scholarly journals Fast Processing RNA-Seq on Multicore Processor

2021 ◽  
Vol 18 (4(Suppl.)) ◽  
pp. 1413
Author(s):  
Lee Jia Bin ◽  
Nor Asilah Wati Abdul Hamid ◽  
Zurita Ismail ◽  
Mohamed Faris Laham
Keyword(s):  
Processing Time ◽  
Sequence Data ◽  
Massive Data ◽  
Control Analysis ◽  
Experimental Sample ◽  
Rna Seq ◽  
Total Processing Time ◽  
Multicore Processor ◽  
Fast Processing ◽  
Speed Up

RNA Sequencing (RNA-Seq) is the sequencing and analysis of transcriptomes. The main purpose of RNA-Seq analysis is to find out the presence and quantity of RNA in an experimental sample under a specific condition. Essentially, RNA raw sequence data was massive. It can be as big as hundreds of Gigabytes (GB). This massive data always makes the processing time become longer and take several days. A multicore processor can speed up a program by separating the tasks and running the tasks’ errands concurrently. Hence, a multicore processor will be a suitable choice to overcome this problem. Therefore, this study aims to use an Intel multicore processor to improve the RNA-Seq speed and analyze RNA-Seq analysis's performance with a multiprocessor. This study only processed RNA-Seq from quality control analysis until sorted the BAM (Binary Alignment/Map) file content. Three different sizes of RNA paired end has been used to make the comparison. The final experiment results showed that the implementation of RNA-Seq on an Intel multicore processor could achieve a higher speedup. The total processing time of RNA-Seq with the largest size of RNA raw sequence data (66.3 Megabytes) decreased from 317.638 seconds to 211.916 seconds. The reduced processing time was 105 seconds and near to 2 minutes. Furthermore, for the smallest RNA raw sequence data size, the total processing time decreased from 212.380 seconds to 163.961 seconds which reduced 48 seconds.

Cell Block Optimization: A Comparative Study of Quality Variables in 4 Different Cell Block Methods

2021 ◽  
pp. 1-7
Author(s):  
Marsali Newman ◽  
Matthew Walsh ◽  
Rosemary Jeffrey ◽  
Richard Hiscock
Keyword(s):  
Comparative Study ◽  
Processing Time ◽  
Morphological Features ◽  
Cell Block ◽  
Total Processing Time ◽  
Block Methods ◽  
Molecular Studies ◽  
Agar Method ◽  
Md Anderson ◽  
Ancillary Studies

<b><i>Objective:</i></b> The cell block (CB) is an important adjunct to cytological preparations in diagnostic cytopathology. Optimizing cellular material in the CB is essential to the success of ancillary studies such as immunohistochemistry (IHC) and molecular studies (MS). Our aim was to identify which CB method was most suitable in a variety of specimen types and levels of cellularity. <b><i>Study Design:</i></b> We assessed 4 different CB methods, thrombin clot method (TCM), MD Anderson method (MDAM), gelatin foam method (GFM), and agar method (AM), with descriptive observations and ranking of the methods based on quantity of cells and morphological features. <b><i>Results:</i></b> TCM performed best in ranking for both quantity of cells and morphological features, followed by MDAM, GFM, and AM. Lack of adjuvant in the MDAM resulted in some unique morphological advantages which, however, also resulted in inconsistent performance. In low cellularity cases insufficient cells were frequently identified on slides from MDAM and AM CBs. Technique touch time was similar for all methods, with total processing time being shortest for TCM followed by MDAM, GFM, and AM. <b><i>Conclusions:</i></b> TCM was the most robust CB technique, retaining high scores for ranking of quantity and morphology in a variety of specimen cellularities and specimen types.

scholarly journals Semi-Online Scheduling on Two Machines withGoSLevels and Partial Information of Processing Time

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Taibo Luo ◽  
Yinfeng Xu
Keyword(s):  
Processing Time ◽  
Parallel Machines ◽  
Partial Information ◽  
Online Scheduling ◽  
Scheduling Problems ◽  
Total Processing Time ◽  
Grade Of Service ◽  
Two Machines

This paper investigates semi-online scheduling problems on two parallel machines under a grade of service (GoS) provision subject to minimize the makespan. We consider three different semi-online versions with knowing the total processing time of the jobs with higherGoSlevel, knowing the total processing time of the jobs with lowerGoSlevel, or knowing both in advance. Respectively, for the three semi-online versions, we develop algorithms with competitive ratios of3/2,20/13, and4/3which are shown to be optimal.

scholarly journals Comparison of Quantitative PCR (qPCR) Paenibacillus larvae Targeted Assays and Definition of Optimal Conditions for Its Detection/Quantification in Honey and Hive Debris

2018 ◽  
Author(s):  
Franca Rossi ◽  
Carmela Amadoro ◽  
Addolorato Ruberto ◽  
Luciano Ricchiuti
Keyword(s):  
Quantitative Pcr ◽  
Clinical Symptoms ◽  
Sequence Data ◽  
Standard Procedure ◽  
Paenibacillus Larvae ◽  
Optimal Conditions ◽  
Target Species ◽  
Speed Up ◽  
Definition Of ◽  
Culture Dependent

