Sequencing Machines Research Articles

Tumor-related Microbiome in the Breast Microenvironment and Breast Cancer.

Despite the significant progress in diagnosis and treatment over the past years in the understanding of breast cancer pathophysiology, it remains one of the leading causes of mortality worldwide among females. Novel technologies are needed to improve better diagnostic and therapeutic approaches, and to better understand the role of tumor-environment microbiome players involved in the progression of this disease. The gut environment is enriched with over 100 trillion microorganisms, which participate in metabolic diseases, obesity, and inflammation, and influence the response to therapy. In addition to the direct metabolic effects of the gut microbiome, accumulating evidence has revealed that a microbiome also exists in the breast and in breast cancer tissue. This microbiome enriched in the breast environment and the tumor microenvironment may modulate effects potentially associated with carcinogenesis and therapeutic interventions in breast tissue, which to date have not been properly acknowledged. Herein, we review the most recent works associated with the population dynamics of breast microbes and explore the significance of the microbiome on diagnosis, tumor development, response to chemotherapy, endocrine therapy, and immunotherapy. To overcome the low reproducibility of evaluations of tumor-related microbiome, sequencing technical escalation and machine deep learning algorithms may be valid for standardization of assessment for breast-related microbiome and their applications as powerful biomarkers for prognosis and predictive response in the future.

Intention learning for decision of machining sequence via Deep Learning

Intention learning for decision of machining sequence via Deep Learning

Evaluation of the Utility of Mitochondrial DNA Testing in Personal Identification Work in the Great East Japan Earthquake of 2011.

Although the Great East Japan Earthquake occurred on March 11, 2011, identification of victims is still ongoing. Typically, mitochondrial DNA (mtDNA) is performed when it is difficult to identify an individual using nuclear DNA. In Japan, samples from criminal investigations are subjected to nuclear DNA testing at the Scientific Research Institute belonging to each prefectural police headquarters, while all mtDNA tests were originally conducted at the National Research Institute of Police Science. However, the appraisal work using mtDNA became more time-consuming as the number of target samples increased. Because our department is capable of performing mtDNA testing, the Miyagi Prefectural Police requested that our department perform mtDNA testing. Specifically, we focused on 16 individuals as putative candidates for 11 unidentified human remains; efforts to identify these remains were performed using samples from 20 relatives. These efforts positively identified six victims. This included confirmation that one corpse had originally been identified incorrectly. Although disasters of a similar scale can strike Japan again, there are limited facilities that can consistently perform mtDNA testing. Expensive sequencing machines and properly trained operators are essential for mtDNA testing, but they cannot be established at the forensic departments of all medical schools. There is thus an urgent need to establish core facilities at appropriate sites, such as Tohoku University in the Tohoku Region, to build a mtDNA testing system suitable for the aftermath of any disaster.

Improved Analysis of High-Throughput Sequencing Data Using Small Universal k-Mer Hitting Sets.

High-throughput sequencing machines can read millions of DNA molecules in parallel in a short time and at a relatively low cost. As a consequence, researchers have access to databases with millions of genomic samples. Searching and analyzing these large amounts of data require efficient algorithms.Universal hitting sets are sets of words that must be present in any long enough string. Using small universal hitting sets, it is possible to increase the efficiency of many high-throughput sequencing data analyses. But, generating minimum-size universal hitting sets is a hard problem. In this chapter, we cover our algorithmic developments to produce compact universal hitting sets and some of their potential applications.

HAssembler: A hybrid de novo genome assembly approach for large genomes

Genome assembly is a process where large contigs and scaffolds are constructed from raw reads generated by sequencing machines. Based on the size of the generated reads they can be primarily categorized into short reads and long reads. Modern genome assemblers follow De Bruijn Graph (DBG) approach for assembly of short reads, whereas Overlap Layout Consensus (OLC) approach for assembly of long reads. For de novo genome assembly, DBG based assemblers are very efficient at repeat resolution but are computation intensive and sensitive to sequencing errors. On the other hand, OLC based assemblers are intuitive and very time efficient but not efficient at resolving repeat regions. Here, we developed an hAssembler, which leverages the advantages of both DBG and OLC approaches and compared its performance with the existing hybrid assemblers. It uses both long reads and short reads and run OLC and DBG in parallel. By using both the long and short reads, the time complexity of hAssembler was reduced considerably. The results showed that hAssembler outperformed the existing hybrid assemblers in terms of time and performance (N50) while assembling the large genomes.

