SeqWare Query Engine: storing and searching sequence data in the cloud

<p>Abstract</p> <p>Background</p> <p>Since the introduction of next-generation DNA sequencers the rapid increase in sequencer throughput, and associated drop in costs, has resulted in more than a dozen human genomes being resequenced over the last few years. These efforts are merely a prelude for a future in which genome resequencing will be commonplace for both biomedical research and clinical applications. The dramatic increase in sequencer output strains all facets of computational infrastructure, especially databases and query interfaces. The advent of cloud computing, and a variety of powerful tools designed to process petascale datasets, provide a compelling solution to these ever increasing demands.</p> <p>Results</p> <p>In this work, we present the SeqWare Query Engine which has been created using modern cloud computing technologies and designed to support databasing information from thousands of genomes. Our backend implementation was built using the highly scalable, NoSQL HBase database from the Hadoop project. We also created a web-based frontend that provides both a programmatic and interactive query interface and integrates with widely used genome browsers and tools. Using the query engine, users can load and query variants (SNVs, indels, translocations, etc) with a rich level of annotations including coverage and functional consequences. As a proof of concept we loaded several whole genome datasets including the U87MG cell line. We also used a glioblastoma multiforme tumor/normal pair to both profile performance and provide an example of using the Hadoop MapReduce framework within the query engine. This software is open source and freely available from the SeqWare project (<url>http://seqware.sourceforge.net</url>).</p> <p>Conclusions</p> <p>The SeqWare Query Engine provided an easy way to make the U87MG genome accessible to programmers and non-programmers alike. This enabled a faster and more open exploration of results, quicker tuning of parameters for heuristic variant calling filters, and a common data interface to simplify development of analytical tools. The range of data types supported, the ease of querying and integrating with existing tools, and the robust scalability of the underlying cloud-based technologies make SeqWare Query Engine a nature fit for storing and searching ever-growing genome sequence datasets.</p

Distributed hybrid-indexing of compressed pan-genomes for scalable and fast sequence alignment

Computational pan-genomics utilizes information from multiple individual genomes in large-scale comparative analysis. Genetic variation between case-controls, ethnic groups, or species can be discovered thoroughly using pan-genomes of such subpopulations. Whole-genome sequencing (WGS) data volumes are growing rapidly, making genomic data compression and indexing methods very important. Despite current space-efficient repetitive sequence compression and indexing methods, the deployed compression methods are often sequential, computationally time-consuming, and do not provide efficient sequence alignment performance on vast collections of genomes such as pan-genomes. For performing rapid analytics with the ever-growing genomics data, data compression and indexing methods have to exploit distributed and parallel computing more efficiently. Instead of strict genome data compression methods, we will focus on the efficient construction of a compressed index for pan-genomes. Compressed hybrid-index enables fast sequence alignments to several genomes at once while shrinking the index size significantly compared to traditional indexes. We propose a scalable distributed compressed hybrid-indexing method for large genomic data sets enabling pan-genome-based sequence search and read alignment capabilities. We show the scalability of our tool, DHPGIndex, by executing experiments in a distributed Apache Spark-based computing cluster comprising 448 cores distributed over 26 nodes. The experiments have been performed both with human and bacterial genomes. DHPGIndex built a BLAST index for n = 250 human pan-genome with an 870:1 compression ratio (CR) in 342 minutes and a Bowtie2 index with 157:1 CR in 397 minutes. For n = 1,000 human pan-genome, the BLAST index was built in 1520 minutes with 532:1 CR and the Bowtie2 index in 1938 minutes with 76:1 CR. Bowtie2 aligned 14.6 GB of paired-end reads to the compressed (n = 1,000) index in 31.7 minutes on a single node. Compressing n = 13,375,031 (488 GB) GenBank database to BLAST index resulted in CR of 62:1 in 575 minutes. BLASTing 189,864 Crispr-Cas9 gRNA target sequences (23 MB in total) to the compressed index of human pan-genome (n = 1,000) finished in 45 minutes on a single node. 30 MB mixed bacterial sequences were (n = 599) were blasted to the compressed index of 488 GB GenBank database (n = 13,375,031) in 26 minutes on 25 nodes. 78 MB mixed sequences (n = 4,167) were blasted to the compressed index of 18 GB E. coli sequence database (n = 745,409) in 5.4 minutes on a single node.Peer reviewe

