Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Protein entries falling below this length are considered as fragments, since the structure of the lipase A from Bacillus subtilis suggests that shorter sequences likely will lack secondary structure elements that are essential for the α/β-hydrolase fold. Protein entries exceeding this length mainly comprise mammalian carboxylesterases that include an additional transmembrane region, as well as putative proteins from C. elegans and putative alpha esterases. The GGGX-class consists of 6 superfamilies and 20 homologous families, containing 1146 protein entries, 1780 sequence entries and 167 structure data sets. This class mainly consists of bacterial esterases, α-esterases, eukaryotic carboxylesterases, bile-salt activated lipases, juvenile hormone esterases, hormone-sensitive lipases, acetylcholinesterases, brefeldin A esterases, and thioesterases (Fig. 2a)Figure 2Distribution of protein functions for the three α/β-hydrolase classes GGGX (a), GX (b), and Y (c). Putative proteins with no function assigned and functions with a low percentage were grouped into "others". Full size imageThe GX class consists of 27 superfamilies and 79 homologous families, containing 2628 protein entries, 3623 sequence entries and 349 structure data sets. This class mainly consists of bacterial and fungal lipases, eukaryotic lipases (hepatic, lipoprotein, pancreatic, gastric and lysosomal acid lipases), cutinases, phospholipases, non-heme peroxidases, acyltransferases, epoxide hydrolases, haloalkane dehalogenases, deacetylases, hydroxynitril lyases and thioesterases (Fig. 2b). class consists of 4 superfamilies and 4 homologous families, containing 548 protein entries, 735 sequence entries and 58 structure data sets. This class mainly consists of dipeptidylpeptidasen, propylendopeptidasen and cocaine esterases (Fig 2c). AlignmentsFully and consistently annotated multisequence alignments are provided for each superfamily and homologous family of the α/β-hydrolases.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: With the ontology and knowledgebase developing [34–40], researchers can easily annotate the genes containing DMPs or DMRs to ontology entries, which brings convenience for understanding the function of genes in the pathogenesis of diseases. Obviously, a phenotype is associated with several -omics data, such as mRNA expression and protein expression, which suggests researchers should utilize integrated analysis with multi-dimension data like TCGA project does [41, 42]. ResultsReduce the technical variation and type biasWe evaluated the different modules of correcting methods including and RCP methods on Dataset 1, described in Table 1. The methylated and unmethylated intensities are imported into R environment and some results are displayed in following figures. For each method, we first plotted the density curves of β-value for three samples in Dataset 1. Then, we computed the standard deviation across the three replicates. Table 1 Normalization methods for Illumina 450K array dataFull size table As seen, SQN, Dasen and RCP can significantly reduce the technical variation (Fig. 3) and the same result can be seen base on standard deviation of replicates (Fig. 4). Because BMIQ and RCP do not change the β-values of probes, the standard deviation of probes of the two methods stays the same as the raw data. SWAN shows the least ability of reducing the variation among replicates. Fig. 3Density curves of β-value of replicates. Density curves of all probes after normalization using different methods are shown in this figure. The separate color represents replicates of the dataset. Raw data shows that there are differences among the replicates, while SQN, Dasen and RCP significantly reduce the technical variationFull size imageFig. 4Box plot of standard deviation across replicates. The standard deviation between replicates after normalization shows that SQN, Dasen and RCP make the normalized β-values of replicates more similar than other two methodsFull size image We also plotted the density curves of β-value of Infinium I/II probes for different methods. and SQN show similar performance on the sample GSM815136 such that the I probes have more similar distribution modes with same local maximum values, while Dasen and SWAN underperformed others regarding removing the type bias (Fig. 5). Fig. 5Density curves of β-value of two types in sample GSM815136 for different methods.

