TRStalker: an Efficient Heuristic for Finding NP-Complete Tandem Repeats

Genomic sequences in higher eucaryotic organisms contain a substantial amount of (almost) repeated sequences. Tandem Repeats (TRs) constitute a large class of repetitive sequences that are originated via phenomena such as replication slippage, are characterized by close spatial contiguity, and play an important role in several molecular regulatory mechanisms. Certain types of tandem repeats are highly polymorphic and constitute a fingerprint feature of individuals. Abnormal TRs are known to be linked to several diseases. Researchers in bio-informatics in the last 20 years have proposed many formal definitions for the rather loose notion of a Tandem Repeat and have proposed exact or heuristic algorithms to detect TRs in genomic sequences. The general trend has been to use formal (implicit or explicit) definitions of TR for which verification of the solution was easy (with complexity linear, or polynomial in the TR\u27s length and substitution+indel rates) while the effort was directed towards identifying efficiently the sub-strings of the input to submit to the verification phase (either implicitly or explicitly). In this paper we take a step forward: we use a definition of TR for which also the verification step is difficult (in effect, NP-complete) and we develop new filtering techniques for coping with high error levels. The resulting heuristic algorithm, christened TRStalker, is approximate since it cannot guarantee that all NP-Complete Tandem Repeats satisfying the target definition in the input string will be found. However, in synthetic experiments with 30% of errors allowed, TRStalker has demonstrated a very high recall (ranging from 100% to 60%, depending on motif length and repetition number) for the NP-complete TRs. TRStalker has consistently better performance than some stateof- the-art methods for a large range of parameters on the class of NP-complete Tandem Repeats. TRStalker aims at improving the capability of TR detection for classes of TRs for which existing methods do not perform well

NTRFinder: a software tool to find nested tandem repeats

We introduce the software tool NTRFinder to search for a complex repetitive structure in DNA we call a nested tandem repeat (NTR). An NTR is a recurrence of two or more distinct tandem motifs interspersed with each other. We propose that NTRs can be used as phylogenetic and population markers. We have tested our algorithm on both real and simulated data, and present some real NTRs of interest. NTRFinder can be downloaded from http://www.maths.otago.ac.nz/~aamatroud/

TRStalker: an efficient heuristic for finding fuzzy tandem repeats

Motivation: Genomes in higher eukaryotic organisms contain a substantial amount of repeated sequences. Tandem Repeats (TRs) constitute a large class of repetitive sequences that are originated via phenomena such as replication slippage and are characterized by close spatial contiguity. They play an important role in several molecular regulatory mechanisms, and also in several diseases (e.g. in the group of trinucleotide repeat disorders). While for TRs with a low or medium level of divergence the current methods are rather effective, the problem of detecting TRs with higher divergence (fuzzy TRs) is still open. The detection of fuzzy TRs is propaedeutic to enriching our view of their role in regulatory mechanisms and diseases. Fuzzy TRs are also important as tools to shed light on the evolutionary history of the genome, where higher divergence correlates with more remote duplication events

Efficient, Dependable Storage of Human Genome Sequencing Data

A compreensão do genoma humano impacta várias áreas da vida. Os dados oriundos do genoma humano são enormes pois existem milhões de amostras a espera de serem sequenciadas e cada genoma humano sequenciado pode ocupar centenas de gigabytes de espaço de armazenamento. Os genomas humanos são críticos porque são extremamente valiosos para a investigação e porque podem fornecer informações delicadas sobre o estado de saúde dos indivíduos, identificar os seus dadores ou até mesmo revelar informações sobre os parentes destes. O tamanho e a criticidade destes genomas, para além da quantidade de dados produzidos por instituições médicas e de ciências da vida, exigem que os sistemas informáticos sejam escaláveis, ao mesmo tempo que sejam seguros, confiáveis, auditáveis e com custos acessíveis. As infraestruturas de armazenamento existentes são tão caras que não nos permitem ignorar a eficiência de custos no armazenamento de genomas humanos, assim como em geral estas não possuem o conhecimento e os mecanismos adequados para proteger a privacidade dos dadores de amostras biológicas. Esta tese propõe um sistema de armazenamento de genomas humanos eficiente, seguro e auditável para instituições médicas e de ciências da vida. Ele aprimora os ecossistemas de armazenamento tradicionais com técnicas de privacidade, redução do tamanho dos dados e auditabilidade a fim de permitir o uso eficiente e confiável de infraestruturas públicas de computação em nuvem para armazenar genomas humanos. As contribuições desta tese incluem (1) um estudo sobre a sensibilidade à privacidade dos genomas humanos; (2) um método para detetar sistematicamente as porções dos genomas que são sensíveis à privacidade; (3) algoritmos de redução do tamanho de dados, especializados para dados de genomas sequenciados; (4) um esquema de auditoria independente para armazenamento disperso e seguro de dados; e (5) um fluxo de armazenamento completo que obtém garantias razoáveis de proteção, segurança e confiabilidade a custos modestos (por exemplo, menos de $1/Genoma/Ano), integrando os mecanismos propostos a configurações de armazenamento apropriadasThe understanding of human genome impacts several areas of human life. Data from human genomes is massive because there are millions of samples to be sequenced, and each sequenced human genome may size hundreds of gigabytes. Human genomes are critical because they are extremely valuable to research and may provide hints on individuals’ health status, identify their donors, or reveal information about donors’ relatives. Their size and criticality, plus the amount of data being produced by medical and life-sciences institutions, require systems to scale while being secure, dependable, auditable, and affordable. Current storage infrastructures are too expensive to ignore cost efficiency in storing human genomes, and they lack the proper knowledge and mechanisms to protect the privacy of sample donors. This thesis proposes an efficient storage system for human genomes that medical and lifesciences institutions may trust and afford. It enhances traditional storage ecosystems with privacy-aware, data-reduction, and auditability techniques to enable the efficient, dependable use of multi-tenant infrastructures to store human genomes. Contributions from this thesis include (1) a study on the privacy-sensitivity of human genomes; (2) to detect genomes’ privacy-sensitive portions systematically; (3) specialised data reduction algorithms for sequencing data; (4) an independent auditability scheme for secure dispersed storage; and (5) a complete storage pipeline that obtains reasonable privacy protection, security, and dependability guarantees at modest costs (e.g., less than$1/Genome/Year) by integrating the proposed mechanisms with appropriate storage configurations