Skaalautuvat laskentamenetelmät suuren kapasiteetin sekvensointidatan analytiikkaan populaatiogenomiikassa

High-throughput sequencing (HTS) technologies have enabled rapid DNA sequencing of whole-genomes collected from various organisms and environments, including human tissues, plants, soil, water, and air. As a result, sequencing data volumes have grown by several orders of magnitude, and the number of assembled whole-genomes is increasing rapidly as well. This whole-genome sequencing (WGS) data has revealed the genetic variation in humans and other species, and advanced various fields from human and microbial genomics to drug design and personalized medicine. The amount of sequencing data has almost doubled every six months, creating new possibilities but also big data challenges in genomics. Diverse methods used in modern computational biology require a vast amount of computational power, and advances in HTS technology are even widening the gap between the analysis input data and the analysis outcome. Currently, many of the existing genomic analysis tools, algorithms, and pipelines are not fully exploiting the power of distributed and high-performance computing, which in turn limits the analysis throughput and restrains the deployment of the applications to clinical practice in the long run. Thus, the relevance of harnessing distributed and cloud computing in bioinformatics is more significant than ever before. Besides, efficient data compression and storage methods for genomic data processing and retrieval integrated with conventional bioinformatics tools are essential. These vast datasets have to be stored and structured in formats that can be managed, processed, searched, and analyzed efficiently in distributed systems. Genomic data contain repetitive sequences, which is one key property in developing efficient compression algorithms to alleviate the data storage burden. Moreover, indexing compressed sequences appropriately for bioinformatics tools, such as read aligners, offers direct sequence search and alignment capabilities with compressed indexes. Relative Lempel-Ziv (RLZ) has been found to be an efficient compression method for repetitive genomes that complies with the data-parallel computing approach. RLZ has recently been used to build hybrid-indexes compatible with read aligners, and we focus on extending it with distributed computing. Data structures found in genomic data formats have properties suitable for parallelizing routine bioinformatics methods, e.g., sequence matching, read alignment, genome assembly, genotype imputation, and variant calling. Compressed indexing fused with the routine bioinformatics methods and data-parallel computing seems a promising approach to building population-scale genome analysis pipelines. Various data decomposition and transformation strategies are studied for optimizing data-parallel computing performance when such routine bioinformatics methods are executed in a complex pipeline. These novel distributed methods are studied in this dissertation and demonstrated in a generalized scalable bioinformatics analysis pipeline design. The dissertation starts from the main concepts of genomics and DNA sequencing technologies and builds routine bioinformatics methods on the principles of distributed and parallel computing. This dissertation advances towards designing fully distributed and scalable bioinformatics pipelines focusing on population genomic problems where the input data sets are vast and the analysis results are hard to achieve with conventional computing. Finally, the methods studied are applied in scalable population genomics applications using real WGS data and experimented with in a high performance computing cluster. The experiments include mining virus sequences from human metagenomes, imputing genotypes from large-scale human populations, sequence alignment with compressed pan-genomic indexes, and assembling reference genomes for pan-genomic variant calling.Suuren kapasiteetin sekvensointimenetelmät (High-Throughput