Secure secondary utilization system of genomic data using quantum secure cloud

This review chapter provides an in-depth exploration of various aspects of data security, ranging from foundational principles to advanced techniques. It sheds light on the importance of maintaining confidentiality, integrity, and availability of data and presents a comprehensive overview of technologies and methodologies that can be employed to achieve robust data security. In today's digital age, ensuring the security and protection of data has become paramount. This research chapter explores various topics related to data security, including confidentiality, integrity, and availability. It also delves into the role of blockchain technology, zero-trust architecture, secure coding practices, legal and ethical considerations, homomorphic encryption, secure multi-party computation, hash functions and digital signatures, secure communication protocols, cryptocurrency and blockchain security, as well as cryptanalysis and side-channel attacks.It highlights various techniques and technologies that can be employed to protect data confidentiality, including encryption and access control mechanisms. Blockchain technology has emerged as a groundbreaking solution for enhancing data security. Zero-trust architecture challenges traditional security models by adopting a holistic and dynamic approach to data protection. Secure coding practices and DevSecOps play a vital role in developing secure software applications. The chapter investigates the importance of integrating security measures throughout the software development lifecycle. It emphasizes the adoption of secure coding practices and the implementation of DevSecOps methodologies to build resilient and secure software systems. Legal and ethical considerations are crucial when addressing data security. Homomorphic encryption and secure multi-party computation are advanced cryptographic techniques that allow data to be processed securely while encrypted. The chapter examines the principles and applications of these techniques, highlighting their significance in scenarios where privacy is paramount. Hash functions and digital signatures form the backbone of data authentication and integrity. The chapter discusses the properties and applications of hash functions and digital signatures in verifying the integrity and authenticity of data. Secure communication protocols are essential for protecting data during transmission. The chapter explores various secure communication protocols, such as Transport Layer Security (TLS), and examines their role in ensuring data confidentiality, integrity, and authenticity. Cryptocurrency and blockchain security focus on the secure storage and transfer of digital assets. The research chapter investigates the security considerations specific to cryptocurrencies and explores how block chain technology can mitigate vulnerabilities and ensure the trustworthiness of transactions. Cryptanalysis and side-channel attacks are threats to cryptographic systems. The chapter discusses these attack techniques, highlighting their potential risks and the measures that can be taken to counter them.

Pathogen genomic data analysis can be extremely bespoke and diverse. This paper presents our plan and progress towards creating a Scalable Pathogen Pipeline Platform (SP 3 ) providing an efficient and unified process of collecting, analysing and comparing genomic data analysis with the benefit of elastic cloud computing. SP 3 enables container-centric bioinformatic workflows run on personal computers, High-performance computing (HPC) clusters and cloud platforms. We have deployed and tested SP 3 on local HPC, Google Cloud Platform (GCP), Microsoft Azure and OpenStack Platforms. SP 3 allows users to fetch genomic sequencing data from European Nucleotide Archive (ENA) and conduct analysis with open-source bioinformatic pipelines. We believe SP 3 will promote common standards around pathogen genomic data quality, data processing and data analysis, helping answer the challenges of tools divergence and leveraging a pool of public genomic data repository and cloud resources.

The content of the 13th Mutation Detection meeting (Leiden, April 2015) is summarized in this report. Topics discussed at the meeting included current challenges of clinical NGS, advances in bioinformatics, data quality control, single cell analysis and RNA sequencing, among others. Social, ethical and regulatory challenges of genomic data handling and data sharing were the focus of an expert panel debate. The 14th International Symposium on Variants in the Genome will take place in Santiago de Compostela, June 5-8, 2017. http://isv.variome.org.

The importance of genomic predictors for clinical outcome of hematological malignancies

Whole genome sequence (WGS) information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS) offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR). A key design feature is that genome data can be stored separately from the EHR. This paper describes in detail: (1) each component of the architecture; (2) the interaction of the components; and (3) how the architecture attempts to overcome the challenges associated with WGS information. We believe that service-oriented CDS capabilities will be essential to using WGS information for personalized medicine.