The application of quantitative PCR (qPCR) as a routine method to detect and enumerate Paenibacillus larvae in honey and hive debris could greatly speed up the estimation of prevalence and outbreak risk of the American foulbrood (AFB) disease of Apis mellifera. However, none of the qPCR tests described so far has been officially proposed as a standard procedure for P. larvae detection and enumeration for surveillance purposes. Therefore, in this study inclusivity, exclusivity and sensitivity in detection of P. larvae spores directly in samples of honey and hive debris were re-evaluated for the previously published qPCR methods. To this aim recently acquired P. larvae sequence data were considered to assess inclusivity in silico and more appropriate non-target species were used to verify exclusivity experimentally. This led to the modification of one of the previously described methods resulting in a new test capable to allow the detection of P. larvae spores in honey and hive debris down to 1 CFU/g. The application of the qPCR test optimized in this study can allow to reliably detect and quantify P. larvae in honey and hive debris, thus circumventing the disadvantages of late AFB diagnosis based on clinical symptoms and possible underestimation of spore numbers that is the main drawback of culture-dependent procedures.

scholarly journals Importance of experimental information (metadata) for archived sequence data: case of specific gene bias due to lag time between sample harvest and RNA protection in RNA sequencing

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11875
Author(s):  
Tomoko Matsuda
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Time Course ◽  
Sequence Data ◽  
Specific Gene ◽  
Time Interval ◽  
Short Time Interval ◽  
Rna Seq ◽  
Lysis Buffer ◽  
Rna Protection

Large volumes of high-throughput sequencing data have been submitted to the Sequencing Read Archive (SRA). The lack of experimental metadata associated with the data makes reuse and understanding data quality very difficult. In the case of RNA sequencing (RNA-Seq), which reveals the presence and quantity of RNA in a biological sample at any moment, it is necessary to consider that gene expression responds over a short time interval (several seconds to a few minutes) in many organisms. Therefore, to isolate RNA that accurately reflects the transcriptome at the point of harvest, raw biological samples should be processed by freezing in liquid nitrogen, immersing in RNA stabilization reagent or lysing and homogenizing in RNA lysis buffer containing guanidine thiocyanate as soon as possible. As the number of samples handled simultaneously increases, the time until the RNA is protected can increase. Here, to evaluate the effect of different lag times in RNA protection on RNA-Seq data, we harvested CHO-S cells after 3, 5, 6, and 7 days of cultivation, added RNA lysis buffer in a time course of 15, 30, 45, and 60 min after harvest, and conducted RNA-Seq. These RNA samples showed high RNA integrity number (RIN) values indicating non-degraded RNA, and sequence data from libraries prepared with these RNA samples was of high quality according to FastQC. We observed that, at the same cultivation day, global trends of gene expression were similar across the time course of addition of RNA lysis buffer; however, the expression of some genes was significantly different between the time-course samples of the same cultivation day; most of these differentially expressed genes were related to apoptosis. We conclude that the time lag between sample harvest and RNA protection influences gene expression of specific genes. It is, therefore, necessary to know not only RIN values of RNA and the quality of the sequence data but also how the experiment was performed when acquiring RNA-Seq data from the database.

Restructured Pork From Hot Processed Sow Meat: Effect of Mechanical Tenderization and Liquid Smoke

1986 ◽  
Vol 49 (8) ◽  
pp. 639-642 ◽  
Author(s):  
JOSEPH C. CORDRAY ◽  
DALE L. HUFFMAN ◽  
WILLIAM R. JONES
Keyword(s):  
Connective Tissue ◽  
Factorial Design ◽  
Fat Mass ◽  
Processing Time ◽  
Total Processing Time ◽  
Treatment Groups ◽  
Liquid Smoke ◽  
Fresh Frozen ◽  
Fully Cooked ◽  
Restructured Pork

A 2 × 2 factorial design was used to study the effect of tenderization and liquid smoke on sensory and physical attribution of a fully cooked restructured pork item. The lean and fat mass was removed intact within 30 min postmortem from sow carcasses and assigned to a tenderized or non-tenderized treatment with and without liquid smoke. The four treatment groups were: non-tenderized, no liquid smoke (NTNS); non-tenderized with liquid smoke (NTS); tenderized, no liquid smoke (TNS); and tenderized with liquid smoke (TS). Mechanical tenderization was accomplished 1 h postmortem and the two original portions were subdivided for a 1% acid-neutralized liquid smoke treatment. Total processing time from exsanguination to a fully cooked product was 8 h. There were no differences (P&gt;0.05) among any of the treatments for cohesiveness, juiciness, flavor or connective tissue scores or cooking loss. The TNS treatment had higher (P&lt;0.06) tension values as determined by Instron measurements than the NTNS treatment. There were initially no practical differences between TBA values for fresh-frozen and cooked-frozen restructured pork. However, after 30 d of storage (−23°C), the cooked-frozen product had significantly higher TBA values than fresh-frozen product.