Sequencing, HTS) ovat mahdollistaneet kokonaisten genomien nopean ja huokean sekvensoinnin eri organismeista ja ympäristöistä, mukaan lukien kudos-, maaperä-, vesistö- ja ilmastonäytteet. Tämän seurauksena sekvensointidatan ja koostettujen kokogenomien määrät ovat kasvaneet nopeasti. Kokogenomin sekvensointi on lisännyt ihmisen ja muiden lajien geneettisen perimän tietämystä ja edistänyt eri tieteenaloja ympäristötieteistä lääkesuunnitteluun ja yksilölliseen lääketieteeseen. Sekvensointidatan määrä on lähes kaksinkertaistunut puolivuosittain, mikä on luonut uusia mahdollisuuksia läpimurtoihin, mutta myös suuria datankäsittelyn haasteita. Nykyaikaisessa laskennallisessa biologiassa käytettävät monimutkaiset analyysimenetelmät vaativat yhä enemmän laskentatehoa HTS-datan kasvaessa, ja siksi HTS-menetelmien edistyminen kasvattaa kuilua raakadatasta lopullisiin analyysituloksiin. Useat tällä hetkellä käytetyistä genomianalyysityökaluista, algoritmeista ja ohjelmistoista eivät hyödynnä hajautetun laskennan tehoa kokonaisvaltaisesti, mikä puolestaan ​​hidastaa uusimpien analyysitulosten saamista ja rajoittaa tieteellisten ohjelmistojen käyttöönottoa kliinisessä lääketieteessä pitkällä aikavälillä. Näin ollen hajautetun ja pilvilaskennan hyödyntämisen merkitys bioinformatiikassa on tärkeämpää kuin koskaan ennen. Genomitiedon suoraa hakua ja käsittelyä tukevat pakkaus- ja tallennusmenetelmät mahdollistavat nopean ja tilatehokkaan genomianalytiikan. Uusia hajautettuihin järjestelmiin soveltuvia tietorakenteita tarvitaan, jotta näitä suuria datamääriä voidaan hallita, käsitellä, hakea ja analysoida tehokkaasti. Genomidata sisältää runsaasti toistuvia sekvenssejä, mikä on yksi keskeinen ominaisuus kehitettäessä tehokkaita pakkausalgoritmeja tiedontallennustaakkaa ja analysointia keventämään. Lisäksi pakattujen sekvenssien indeksointi yhdistettynä sekvenssilinjausmenetelmiin mahdollistaa sekvenssien satunnaishaun ja suoran linjauksen pakattuihin sekvensseihin. Relative Lempel-Ziv (RLZ) pakkausmenetelmä on todettu tehokkaaksi toistuville genomisekvensseille rinnakkaislaskentaa hyödyntäen. RLZ-menetelmää on viime aikoina sovellettu sekvenssilinjaukseen yhteensopiviin hybridi-indekseihin, joita tässä työssä on nopeutettu hajautetulla laskennalla. Genomiikan dataformaateista löytyvillä tietorakenteilla on ominaisuuksia, jotka soveltuvat hajautettuun sekvenssihakuun, sekvenssilinjaukseen, genomien koostamiseen, genotyyppien imputointiin ja varianttien havaitsemiseen. Pakattu indeksointi sovellettuna hajautetulla laskennalla tehostettuihin menetelmiin vaikuttaa lupaavalta lähestymistavalta populaatiogenomiikan analyysiohjelmistojen mukauttamiseksi suuriin datamääriin. Erilaisia ​​tiedon osittamis- ja muunnosstrategioita hyödynnetään suorituskyvyn tehostamiseen monivaiheisessa hajautetussa genomidatan prosessoinnissa. Näitä uusia skaalautuvia hajautettuja laskentamenetelmiä tutkitaan tässä väitöskirjassa ja demonstroidaan yleisluontoisella bioinformatiikan analyysiohjelmiston arkkitehtuurilla. Tässä työssä johdatellaan genomiikan ja DNA-sekvensointitekniikoiden peruskäsitteisiin ja esitellään rutiininomaisia ​​bioinformatiikan menetelmiä perustuen hajautetun ja rinnakkaislaskennan periaatteille. Väitöskirjassa edetään kohti täysin hajautettujen ja skaalautuvien bioinformatiikan ohjelmistojen suunnittelua keskittyen populaatiogenomiikan ongelmiin, joissa syötedatan määrät ovat suuria ja analyysitulosten saavuttaminen on hidasta tai jopa mahdotonta tavanomaisella laskennalla. Lopuksi tutkittuja menetelmiä sovelletaan tässä työssä kehitettyihin skaalautuviin populaatiogenomiikan sovelluksiin, joita koestetaan kokogenomidatalla supertietokoneen laskentaklusterissa. Kokeet sisältävät virussekvenssien louhintaa ihmisten metagenominäytteistä, genotyyppien täydentämistä (imputointia) suurista ihmispopulaatioista ja pan-genomisen indeksin pakkaamista sekvenssilinjauksen nopeuttamista varten. Lisäksi pakattua pan-genomia kokeillaan referenssigenomin koostamiseen populaatioon perustuvien varianttien havaitsemista varten

