P/Key: PUF based second factor authentication.

One-time password (OTP) mechanisms are widely used to strengthen authentication processes. In time-based one-time password (TOTP) mechanisms, the client and server store common secrets. However, once the server is compromised, the client's secrets are easy to obtain. To solve this issue, hash-chain-based second-factor authentication protocols have been proposed. However, these protocols suffer from latency in the generation of OTPs on the client side because of the hash-chain traversal. Secondly, they can generate only a limited number of OTPs as it depends on the length of the hash-chain. In this paper, we propose a second-factor authentication protocol that utilizes Physically Unclonable Functions (PUFs) to overcome these problems. In the proposed protocol, PUFs are used to store the secrets of the clients securely on the server. In case of server compromise, the attacker cannot obtain the seeds of clients' secrets and can not generate valid OTPs to impersonate the clients. In the case of physical attacks, including side-channel attacks on the server side, our protocol has a mechanism that prevents attackers from learning the secrets of a client interacting with the server. Furthermore, our protocol does not incur any client-side delay in OTP generation.

Efficient privacy-preserving whole-genome variant queries.

MOTIVATION: Diagnosis and treatment decisions on genomic data have become widespread as the cost of genome sequencing decreases gradually. In this context, disease-gene association studies are of great importance. However, genomic data are very sensitive when compared to other data types and contains information about individuals and their relatives. Many studies have shown that this information can be obtained from the query-response pairs on genomic databases. In this work, we propose a method that uses secure multi-party computation to query genomic databases in a privacy-protected manner. The proposed solution privately outsources genomic data from arbitrarily many sources to the two non-colluding proxies and allows genomic databases to be safely stored in semi-honest cloud environments. It provides data privacy, query privacy and output privacy by using XOR-based sharing and unlike previous solutions, it allows queries to run efficiently on hundreds of thousands of genomic data. RESULTS: We measure the performance of our solution with parameters similar to real-world applications. It is possible to query a genomic database with 3 000 000 variants with five genomic query predicates under 400 ms. Querying 1 048 576 genomes, each containing 1 000 000 variants, for the presence of five different query variants can be achieved approximately in 6 min with a small amount of dedicated hardware and connectivity. These execution times are in the right range to enable real-world applications in medical research and healthcare. Unlike previous studies, it is possible to query multiple databases with response times fast enough for practical application. To the best of our knowledge, this is the first solution that provides this performance for querying large-scale genomic data. AVAILABILITY AND IMPLEMENTATION: https://gitlab.com/DIFUTURE/privacy-preserving-variant-queries. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying disease-causing mutations with privacy protection.

MOTIVATION: The use of genome data for diagnosis and treatment is becoming increasingly common. Researchers need access to as many genomes as possible to interpret the patient genome, to obtain some statistical patterns and to reveal disease-gene relationships. The sensitive information contained in the genome data and the high risk of re-identification increase the privacy and security concerns associated with sharing such data. In this article, we present an approach to identify disease-associated variants and genes while ensuring patient privacy. The proposed method uses secure multi-party computation to find disease-causing mutations under specific inheritance models without sacrificing the privacy of individuals. It discloses only variants or genes obtained as a result of the analysis. Thus, the vast majority of patient data can be kept private. RESULTS: Our prototype implementation performs analyses on thousands of genomic data in milliseconds, and the runtime scales logarithmically with the number of patients. We present the first inheritance model (recessive, dominant and compound heterozygous) based privacy-preserving analyses of genomic data to find disease-causing mutations. Furthermore, we re-implement the privacy-preserving methods (MAX, SETDIFF and INTERSECTION) proposed in a previous study. Our MAX, SETDIFF and INTERSECTION implementations are 2.5, 1122 and 341 times faster than the corresponding operations of the state-of-the-art protocol, respectively. AVAILABILITY AND IMPLEMENTATION: https://gitlab.com/DIFUTURE/privacy-preserving-genomic-diagnosis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A fully automated microfluidic-based electrochemical sensor for real-time bacteria detection.

A fully automated microfluidic-based electrochemical biosensor was designed and manufactured for pathogen detection. The quantification of Escherichia coli was investigated with standard and nanomaterial amplified immunoassays in the concentration ranges of 0.99 × 1043.98 × 109 cfu mL-1 and 103.97 × 107 cfu mL-1 which resulted in detection limits of 1.99 × 104 cfu mL-1 and 50 cfu mL-1, respectively. The developed methodology was then applied for E. coli quantification in water samples using nanomaterial modified assay. Same detection limit for E. coli was achieved for real sample analysis with a little decrease on the sensor signal. Cross-reactivity studies were conducted by testing Shigella, Salmonella spp., Salmonella typhimurium and Staphylococcus aureus on E. coli specific antibody surface that confirmed the high specificity of the developed immunoassays. The sensor surface could be regenerated multiple times which significantly reduces the cost of the system. Our custom-designed biosensor is capable of detecting bacteria with high sensitivity and specificity, and can serve as a promising tool for pathogen detection.