Developing molecular tools for probing and modulating genomic spatial adjacency

In addition to the vast information encoded in DNA sequence, the genome has physical features that are also essential for its function, including its organization in threedimensional space. The development of high-throughput technology has greatly advanced our understanding of the spatial organization of the genome but has also raised more questions. In this thesis, we developed molecular tools to address the remaining challenges regarding the interplay between genomic organization and function. By breaking down the subject from the global architecture of the genome into an ensemble of spatially adjacent chromatin segments, we came up with different methods covering various aspects. We demonstrated in Paper I that global spatial information can be transferred in the format of DNA sequence encoding pairwise spatial proximity between two distinct molecular objects. We have shown that by growing network from pairwise relationship encoded in DNA sequence, spatial features at a global scale can be recovered. The results from this work highlighted the potential of using pairwise adjacency as a fundamental unit for recording the spatial organization of complex molecular systems. The high programmability and versatility of nucleic acids make them an ideal medium for encoding this information. With the aim of studying the pairwise relationship between genomic DNA in cells, we devised a CRISPR-dCas9 system for different purposes by leveraging its high programmability for genome targeting. In Paper III, we have shown that the re-designed guide RNA can direct dCas9 to a pair of genomic loci, inducing DNA contacts. This system can be applied as a modulation tool to introduce pairwise contacts for decoding functional implications in cells. In Paper IV, we developed a method for the direct detection of pairwise interactions between genomic loci at the single-cell level in situ. This method is achieved by conjugating oligonucleotide tags to Cas9 and using the tags for probing the spatial adjacency between a pair of genomic loci targeted by Cas9 Meanwhile, we developed an efficient method to fabricate and purify DNA origami with modifications in Paper II. This method makes the production of functionalized nanostructures more time and material-efficient compared to established techniques. The ease of production allows broader applications of functionalized nanostructures, including characterizing the effect of nanoscale distance on biochemical assays, as shown in Paper IV

Repetition Detection in a Dynamic String

A string UU for a non-empty string U is called a square. Squares have been well-studied both from a combinatorial and an algorithmic perspective. In this paper, we are the first to consider the problem of maintaining a representation of the squares in a dynamic string S of length at most n. We present an algorithm that updates this representation in n^o(1) time. This representation allows us to report a longest square-substring of S in O(1) time and all square-substrings of S in O(output) time. We achieve this by introducing a novel tool - maintaining prefix-suffix matches of two dynamic strings. We extend the above result to address the problem of maintaining a representation of all runs (maximal repetitions) of the string. Runs are known to capture the periodic structure of a string, and, as an application, we show that our representation of runs allows us to efficiently answer periodicity queries for substrings of a dynamic string. These queries have proven useful in static pattern matching problems and our techniques have the potential of offering solutions to these problems in a dynamic text setting