Latent Semantic Indexing (LSI) Based Distributed System and Search On Encrypted Data

Latent semantic indexing (LSI) was initially introduced to overcome the issues of synonymy and polysemy of the traditional vector space model (VSM). LSI, however, has challenges of its own, mainly scalability. Despite being introduced in 1990, there are few attempts that provide an efficient solution for LSI, most of the literature is focuses on LSI’s applications rather than improving the original algorithm. In this work we analyze the first framework to provide scalable implementation of LSI and report its performance on the distributed environment of RAAD. The possibility of adopting LSI in the field of searching over encrypted data is also investigated. The importance of that field is stemmed from the need for cloud computing as an effective computing paradigm that provides an affordable access to high computational power. Encryption is usually applied to prevent unauthorized access to the data (the host is assumed to be curious), however this limits accessibility to the data given that search over encryption is yet to catch with the latest techniques adopted by the Information Retrieval (IR) community. In this work we propose a system that uses LSI for indexing and free-query text for retrieving. The results show that the available LSI framework does scale on large datasets, however it had some limitations with respect to factors like dictionary size and memory limit. When replicating the exact settings of the baseline on RAAD, it performed relatively slower. This could be resulted by the fact that RAAD uses a distributed file system or because of network latency. The results also show that the proposed system for applying LSI on encrypted data retrieved documents in the same order as the baseline (unencrypted data)

SeqWare Query Engine: storing and searching sequence data in the cloud

Abstract Background Since the introduction of next-generation DNA sequencers the rapid increase in sequencer throughput, and associated drop in costs, has resulted in more than a dozen human genomes being resequenced over the last few years. These efforts are merely a prelude for a future in which genome resequencing will be commonplace for both biomedical research and clinical applications. The dramatic increase in sequencer output strains all facets of computational infrastructure, especially databases and query interfaces. The advent of cloud computing, and a variety of powerful tools designed to process petascale datasets, provide a compelling solution to these ever increasing demands. Results In this work, we present the SeqWare Query Engine which has been created using modern cloud computing technologies and designed to support databasing information from thousands of genomes. Our backend implementation was built using the highly scalable, NoSQL HBase database from the Hadoop project. We also created a web-based frontend that provides both a programmatic and interactive query interface and integrates with widely used genome browsers and tools. Using the query engine, users can load and query variants (SNVs, indels, translocations, etc) with a rich level of annotations including coverage and functional consequences. As a proof of concept we loaded several whole genome datasets including the U87MG cell line. We also used a glioblastoma multiforme tumor/normal pair to both profile performance and provide an example of using the Hadoop MapReduce framework within the query engine. This software is open source and freely available from the SeqWare project (http://seqware.sourceforge.net). Conclusions The SeqWare Query Engine provided an easy way to make the U87MG genome accessible to programmers and non-programmers alike. This enabled a faster and more open exploration of results, quicker tuning of parameters for heuristic variant calling filters, and a common data interface to simplify development of analytical tools. The range of data types supported, the ease of querying and integrating with existing tools, and the robust scalability of the underlying cloud-based technologies make SeqWare Query Engine a nature fit for storing and searching ever-growing genome sequence datasets

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data