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Explanation of the meaning of high-throughput


Almost all of the papers about bioinformatics, I faced with the high-throughput word, but I could not find any explanation about it (I think it is so easy to understand and that's why anyone explains it but I could not do it ) . Is there anyone who can explain me what do they mean by high-throughput ? Thanks in advance.


High-throughput sequencing specifically refers to sequencing techniques like Illumina that allow you to sequence massive amounts of DNA at once (hundreds of thousands of strands), as opposed to older techniques such as cloning the cDNA in plasmids, followed by sequencing.


High-throughput, as indicated by canadianer in their comments refers to amount of data that is processed by the system. Though the answer by CactusWoman would be correct for the case of DNA sequencing, high-throughput is not really confined to that domain.

Any high-throughput technique tries to measure several variables simultaneously. The examples include, other than the Next Gen DNA sequencing, RNA sequencing, protein identification and quantification by mass spectrometry (LC-MS), lipid profiling by GC-MS etc.

There are also medium-throughput techniques that can measure several variables but much less than high-throughput techniques. Many low-throughput techniques can be converted to medium-throughput by some level of automation and experimental planning. Example would include real-time PCR.


There is nothing particular about the bioinformatic use of this term; it is normal English not specialised jargon. 'Throughput' simply means the rate at which something can be processed. A high throughput system is one which handles things at a high rate, and it could be equally applied to genome sequencing, a city transit network, the ticketing gates at a football match, a factory processing potatoes into chips (fries), or the processor in your computer.

In biology, though, throughput usually refers to the rate at which samples can be processed. A 'low' throughput method is therefore one that can takes a longer time to carry out, can only be applied to a few samples, or needs to be repeated in order to obtain a full set of measurements of different factors. A 'high' throughput method is simply a faster method, which allows a greater number of samples to be processed in the same, or less, time. This can be achieved by working faster, processing multiple samples at once, or simultaneously handling multiple aspects of the same sample.

Note that there is no definition of when something becomes 'high throughput'; it is simply 'high' relative to earlier methods, although you would usually expect it only to be applied when the new method is many times faster than the existing method, not a small incremental improvement.


High-throughput sequencing specifically refers to sequencing techniques like Illumina that allow you to sequence massive amounts of DNA at once means hundreds of thousands of strands. Producing multitude of data sequence simultaneously.


Throughput

Throughput is the amount of a product or service that a company can produce and deliver to a client within a specified period of time. The term is often used in the context of a company's rate of production or the speed at which something is processed.

Businesses with high throughput levels can take market share away from their lower throughput peers because high throughput generally indicates that a company can produce a product or service more efficiently than its competitors.

Key Takeaways

  • Throughput is a term used to describe the rate at which a company produces or processes its products or services.
  • The goal behind measuring the throughput concept is often to identify and minimize the weakest links in the production process.
  • Assumptions about capacity and the company's supply chain can affect throughput.
  • Maintaining high throughput becomes a challenge when different products are being produced using a combination of joint and separate processes.
  • When a company can maximize its throughput, it can maximize its revenues.

Circulating Tumor Cells: High-Throughput Imaging of CTCs and Bioinformatic Analysis

Circulating tumor cells (CTCs) represent novel biomarkers, since they are obtainable through a simple and noninvasive blood draw or liquid biopsy. Here, we review the high-definition single-cell analysis (HD-SCA) workflow, which brings together modern methods of immunofluorescence with more sophisticated image processing to rapidly and accurately detect rare tumor cells among the milieu of platelets, erythrocytes, and leukocytes in the peripheral blood. In particular, we discuss progress in methods to measure CTC morphology and subcellular protein expression, and we highlight some initial applications that lead to fundamental new insights about the hematogenous phase of cancer, as well as its performance in early-stage diagnosis and treatment monitoring. We end with an outlook on how to further probe CTCs and the unique advantages of the HD-SCA workflow for improving the precision of cancer care.

Keywords: Biomarkers Circulating tumor cells Diagnostics High throughput Image processing Intratumor heterogeneity Liquid biopsy Morphometry Physical oncology Precision medicine.

Figures

A schematic overview of the…

A schematic overview of the HD-SCA workflow. Received patient whole blood is treated…

HD-SCA automated fluorescence scanning microscopy…

HD-SCA automated fluorescence scanning microscopy system. A First, the focus and exposure (for…

Gallery of representative CTCs detected…

Gallery of representative CTCs detected in the blood of a patient with prostate…

Downstream characterization with the HD-SCA…

Downstream characterization with the HD-SCA workflow Left panel: As an example of an…


Throughput

Throughput refers to how much data can be transferred from one location to another in a given amount of time. It is used to measure the performance of hard drives and RAM, as well as Internet and network connections.

