SQC: secure quality control for meta-analysis of genome-wide association studies.

Due to the limited power of small-scale genome-wide association studies (GWAS), researchers tend to collaborate and establish a larger consortium in order to perform large-scale GWAS. Genome-wide association meta-analysis (GWAMA) is a statistical tool that aims to synthesize results from multiple independent studies to increase the statistical power and reduce false-positive findings of GWAS. However, it has been demonstrated that the aggregate data of individual studies are subject to inference attacks, hence privacy concerns arise when researchers share study data in GWAMA. In this article, we propose a secure quality control (SQC) protocol, which enables checking the quality of data in a privacy-preserving way without revealing sensitive information to a potential adversary. SQC employs state-of-the-art cryptographic and statistical techniques for privacy protection. We implement the solution in a meta-analysis pipeline with real data to demonstrate the efficiency and scalability on commodity machines. The distributed execution of SQC on a cluster of 128 cores for one million genetic variants takes less than one hour, which is a modest cost considering the 10-month time span usually observed for the completion of the QC procedure that includes timing of logistics. SQC is implemented in Java and is publicly available at https://github.com/acs6610987/secureqc. [email protected]. Supplementary data are available at Bioinformatics online

third generation sequencing data analytics on mobile devices cache oblivious and out of core approaches as a proof of concept

Abstract Mobile (third-generation) sequencing technologies, including Oxford Nanopore's MinION and SmidgION, have the benefit of outputting long sequence reads (up to hundred thousands of bases) in a portable manner. These sequencing devices fit in the palm of a hand and only require a USB outlet. Unfortunately, the development of data analysis tools for these technologies is in a nascent stage, impeding on the portability of these devices. The objective of this work is to introduce an out-of-core approach to port Nanopore analytics on mobile devices such as tablets or smartphones, often used in extreme experimental settings with special ergonomics needs and ease of sterilization. In this paper, we present a serial k-mer parser/counter for FAST5 files, and a de Bruijn graph construction method which can run on a hand-held device. In order to accomplish this portability we develop novel cache oblivious data structures and out-of-core chunked processing methods. Our toolset, which we refer to as Nanopore Portable Analytics Library (NanoPAL), wase implemented in ISO C++ v.14 and compiled for Android devices. Using MinION data (Zaire Ebolavirus species and others), we evaluate the time required to parse and build the de Bruijn graph with respect to the file sizes and RAM allocation. These metrics were compared to those of minimap/miniasm. On an LG Nexus 5 with 2GB or RAM, 2MB L2 cache and 16GB storage, the out-of-core NanoPAL is able to process FAST5 files at about 30 minutes per 0.5 GB, creating sorted k-mer and de Bruijn graph files. The recompiled minimap/miniasm tool cannot complete FAST5 files larger than 170MB. In conjunction with base calling/error correction, and with addition of assembly procedures downstream, NanoPAL can be effectively used to perform analyses of MinION/SmidgION data locally on a mobile device

Secure and Distributed Assessment of Privacy-Preserving Releases of GWAS

Genome-wide association studies (GWAS) identify correlations between the genetic variants and an observable characteristic such as a disease. Previous works presented privacy-preserving distributed algorithms for a federation of genome data holders that spans multiple institutional and legislative domains to securely compute GWAS results. However, these algorithms have limited applicability, since they still require a centralized instance to decide whether GWAS results can be safely disclosed, which is in violation to privacy regulations, such as GDPR. In this work, we introduce GenDPR, a distributed middleware that leverages Trusted Execution Environments (TEEs) to securely determine a subset of the potential GWAS statistics that can be safely released. GenDPR achieves the same accuracy as centralized solutions, but requires transferring significantly less data because TEEs only exchange intermediary results but no genomes. Additionally, GenDPR can be configured to tolerate all-but-one honest-but-curious federation members colluding with the aim to expose genomes of correct members

A resource-frugal probabilistic dictionary and applications in (meta)genomics

Genomic and metagenomic fields, generating huge sets of short genomic sequences, brought their own share of high performance problems. To extract relevant pieces of information from the huge data sets generated by current sequencing techniques, one must rely on extremely scalable methods and solutions. Indexing billions of objects is a task considered too expensive while being a fundamental need in this field. In this paper we propose a straightforward indexing structure that scales to billions of element and we propose two direct applications in genomics and metagenomics. We show that our proposal solves problem instances for which no other known solution scales-up. We believe that many tools and applications could benefit from either the fundamental data structure we provide or from the applications developed from this structure.Comment: Submitted to PSC 201

mrsFAST-Ultra: a compact, SNP-aware mapper for high performance sequencing applications

Cataloged from PDF version of article.High throughput sequencing (HTS) platforms generate unprecedented amounts of data that introduce challenges for processing and downstream analysis. While tools that report the 'best' mapping location of each read provide a fast way to process HTS data, they are not suitable for many types of downstream analysis such as structural variation detection, where it is important to report multiple mapping loci for each read. For this purpose we introduce mrsFAST-Ultra, a fast, cache oblivious, SNP-aware aligner that can handle the multi-mapping of HTS reads very efficiently. mrsFAST-Ultra improves mrsFAST, our first cache oblivious read aligner capable of handling multi-mapping reads, through new and compact index structures that reduce not only the overall memory usage but also the number of CPU operations per alignment. In fact the size of the index generated by mrsFAST-Ultra is 10 times smaller than that of mrsFAST. As importantly, mrsFAST-Ultra introduces new features such as being able to (i) obtain the best mapping loci for each read, and (ii) return all reads that have at most n mapping loci (within an error threshold), together with these loci, for any user specified n. Furthermore, mrsFAST-Ultra is SNP-aware, i.e. it can map reads to reference genome while discounting the mismatches that occur at common SNP locations provided by db-SNP; this significantly increases the number of reads that can be mapped to the reference genome. Notice that all of the above features are implemented within the index structure and are not simple post-processing steps and thus are performed highly efficiently. Finally, mrsFAST-Ultra utilizes multiple available cores and processors and can be tuned for various memory settings. Our results show that mrsFAST-Ultra is roughly five times faster than its predecessor mrsFAST. In comparison to newly enhanced popular tools such as Bowtie2, it is more sensitive (it can report 10 times or more mappings per read) and much faster (six times or more) in the multi-mapping mode. Furthermore, mrsFAST-Ultra has an index size of 2GB for the entire human reference genome, which is roughly half of that of Bowtie2. mrsFAST-Ultra is open source and it can be accessed at http://mrsfast.sourceforge.net

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

BLOOM: BLoom filter based oblivious outsourced matchings

Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations