analysointimahdollisuuksia. Kuitenkin suurempi virhemäärä, korkeampi hinta ja pienempi määrä tuotettua dataa tuovat omat haasteensa. Toisen ja kolmannen sukupolven teknologioiden käyttäminen yhdessä, ns. hybridilähestymistapa, on tutkimussuunta joka on kerännyt paljon kiinnostusta viimeaikoina. Tämän tutkielman ensimmäinen osa keskittyy toisen sukupolven, eli ns. lyhyiden RNA-lukujaksojen (short read), analyysiin. Näiden lyhyiden lukujaksojen linjausta referenssigenomiin on tutkittu jo 2000-luvulla, joten tällä alueella keskitymme olemassaolevaan kirjallisuuteen. Transkriptien kokoamisen alalta esittelemme metodin, joka käyttää vähimmäiskustannusvirtauksen (minimum-cost flow) mallia. Vähimmäiskustannusvirtauksen mallissa lukujaksoista luotu verkko peitetään joukolla polkuja, joiden kustannus on pienin mahdollinen. Virtausmalleja on käytetty myös muiden tutkijoiden kehittämissä analyysityökaluissa. Tämän tutkielman suurin kontribuutio on toisessa osassa, joka keskittyy ns. pitkien RNA-lukujaksojen (long read) analysointiin. Tutkimuksemme lähtökohtana on ollut malli, jossa pienimmän polkupeitteen (Minimum Path Cover) ongelmaan lisätään alipolkurajoitus (subpath constraint). Jokainen alipolkurajoitus vastaa eksoniketjua (exon chain), jotka jokin pitkä lukujakso peittää, ja jokaisen alipolkurajoituksen täytyy sisältyä kokonaan johonkin polkupeitteen polkuun. Tämän konseptin toteuttamisen lisäksi testasimme kokeellisesti erilaisia lähestymistapoja eksoniketjujen löytämiseksi. Näihin testattaviin lähestymistapoihin kuului pitkien lukujaksojen linjaaminen suoraan lyhyistä lukujaksoista luotuun verkkoon referenssigenomin sijaan. Tämä lähestymistapa johti tämän tutkielman viimeiseen kontribuutioon: kolineaarisen ketjun (co-linear chaining) algoritmin yleistäminen kahden sekvenssin sijasta sekvenssiin ja suunnattuun syklittömään verkkoon