Output: corroborate


Input: Claim: All microbes can survive in extreme conditions indefinitely. [SEP] Text: Previous launches a few months back led the students to discover that the organisms could survive one trip into the stratosphere, but then the team came up with a batch of new questions: What would happen if the same test tube full of microbes journeyed to the stratosphere multiple times? How would the growth of those microbes compare to that of haloarchaea that never left Earth? “I thought we could try to refine [the experiment] by getting growth curves and actually trying to produce data, not just saying ‘it grows’” after a trip into the upper atmosphere, said Carson Reid, an 11th grader at and leader of the current project. The microbes face an atmospheric gauntlet while on their journey skyward and back. The microbes face an atmospheric gauntlet while on their journey skyward and back. First, they pass through the boundary between the troposphere and stratosphere, where temperatures drop below −60°C. Higher up, the microbes enter the ozone layer, which exposes them to such high dose rates of cosmic rays—more than 40 times that found at sea level—that the microbes can mutate. Air pressures are exceedingly low that high up, so the balloon explodes, sending its payload into a free fall at nearly 800 kilometers per hour until a parachute opens and slows its descent to Earth. Oftentimes, the cargo lands in Death Valley, just 80 kilometers away from the launch site, where surface temperatures can reach 50°C. Using GPS signals emanating from the payload, the students and adult advisers track down the microbes and set out to collect them from wherever they land, be it in the middle of a Joshua tree forest, a day’s hike into the mountains, or on the side of a dusty highway. Adventures in the students have become well versed in atmosphere physics and chemistry, analyzing archaea colonies in the laboratory is an entirely different beast. That’s where the microbiologists come in. Shil and Priya DasSarma’s lives revolve around haloarchaea. Their laboratory at the University of of Medicine is dedicated not only to studying the organisms but also to partnering with outside groups, such as schools, for educational purposes. The students of Earth to reached out to the DasSarmas last summer and formed an informal collaboration.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Table 1 The list of cancer types and sample counts in the BOUN10datasetFull size table Data representation modelsBinary mutation modelIn the binary model, a gene is represented with 1 if there is a mutation in that gene, and it is represented with 0 otherwise. Hence, the resulting data set has the gene labels as features and binary values for each feature. The binary value for each gene feature is extracted from the annotated VCF file. If the gene label exists in the annotated file, the value is 1, and it is 0 otherwise. If there are more than 1 mutation for a gene, the feature value for that gene is still set to 1. This representation model is applied by using two different gene lists. The first model is constructed with the known causal genes for the selected cancer types. The causal genes are obtained from OMIM [28] by using the MSC tool [29] of HGNC [30]. We extracted 434 causal genes for the cancer types in the BOUN10dataset. The second model is constructed by using all mutated genes in the annotated VCF files. We extracted 16,383 mutated genes for the cancer types in the BOUN10dataset. C-score based mutation modelThe CADD framework [22, 23] is a Machine (SVM) based framework which calculates C-scores for variants. The C-score integrates diverse annotations and creates a single score for a variant. In the C-score based mutation model, the sum of C-scores for all mutations in a gene is used as the feature value for that gene. For example, if a gene has two mutations in a sample, the feature value for that gene in that sample is the sum of the C-scores of these two mutations. The sum of C-scores approach was also used in [24] for breast cancer patient classification. We evaluate this approach for cancer type classification and compare it with the proposed information retrieval based mutation models described in the following subsections. Tf-idf based mutation modelComplex diseases are in general developed from the combination of various mutations in the genes. Each mutation may influence the evolution of the disease at different levels. In order to express these differences, we proposed to utilize the tf-idf (term frequency-inverse document frequency) weighting method.