New Approaches to Long-Read Assembly under High Error Rates

Das Gebiet der Genomassemblierung beschäftigt sich mit der Entwicklung von Algorithmen, die Genome am Computer anhand von Sequenzierungsdaten rekonstruieren. Es geriet erstmals in den Neunzigern mit dem Human Genome Project in den Fokus der Öffentlichkeit. Da nur kurze Abschnitte des menschlichen Genoms ausgelesen werden konnten, musste die Rekonstruktion längerer Genomsequenzen aus den ausgelesenen Abschnitten im Nachhinein am Computer erfolgen. Auch fast 20 Jahre nach der Veröffentlichung der menschlichen Genomsequenzen stellt die Genomeassemblierung nach wie vor noch einen essentiellen Verarbeitungsschritt für Sequenzierungsdaten dar. Nur Datendurchsatz, Länge und Fehlerprofil der ausgelesenen Genomabschnitte haben sich verändert und damit einhergehend auch die algorithmischen Anforderungen. Damit komplementiert das Forschungsgebiet der Genomeassemblierung die Sequenzierungstechnologien, die sich mit enormer Geschwindigkeit weiter entwickelt haben. Zusammen erlauben sie die Entschlüsselung der Genome einer stark zunehmenden Anzahl von Lebewesen und bilden damit die Grundlage für einen Großteil der Forschung in verschiedensten Bereichen der Biologie und Medizin. Trotz der beeindruckenden technologischen und algorithmischen Entwicklungen der vergangenen Jahrzehnte ist es bisher nur für bakterielle Genome gelungen, die komplette Genomsequenz zu rekontruieren. Bei der Assemblierung der wesentlich größeren eukaryotischen Genome bestehen mehrere ungelöste algorithmische Probleme. Diese Probleme hängen mit verschiedenen repetitiven Strukturen zusammen, die in fast allen Genomen höherer Lebewesen vorkommen. Deshalb werden eukaryotische Genome immer in wesentlich mehr unzusammenhängenden Sequenzen veröffentlicht als die jeweiligen Lebewesen Chromosomen haben. Die repetitiven Strukturen, die für die Lücken in den Genomsequenzen verantwortlich sind, lassen sich grob in drei Klassen unterteilen. Mikrosatelliten und Minisatelliten sind sehr kurze Sequenzen, die sich tausende oder zehntausende Male direkt aufeinander folgend wiederholen können. Dieses Muster ist typisch für sogenannte Centromere und Telomere, die sich in der Mitte und an den Enden vieler Chromosome befinden. Sogenannte Interspersed Repeats, oft auch als Transposons bezeichnet, sind längere Sequenzen, die häufig in fast identischer Form an unterschiedlichen Stellen im Genome vorkommen. Sogenannte Tandem Repeats dagegen sind längere Sequenzen, die direkt aufeinanderfolgend mehrere Male in einem Genom auftreten können. Oft sind Tandem Repeats Genkomplexe, das heißt Ansammlungen fast identischer proteinkodierender Abschnitte, die es der Zelle erlauben, die kodierten Proteine besonders schnell zu produzieren. Jede dieser repetitive Strukturen stellt spezifische Anforderung an Assemblierungsalgorithmen. In dieser Doktorarbeit leisten wir mehrere Beiträge zur Lösung der letzteren zwei vorgestellten Probleme, der Assemblierung von Interspersed Repeats und Tandem Repeats. In Teil 1 der Arbeit stellen wir mehrere Datenverarbeitungsprozeduren vor, die Sequenzierungsdaten aufbereiten, um die seltenen Unterschiede zwischen mehrfach auftretenden Genomsequenzen zu identifizieren. Diese beinhalten Softwareprogramme zur Berechnung und Optimierung von Multiplen Sequenz Alignments (MSA) anhand dynamischer Programmierung und zur statistischen Modellierung und Analyse der Unterschiede, wie das MSA sie präsentiert. In Teil 2 bauen wir auf dieser Analyse auf und präsentieren ein Softwareprogramm zur Assemblierung von Interspersed Repeats. Dieses Programm baut auf mehreren algorithmischen Neuerungen auf und ist in der Lage, Transposonfamilien mit sehr langen Sequenzen und sehr vielen verschiedenen Kopien effektiv zu assemblieren. Es ist das erste Programm dieser Art, welches in der Lage ist, Transposonfamilien mit dutzenden von Kopien zu assemblieren. Es gelingt uns zu zeigen, dass es auch für kleinere Transposonfamilien akkurater und schneller ist als das bisher einzige Konkurrenzprogramm, welches auf dieses Assemblierungsproblem spezialisiert ist. In Teil 3 beschreiben wir eine Analysepipeline, die es uns ermöglicht, Genkomplexe aus dutzenden von Tandem Repeats zu assemblieren. Diese Pipeline enthält Clustering und Graph Drawing Algorithmen. Ihr Herzstück ist ein Fehlerkorrekturalgorithmus, der auf Neuronalen Netzwerken basiert. Wir demonstrieren den praktischen Nutzen dieser Pipeline durch die Assemblierung des Drosophila Histone Komplexes. Im Abschluss diskutieren wir die Möglichkeit, Mikro- und Minisatelliten zu assemblieren und schlagen Forschungsansätze für weitere Verbesserungen im Bereich der Interspersed Repeat- und Genkomplexassemblierung vor

