Alignments with non-overlapping moves, inversions and tandem duplications in O ( n 4) time

Sequence alignment is a central problem in bioinformatics. The classical dynamic programming algorithm aligns two sequences by optimizing over possible insertions, deletions and substitutions. However, other evolutionary events can be observed, such as inversions, tandem duplications or moves (transpositions). It has been established that the extension of the problem to move operations is NP-complete. Previous work has shown that an extension restricted to non-overlapping inversions can be solved in O(n 3) with a restricted scoring scheme. In this paper, we show that the alignment problem extended to non-overlapping moves can be solved in O(n 5) for general scoring schemes, O(n 4log n) for concave scoring schemes and O(n 4) for restricted scoring schemes. Furthermore, we show that the alignment problem extended to non-overlapping moves, inversions and tandem duplications can be solved with the same time complexities. Finally, an example of an alignment with non-overlapping moves is provide

Dissect: detection and characterization of novel structural alterations in transcribed sequences

Motivation: Computational identification of genomic structural variants via high-throughput sequencing is an important problem for which a number of highly sophisticated solutions have been recently developed. With the advent of high-throughput transcriptome sequencing (RNA-Seq), the problem of identifying structural alterations in the transcriptome is now attracting significant attention

Multiple non-collinear TF-map alignments of promoter regions

<p>Abstract</p> <p>Background</p> <p>The analysis of the promoter sequence of genes with similar expression patterns is a basic tool to annotate common regulatory elements. Multiple sequence alignments are on the basis of most comparative approaches. The characterization of regulatory regions from co-expressed genes at the sequence level, however, does not yield satisfactory results in many occasions as promoter regions of genes sharing similar expression programs often do not show nucleotide sequence conservation.</p> <p>Results</p> <p>In a recent approach to circumvent this limitation, we proposed to align the maps of predicted transcription factors (referred as TF-maps) instead of the nucleotide sequence of two related promoters, taking into account the label of the corresponding factor and the position in the primary sequence. We have now extended the basic algorithm to permit multiple promoter comparisons using the progressive alignment paradigm. In addition, non-collinear conservation blocks might now be identified in the resulting alignments. We have optimized the parameters of the algorithm in a small, but well-characterized collection of human-mouse-chicken-zebrafish orthologous gene promoters.</p> <p>Conclusion</p> <p>Results in this dataset indicate that TF-map alignments are able to detect high-level regulatory conservation at the promoter and the 3'UTR gene regions, which cannot be detected by the typical sequence alignments. Three particular examples are introduced here to illustrate the power of the multiple TF-map alignments to characterize conserved regulatory elements in absence of sequence similarity. We consider this kind of approach can be extremely useful in the future to annotate potential transcription factor binding sites on sets of co-regulated genes from high-throughput expression experiments.</p

Efficient Alignment Algorithms for DNA Sequencing Data

The DNA Next Generation Sequencing (NGS) technologies produce data at a low cost, enabling their application to many ambitious fields such as cancer research, disease control, personalized medicine etc. However, even after a decade of research, the modern aligners and assemblers are far from providing efficient and error free genome alignments and assemblies respectively. This is due to the inherent nature of the genome alignment and assembly problem, which involves many complexities. Many algorithms to address this problem have been proposed over the years, but there still is a huge scope for improvement in this research space. Many new genome alignment algorithms are proposed over time and one of the key differentiator among these algorithms is the efficiency of the genome alignment process. I present a new algorithm for efficiently finding Maximal Exact Matches (MEMs) between two genomes: E-MEM (Efficient computation of maximal exact matches for very large genomes). Computing MEMs is one of the most time consuming step during the alignment process. E-MEM can be used to find MEMs which are used as seeds in genome aligner to increase its efficiency. The E-MEM program is the most efficient algorithm as of today for computing MEMs and it surpasses all competition by large margins. There are many genome assembly algorithms available for use, but none produces perfect genome assemblies. It is important that assemblies produced by these algorithms are evaluated accurately and efficiently.This is necessary to make the right choice of the genome assembler to be used for all the downstream research and analysis. A fast genome assembly evaluator is a key factor when a new genome assembler is developed, to quickly evaluate the outcome of the algorithm. I present a fast and efficient genome assembly evaluator called LASER (Large genome ASsembly EvaluatoR), which is based on a leading genome assembly evaluator QUAST, but significantly more efficient both in terms of memory and run time. The NGS technologies limit the potential of genome assembly algorithms because of short read lengths and nonuniform coverage. Recently, third generation sequencing technologies have been proposed which promise very long reads and a uniform coverage. However, this technology comes with its own drawback of high error rate of 10 - 15% consisting mostly of indels. The long read sequencing data is useful only after error correction obtained using self read alignment (or read overlapping) techniques. I propose a new self read alignment algorithm for Pacific Biosciences sequencing data: HISEA (Hierarchical SEed Aligner), which has very high sensitivity and precision as compared to other state-of-the-art aligners. HISEA is also integrated into Canu assembly pipeline. Canu+HISEA produces better assemblies than Canu with its default aligner MHAP, at a much lower coverage