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: 67% of them are identified by only this methodFull size imageTable 2 The number of obtained DMPs and GO terms by five methodsFull size table Efficiency of large dataset analysisWe use Dataset3 to evaluate the performance of minfi and meffil [43] in term of importing large data. It took 48 min and less than 3 Gb memory to import data of 485 samples (970 *. idat files) using meffil package on a computer with 8 Gb of RAM and 4 processors while minfi could not run on the same computer. Minfi took two hours and ∼ 23 Gb memory on a server to import the same dataset. DiscussionIn this study we propose a generalized framework for Illumina 450K data analysis. We evaluate five methods for correcting type bias. Analysis of reducing technical replicates showed that different methods optimized different assessment criterion. The summary table of evaluation is shown in Table 3, where the score is set to 1 if there is significant change using the method, otherwise the score is set to 0. 5. RCP gets the highest total score based on mentioned criteria. Table 3 Summary of evaluation of five methodsFull size table The SQN, Dasen and RCP methods could significantly improve the replicated data quality, while SWAN and BMIQ didn’t show improvement of the replicates. SWAN and Dasen didn’t remove type bias as other methods, which might be due to that models they applying cannot fit the distribution of well as other methods. When evaluation focused on detecting and RCP got more overlapped DMPs and credible GO terms than other methods. It is should pointed out that result may vary largely when using different datasets, which will be validated in further work. [44] microarrays have been used in some project, which contain more probes on a single array. More efficient tools are in urgent need of merging 450K and EPIC array data and the efficiency of analysis should be considered. Minfi has been utilized widely but it cannot handle large dataset on personal computer in our view while the newly package meffil displayed surprising performance. During the evaluating processing, there were conflicts between R packages, for example, the MethylSet object in wateRmelon and minfi are different because the one in minfi has been updated in the newest version while the wateRmelon still use the previous object constructor. It is should be noticed in case of using different version of them.

Output: corroborate


Input: Claim: All microbes can survive in extreme conditions indefinitely. [SEP] Text: “Generally, when we’re studying these organisms in the lab, we’re subjecting them to a single stressor and seeing what the effect is in terms of survival,” said S. DasSarma. However, “ultimately, modeling something in the lab has limited value because what you really want to know is what happens in the real world,” he continued. Although normal haloarchaea are bubblegum pink, the mutants change color. Scientists are not sure why they manifest in such strange color patterns. Credit: Shil DasSarma and Priya Dassee whether the extreme conditions—notably the high doses of cosmic radiation—caused any mutations, the students need to culture their archaea colonies and analyze them under a microscope. Finding any mutated microbes will not be terribly difficult for the students, P. DasSarma said. Normally, haloarchaea are bright pink, but when mutated, they change color and have a distinctively odd look, like a glass marble or a foggy crystal ball. After the students culture their archaea and identify potential mutants, the DasSarmas will come aboard for the more technical analysis of the survivors. This analysis includes tests to identify how the mutant archaea differ genetically from their counterparts, which S. DasSarma said could lead to discovering strains that are more resistant to radiation. are a model microbe for studying the type of life that could survive outside of Earth’s protective pocket of atmosphere, explained Tony Phillips, an astronomer who runs the website spaceweather. com. Phillips started the Earth to club several years ago when his daughter and some of her classmates wanted to pursue more science and math projects outside of school, and he continues to serve as an adviser to the group. “By sending these microbes 30 to 35 kilometers above our head, we’re sort of transporting them to Mars for a little while. ” “By sending these microbes 30 to 35 kilometers above our head, we’re sort of transporting them to Mars for a little while,” Phillips said. High in the stratosphere, the conditions are much like those on Mars—where rovers are currently searching for signs of past or present life, Phillips said. Astrobiologists have long wondered what kinds of organisms could have lived—or might currently live—on the Red Planet.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: By this strategy, the instances in the independent test set are not included in any step of either parameter tuning or training, and the effects of minor changes of the training data are also indicated. The reported results are the micro-averaged scores and standard deviations on the independent test set. Accuracy, f-score, precision, recall, false positive rate (FPR) and area under the receiver operating curve (roc-auc) are used as the performance measures. Parameter tuning for the representation modelsFor BM25-tf, a range of k values between 0. 