Update Query Time Trade-Off for Dynamic Suffix Arrays

The Suffix Array SA(S) of a string S[1 ... n] is an array containing all the suffixes of S sorted by lexicographic order. The suffix array is one of the most well known indexing data structures, and it functions as a key tool in many string algorithms. In this paper, we present a data structure for maintaining the Suffix Array of a dynamic string. For every $0 \leq \varepsilon \leq 1$, our data structure reports SA[i] in $\tilde{O}(n^{\varepsilon})$ time and handles text modification in $\tilde{O}(n^{1-\varepsilon})$ time. Additionally, our data structure enables the same query time for reporting iSA[i], with iSA being the Inverse Suffix Array of S[1 ... n]. Our data structure can be used to construct sub-linear dynamic variants of static strings algorithms or data structures that are based on the Suffix Array and the Inverse Suffix Array.Comment: 19 pages, 3 figure

Microsatellite marker development by partial sequencing of the sour passion fruit genome (Passiflora edulis Sims).

Abstract Background The Passiflora genus comprises hundreds of wild and cultivated species of passion fruit used for food, industrial, ornamental and medicinal purposes. Efforts to develop genomic tools for genetic analysis of P. edulis, the most important commercial Passiflora species, are still incipient. In spite of many recognized applications of microsatellite markers in genetics and breeding, their availability for passion fruit research remains restricted. Microsatellite markers in P. edulis are usually limited in number, show reduced polymorphism, and are mostly based on compound or imperfect repeats. Furthermore, they are confined to only a few Passiflora species. We describe the use of NGS technology to partially assemble the P. edulis genome in order to develop hundreds of new microsatellite markers. Results A total of 14.11 Gbp of Illumina paired-end sequence reads were analyzed to detect simple sequence repeat sites in the sour passion fruit genome. A sample of 1300 contigs containing perfect repeat microsatellite sequences was selected for PCR primer development. Panels of di- and tri-nucleotide repeat markers were then tested in P. edulis germplasm accessions for validation. DNA polymorphism was detected in 74% of the markers (PIC = 0.16 to 0.77; number of alleles/locus = 2 to 7). A core panel of highly polymorphic markers (PIC = 0.46 to 0.77) was used to cross-amplify PCR products in 79 species of Passiflora (including P. edulis), belonging to four subgenera (Astrophea, Decaloba, Distephana and Passiflora). Approximately 71% of the marker/species combinations resulted in positive amplicons in all species tested. DNA polymorphism was detected in germplasm accessions of six closely related Passiflora species (P. edulis, P. alata, P. maliformis, P. nitida, P. quadrangularis and P. setacea) and the data used for accession discrimination and species assignment. Conclusion A database of P. edulis DNA sequences obtained by NGS technology was examined to identify microsatellite repeats in the sour passion fruit genome. Markers were submitted to evaluation using accessions of cultivated and wild Passiflora species. The new microsatellite markers detected high levels of DNA polymorphism in sour passion fruit and can potentially be used in genetic analysis of P. edulis and other Passiflora species

Automated copy number variation concordance analysis

Rapid growth and advancement of next generation sequencing (NGS) technologies have changed the landscape of genomic medicine. Today, clinical laboratories perform DNA sequencing on a regular basis, which is an error prone process. Erroneous data affects downstream analysis and produces fallacious result. Therefore, external quality assessment (EQA) of laboratories working with NGS data is crucial. Validation of variations such as single nucleotide polymor- phism (SNP) and InDels (<50 bp) is fairly accurate these days. However, detection and quality assessment of large changes such as the copy number variation (CNV) continues to be a concern. In this work, we aimed to study the feasibility of an automated CNV concordance analysis for the laboratory EQA services. We benchmarked variants reported by 25 laboratories against the highly curated gold standard for the son (HG002/NA24385) of the askenazim trio from the Personal Genome Project published by the Genome in a Bottle Consortium (GIAB). We employed two methods to conduct concordance of CNVs, the sequence based comparison with Truvari and the in-house exome-based comparison. For deletion calls of two whole genome sequencing (WGS) submissions, Truvari gained a value greater than 88% and 68% for precision and recall respectively. Conversely, the in-house method’s precision and recall score peaked at 39% and 7.9% respectively for one WGS submission for both deletion and duplication calls. The results indicate that automated CNV concordance analysis of the deletion calls for the WGS-based callset might be feasible with Truvari. On the other hand, results for panel-based targeted sequencing for the deletion calls showed precision and recall rates ranging from 0-80% and 0-5.6% respectively with Truvari. The result suggests that automated concordance analysis of CNVs for targeted sequencing remains a challenge. In conclusion, CNV concordance analysis depends on how the sequence data is generated