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

Signal Track Analytical Research Tool (START), is a parallel system for analyzing large-scale genomic data. Currently, genomic data analyses are usually performed by using custom scripts developed by individual research groups, and/or by the integrated use of multiple existing tools (such as BEDTools and Galaxy). The goals of START are 1) to provide a single tool that supports a wide spectrum of genomic data analyses that are commonly done by analysts; and 2) to greatly simplify these analysis tasks by means of a simple declarative language (STQL) with which users only need to specify what they want to do, rather than the detailed computational steps as to how the analysis task should be performed.
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\nSTART consists of four major components: 1) A declarative language called Signal Track Query Language (STQL), which is a SQL-like language we specifically designed to suit the needs for analyzing genomic signal tracks. 2) A STQL processing system built on top of a large-scale distributed architecture. The system is based on the Hadoop distributed storage and the MapReduce Big Data processing framework. It processes each user query using multiple machines in parallel. 3) A simple and user-friendly web site that helps users construct and execute queries, upload/download compressed data files in various formats, man-age stored data, queries and analysis results, and share queries with other users.
\nIt also provides a complete help system, detailed specification of STQL, and a large number of sample queries for users to learn STQL and try START easily. Private files and queries are not accessible by other users. 4) A repository of public data popularly used for large-scale genomic data analysis, including data from ENCODE and Roadmap Epigenomics, that users can use in their analyses.

Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data. We carried out a narrative review of clinical informatics studies related to sharing protected health data within cancer studies published from 2018-2022, with a focus on domains such as decentralized analytics, homomorphic encryption, and common data models. Clinical informatics studies that investigated cancer data sharing were identified. A particular focus of the search yielded studies on decentralized analytics, homomorphic encryption, and common data models. Decentralized analytics has been prototyped across genomic, imaging, and clinical data with the most advances in diagnostic image analysis. Homomorphic encryption was most often employed on genomic data and less on imaging and clinical data. Common data models primarily involve clinical data from the electronic health record. Although all methods have robust research, there are limited studies showing wide scale implementation. Decentralized analytics, homomorphic encryption, and common data models represent promising solutions to improve cancer data sharing. Promising results thus far have been limited to smaller settings. Future studies should be focused on evaluating the scalability and efficacy of these methods across clinical settings of varying resources and expertise.

The interpretation of variants in cancer is frequently focused on direct protein coding alterations. However, most somatic mutations are in noncoding regions of the genome, and even exonic mutations may have unidentified noncoding consequences. Here we present Regtools, a software package designed to efficiently identify variants that may cause aberrant splicing in tumors. Our tool integrates variant calls from genomic data with junctions extracted from transcriptomic data in order to examine potential cis alterations to splicing near a somatic variant. Based on user-defined parameters and position relative to known exons, variants are first annotated as splicing relevant or not. Splicing junctions are inferred from transcriptomic sequencing data, and comparison of junctions to a reference transcriptome allows for identification and annotation of novel junctions and nearby regulatory or splicing motifs. From there, mutations are associated with junctions that overlap with a flanking region. In order to evaluate Regtools, we used it to analyze the transcriptional output of tumor-sequencing data from two cohorts of cancer patients, one of hepatocellular carcinoma and one of small cell lung cancer with 28 and 21 samples, respectively. We performed whole-exome and RNA sequencing on each sample. Somatic variants were called on whole-exome alignment data. For each cohort, we compared the junctional profiles between tumors and identified numerous examples of variants for which there are elevated levels of proximal novel or known junctions. Moreover, out of 754 (153 in HCC; 601 in SCLC) variants identified as splicing relevant by our approach, only 165 (20 in HCC; 145 in SCLC) were annotated as splicing relevant by Ensembl's Variant Effect Predictor, using the recommended “per_gene” option. This preliminary analysis illustrates the importance of an efficient, user-friendly computational tool for identifying important noncoding variants that would otherwise be undervalued or perhaps even completely ignored by traditional methods and annotators. Regtools is freely available and open source (https://github.com/griffithlab/regtools). Citation Format: Yang-Yang Feng, Avinash Ramu, Zachary L. Skidmore, Jason Kunisaki, Kelsy C. Cotto, Obi L. Griffith, Malachi Griffith. Regtools: Integrated analysis of genomic and transcriptomic data for discovery of mutations associated with aberrant splicing in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2285.