Co-Linear Chaining on Pangenome Graphs

Pangenome reference graphs are useful in genomics because they compactly represent the genetic diversity within a species, a capability that linear references lack. However, efficiently aligning sequences to these graphs with complex topology and cycles can be challenging. The seed-chain-extend based alignment algorithms use co-linear chaining as a standard technique to identify a good cluster of exact seed matches that can be combined to form an alignment. Recent works show how the co-linear chaining problem can be efficiently solved for acyclic pangenome graphs by exploiting their small width [Makinen et al., TALG\u2719] and how incorporating gap cost in the scoring function improves alignment accuracy [Chandra and Jain, RECOMB\u2723]. However, it remains open on how to effectively generalize these techniques for general pangenome graphs which contain cycles. Here we present the first practical formulation and an exact algorithm for co-linear chaining on cyclic pangenome graphs. We rigorously prove the correctness and computational complexity of the proposed algorithm. We evaluate the empirical performance of our algorithm by aligning simulated long reads from the human genome to a cyclic pangenome graph constructed from 95 publicly available haplotype-resolved human genome assemblies. While the existing heuristic-based algorithms are faster, the proposed algorithm provides a significant advantage in terms of accuracy

A Geometric Approach to Mapping Bitext Correspondence

The first step in most corpus-based multilingual NLP work is to construct a detailed map of the correspondence between a text and its translation. Several automatic methods for this task have been proposed in recent years. Yet even the best of these methods can err by several typeset pages. The Smooth Injective Map Recognizer (SIMR) is a new bitext mapping algorithm. SIMR's errors are smaller than those of the previous front-runner by more than a factor of 4. Its robustness has enabled new commercial-quality applications. The greedy nature of the algorithm makes it independent of memory resources. Unlike other bitext mapping algorithms, SIMR allows crossing correspondences to account for word order differences. Its output can be converted quickly and easily into a sentence alignment. SIMR's output has been used to align over 200 megabytes of the Canadian Hansards for publication by the Linguistic Data Consortium.Comment: 15 pages, minor revisions on Sept. 30, 199

Single-cell strand sequencing for structural variant analysis and genome assembly