Fast and inexpensive whole-genome sequencing library preparation from intact yeast cells.

Through the increase in the capacity of sequencing machines massively parallel sequencing of thousands of samples in a single run is now possible. With the improved throughput and resulting drop in the price of sequencing, the cost and time for preparation of sequencing libraries have become the major bottleneck in large-scale experiments. Methods using a hyperactive variant of the Tn5 transposase efficiently generate libraries starting from cDNA or genomic DNA in a few hours and are highly scalable. For genome sequencing, however, the time and effort spent on genomic DNA isolation limit the practicability of sequencing large numbers of samples. Here, we describe a highly scalable method for preparing high-quality whole-genome sequencing libraries directly from Saccharomyces cerevisiae cultures in less than 3 h at 34 cents per sample. We skip the rate-limiting step of genomic DNA extraction by directly tagmenting lysed yeast spheroplasts and add a nucleosome release step prior to enrichment PCR to improve the evenness of genomic coverage. Resulting libraries do not show any GC bias and are comparable in quality to libraries processed from genomic DNA with a commercially available Tn5-based kit. We use our protocol to investigate CRISPR/Cas9 on- and off-target edits and reliably detect edited variants and shared polymorphisms between strains. Our protocol enables rapid preparation of unbiased and high-quality, sequencing-ready indexed libraries for hundreds of yeast strains in a single day at a low price. By adjusting individual steps of our workflow, we expect that our protocol can be adapted to other organisms.

A hybrid multiobjective GRASP for a multi-row facility layout problem with extra clearances

The multi-row facility layout problem (MRLP) is an important design problem often encountered in real life. Existing studies on MRLPs typically either ignore clearances between adjacent machines or consider only the minimum clearances. However, separating adjacent machines with clearances greater than the minimum ones may achieve lower material flow cost. In addition, current studies on MRLPs ignore the optimisation of layout area. In this paper, we study a multi-row facility layout problem with extra clearances (MRLP-EC), with the objectives of minimising material flow cost and layout area. A mixed integer programming formulation is established for MRLP-EC. A hybrid approach combining an improved multi-objective greedy randomised adaptive search procedure (mGRASP) and linear programming (LP) is proposed for the problem. The mGRASP is used to optimise machine sequences to obtain a set of non-dominated machine sequences. A segments-based dominance method is suggested to measure the dominance relationship of any pair of machine sequences. LP is used to optimise extra clearances between adjacent machines (i.e. the exact location of each machine) for each non-dominated machine sequence. The proposed approach is compared against an exact method and two multi-objective heuristics. Experiments show that the approach is effective for MRLP-EC and outperforms comparative approaches.

An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis.

Tuberculosis is the leading cause of death from a single infectious agent. The emergence of antimicrobial resistant Mycobacterium tuberculosis strains makes the problem more severe. Pyrazinamide (PZA) is an important component for short-course treatment regimens and first- and second-line treatment regimens. This research aims for fast diagnosis of M. tuberculosis resistance to PZA and identification of genetic features causing resistance. We use clinically collected genomic data of M. tuberculosis that are resistant or susceptible to PZA. A machine learning platform is built to diagnose PZA resistance using the whole genome sequence data, and to identify resistance genes and mutations. The platform consists of a deep convolutional neural network (DCNN) model for resistance diagnosis and a support vector machine (SVM) model as a surrogate to identify resistance genes and mutations. The DCNN model achieves a PZA resistance diagnosis accuracy of 93%. Each prediction takes less than a second. The SVM has revealed 2 novel genes, embB and gyrA, besides the well-known pncA gene, and 9 mutations that harbor PZA resistance. The DCNN and SVM machine learning platform, if used together with the real-time genome sequencing machines, could allow for rapid PZA diagnosis, allowing for critical time to ensure good patient outcomes, and preventing outbreaks of deadly infections. Furthermore, identifying pertinent resistance genes and mutations will help researchers better understand the biological mechanisms behind resistance. Machine learning can be used to achieve high-accuracy resistance prediction, and identify genes and mutations causing the resistance.

