Irregular alignment of arbitrarily long DNA sequences on GPU

The use of Graphics Processing Units to accelerate computational applications is increasingly being adopted due to its affordability, flexibility and performance. However, achieving top performance comes at the price of restricted data-parallelism models. In the case of sequence alignment, most GPU-based approaches focus on accelerating the Smith-Waterman dynamic programming algorithm due to its regularity. Nevertheless, because of its quadratic complexity, it becomes impractical when comparing long sequences, and therefore heuristic methods are required to reduce the search space. We present GPUGECKO, a CUDA implementation for the sequential, seed-and-extend sequence-comparison algorithm, GECKO. Our proposal includes optimized kernels based on collective operations capable of producing arbitrarily long alignments while dealing with heterogeneous and unpredictable load. Contrary to other state-of-the-art methods, GPUGECKO employs a batching mechanism that prevents memory exhaustion by not requiring to fit all alignments at once into the device memory, therefore enabling to run massive comparisons exhaustively with improved sensitivity while also providing up to 6x average speedup w.r.t. the CUDA acceleration of BLASTN.Funding for open access publishing: Universidad Málaga/CBUA /// This work has been partially supported by the European project ELIXIR-EXCELERATE (grant no. 676559), the Spanish national project Plataforma de Recursos Biomoleculares y Bioinformáticos (ISCIII-PT13.0001.0012 and ISCIII-PT17.0009.0022), the Fondo Europeo de Desarrollo Regional (UMA18-FEDERJA-156, UMA20-FEDERJA-059), the Junta de Andalucía (P18-FR-3130), the Instituto de Investigación Biomédica de Málaga IBIMA and the University of Málaga

Technology dictates algorithms: Recent developments in read alignment

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to todays diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies

Algorithmic methods for large-scale genomic and metagenomic data analysis

DNA sequencing technologies have advanced into the realm of big data due to frequent and rapid developments in biologic medicine. This has caused a surge in the necessity of efficient and highly scalable algorithms.This dissertation focuses on central work in read-to-reference alignments, resequencing studies, and metagenomics that were designed with these principles as the guiding reason for their construction.First, consider the computing intensive task of read-to-reference alignments, where the difficulty of aligning reads to a genome is directly related their complexity. We investigated three different formulations of sequence complexity as viable tools for measuring genome complexity along with how they related to short read alignments and found that repeat measures of complexity were best suited for this task. In particular, the fraction of distinct substrings of lengths close to the read length was found to correlate very highly to alignment accuracy in terms of precision and recall. All this demonstrated how to build models to predict accuracy of short read aligners with predictably low errors. As a result, practitioners can select the most accurate aligners for an unknown genome by comparing how different models predict alignment accuracy based on the genomes complexity. Furthermore, accurate recall rate prediction may help practitioners reduce expenses by using just enough reads to get sufficient sequencing coverage.Next, focus on the comprehensive task of resequencing studies for analyzing genetic variants of the human population. By using optimal alignments, we revealed that the current variant profiles contained thousands of insertion/deletion (INDEL) that were constructed in a biased manner. The bias is caused by the existence of many theoretically optimal alignments between the reference genome and reads containing alternative alleles at those INDEL locations. We examined several popular aligners and showed that these aligners could be divided into groups whose alignments yielded INDELs that either strongly agreed or disagreed with reported INDELs. This finding suggests that the agreement or disagreement between the aligners called INDEL and the reported INDEL is merely a result of the arbitrary selection of an optimal alignment. Also of note is LongAGE, a memory efficient of Alignment with Gap Excision (AGE) for defining geneomic variant breakpoints, which enables the precise alignment of longer reads or contigs that potentially contain SVs/CNVs while having a trade off of time compared to AGE.Finally, consider several resource-intensive tasks in metagenomics. We introduce a new algorithmic method for detecting unknown bacteria, those whose genomes have not been sequenced, in microbial communities. Using the 16S ribosomal RNA (16S rRNA) gene instead of the whole genomes information is not only computational efficient, but also economical; an analysis that demonstrates the 16S rRNA gene retains sufficient information to allow us to detect unknown bacteria in the context of oral microbial communities is provided. Furthermore, the main hypothesis that the classification or identification of microbes in metagenomic samples is better done with long reads than with short reads is iterated upon, by investigating the performance of popular metagenomic classifiers on short reads and longer reads assembled from those short reads. Higher overall performance of species classification was achieved simply by assembling short reads.These topics about read-to-reference alignments, resequencing studies, and metagenomics are all key focal points in the pages to come. My dissertation delves deeper into these as I cover the contributions my work has made to the field

Genome assembly in the telomere-to-telomere era

De novo assembly is the process of reconstructing the genome sequence of an organism from sequencing reads. Genome sequences are essential to biology, and assembly has been a central problem in bioinformatics for four decades. Until recently, genomes were typically assembled into fragments of a few megabases at best but technological advances in long-read sequencing now enable near complete chromosome-level assembly, also known as telomere-to-telomere assembly, for many organisms. Here we review recent progress on assembly algorithms and protocols. We focus on how to derive near telomere-to-telomere assemblies and discuss potential future developments

