Accurate long read mapping using enhanced suffix arrays

With the rise of high throughput sequencing, new programs have been developed for dealing with the alignment of a huge amount of short read data to reference genomes. Recent developments in sequencing technology allow longer reads, but the mappers for short reads are not suited for reads of several hundreds of base pairs. We propose an algorithm for mapping longer reads, which is based on chaining maximal exact matches and uses heuristics and the Needleman-Wunsch algorithm to bridge the gaps. To compute maximal exact matches we use a specialized index structure, called enhanced suffix array. The proposed algorithm is very accurate and can handle large reads with mutations and long insertions and deletions

Effiicient Computation of Maximal Exact Matches Between Genomic Sequences

Sequence alignment is one of the most accomplished methods in the field of bioinformatics, being crucial to determine similarities between sequences, from finding genes to predicting functions. The computation of Maximal Exact Matches (MEM) plays a fundamental part in some algorithms for sequence alignment. MEMs between a reference-query genome are often utilized as seeds in a genome aligner to increase its efficiency. The MEM computation is a time consuming step in the sequence alignment process and increasing the performance of this step increases significantly the whole process of the alignment between the sequences. As of today, there are many programs available for MEM computing, from algorithms based full text indexes, like essaMEM; to more effective ones, such as E-MEM, copMEM and bfMEM. However, none of the available programs for the computation of MEMs are able to work with highly related sequences. In this study, we propose an improved version, E-MEM2, of the well known MEM computing software, E-MEM. With a trade-off between time and memory, the improved version shows to run faster than its previous version, presenting very large improvements when comparing closely-related sequences

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

Indexing arbitrary-length kk-mers in sequencing reads

We propose a lightweight data structure for indexing and querying collections of NGS reads data in main memory. The data structure supports the interface proposed in the pioneering work by Philippe et al. for counting and locating $k$-mers in sequencing reads. Our solution, PgSA (pseudogenome suffix array), based on finding overlapping reads, is competitive to the existing algorithms in the space use, query times, or both. The main applications of our index include variant calling, error correction and analysis of reads from RNA-seq experiments

Computing all-vs-all MEMs in run-length encoded collections of HiFi reads

Peer reviewe

Ψ-RA: a parallel sparse index for genomic read alignment

Background Genomic read alignment involves mapping (exactly or approximately) short reads from a particular individual onto a pre-sequenced reference genome of the same species. Because all individuals of the same species share the majority of their genomes, short reads alignment provides an alternative and much more efficient way to sequence the genome of a particular individual than does direct sequencing. Among many strategies proposed for this alignment process, indexing the reference genome and short read searching over the index is a dominant technique. Our goal is to design a space-efficient indexing structure with fast searching capability to catch the massive short reads produced by the next generation high-throughput DNA sequencing technology. Results We concentrate on indexing DNA sequences via sparse suffix arrays (SSAs) and propose a new short read aligner named Ψ-RA (PSI-RA: parallel sparse index read aligner). The motivation in using SSAs is the ability to trade memory against time. It is possible to fine tune the space consumption of the index based on the available memory of the machine and the minimum length of the arriving pattern queries. Although SSAs have been studied before for exact matching of short reads, an elegant way of approximate matching capability was missing. We provide this by defining the rightmost mismatch criteria that prioritize the errors towards the end of the reads, where errors are more probable. Ψ-RA supports any number of mismatches in aligning reads. We give comparisons with some of the well-known short read aligners, and show that indexing a genome with SSA is a good alternative to the Burrows-Wheeler transform or seed-based solutions. Conclusions Ψ-RA is expected to serve as a valuable tool in the alignment of short reads generated by the next generation high-throughput sequencing technology. Ψ-RA is very fast in exact matching and also supports rightmost approximate matching. The SSA structure that Ψ-RA is built on naturally incorporates the modern multicore architecture and thus further speed-up can be gained. All the information, including the source code of Ψ-RA, can be downloaded at: http://www.busillis.com/o_kulekci/PSIRA.zip webcite

Gsufsort: Constructing suffix arrays, LCP arrays and BWTs for string collections

Background: The construction of a suffix array for a collection of strings is a fundamental task in Bioinformatics and in many other applications that process strings. Related data structures, as the Longest Common Prefix array, the Burrows-Wheeler transform, and the document array, are often needed to accompany the suffix array to efficiently solve a wide variety of problems. While several algorithms have been proposed to construct the suffix array for a single string, less emphasis has been put on algorithms to construct suffix arrays for string collections. Result: In this paper we introduce gsufsort, an open source software for constructing the suffix array and related data indexing structures for a string collection with N symbols in O(N) time. Our tool is written in ANSI/C and is based on the algorithm gSACA-K (Louza et al. in Theor Comput Sci 678:22-39, 2017), the fastest algorithm to construct suffix arrays for string collections. The tool supports large fasta, fastq and text files with multiple strings as input. Experiments have shown very good performance on different types of strings. Conclusions: gsufsort is a fast, portable, and lightweight tool for constructing the suffix array and additional data structures for string collections

Tilatehokas metagenomisten DNA-fragmenttien ryhmittely

The collection of all genomes in an environment is called the metagenome of the environment. In the past 15 years, high-throughput sequencing has made it feasible to sequence entire environments at once for the first time in history, which has resulted in a variety of interesting new algorithmic problems. This thesis focuses on the basic problem of clustering the reads from an environment according to which species, or more generally, taxonomic unit they originate from. In this work, we identify and formalize two fundamental string processing tasks useful in clustering metagenomic read sets. We solve the two problems with space efficiency in mind using the recently developed bidirectional Burrows-Wheeler index. The algorithms were implemented in a way which makes parallel processing possible. Our tool is experimentally shown to give good results for simple simulated datasets, and to use less than 10 times less space and time compared to two recently published metagenome clustering tools.Kaikkien ympäristössä esiintyvien genomien joukkoa kutsutaan kyseisen ympäristön \emph{metagenomiksi}. Viimeisen 15 vuoden aikana kehitetyt korkean läpisyötön sekvenssoriteknologiat ovat mahdollistaneet ensimmäistä kertaa historiassa kokonaisen ympäristön metagenomin kartoittamisen. Tämä kehityssuunta on johtanut uusiin mielenkiintoisiin algoritmisiin ongelmiin. Tämä työ käsittelee ympäristöistä näytteistettyjen DNA-fragmenttejen ryhmittelyä lajien, tai yleisemmin taksonomisten yksiköiden mukaan. Työssä tunnistetaan ja formalisoidaan kaksi merkkijono-ongelmaa, jotka ilmentyvät metagenomisten DNA-fragmentteja ryhmittelyssä. Ongelmiin esitetään tilatehokkaat ratkaisut käyttäen hiljattain kehitettyä kaksisuuntaista Burrows-Wheeler indeksiä. Algoritmit toteutettiin pitäen silmällä rinnakkaista laskentaa. Työssä osoitetaan, että uusi toteutus antaa hyviä tuloksia yksinkertaisille simuloiduille näytteille, ja että työkalu on kymmenen kertaa nopeampi ja tilatehokkaampi, kuin kaksi hiljattain julkaistua metagenomisten näytteiden ryhmittelyyn tarkoitettua työkalua