Bi-objective parallel machine scheduling with additional resources during setups

We present a bi-objective parallel machine scheduling problem with machine and job sequence dependent setup times, with the additional consideration of resources needed during setups. The availability of such resources is limited. This models many practical situations where setup times imply, for example, cleaning and/or the reconfiguration of productive equipment. These setups are performed by personnel, who are of course limited in number. The objectives considered are the minimization of the makespan and the minimization of the number of resources. Fewer available resources reduce production costs but inevitably increase the makespan. On the contrary, a greater number of resources increase costs but allow for more setups to be done in parallel and a reduced makespan. An algorithm based on iterated greedy approaches is proposed to search for the Pareto front of the problem. This algorithm is compared with state-of-the art methods adapted to the problem. Computational experiments, supported by statistical analyses, indicate that the proposed approach outperforms all other tested procedures.

Penerapan Algoritma Campbell Dudek Smith (CDS) untuk Optimasi Waktu Produksi Pada Penjadwalan Produksi

Research on optimizing production time using the dudek smith campbell algorithm (CDS) in scheduling the production process aims to makepan optimization for machine operations to produce 12 size griddle products, 14 griddle sizes, 16 griddle sizes, 18 griddle sizes and 20 griddle sizes to get the makepan values. optimal. The method applied by the Campbell Dudek and Smith (CDS) algorithm, CDS is a method used in flowshop scheduling developed from Johnson's rule which is able to minimize the makespan of 2 machines arranged in series. The CDS method is very suitable for production characters who apply machine sequences to the production process. CDS produces several iterations that have the value of makespan, from those iterations, the minimum value of makespan is to determine the order of products to be produced. This research resulted in an application that can schedule products to be produced by machines automatically. From the test results with a total production of 12 pieces on each product with 6 repetitions, the minimum value of makespan is 210.12 minutes with a sequence of product works 20 pans, 18 pans, 16 pans, 14 pans, and 12 pans. Accuracy of results Application testing shows 99.99% for the first time and 99.96% for the second time when compared to manual calculations

Machining process analysis for machine tool selection based on form-shaping motions

With the increase in machine tool types, including multi-axis control and multi-function machine tools, it can be difficult to select the appropriate machine tools for targeted parts machining. This study aims to automate machine tool selection by evaluating and comparing machining processes on a variety of machine tools. A system to analyze machining process based on the functional description of machine tools is developed as a first step. In the system, supposed machine tool structures are represented first corresponding to their form-shaping motions. Then, machining features are recognized using both CAD models of the workpiece and target shapes. The machining process consisting of a number of machining operations is analyzed using specific notations, and the operation expression matching the axis configuration is assigned to each recognized machining feature to decide the feasible machining operation. An enumerated machining operations list in the order of machining sequence is finally obtained as the machining process according to the machine tool structure. From a conducted case study, it is confirmed that the machining process analysis results could successfully support machine tool selection.

Incorporating Deep Learning With Word Embedding to Identify Plant Ubiquitylation Sites.

Protein ubiquitylation is an important posttranslational modification (PTM), which is involved in diverse biological processes and plays an essential role in the regulation of physiological mechanisms and diseases. The Protein Lysine Modifications Database (PLMD) has accumulated abundant ubiquitylated proteins with their substrate sites for more than 20 kinds of species. Numerous works have consequently developed a variety of ubiquitylation site prediction tools across all species, mainly relying on the predefined sequence features and machine learning algorithms. However, the difference in ubiquitylated patterns between these species stays unclear. In this work, the sequence-based characterization of ubiquitylated substrate sites has revealed remarkable differences among plants, animals, and fungi. Then an improved word-embedding scheme based on the transfer learning strategy was incorporated with the multilayer convolutional neural network (CNN) for identifying protein ubiquitylation sites. For the prediction of plant ubiquitylation sites, the proposed deep learning scheme could outperform the machine learning-based methods, with the accuracy of 75.6%, precision of 73.3%, recall of 76.7%, F-score of 0.7493, and 0.82 AUC on the independent testing set. Although the ubiquitylated specificity of substrate sites is complicated, this work has demonstrated that the application of the word-embedding method can enable the extraction of informative features and help the identification of ubiquitylated sites. To accelerate the investigation of protein ubiquitylation, the data sets and source code used in this study are freely available at https://github.com/wang-hong-fei/DL-plant-ubsites-prediction.