VCF-Explorer: filtering and analysing whole genome VCF files.

SUMMARY: The decreasing cost in high-throughput technologies led to a number of sequencing projects consisting of thousands of whole genomes. The paradigm shift from exome to whole genome brings a significant increase in the size of output files. Most of the existing tools which are developed to analyse exome files are not adequate for larger VCF files produced by whole genome studies. In this work we present VCF-Explorer, a variant analysis software capable of handling large files. Memory efficiency and avoiding computationally costly pre-processing step enable to carry out the analysis to be performed with ordinary computers. VCF-Explorer provides an easy to use environment where users can define various types of queries based on variant and sample genotype level annotations. VCF-Explorer can be run in different environments and computational platforms ranging from a standard laptop to a high performance server. AVAILABILITY AND IMPLEMENTATION: VCF-Explorer is freely available at: http://vcfexplorer.sourceforge.net/. CONTACT: mete.akgun@tubitak.gov.tr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Lab-on-a-chip based biosensor for the real-time detection of aflatoxin.

Polymers were synthesized and utilized for aflatoxin detection coupled with a novel lab-on-a-chip biosensor: MiSens and high performance liquid chromatography (HPLC). Non-imprinted polymers (NIPs) were preferred to be designed and used due to the toxic nature of aflatoxin template and also to avoid difficult clean-up protocols. Towards an innovative miniaturized automated system, a novel biochip has been designed that consists of 6 working electrodes (1mm diameter) with shared reference and counter electrodes. The aflatoxin detection has been achieved by a competition immunoassay that has been performed using the new biochips and the automated MiSens electrochemical biosensor device. For the assay, aflatoxin antibody has been captured on the Protein A immobilized electrode. Subsequently the sample and the enzyme-aflatoxin conjugate mixture has been injected to the electrode surfaces. The final injection of the enzyme substrate results in an amperometric signal. The sensor assays for aflatoxin B1 (AFB1) in different matrices were also performed using enzyme link immunosorbent assay (ELISA) and HPLC for confirmation. High recovery was successfully achieved in spiked wheat samples using NIP coupled HPLC and NIP coupled MiSens biosensor [2ppb of aflatoxin was determined as 1.86ppb (93% recovery), 1.73ppb (86.5% recovery), 1.96ppb (98% recovery) and 1.88ppb (94.0% recovery) for immunoaffinity column (IAC)-HPLC, NIP-HPLC, Supel™ Tox SPE Cartridges (SUP)-HPLC and NIP-MiSens, respectively]. Aflatoxin detection in fig samples were also investigated with MiSens biosensor and the results were compared with HPLC method. The new biosensor allows real-time and on-site detection of AFB1 in foods with a rapid, sensitive, fully automated and miniaturized system and expected to have an immense economic impact for food industry.

An integrated lab-on-a-chip-based electrochemical biosensor for rapid and sensitive detection of cancer biomarkers.

Recent advances in the area of biosensor technology and microfluidic applications have enabled the miniaturisation of the sensing platforms. Here we describe a new integrated and fully automated lab-on-a-chip-based biosensor device prototype (MiSens) that has potential to be used for point-of-care cancer biomarker testing. The key features of the device include a new biochip, a device integrated microfluidic system and real-time amperometric measurements during the flow of enzyme substrate. For ease of use, a new plug and play type sensor chip docking station has been designed. This system allows the formation of an ∼7 µL capacity flow cell on the electrode array with the necessary microfluidic and electronic connections with one move of a handle. As a case study, the developed prototype has been utilised for the detection of prostate-specific antigen (PSA) level in serum that is routinely used as a biomarker for the diagnosis of prostate cancer. The patient samples from a nearby hospital have been collected and tested using the MiSens device, and the results have been compared to the hospital results. The obtained results indicate the potential of the MiSens device as a useful tool for point-of-care testing. Graphical abstract Microfluidics integrated and automated electrochemical biosensor device "MiSens" has been designed and fabricated by a multidisciplinary team and utilised to detect PSA from clinical samples.

FMFilter: A fast model based variant filtering tool.