6 and 2 are used in the parameter tuning phase. The BM25-tf-rf representation model and the NN are used in the parameter tuning setup. The classification results for different values of k are presented in Table 2 and the best performing row is shown in bold. k =0. 8 leads to the best accuracy and f-score values. Therefore, this value is used in our experiments for the BM25-tf calculations. Table 2 Parameter tuning results for the parameter k in the BM25-tf formulaFull size table Parameter tuning for the classification modelsA parameter tuning phase is applied for each machine learning algorithm and data representation model. The best parameter set is used in the test phase. The default values are used for the parameters that are not tuned. NB and LR are applied with the default parameters. The range or list of values used in parameter tuning for the other machine learning algorithms are presented in Table 3. The best parameters for the classification models are presented in Table 4. Table 3 The range (or list) of parameters used in the parameter tuning phase for the classification modelsFull size tableTable 4 The best parameters found as a result of the parameter tuning phase for the classification modelsFull size table ResultsSelection of gene setsThe success of the mutation based data representation schemes also depends on the selected gene list. The initial experiments are applied on the binary representation to observe the classification performances over the causal and full gene sets. LR, SVM with linear kernel and Perceptron are selected as pilot algorithms. As shown in Table 5, the accuracy values are between 33% and 37% for the binary causal gene dataset and between 66% and 69% for the binary full gene dataset. It is observed that, the addition of extra gene information nearly doubles the classification accuracy. The mutation data of the additional genes increases the accuracy. The dramatic increase can be observed in f-score results too.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: OmicsVis: an interactive tool for visually analyzing metabolomics data 8 Highlights of the 1st IEEE Symposium on Visualization (BioVis 2011) access Published: 18 May 2012 OmicsVis: an interactive tool for visually analyzing metabolomics data Philip Livengood1, Ross Maciejewski2, Wei Chen3 & …Ebert1 Show authors BMC Bioinformatics 1. A comparative visualization system that allows multiple samples (and multiple views of individual samples) to be displayed and explored simultaneously, 2. Data exploration tools for exploring mass spectra and filtering and comparing TIC images in real-time, 3. Grouping and rendering of samples and calculation of group means for comparison and difference calculation, 4. Comparative volume rendering across samples. Related workIn order to develop a system that is both effective and efficient at finding differences across samples, this system builds on previous work from several areas. In particular, previous work from GCxGC-MS analysis and data visualization were examined. With regards to visualization, areas of relevance include focusing and linking techniques, comparative visualization, multi-dimensional visualization, user interface techniques and design, and the use of color as it relates to data and difference visualization. Two-dimensional gas chromatography-mass spectrometryA traditional gas chromatograph consists of a carrier gas, injector port, column, detector and a recorder. The injector is set to be hotter than the boiling point of the sample so that the sample will be vaporized. The carrier gas flows through the system and pushes the gaseous components of the sample into the column. Generally, an inert gas, such as helium, is used. As the carrier/sample mixture moves through the column, the different components of the sample interact with the column and these interactions cause the samples to pass through the column at different rates. This chemical separation is used to classify the compounds within the sample, as each compound will exhibit a characteristic retention time. A detector resides at the end of the column, quantizing the output. The most common type of detector is a mass spectrometer that ionizes the gas as it elutes from the column, producing additional separation in the mass spectral domain [3]. This type of setup is referred to as Gas Chromatography-Mass Spectrometry, or GC-MS. GC-MS has proven to be very effective for simple mixtures. However, as the complexity of the samples increases, it becomes difficult to achieve adequate chemical separation.