For example, a hard drive that has a maximum transfer rate of 100 Mbps has twice the throughput of a drive that can only transfer data at 50 Mbps. Similarly, a 54 Mbps wireless connection has roughly 5 times as much throughput as a 11 Mbps connection. However, the actual data transfer speed may be limited by other factors such as the Internet connection speed and other network traffic. Therefore, it is good to remember that the maximum throughput of a device or network may be significantly higher than the actual throughput achieved in everyday use.

TechTerms - The Tech Terms Computer Dictionary

This page contains a technical definition of Throughput. It explains in computing terminology what Throughput means and is one of many technical terms in the TechTerms dictionary.

All definitions on the TechTerms website are written to be technically accurate but also easy to understand. If you find this Throughput definition to be helpful, you can reference it using the citation links above. If you think a term should be updated or added to the TechTerms dictionary, please email TechTerms!

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Throughput was conceived to evaluate the productivity of computer processors. This was generally calculated in terms of batch jobs or tasks per second and millions of instructions per second. Some derivatives measure a system's overall throughput by evaluating the amount and complexity of work, number of simultaneous users and application/system responsiveness.

Similarly, for network communications, throughput is measured by calculating the amount of data transferred between locations during a specified period, generally resulting as bits per second (bps), which has evolved to bytes per second (Bps), kilobytes per second (KBps), megabytes per second (MBps) and gigabytes per second (GBps).


Conclusion

ReSeq improves the faithfulness of simulated data for all tested datasets. To achieve this, we solved three major challenges. First, we developed a coverage model that can be trained on complete large genomes. Second, we included systematic errors into the simulation. Third, we efficiently represented the important statistics, such that memory requirements remain constrained and the parameters can still be learned from a single real dataset.

Furthermore, ReSeq provides an easy-to-use training of all required models. No manual choice of parameters is needed, which simplifies usage over a wide range of genomes, Illumina machines, and DNA preparations. The results from the At-BGI dataset suggest that sequencers from BGI can also be successfully simulated. Additionally, ReSeq is more robust to fragmented references during the profile generation compared to pIRS and NEAT.

The simulation of eleven diverse datasets showed the importance of choosing good training data that fit the desired simulation in terms of genomic GC distribution, preprocessing (PCR, fragmentation), and sequencing machine. Therefore, hard-coded profiles should be avoided.

ReSeq and all of its code are available [45].


Background

Biomarker discovery has proven to be one of the most broadly applicable and successful means of translating molecular and genomic data into clinical practice. Comparisons between healthy and diseased tissues have highlighted the importance of tasks such as class discovery (detecting novel subtypes of a disease) and class prediction (determining the subtype of a new sample) [1–4], and recent metagenomic assays have shown that human microbial communities can be used as biomarkers for host factors such as lifestyle [5–7] and disease [7–10]. As sequencing technology continues to develop and makes microbial biomarkers increasingly easily detected, this enables clinical diagnostic and microbiological applications through the comparison of microbial communities [11, 12].

The human microbiome, consisting of the total microbial complement associated with human hosts, is an important emerging area for metagenomic biomarker discovery [13, 14]. Changes in microbial abundances in the gut, oral cavity, and skin have been associated with disease states ranging from obesity [15–17] to psoriasis [18]. More generally, the metagenomic study of microbial communities is an effective approach for identifying the microorganisms or microbial metabolic characteristics of any uncultured sample [19, 20]. Analyses of metagenomic data typically seek to identify the specific organisms, clades, operational taxonomic units, or pathways whose relative abundances differ between two or more groups of samples, and several features of microbial communities have been proposed as potential biomarkers for various disease states. For example, single pathogenic organisms can signal disease if present in a community [21, 22], and increases and decreases in community complexity have been observed in bacterial vaginosis [23] and Crohn's disease [8]. Each of these different types of microbial biomarkers is correlated with disease phenotypes, but few bioinformatic methods exist to explain the class comparisons afforded by metagenomic data.

Identifying the most biologically informative features differentiating two or more phenotypes can be challenging in any genomics dataset, and this is particularly true for metagenomic biomarkers. Robust statistical tools are needed to ensure the reproducibility of conclusions drawn from metagenomic data, which is crucial for clinical application of the biological findings. Related challenges are associated with high-dimensional data regardless of the data type or experimental platform the number of potential biomarkers, for example, is typically much higher than the number of samples [24–26]. Metagenomic analyses additionally present their own specific issues, including sequencing errors, chimeric reads [27, 28], and complex underlying biology many microbial communities have been found to show remarkably high inter-subject variability. For example, large differences are detected even among the gut microbiomes of twins [29], and both human microbiomes and environmental communities are thought to be characterized by the presence of a long tail of rare organisms [30–32]. Moreover, simply identifying potential biomarkers without elucidating their biological consistency and roles is only a precursor to understanding the underlying mechanisms of microbe-microbe or host-microbe interactions [33]. In many cases, it is necessary to explain not just how two biological samples differ, but why. This problem is referred to as class comparison: how can the differences between phenotypes such as tumor subtype or disease state be explained in terms of consistent biological pathways or molecular mechanisms?

