Bulk segregation analysis in the NGS era: a review of its teenage years

Bulk segregation analysis (BSA) utilizes a strategy of pooling individuals with extreme phenotypes to conduct economical and rapidly linked marker screening or quantitative trait locus (QTL) mapping. With the development of next-generation sequencing (NGS) technology in the past 10 years, BSA methods and technical systems have been gradually developed and improved. At the same time, the ever-decreasing costs of sequencing accelerate NGS-based BSA application in different species, including eukaryotic yeast, grain crops, economic crops, horticultural crops, trees, aquatic animals, and insects. This paper provides a landscape of BSA methods and reviews the BSA development process in the past decade, including the sequencing method for BSA, different populations, different mapping algorithms, associated region threshold determination, and factors affecting BSA mapping. Finally, we summarize related strategies in QTL fine mapping combining BSA.

3D NoC Low-Power Mapping Optimization Based on Improved Genetic Algorithm

Power optimization is an important part of network-on-chip(NoC) design. This paper proposes an improved algorithm based on genetic algorithm on how to properly map IP (Intellectual Property) cores to 3D NoC. First, in view of the randomness of the traditional genetic algorithm in individual selection, an improved greedy algorithm is used in the initial population generation stage to make the generated individuals reach the optimal. Secondly, in view of the weak local optimization ability of the traditional genetic algorithm and prone to premature problems, the simulated annealing algorithm is added in the crossover operation stage to make the offspring reach the global optimum. The experimental results show that compared with the traditional genetic algorithm, the algorithm has better convergence and low power consumption performance, which can quickly search for a better solution, in the case of a large number of cores (124 IP cores), the average power consumption can be reduced by 42.2%.

Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System

Spiking Neuromorphic systems have been introduced as promising platforms for energy-efficient spiking neural network (SNNs) execution. SNNs incorporate neuronal and synaptic states in addition to the variant time scale into their computational model. Since each neuron in these networks is connected to many others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, a precise communication latency is also needed, although SNN is tolerant to the spike delay variation in some limits when it is seen as a whole. The two-dimensional packet-switched network-on-chip was proposed as a solution to provide a scalable interconnect fabric in large-scale spike-based neural networks. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. Combining these two emerging technologies provides a new horizon for IC design to satisfy the high requirements of low power and small footprint in emerging AI applications. Moreover, although fault-tolerance is a natural feature of biological systems, integrating many computation and memory units into neuromorphic chips confronts the reliability issue, where a defective part can affect the overall system's performance. This paper presents the design and simulation of R-NASH-a reliable three-dimensional digital neuromorphic system geared explicitly toward the 3D-ICs biological brain's three-dimensional structure, where information in the network is represented by sparse patterns of spike timing and learning is based on the local spike-timing-dependent-plasticity rule. Our platform enables high integration density and small spike delay of spiking networks and features a scalable design. R-NASH is a design based on the Through-Silicon-Via technology, facilitating spiking neural network implementation on clustered neurons based on Network-on-Chip. We provide a memory interface with the host CPU, allowing for online training and inference of spiking neural networks. Moreover, R-NASH supports fault recovery with graceful performance degradation.

Mapping the Kansas City Cardiomyopathy Questionnaire (KCCQ) Onto EQ-5D-3L in Heart Failure Patients: Results for the Japanese and UK Value Sets

Background. Health technology assessment bodies in several countries, including Japan and the United Kingdom, recommend mapping techniques to obtain utility scores in clinical trials that do not have a preference-based measure of health. This study sought to develop mapping algorithms to predict EQ-5D-3L scores from the Kansas City Cardiomyopathy Questionnaire (KCCQ) in patients with heart failure (HF). Methods. Data from the randomized, double-blind PARADIGM-HF trial were analyzed, and EQ-5D-3L scores were calculated using the Japanese and UK value sets. Several different model specifications were explored to best fit EQ-5D data collected at baseline with KCCQ scores, including ordinary least square regression, two-part, Tobit, and three-part models. Generalized estimating equations models were also fitted to analyze longitudinal EQ-5D data. To validate model predictions, the data set was split into a derivation (n = 4,465) from which the models were developed and a separate sample (n = 1,892) for validation. Results. There were only small differences between the different model classes tested. Model performance and predictive power was better for the item-level models than for the models including KCCQ domain scores. R ² statistics for the item-level models ranged from 0.45 to 0.52. Mean absolute error in the validation sample was 0.10 for the models using the Japanese value set and 0.114 for the UK models. All models showed some underprediction of utility above 0.75 and overprediction of utility below 0.5, but performed well for population-level estimates. Conclusions. Using data from a large clinical trial in HF, we found that EQ-5D-3L scores can be estimated from responses to the KCCQ and can facilitate cost-utility analysis from existing HF trials where only the KCCQ was administered. Future validation in other HF populations is warranted.