scholarly journals VIRTUS: a pipeline for comprehensive virus analysis from conventional RNA-seq data

2020 ◽  
Author(s):  
Yoshiaki Yasumizu ◽  
Atsushi Hara ◽  
Shimon Sakaguchi ◽  
Naganari Ohkura
Keyword(s):  
Sequence Data ◽  
Supplementary Information ◽  
Clinical Samples ◽  
Host Gene Expression ◽  
Rna Seq ◽  
Viral Transcript ◽  
Rna Transcripts ◽  
Copy Numbers ◽  
Infected Cells ◽  
New Treatments

Abstract Summary The possibility that RNA transcripts from clinical samples contain plenty of virus RNAs has not been pursued actively so far. We here developed a new tool for analyzing virus-transcribed mRNAs, not virus copy numbers, in the data of bulk and single-cell RNA-sequencing of human cells. Our pipeline, named VIRTUS (VIRal Transcript Usage Sensor), was able to detect 762 viruses including herpesviruses, retroviruses and even SARS-CoV-2 (COVID-19), and quantify their transcripts in the sequence data. This tool thus enabled simultaneously detecting infected cells, the composition of multiple viruses within the cell, and the endogenous host-gene expression profile of the cell. This bioinformatics method would be instrumental in addressing the possible effects of covertly infecting viruses on certain diseases and developing new treatments to target such viruses. Availability and implementation : VIRTUS is implemented using Common Workflow Language and Docker under a CC-NC license. VIRTUS is freely available at https://github.com/yyoshiaki/VIRTUS. Supplementary information Supplementary data are available at Bioinformatics online.

Two methods for processing yield maps from multiple sensors in large vineyards in California

2017 ◽  
Vol 8 (2) ◽  
pp. 530-533 ◽  
Author(s):  
B. Sams ◽  
C. Litchfield ◽  
L. Sanchez ◽  
N. Dokoozlian
Keyword(s):  
Processing Time ◽  
Fruit Weight ◽  
Wine Grape ◽  
Total Processing Time ◽  
Yield Mapping ◽  
Processing Methods ◽  
Multiple Sensors ◽  
Level Method ◽  
Mapping Techniques ◽  
New Processing

Yield mapping techniques have only recently started to be implemented by the Californian wine grape industry, but the advancement has necessitated new processing methods for large vineyards. The process for mapping large blocks harvested with multiple machines has only recently occurred and implies that their yield monitors have to be calibrated and corrected to the same scale. Here we discuss two methods for processing yield maps at the commercial level. Method 1 depends on many calibrations with delivered fruit weight to a winery. Method 2 normalizes raw files automatically can reduce total processing time by up to 90%.

Right Ventricle Segmentation from Heart using Active Contour Model without Edge and Split Plane Decision

2018 ◽  
Vol 7 (3.33) ◽  
pp. 1
Author(s):  
Ho Chul Kang ◽  
. .
Keyword(s):  
Right Ventricle ◽  
Processing Time ◽  
Active Contour Model ◽  
Automatic Segmentation ◽  
Total Processing Time ◽  
Anisotropic Diffusion Filter ◽  
Computational Performance ◽  
Volume Of Interest ◽  
Whole Heart ◽  
Cardiac Diagnosis

In this paper, we propose an automatic segmentation method of right ventricle from computed tomography angiography (CTA) using Chan-Vese model and split plane detection. First, we remove noise in the images by applying anisotropic diffusion filter and extract the whole heart using Otsu Thresholding. Second, the volume of interest (VOI) is detected by Chan-Vese model and morpholotical operation. Third, we divide the heart to left and right region using power watershed. Finally we detect split plane which divide right heart to right ventricle and atrium. We tested our method in ten CT images and they were obtained from a different patient. For the evaluation of the computational performance of the proposed method, we measured the total processing time. The average of total processing time, from first step to third step, was 13.92±1.28 s. We expect for our method to be used in cardiac diagnosis for cardiologist.  

Heating Position Planning in Laser Forming of Single Curved Shapes Based on Probability Convergence

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Hong Shen ◽  
Yutao Zheng ◽  
Han Wang ◽  
Zhenqiang Yao
Keyword(s):  
Experimental Data ◽  
Inverse Problem ◽  
Minimum Distance ◽  
Processing Time ◽  
Probability Function ◽  
Laser Forming ◽  
Total Processing Time ◽  
Processing Efficiency ◽  
Bending Angle

Inverse problem in laser forming involves the heating position planning and the determination of heating parameters. In this study, the heating positions are optimized in laser forming of single curved shapes based on the processing efficiency. The algorithm uses a probability function to initialize the heating position that is considered to be the bending points. The optimization process is to minimize the total processing time through adjusting the heating positions by considering the boundary conditions of the offset distances, the minimum bending angle, and the minimum distance between two adjacent heating positions. The optimized results are compared with those obtained by the distance-based model as well as the experimental data.

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