A number of methods have been proposed for class discovery or comparison in metagenomic data. MEGAN [34] is a metagenomic analysis tool with recent additions for phylogenetic comparisons [35] and statistical analyses [36]. MEGAN, however, can only compare single pairs of metagenomes, as is also the case with STAMP [37], which does introduce a concept of 'biological relevance' in the form of confidence intervals. UniFrac [38] compares sets of metagenomes at a strictly taxonomic level using phylogenetic distance, while MG-RAST [39], ShotgunFunctionalizeR [40], mothur [41], and METAREP [42] all process metagenomic data using standard statistical tests (mainly t-tests with some modifications). Most methods for community analysis from an ecological perspective rely on unsupervised cluster analyses based on principal component analysis [43] or principal coordinate analysis [44]. These can successfully detect groups of related samples, but they fail to include prior knowledge of phenotypes or environmental conditions associated with the groups, and they generally do not identify the biological features responsible for group relationships. Metastats [45] is the only current method that explicitly couples statistical analysis (to assess whether metagenomes differ) with biomarker discovery (to detect features characterizing the differences) based on repeated t statistics and Fisher's tests on random permutations. However, none of these methods, even those offering nuanced analyses of metagenomic data, provide biological class explanations to establish statistical significance, biological consistency, and effect size estimation of predicted biomarkers.

In this work, we present the linear discriminant analysis (LDA) effect size (LEfSe) method to support high-dimensional class comparisons with a particular focus on metagenomic analyses. LEfSe determines the features (organisms, clades, operational taxonomic units, genes, or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance. Class comparison methods typically predict biomarkers consisting of features that violate a null hypothesis of no difference between classes we additionally detect the subset of features with abundance patterns compatible with an algorithmically encoded biological hypothesis and estimate the sizes of the significant variations. In particular, effect size provides an estimation of the magnitude of the observed phenomenon due to each characterizing feature and it is thus a valuable tool for ranking the relevance of different biological aspects and for addressing further investigations and analyses. The introduction of prior biological knowledge in the method contributes to constrain the analysis and thus to address the challenges traditionally connected with high-dimensional data mining. LEfSe thus aims to support biologists by suggesting biomarkers that explain most of the effect differentiating phenotypes of interest (two or more) in biomarker discovery comparative and hypothesis-driven investigations. The visualization of the discovered biomarkers on taxonomic trees provides an effective means for summarizing the results in a biologically meaningful way, as this both statistically and visually captures the hierarchical relationships inherent in 16S-based taxonomies/phylogenies or in ontologies of pathways and biomolecular functions.

We validated this approach using data from human microbiomes, a mouse model of ulcerative colitis, and environmental samples, in each case predicting groups of organisms or operational taxonomic units that concisely differentiate the classes being compared. We further evaluated LEfSe using synthetic data, observing that it achieves a substantially better false positive rate compared to standard statistical tests, at the price of a moderately increased false negative rate (that can be adjusted as needed by the user). An implementation of LEfSe including a convenient graphical interface incorporated in the Galaxy framework [46, 47] is provided online at [48].


Free Energies of Hydrated Halide Anions: High throughput Computations on Clusters to Treat Rough Energy-Landscapes

How to cite: Gomez, D. Pratt, L. Rogers, D. Rempe, S. Free Energies of Hydrated Halide Anions: High throughput Computations on Clusters to Treat Rough Energy-Landscapes. Preprints 2021, 2021040583 (doi: 10.20944/preprints202104.0583.v1). Gomez, D. Pratt, L. Rogers, D. Rempe, S. Free Energies of Hydrated Halide Anions: High throughput Computations on Clusters to Treat Rough Energy-Landscapes. Preprints 2021, 2021040583 (doi: 10.20944/preprints202104.0583.v1). Copy

Cite as:

Gomez, D. Pratt, L. Rogers, D. Rempe, S. Free Energies of Hydrated Halide Anions: High throughput Computations on Clusters to Treat Rough Energy-Landscapes. Preprints 2021, 2021040583 (doi: 10.20944/preprints202104.0583.v1). Gomez, D. Pratt, L. Rogers, D. Rempe, S. Free Energies of Hydrated Halide Anions: High throughput Computations on Clusters to Treat Rough Energy-Landscapes. Preprints 2021, 2021040583 (doi: 10.20944/preprints202104.0583.v1). Copy


What is the definition of evil?