Comparison of Burrows-Wheeler Transform-Based Mapping Algorithms Used in High-Throughput Whole-Genome Sequencing: Application to Illumina Data for Livestock Genomes

Ongoing developments and cost decreases in next-generation sequencing (NGS) technologies have led to an increase in their application, which has greatly enhanced the fields of genetics and genomics. Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. Efficient alignment of the reads onto the reference genome with high accuracy is very important because it determines the global quality of downstream analyses. In this study, we evaluate the performance of three Burrows-Wheeler transform-based mappers, BWA, Bowtie2, and HISAT2, in the context of paired-end Illumina whole-genome sequencing of livestock, using simulated sequence data sets with varying sequence read lengths, insert sizes, and levels of genomic coverage, as well as five real data sets. The mappers were evaluated based on two criteria, computational resource/time requirements and robustness of mapping. Our results show that BWA and Bowtie2 tend to be more robust than HISAT2, while HISAT2 was significantly faster and used less memory than both BWA and Bowtie2. We conclude that there is not a single mapper that is ideal in all scenarios but rather the choice of alignment tool should be driven by the application and sequencing technology.

Suitability of Different Mapping Algorithms for Genome-Wide Polymorphism Scans with Pool-Seq Data

The cost-effectiveness of sequencing pools of individuals (Pool-Seq) provides the basis for the popularity and widespread use of this method for many research questions, ranging from unraveling the genetic basis of complex traits, to the clonal evolution of cancer cells. Because the accuracy of Pool-Seq could be affected by many potential sources of error, several studies have determined, for example, the influence of sequencing technology, the library preparation protocol, and mapping parameters. Nevertheless, the impact of the mapping tools has not yet been evaluated. Using simulated and real Pool-Seq data, we demonstrate a substantial impact of the mapping tools, leading to characteristic false positives in genome-wide scans. The problem of false positives was particularly pronounced when data with different read lengths and insert sizes were compared. Out of 14 evaluated algorithms novoalign, bwa mem and clc4 are most suitable for mapping Pool-Seq data. Nevertheless, no single algorithm is sufficient for avoiding all false positives. We show that the intersection of the results of two mapping algorithms provides a simple, yet effective, strategy to eliminate false positives. We propose that the implementation of a consistent Pool-Seq bioinformatics pipeline, building on the recommendations of this study, can substantially increase the reliability of Pool-Seq results, in particular when libraries generated with different protocols are being compared.

Conversion of the Seattle Angina Questionnaire into EQ-5D utilities for ischemic heart disease: a systematic review and catalog of the literature

Background

There is a paucity of preference-based (utility) measures of health-related quality of life for patients with ischemic heart disease (IHD); in contrast, the Seattle Angina Questionnaire (SAQ) is a widely used descriptive measure. Our objective was to perform a systematic review of the literature to identify IHD studies reporting SAQ scores in order to apply a mapping algorithm to convert these to preference-based scores for secondary use in economic evaluations.

Methods

Relevant articles were identified in MEDLINE (Ovid), EMBASE (Ovid), Cochrane Library (Wiley), HealthStar (Ovid), and PubMed from inception to 2012. We previously developed and validated a mapping algorithm that converts SAQ descriptive scores to European Quality of Life-5 Dimensions (EQ-5D) utility scores. In the current study, this mapping algorithm was used to estimate EQ-5D utility scores from SAQ scores.

Results

Thirty-six studies met the inclusion criteria. The studies were categorized into three groups, ie, general IHD (n=13), acute coronary syndromes (n=4), and revascularization (n=19). EQ-5D scores for patients with general IHD were in the range of 0.605–0.843 at baseline, and increased to 0.649–0.877 post follow-up. EQ-5D scores for studies of patients with recent acute coronary syndromes increased from 0.706–0.796 at baseline to 0.795–0.942 post follow-up. The revascularization studies had EQ-5D scores in the range of 0.616–0.790 at baseline, and increased to 0.653–0.928 after treatment; studies that focused only on coronary artery bypass grafting increased from 0.643–0.788 at baseline to 0.653–0.928 after grafting, and studies that focused only on percutaneous coronary intervention increased in score from 0.616–0.790 at baseline to 0.668–0.897 after treatment.

Conclusion

In this review, we provide a catalog of estimated health utility scores across a wide range of disease severity and following various interventions in patients with IHD. Our catalog of EQ-5D scores can be used in IHD-related economic evaluations.