Output: corroborate


Input: Claim: All microbes can survive in extreme conditions indefinitely. [SEP] Text: In fact, scientists have recently discovered seasonal flows on the planet that may be salty—the perfect environment for a salt-loving organism. The microbes can also survive encapsulated within salt crystals, according to S. DasSarma. Considering that salt crystals have been found within meteorites from Mars, the students’ research could provide a preliminary look at whether microbial life could survive a trip across interplanetary space. Students of first launch on 24 March went off without a hitch. The balloon, carrying a test tube full of haloarchaea, lingered in the atmosphere for almost 3 hours before exploding and sending its cargo hurtling toward Earth. On 1 April, graduate Amelia Yarborough, 11th grader Jamie Shultz, and 10th grader launched the second balloon, which carried two test tubes: one full of the microbes that had already visited the stratosphere and one with a fresh batch on its first flight. The third and last launch is scheduled for 11 April but, depending on the weather, could be rescheduled. The last balloon will carry the test tubes from the previous two launches plus one more. Reid, Yarborough, and Shultz were introduced to the group by friends, whereas Herbst joined after tagging along with his sister. They all stay because they love the learning environment and sharing their experiences with like-minded students. graduated from last year, she still enjoys joining launches. “Basically from the first day, I liked the new learning experiences and the comradery,” Yarborough said. “Spreading the knowledge is also a great feeling. ” —’s note: The title and text were changed to reflect that the microbes launched are archea, not bacteria. Citation: Wendel, J. (2015), Can microbes survive multiple trips into the stratosphere? , Eos, 96, doi:10. 1029/2015EO027827. Published on 9 April 2015. Text © 2015. The authors. CC BY-NC 3. 0 Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: This significant difference states that, although the BM25-tf-rf scheme doesn’t utilize the various properties of mutations that are expressed in C-score, it is more successful for the differentiation of cancer types with its class-based statistical approach for the mutations. We can conclude that the BM25-tf-rf scheme is a suitable representation tool for the mutation information in VCF files for the cancer type classification task. The most accurate algorithm (76. 44% accuracy and 76. 95% f-score) is the Feed-Forward NN with the BM25-tf-rf representation scheme, despite the extra network cost. The precision and recall results for the NN on BM25-tf-rf representation are similar with the accuracy value. The roc-auc result is also the highest compared to the other results in Table 6. For the LR and NN f-score results with the BM25-tf-rf representation scheme, the paired t-test produces a p-value < 2. 35e−05. For the Perceptron and NN f-score results with BM25-tf-rf, the paired t-test produces a p-value < 1. 7e−06. Thus, the multilayer feed forward neural network model is found to be significantly more accurate than the single layer perceptron and LR with BM25-tf-rf. Class-based comparison of experiment resultsBM25-tf-rf based representation leads to improved performance results compared to the other representations with almost all machine learning algorithms in our experiments. In addition, the multilayer feed forward neural network model achieves better cancer type classification performance compared to the other machine learning algorithms in our experiments for all data representations except tf-idf. Therefore, we used the results for the NN algorithm with the BM25-tf-rf representation model for further discussions on class-level performance. Table 7 lists the class-based performance metrics. The cancer types are presented in the order of descending sample count. From this list, it is observed that the classification performance differs for each class. The results show that the success level does not entirely depend on the number of samples in the dataset, as there are fewer samples for Skin cancer than Thyroid cancer, but the f-score for Skin cancer is higher. The proposed model doesn’t perform well for cancer types such as Prostate and Thyroid.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: It reveals the location and relative intensity of peaks, and can be useful in helping a user identify any samples that may contain data collection errors. An example of the intensity mode mapping is shown in Figure 3 (left). Figure 3Color mapping examples. Examples of the different color mappings that can be applied to total ion count images. Intensity (left), difference (middle), and standard deviations (right). Full size imageDifferenceThe difference mode calculates the difference between two samples. The result is then displayed by simply mapping the difference to a color. The system uses a separate set of HSV curves for positive and negative differences. By default, the hue for negative differences is set to pure green, and the hue for positive differences is set to pure red. An example of the difference mode is shown in Figure 3 (middle). Here, the user can quickly find areas of high positive or negative differences between two samples. Standard deviationThe standard deviation mode also calculates the difference between samples and renders an image based on that difference. However, a color mapping based solely on the magnitude of the difference in intensities may not be what is most interesting. Even using the mean of two sets of samples, the difference in intensity between two large peaks could be relatively high in magnitude compared to two smaller peaks, but this does not necessarily mean that difference is meaningful. By analyzing the standard deviation within user specified groups of samples, differences can be visualized in more certain terms. We create a standard deviation color mapping from a sample group by first calculating a mean TIC for all the samples within that group. Note that the samples are chosen by the user such that they have been pre-normalized as input to the system. Next a standard deviation TIC is calculated as: σ A = 1 n A ∑ i = 1 n ( x i - μ A ) 2 (1) Here, n A is the number of samples in group A, μ A is the mean TIC of group A, and the x i are the TICs of the i th sample. Once we have computed the standard deviation TIC, it is stored to use for color mapping. This color mapping can then be applied to a sample visualization. Generally, it would be applied to a sample that is part of another group. In order to determine the color at a particular retention time coordinate, the system calculates the corresponding z-value for each point b in the new sample, as shown in Equation 2.