Evil is usually thought of as that which is morally wrong, sinful, or wicked however, the word evil can also refer to anything that causes harm, with or without the moral dimension. The word is used both ways in the Bible. Anything that contradicts the holy nature of God is evil (see Psalm 51:4). On the flip side, any disaster, tragedy, or calamity can also be called an “evil” (see 1 Kings 17:20, KJV).

Evil behavior includes sin committed against other people (murder, theft, adultery) and evil committed against God (unbelief, idolatry, blasphemy). From the disobedience in the Garden of Eden (Genesis 2:9) to the wickedness of Babylon the Great (Revelation 18:2), the Bible speaks of the fact of evil, and man is held responsible for the evil he commits: “The one who sins is the one who will die” (Ezekiel 18:20).

Essentially, evil is a lack of goodness. Moral evil is not a physical thing it is a lack or privation of a good thing. As Christian philosopher J. P. Moreland has noted, “Evil is a lack of goodness. It is goodness spoiled. You can have good without evil, but you cannot have evil without good.” Or as Christian apologist Greg Koukl has said, “Human freedom was used in such a way as to diminish goodness in the world, and that diminution, that lack of goodness, that is what we call evil.”

God is love (1 John 4:8) the absence of love in a person is un-God-like and therefore evil. And an absence of love manifests itself in unloving behavior. The same can be said concerning God’s mercy, justice, patience, etc. The lack of these godly qualities in anyone constitutes evil. That evil then manifests itself in behavior that is unmerciful, unjust, impatient, etc., bringing more harm into the good world that God has made. As it turns out, we lack a lot: “As it is written: ‘There is no one righteous, not even one’” (Romans 3:10).

Moral evil is wrong done to others, and it can exist even when unaccompanied by external action. Murder is an evil action, but it has its start with the moral evil of hatred in the heart (Matthew 5:21&ndash22). Committing adultery is evil, but so is the moral evil of lust in the heart (Matthew 5:27&ndash28). Jesus said, “What comes out of a person is what defiles them. For it is from within, out of a person’s heart, that evil thoughts come&mdashsexual immorality, theft, murder, adultery, greed, malice, deceit, lewdness, envy, slander, arrogance and folly. All these evils come from inside and defile a person” (Mark 7:20&ndash23).

Those who fall into evil behavior usually start slowly. Paul shows the tragic progression into more and more evil in Romans 1. It starts with refusing to glorify God or give thanks to Him (Romans 1:21), and it ends with God giving them over to a “depraved mind” and allowing them to be “filled with every kind of wickedness” (verses 28&ndash29).

Those who practice evil are in Satan’s trap and are slaves to sin: “Opponents [of the Lord’s servant] must be gently instructed, in the hope that God will grant them repentance leading them to a knowledge of the truth, and that they will come to their senses and escape from the trap of the devil, who has taken them captive to do his will” (2 Timothy 2:25&ndash26 see also John 8:34). Only by the grace of God can we be set free.

Physical evil is the trouble that befalls people in the world, and it may or may not be linked to moral evil or divine judgment. Ecclesiastes 11:2 counsels us to diversify our investments, for this reason: “thou knowest not what evil shall be upon the earth” (KJV). The word evil in this case means “disaster,” “misfortune,” or “calamity,” and that’s how other translations word it. Sometimes, physical evil is simply the result of an accident or causes unknown, with no known moral cause examples would include injuries, car wrecks, hurricanes, and earthquakes. Other times, physical evil is God’s retribution for the sins of an individual or group. Sodom and the surrounding cities were destroyed for their sins (Genesis 19), and God “made them an example of what is going to happen to the ungodly” (2 Peter 2:6). Many times, God warned Israel of the calamities that awaited them if they rebelled: “[The LORD] also is wise, and will bring evil, and will not call back his words: but will arise against the house of the evildoers, and against the help of them that work iniquity” (Isaiah 31:2, KJV). In all cases, God works through the situation to bring about His good purpose (Romans 8:28).

God is not the author of moral evil rather, it is His holiness that defines it. Created in God’s image, we bear the responsibility to make moral choices that please God and conform to His will. He wills our sanctification (1 Thessalonians 4:3) and does not wish us to sin (James 1:13). In repentance and faith in Christ, we have forgiveness of sin and a reversal of the moral evil within us (Acts 3:19). As God’s children, we walk according to this command: “Do not be overcome by evil, but overcome evil with good” (Romans 12:21).


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