Bit-Vector-Based Hardware Accelerator for DNA Alignment Tools

Next generation sequencing technologies have noticeably improved in the last decade. Time and cost of whole genome sequencing are important challenges that must be reduced, opening unprecedented opportunities to various research and development areas. The alignment or mapping of small reads produced by sequencing machines to reference genomes of billions of nucleotides is a fundamental task in this sequencing process. It is computationally highly demanding and has become the bottleneck of the DNA analysis process. This paper proposes hardware acceleration based on FPGA of the Myers bit-parallelized algorithm, appropriately modified to be used in the extend stage of DNA alignment tools. The proposed design can be employed in conjunction with software functions, as it constitutes an extremely fast heterogeneous DNA alignment system. The implementation results show a speedup of up to [Formula: see text] relative to a sequential implementation only in software. In addition, due to the limited use of FPGA resources and the modular design, multiple modules can be used to completely populate the chip, further increasing the computing speed.

A collaborative optimization method of machining sequence for deformation control of double-sided structural parts

An appropriate machining sequence can benefit the deformation control of structural parts. However, the existing machining sequence optimization methods can only take single-sided parts into consideration, which is not sufficient to the deformation control of the double-sided parts. To this end, this paper proposes a double-sided collaborative optimization method of machining sequence for deformation control of structural parts. Off-line numerical analysis and on-line deformation monitoring are combined in the proposed method, i.e., the relative residual stiffness of the workpiece in different machining sequence is obtained by off-line numerical analysis, and the residual stress distribution is reflected by on-line deformation monitoring data, respectively. Then, a fuzzy comprehensive evaluation model aiming to decrease the overall deformation is established to realize the collaborative optimization of machining sequence. The machining efficiency is also considered in this model by optimizing the number of alternate flip-over, where the machining features of the part with two sides are machined alternatively according to the optimized sequence. The experiment shows that the proposed collaborative optimization method of machining sequence can decrease the machining deformation of double-sided parts effectively.

Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features

BackgroundDNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem.ResultsThe feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene.ConclusionsThe results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.

Modified differential evolution and heuristic algorithms for dump tippler machine allocation in a typical sugar mill in Thailand

This paper focuses on a computational tool for scheduling and sequencing sugarcane vehicles for dump tippler machines at the mill yard of a sugar mill, which can be represented as an NP-hard problem for parallel capacitated machines with machine restrictions, job grouping, and sequencing independent setup time. This research aims to determine the optimal sequencing of jobs, i.e. minimizing the makespan by considering machine restrictions, capacitated machines, group size, number of jobs, and the constraints of the sugar mill. In the considered problem machines alternatively operate, that distinguishes it from a general parallel machine problem. The mixed integer linear programing model is developed for solving the small-scale problem instances. Large-scale instances are handled by four heuristics, and four differential evolution (DE) metaheuristics. In order to improve the computational results, solution quality and computation time were considered. In addition, modified DE algorithms were used in encoding operation (initial solution), mutation and local search operation. The computational results revealed that the modified DE algorithms had higher relative improvement on the makespan. Furthermore, this decision-making support tool was implemented as a prototype in the sector of cane and sugar industry in Thailand and extended to other similar industries.

Approach to derive golden paths based on machine sequence patterns in multistage manufacturing process

A multistage manufacturing process (MMP) consists of several consecutive process stages, each of which has multiple machines performing the same functions in parallel. A manufacturing path (simply referred to as path) is defined as an ordered set indicating a record of machines assigned to a product at each process stage of an MMP. An MMP usually produces products through various paths. In practice, multiple machines in a process stage have different operational performances, which accumulate during production and affect the quality of products. This study proposes a heuristic approach to derive the golden paths that produce products whose quality exceeds the desired level. The proposed approach consists of the searching phase and the merging phase. The searching phase extracts two types of machine sequence patterns (MSPs) from a path dataset in an MMP. An MSP is a subset of the path that is defined as an ordered set of assigned machines from several process stages. The two extracted types of MSPs are: (1) superior MSP, which affects the production of superior-quality products, and (2) inferior MSP, which affects the production of inferior-quality products, called inferior MSP. The merging phase derives the golden paths by combining superior MSPs and excluding inferior MSPs. The proposed approach is verified by applying it to a hypothetical path dataset and the semiconductor tool fault isolation (SETFI) dataset. This verification shows that the proposed approach derives the golden paths that exceed the predefined product quality level. This outcome demonstrates the practical viability of the proposed approach in an MMP.