Novel computational techniques for mapping and classifying Next-Generation Sequencing data

Since their emergence around 2006, Next-Generation Sequencing technologies have been revolutionizing biological and medical research. Quickly obtaining an extensive amount of short or long reads of DNA sequence from almost any biological sample enables detecting genomic variants, revealing the composition of species in a metagenome, deciphering cancer biology, decoding the evolution of living or extinct species, or understanding human migration patterns and human history in general. The pace at which the throughput of sequencing technologies is increasing surpasses the growth of storage and computer capacities, which creates new computational challenges in NGS data processing. In this thesis, we present novel computational techniques for read mapping and taxonomic classification. With more than a hundred of published mappers, read mapping might be considered fully solved. However, the vast majority of mappers follow the same paradigm and only little attention has been paid to non-standard mapping approaches. Here, we propound the so-called dynamic mapping that we show to significantly improve the resulting alignments compared to traditional mapping approaches. Dynamic mapping is based on exploiting the information from previously computed alignments, helping to improve the mapping of subsequent reads. We provide the first comprehensive overview of this method and demonstrate its qualities using Dynamic Mapping Simulator, a pipeline that compares various dynamic mapping scenarios to static mapping and iterative referencing. An important component of a dynamic mapper is an online consensus caller, i.e., a program collecting alignment statistics and guiding updates of the reference in the online fashion. We provide Ococo, the first online consensus caller that implements a smart statistics for individual genomic positions using compact bit counters. Beyond its application to dynamic mapping, Ococo can be employed as an online SNP caller in various analysis pipelines, enabling SNP calling from a stream without saving the alignments on disk. Metagenomic classification of NGS reads is another major topic studied in the thesis. Having a database with thousands of reference genomes placed on a taxonomic tree, the task is to rapidly assign a huge amount of NGS reads to tree nodes, and possibly estimate the relative abundance of involved species. In this thesis, we propose improved computational techniques for this task. In a series of experiments, we show that spaced seeds consistently improve the classification accuracy. We provide Seed-Kraken, a spaced seed extension of Kraken, the most popular classifier at present. Furthermore, we suggest ProPhyle, a new indexing strategy based on a BWT-index, obtaining a much smaller and more informative index compared to Kraken. We provide a modified version of BWA that improves the BWT-index for a quick k-mer look-up

High Performance Computing for DNA Sequence Alignment and Assembly

Recent advances in DNA sequencing technology have dramatically increased the scale and scope of DNA sequencing. These data are used for a wide variety of important biological analyzes, including genome sequencing, comparative genomics, transcriptome analysis, and personalized medicine but are complicated by the volume and complexity of the data involved. Given the massive size of these datasets, computational biology must draw on the advances of high performance computing. Two fundamental computations in computational biology are read alignment and genome assembly. Read alignment maps short DNA sequences to a reference genome to discover conserved and polymorphic regions of the genome. Genome assembly computes the sequence of a genome from many short DNA sequences. Both computations benefit from recent advances in high performance computing to efficiently process the huge datasets involved, including using highly parallel graphics processing units (GPUs) as high performance desktop processors, and using the MapReduce framework coupled with cloud computing to parallelize computation to large compute grids. This dissertation demonstrates how these technologies can be used to accelerate these computations by orders of magnitude, and have the potential to make otherwise infeasible computations practical

Identification of cyanobacterial non-coding RNAs by comparative genome analysis

BACKGROUND: Whole genome sequencing of marine cyanobacteria has revealed an unprecedented degree of genomic variation and streamlining. With a size of 1.66 megabase-pairs, Prochlorococcus sp. MED4 has the most compact of these genomes and it is enigmatic how the few identified regulatory proteins efficiently sustain the lifestyle of an ecologically successful marine microorganism. Small non-coding RNAs (ncRNAs) control a plethora of processes in eukaryotes as well as in bacteria; however, systematic searches for ncRNAs are still lacking for most eubacterial phyla outside the enterobacteria. RESULTS: Based on a computational prediction we show the presence of several ncRNAs (cyanobacterial functional RNA or Yfr) in several different cyanobacteria of the Prochlorococcus-Synechococcus lineage. Some ncRNA genes are present only in two or three of the four strains investigated, whereas the RNAs Yfr2 through Yfr5 are structurally highly related and are encoded by a rapidly evolving gene family as their genes exist in different copy numbers and at different sites in the four investigated genomes. One ncRNA, Yfr7, is present in at least seven other cyanobacteria. In addition, control elements for several ribosomal operons were predicted as well as riboswitches for thiamine pyrophosphate and cobalamin. CONCLUSION: This is the first genome-wide and systematic screen for ncRNAs in cyanobacteria. Several ncRNAs were both computationally predicted and their presence was biochemically verified. These RNAs may have regulatory functions and each shows a distinct phylogenetic distribution. Our approach can be applied to any group of microorganisms for which more than one total genome sequence is available for comparative analysis