In clinical genomics, the continuous evolution of bioinformatic algorithms and sequencing platforms makes it beneficial to store patients’ complete aligned genomic data in addition to variant calls relative to a reference sequence. Due to the large size of human genome sequence data files (varying from 30 GB to 200 GB depending on coverage), two major challenges facing genomics laboratories are the costs of storage and the efficiency of the initial data processing. In addition, privacy of genomic data is becoming an increasingly serious concern, yet no standard data storage solutions exist that enable compression, encryption, and selective retrieval. Here we present a privacy-preserving solution named SECRAM (Selective retrieval on Encrypted and Compressed Reference-oriented Alignment Map) for the secure storage of compressed aligned genomic data. Our solution enables selective retrieval of encrypted data and improves the efficiency of downstream analysis (e.g., variant calling). Compared with BAM, the de facto standard for storing aligned genomic data, SECRAM uses 18% less storage. Compared with CRAM, one of the most compressed nonencrypted formats (using 34% less storage than BAM), SECRAM maintains efficient compression and downstream data processing, while allowing for unprecedented levels of security in genomic data storage. Compared with previous work, the distinguishing features of SECRAM are that (1) it is position-based instead of read-based, and (2) it allows random querying of a subregion from a BAM-like file in an encrypted form. Our method thus offers a space-saving, privacy-preserving, and effective solution for the storage of clinical genomic data.

With advances in sequencing technology, a vast amount of genomic sequence information has become available. However, annotating biological functions particularly of non-protein-coding regions in genome sequences without experiments is still a challenging task. Recently deep learning-based methods were shown to have the ability to predict gene regulatory regions from genome sequences, promising to aid the interpretation of genomic sequence data. Here, we report an improvement of the prediction accuracy for gene regulatory regions by using the design of convolution layers that efficiently process genomic sequence information, and developed a software, DeepGMAP, to train and compare different deep learning-based models (https://github.com/koonimaru/DeepGMAP). First, we demonstrate that our convolution layers, termed forward- and reverse-sequence scan (FRSS) layers, integrate both forward and reverse strand information, and enhance the power to predict gene regulatory regions. Second, we assessed previous studies and identified problems associated with data structures that caused overfitting. Finally, we introduce visualization methods to examine what the program learned. Together, our FRSS layers improve the prediction accuracy for gene regulatory regions.

BackgroundAdenoviruses are common pathogens infecting animals and humans. They are classified based on serology, or genome sequence information. These methods have limitations due to lengthy procedures or lack of infectivity data. Adenoviruses are easy to produce and amenable to genetic and biochemical modifications, which makes them a powerful tool for biological studies, and clinical gene-delivery and vaccine applications. Antibodies directed against adenoviral proteins are important diagnostic tools for virus identification in vivo and in vitro, and are used to elucidate infection mechanisms, often in combination with genomic sequencing and type specific information from hyper-variable regions of structural proteins.ResultsHere we describe a novel and readily useable method for cloning, expressing and purifying small fragments of hyper-variable regions 1-6 of the adenoviral hexon protein. We used these polypeptides as antigens for generating polyclonal rabbit antibodies against human adenovirus 3 (HAdV-B3), mouse adenovirus 1 (MAdV-1) and MAdV-2 hexon. In Western immunoblots with lysates from cells infected from thirteen human and three mouse viruses, these antibodies bound to homologous full-length hexon protein and revealed variable levels of cross-reactivity to heterologous hexons. Results from immuno-fluorescence and electron microscopy studies indicated that HAdV-B3 and MAdV-2 hexon antibodies recognized native forms of hexon.ConclusionsThe procedure described here can in principle be applied to any adenovirus for which genome sequence information is available. It provides a basis for generating novel type-specific tools in diagnostics and research, and extends beyond the commonly used anti-viral antibodies raised against purified viruses or subviral components.

The 1000 Genomes Project: paving the way for personalized genomic medicine.

With the rapid development of Internet of Things, the demand of data sharing is not only to ensure the data integrity, but also to protect user's individual privacy. Secure multi-party computation, as a technology paradigm based on cryptography technology, enables privacy protection in data sharing among multiple parties, while the implement of secure multi-party computation is limited due to the inefficient, complex computation protocol and frequent interaction. Fortunately, the emergence of blockchain technology provides excellent properties of decentralization, verifiability and high reliability for secure multi-party computation. In this paper, we propose a blockchain-based secure multi-party computation architecture for data sharing, where we design an aggregator consortium for data storage, verification and joint computation. Moreover, we introduce a straightforward secure multi-party computation scheme based on homomorphic encryption. In addition, we analyze and discuss this scheme in terms of data security, access flexibility, attack resistance and potential applications.

SM algorithms-based encryption scheme for large genomic data files