IonCRAM: a reference-based compression tool for ion torrent sequence files

BackgroundIon Torrent is one of the major next generation sequencing (NGS) technologies and it is frequently used in medical research and diagnosis. The built-in software for the Ion Torrent sequencing machines delivers the sequencing results in the BAM format. In addition to the usual SAM/BAM fields, the Ion Torrent BAM file includes technology-specific flow signal data. The flow signals occupy a big portion of the BAM file (about 75% for the human genome). Compressing SAM/BAM into CRAM format significantly reduces the space needed to store the NGS results. However, the tools for generating the CRAM formats are not designed to handle the flow signals. This missing feature has motivated us to develop a new program to improve the compression of the Ion Torrent files for long term archiving.ResultsIn this paper, we present IonCRAM, the first reference-based compression tool to compress Ion Torrent BAM files for long term archiving. For the BAM files, IonCRAM could achieve a space saving of about 43%. This space saving is superior to what achieved with the CRAM format by about 8–9%.ConclusionsReducing the space consumption of NGS data reduces the cost of storage and data transfer. Therefore, developing efficient compression software for clinical NGS data goes beyond the computational interest; as it ultimately contributes to the overall cost reduction of the clinical test. The space saving achieved by our tool is a practical step in this direction. The tool is open source and available at Code Ocean, github, and http://ioncram.saudigenomeproject.com.

The multi-layered job-shop automatic scheduling system of mould manufacturing for Industry 3.5

This study combined the First In First Out (FIFO) with the Earliest Due Date (EDD) heuristics and the on-site experience of actual production lines. According to such restrictive conditions as the available processing machine groups, the hierarchical relations among components, the due date of moulds as designated in the plan of mould manufacturing, this study conducted a quick analysis with an expert system to determine the optimal sequence and scheduling of machines. The machine management agent was then used for automatic pre-scheduling of the earliest available capacities of the machines to obtain preliminary scheduling. Firstly, the Genetic Algorithm (GA) was used to find the better scheduling sequences. After that, Ant Colony Optimization (ACO) was adopted to optimize the sequence determined by the expert system. It was able to effectively solve the problem of job-shop scheduling featuring a complicated hierarchical procedure for Industry 3.5 as a hybrid strategy between Industry 3.0 and to-be Industry 4.0. Practical mould cases with four-layer components were next used for a comparison of scheduling results. Three case studies show better results with integration of GA and ACO (GA + ACO) than GA or ACO alone. For the in-depth case study of 92 jobs, the scheduling based on the automatic scheduling of EDD spent 8% less work time than that based on FIFO. After the GA and ACO was adopted for the optimization of scheduling, the work span was further shortened by 10% with less computing time.

Hybrid particle swarm optimization and neighborhood strategy search for scheduling machines and equipment and routing of tractors in sugarcane field preparation

This paper presents the Hybrid Particle Swarm Optimization and Neighborhood Strategy Search (HPSO-NS) to solve a tractor scheduling and routing problem with equipment allocation constraint in sugarcane field preparation, to help the growers catch the season and ensure advantageous production of sugar from sugarcane. This problem can be formulated as the flexible flow shop scheduling problem with machine eligibility, time windows, sequence dependent setup time (SDST), blocking, machine restriction and machine grouping (FFS |Ssmt,Mj, Grouping, block, 6-stage, Tool, Tw |∑iNRi). A mixed-integer programming model was developed to solve small-scale problems. The HPSO-NS was developed for large-scale problems, and three neighborhood strategies were added to the PSO procedure and developed. Moreover, two new formulae which were used to select the neighborhood strategy in HPSO-NS are presented in this paper to increase the performance of the proposed method. The computational results show that the HPSO-NS outperforms the original PSO and the lower bound obtained from the optimization software, while using 97% less computational time.