Rapid advances of DNA sequencing technologies and development of computational tools to analyze sequencing data has started a revolution in the field of genetics. DNA sequencing has applications in medical research, disease diagnosis and treatment, and population genetic studies. Different sequencing techniques have their own advantages and limitations, and they can be used together to solve genome assembly and genetic variant detection. The focus of this thesis is on a specific single-cell sequencing technology, called strand sequencing. With its chromosome and haplotype-specific strand information, this technique has very powerful signals for discovery of genomic structural variations, haplotype phasing, and chromosome clustering. We developed statistical and compuptational tools to exploit this information from strand sequencing technology. I first present a computational framework for detecting structural variations in single cells using strand sequencing data. The presented tool is able to detect different types of structural variations in single cells including copy number variations, inversions, and inverted duplications, and also more complex biological events such as translocations and breakage-fusion-bridge (BFB) cycles. These variations and genomic rearrangements have been observed in cancer, therefore the discovery of such events within cell populations can lead to a more accurate picture of cancer genomes and help in diagnosis. In the remainder of this thesis, I elaborate on two computational pipelines for clustering long DNA sequences by their original chromosome and haplotype in the absence of a reference genome. These pipelines are developed to facilitate genome assembly and de novo haplotype phasing in a fast and accurate manner. The resulting haplotype assemblies can be useful in studying genomic variations with no reference bias, gaining insights in population genetics, and detection of compound heterozygosity.Die rasanten Fortschritte im Bereich der DNA-Sequenzierung und die Entwicklung von Computerwerkzeugen für die Analyse von Sequenzierdaten haben eine Revolution auf dem Gebiet der Genetik ausgelöst. Die DNA-Sequenzierung findet Anwendung in der medizinischen Forschung, bei der Diagnose und Behandlung von Krankheiten und bei populationsgenetischen Studien. Verschiedene Sequenzierungstechniken haben jeweils ihre Vorteile und Grenzen, können aber kombiniert werden, um Genome zu assemblieren oder um genetische Varianten zu finden. Der Schwerpunkt dieser Arbeit liegt auf einer speziellen Einzelzell Sequenzierungstechnologie, genannt Strand-Seq. Mit ihren chromosomen- und haplotypspezifischen Stranginformationen liefert diese Technik sehr starke Signale für die Entdeckung genomischer Strukturvariationen, die Rekonstruktion von Haplotypen und das Chromosomenclustering. Wir haben statistische und computergestützte Werkzeuge entwickelt, um diese Informationen der Strand-Seq Technologie zu nutzen. Zunächst präsentiere ich einen mathematisches Modell für die Erkennung struktureller Variationen in einzelnen Zellen unter Verwendung von Strand-Seq Daten. Das vorgestellte Tool ist in der Lage, verschiedene Arten von Strukturvariationen in Einzelzellen zu erkennen, darunter Kopienzahlvariationen, Inversionen und invertierte Duplikationen sowie komplexere biologische Ereignisse wie Translokationen und Break-Fusion- Bridge-Zyklen (BFB). Diese Variationen und genomischen Umlagerungen wurden bei Krebs beobachtet, sodass der Nachweis solcher Ereignisse in Zellpopulationen zu einem genaueren Bild des Krebsgenoms führen und bei der Diagnose helfen kann. Im Folgenden stelle ich zwei Computerpipelines vor, mit denen lange DNA-Sequenzen nach ihrem ursprünglichen Chromosom und Haplotyp geclustert werden können, wenn kein Referenzgenom verfügbar ist. Diese Pipelines wurden entwickelt, um die Genomassemblierung und die de novo Rekonstruktion von Haplotypen auf schnelle und genaue Weise zu erleichtern. Die daraus resultierenden Haplotypen können bei der Untersuchung genomischer Variationen ohne Referenzverzerrung, bei der Gewinnung von Einblicken in die Populationsgenetik und beim Nachweis von zusammengesetzter Heterozygotie nützlich sein

Large Genomes Assembly Using MAPREDUCE Framework

Knowing the genome sequence of an organism is the essential step toward understanding its genomic and genetic characteristics. Currently, whole genome shotgun (WGS) sequencing is the most widely used genome sequencing technique to determine the entire DNA sequence of an organism. Recent advances in next-generation sequencing (NGS) techniques have enabled biologists to generate large DNA sequences in a high-throughput and low-cost way. However, the assembly of NGS reads faces significant challenges due to short reads and an enormously high volume of data. Despite recent progress in genome assembly, current NGS assemblers cannot generate high-quality results or efficiently handle large genomes with billions of reads. In this research, we proposed a new Genome Assembler based on MapReduce (GAMR), which tackles both limitations. GAMR is based on a bi-directed de Bruijn graph and implemented using the MapReduce framework. We designed a distributed algorithm for each step in GAMR, making it scalable in assembling large-scale genomes. We also proposed novel gap-filling algorithms to improve assembly results to achieve higher accuracy and more extended continuity. We evaluated the assembly performance of GAMR using benchmark data and compared it against other NGS assemblers. We also demonstrated the scalability of GAMR by using it to assemble loblolly pine (~22Gbp). The results showed that GAMR finished the assembly much faster and with a much lower requirement of computing resources