The availability of whole exome and genome sequencing has completely changed the structure of genetic disease studies. It is now possible to solve the disease causing mechanisms within shorter time and budgets. For this reason, mining out the valuable information from the huge amount of data produced by next generation techniques becomes a challenging task. Current tools analyze sequencing data in various methods. However, there is still need for fast, easy to use and efficacious tools. Considering genetic disease studies, there is a lack of publicly available tools which support compound heterozygous and de novo models. Also, existing tools either require advanced IT expertise or are inefficient for handling large variant files. In this work, we provide FMFilter, an efficient sieving tool for next generation sequencing data produced by genetic disease studies. We develop a software which allows to choose the inheritance model (recessive, dominant, compound heterozygous and de novo), the affected and control individuals. The program provides a user friendly Graphical User Interface which eliminates the requirement of advanced computer techniques. It has various filtering options which enable to eliminate the majority of the false alarms. FMFilter requires negligible memory, therefore it can easily handle very large variant files like multiple whole genomes with ordinary computers. We demonstrate the variant reduction capability and effectiveness of the proposed tool with public and in-house data for different inheritance models. We also compare FMFilter with the existing filtering software. We conclude that FMFilter provides an effective and easy to use environment for analyzing next generation sequencing data from Mendelian diseases.

Privacy preserving processing of genomic data: A survey.

Recently, the rapid advance in genome sequencing technology has led to production of huge amount of sensitive genomic data. However, a serious privacy challenge is confronted with increasing number of genetic tests as genomic data is the ultimate source of identity for humans. Lately, privacy threats and possible solutions regarding the undesired access to genomic data are discussed, however it is challenging to apply proposed solutions to real life problems due to the complex nature of security definitions. In this review, we have categorized pre-existing problems and corresponding solutions in more understandable and convenient way. Additionally, we have also included open privacy problems coming with each genomic data processing procedure. We believe our classification of genome associated privacy problems will pave the way for linking of real-life problems with previously proposed methods.

Comparative transcriptome profiling approach to glean virulence and immunomodulation-related genes of Fasciola hepatica.

BACKGROUND: Fasciola hepatica causes chronic liver disease, fasciolosis, leading to significant losses in the livestock economy and concerns for human health in many countries. The identification of F. hepatica genes involved in the parasite's virulence through modulation of host immune system is utmost important to comprehend evasion mechanisms of the parasite and develop more effective strategies against fasciolosis. In this study, to identify the parasite's putative virulence genes which are associated with host immunomodulation, we explored whole transcriptome of an adult F. hepatica using current transcriptome profiling approaches integrated with detailed in silico analyses. In brief, the comparison of the parasite transcripts with the specialised public databases containing sequence data of non-parasitic organisms (Dugesiidae species and Caenorhabditis elegans) or of numerous pathogens and investigation of the sequences in terms of nucleotide evolution (directional selection) and cytokine signaling relation were conducted. RESULTS: NGS of the whole transcriptome resulted in 19,534,766 sequence reads, yielding a total of 40,260 transcripts (N50 = 522 bp). A number of the parasite transcripts (n = 1,671) were predicted to be virulence-related on the basis of the exclusive homology with the pathogen-associated data, positive selection or relationship with cytokine signaling. Of these, a group of the virulence-related genes (n = 62), not previously described, were found likely to be associated with immunomodulation based on in silico functional categorisation, showing significant sequence similarities with various immune receptors (i.e. MHC I class, TGF-ß receptor, toll/interleukin-1 receptor, T-cell receptor, TNF receptor, and IL-18 receptor accessory protein), cytokines (i.e. TGF-ß, interleukin-4/interleukin-13 and TNF-α), cluster of differentiations (e.g. CD48 and CD147) or molecules associated with other immunomodulatory mechanisms (such as regulation of macrophage activation). Some of the genes (n = 5) appeared to be under positive selection (Ka/Ks > 1), imitating proteins associated with cytokine signaling (through sequence homologies with thrombospondin type 1, toll/interleukin-1 receptor, TGF-ß receptor and CD147). CONCLUSIONS: With a comparative transcriptome profiling approach, we have identified a number of potential immunomodulator genes of F. hepatica (n = 62), which are firstly described here, could be employed for the development of better strategies (including RNAi) in the battle against both zoonotically and economically important disease, fasciolosis.

Generating a detailed protein profile of Fasciola hepatica during the chronic stage of infection in cattle.

Fasciola hepatica is a trematode helminth causing a damaging disease, fasciolosis, in ruminants and humans. Comprehensive proteomic studies broaden our knowledge of the parasite's protein profile, and provide new insights into the development of more effective strategies to deal with fasciolosis. The objective of this study was to generate a comprehensive profile of F. hepatica proteins expressed during the chronic stage of infection in cattle by building on previous efforts in this area. The approach included an improved sample preparation procedure for surface and internal layers of the parasite, the application of nano-UPLC-ESI-qTOF-MS (nano-ultra-performance LC and ESI quadrupole TOF MS) integrated with different acquisition methods and in silico database search against various protein databases and a transcript database including a new assembly of publically available EST. Of a total of 776 identified proteins, 206 and 332 were specific to the surface and internal layers of the parasite, respectively. Furthermore, 238 proteins were common to both layers, with comparative differences of 172 proteins detected. Specific proteins not previously identified in F. hepatica, but shown to be immunomodulatory or potential drug targets for other parasites, are discussed.