Output: corroborate


Input: Claim: Genetic engineering can produce organisms that never mutate. [SEP] Text: In agriculture, animals and plants are engineered to produce pharmaceuticals, and crops are modified to be nutritious, tolerant of herbicides and the natural environment, and resistant to disease. While these goals may seem practical, is it wise to modify plant systems without a clear idea of the long-term effects of genetic engineering in animals and humans? [1]. Are pharmaceutical companies in a race to increase their profits in the relatively new market of biopharming? If so, these companies are just responding to the pressures of a capitalist system, but what is the long-term risk? Before considering these questions, first consider some examples of current technology. Transgenic crops Tomato, tobacco, and soybean plants are infected by the bacterium Agrobacterium tumefaciens using its tumor-inducing plasmid [2]. How can biologists alter the characteristics of these plants? They isolate a tumor-preventing gene from the DNA of another organism and insert it into the plasmid [3]. The plasmid is then returned to the Agrobacterium. infects a plant, the new tumor-preventing gene is transferred into a chromosome of the plant cell. Glyphosate is a powerful herbicide that kills most actively growing plants by inhibiting an enzyme called 5-enolpyruvyl-shikimate-3-phosphate (EPSP) synthetase, which plants need to produce aromatic amino acids [4]. Extra copies of the EPSP synthetase gene are inserted into plants via tumor-inducing plasmid. Subsequently, these new transgenic plants produce more than the normal level of EPSP synthetase and are glyphosate resistant. At present, corn, cotton, soybeans, and canola have been genetically engineered to be glyphosate resistant [4]. These modified crop plants are popularly known as genetically modified (GM) plants, and glyphosate-resistant soybeans are grown in nine countries [5]. In fact, 90 percent of soybeans grown in the are GM soy. Historically, Western countries have taken steps to protect the public from scientific research. However, in this new frontier of genetic engineering, and GM crops in particular, US biologists are moving quickly and somewhat recklessly. Interestingly, their speed in creating transgenic plants is similar to that of biologists in Asia who have generated the largest growth of GM crops in an environment of limited safety regulations [5].

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Other cancer types such as Lung, Breast, Colorectal, and Skin are classified with better f-scores. This suggests that there may be more distinctive and class specific mutations in these cancer types, which the BM25-tf-tr scheme can model successfully. Table 7 Class based experiment test results with NN on full gene BM25-tf-rf datasetFull size table Location-based comparison of gene mutationsIn the previous sections all exonic and intronic mutations in the dataset have been utilized to compare the data representation and classification models. The results have shown that the BM25-tf-rf is the best performing representation model and the multilayer feed forward neural network is the best performing classification model in our experiments. By using these best models, a new experiment setup is created to explore the effect of the location of the mutations in the classification result. The exonic and intronic mutations are used separately. The experiment results are presented in Table 8. When only the exonic mutations are used, the classification performance decreases dramatically to 54. 56% accuracy and 55. 52% f-score. This decrease can be dependent on the fact that only 15% of all mutations are exonic. When only intronic mutations are used, the classification performance decreases to 74. 39% accuracy and 75. 54% f-score. This relatively lower decrease in the performance can be explained by the vast majority of mutations being intronic. The paired t-test produces a p-value < 4. 8e−02 for the f-score results of the Feed-Forward NN with BM25-tf-rf representation with only intronic and all mutations. The utilization of all exonic and intronic mutations for input representation leads to statistically significant improvement in f-score performance. Similar to recent studies stating that malignancy-driving mutations can also occur outside the coding region [37, 38], our location based comparison results support the need for further research in non-coding variants. Table 8 Machine learning experiment test results on the separated exonic and intronic mutationsFull size table DiscussionThe main goal of genomic studies for diseases is to propose target or causal genes. BM25-tf-rf is found to be superior compared to binary, tf-idf and C-score for the representation of DNA mutations for the task of cancer classification. We further analyze our best model (BM25-tf-rf and NN) for the most effective genes in decision making.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Volume visualization of selected regions (the first retention time: [1104]; the second retention time: [2. 0,2. 64]; the m/z values: [50,114]). Top: two healthy samples; bottom: two cancer samples. Visible differences favor locating interesting points for further study. Three circles (red for healthy samples and green for cancer samples) indicate three regions with significant differences, whose locations can be easily. Full size imageFor datasets whose classifications are known, we can use volume visualization to depict the difference among two classes. For the healthy sample set and the cancer sample set, we compute the mean and normalized variance of the values in each voxel, yielding a template data for each set, respectively. We then visualize the mean values of each template. The variance is used to modulate the opacity after the volume illumination is performed: α o u t = α i n × ( 1. 0 - k × v a r i a n c e ) (3) where k is an adjustable constant. In our system, k is set to be 0. 5. The variance-dependent opacity modulation enables an emphasis of the common features in each class. Thus, color mapping schemes as described in the TIC section may be applied. Mass spectrum based visual explorationTo accurately study the behaviour of a bio-marker or to differentiate different samples, cancer researchers need to check the mass spectrum at given locations, or of a specific potential bio-marker. However, the cycle of statistical bio-marker detection typically produces more than 6,000 candidates. Numerically checking their mass spectra would be a tedious and error-prone procedure. Our novel exploration scheme allows the users to study the mass spectrum of a set of peaks visually with both the two-dimensional and three-dimensional visual representations of the mass spectra. We also provide a comparative visualization approach that is capable of rapidly locating significant bio-marker candidates from a large set of metabolites, thus greatly reducing the user's time, and improving the exploration accuracy. Two-dimensional mass spectrum exploration of mouse cancerThe results shown in this section were obtained using raw data exported from LECO's ChromaTOF software. Pre-processing could be applied to remove background levels, normalize samples, and align peaks. In these examples, however, it was interesting to see how the system performs without applying any of these transformations.

Output: corroborate


Input: Claim: Genetic engineering can produce organisms that never mutate. [SEP] Text: In contrast, European governments are being more cautious about GM crops [6]. Biologists have also engineered staple crops to express key vitamins and minerals. For example, they genetically modified rice to produce β-carotene (provitamin A), which gives the engineered rice a golden color in the outer layer or endosperm [7,8]. This GM rice is possible because rice generates a precursor, geranylgeranyl diphosphate, in the endosperm tissue, which can be converted by three enzymes (phytoene synthase, phytoene desaturase, and lycopene β-cyclase) to β-carotene [7]. Genes for these three enzymes were introduced into the rice genome to allow expression of the pathway producing β-carotene in the endosperm. Using the gene for phytoene synthase from maize (instead of the original daffodil gene) leads to even higher levels of β-carotene. In essence, this so-called provides provitamin A in the diet, which enzymes in the human body can convert to vitamin A [8]. Deficiency in this vitamin affects preschool children worldwide [9]. Biopharmaceuticals crops Insects feast on many commercially important plants, which costs billions of dollars each year. The usual defense against such feasts is to apply insecticides. However, within several generations, insects become resistant. In addition, chemical and pharmaceutical companies do not produce large stocks of insecticides because they are expensive to make, and the demand is small [10]. At present, the targets of more than half of all insecticides used are boll weevils, bollworms, and other insects that eat cotton. As a result, biologists have engineered plants that are resistant to insects. Obviously, any engineered gene must be harmful only to the targeted organisms—boll weevils and bollworms, for example—and not other organisms. This is the source of long-term uncertainty. Nevertheless, genes for the Bt toxin produced by the soil bacterium Bacillus thuringiensis have been introduced in crop plants [11]. When insects ingest the engineered crop plants, Bt toxin enters their systems [12]. For the insects, irreversible cascading events of paralysis and death occurs as their endogenous enzymes convert Bt toxin into an insect-specific toxin.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: In Figs. 3 and 4, the heat maps show the most effective genes in the NN model with BM25-tf-rf representation for the classification of the breast and lung cancer types, respectively. The heat maps are constructed by giving one hot vectors, where only one gene feature is set to 1 and the others are set to 0, as input to the 10-fold trained NN classifiers. The output values from the 10 output nodes corresponding to the probabilities for the 10 cancer types, are saved as a heat map for that fold. The final heat map is the average of the folds. Each output of the NN reflects the effect of the labelled gene (set to 1 in the input) to the prediction of the cancer types. If the probability value for a cancer type is high, this means that the labelled gene is more effective in the prediction of that cancer type, since in the NN calculations, the other features are cancelled as their input values are 0. If the probability value for a cancer type is low, this means that the labelled gene doesn’t play an important role in the prediction of that cancer type. The genes that result in high output probability values for a cancer type are taken as more effective and active genes for the prediction of that cancer type. Fig. 3The heat map of the most effective genes in NN with BM25-tf-rf model for breast cancer. A light colored region for a gene and a cancer type can be interpreted as the gene is more effective in the decision of the cancer type. A dark colored region corresponds to less effective stateFull size imageFig. 4The heat map of the most effective genes in NN with BM25-tf-rf model for lung cancer. A light colored region for a gene and a cancer type can be interpreted as the gene is more effective in the decision of the cancer type. A dark colored region corresponds to less effective stateFull size image For the specific heat maps in Figs. 3 and 4, the final heat map is sorted according to the selected class column and 20 genes, which have the highest impact on the prediction of the selected cancer type, are plotted. The values in the heat-maps represent how effective a gene is for the prediction of the corresponding cancer type. The lighter colors represent higher values which refer to more effective state. In Fig.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Although our mode may introduce occlusion problems, a user can annotate significantly different peaks, and employs other provided operations including two-dimensional and three-dimensional spectrum comparisons and statistics-based filtering to quickly reduce the peak number and remove possible visual clutter. The user can progressively filter the bio-marker candidates and the comparison of multiple mass spectra is visually recognizable, and dramatically eases their differentiation. In addition, the user can further search the most significant ones with the assistance of the two-dimensional comparative exploration described below. Figure 9 depicts a simple procedure that locates an interesting potential biomarker (the second metabolite from the left as indicated with an arrow in (c) and (d)). DiscussionInitial feedback from researchers working with GCxGC-MS data has been very enthusiastic. This system has provided them with their first opportunity to visualize multiple samples simultaneously. Enthusiasm has also been expressed about mean, standard deviation, and difference calculation for a set of samples. By visualizing these calculated data, the human eye can quickly identify differences. This system can be used to identify a particular peak or region of difference, and then the mass spectra can be explored to provide validation of differences, and hypotheses about the compounds involved. With this information, their existing tools can be used to obtain information about compound identification and intensity details much more quickly than was previously possible. The researchers also frequently mentioned how pleased they were with the speed of the software. Other commercial systems will often take tens of seconds or minutes to even display a TIC image. Additionally, these software systems allow masses to be filtered, and individual spectra to be visualized, however, it is a slow and cumbersome process to change parameters and redisplay a new spectra or filter out different mass values. No other system currently used by this group was able to provide the fast, interactive filtering and mass spectra exploration of our system. As an example, the researchers can now quickly change the mass filter for a specific value, and slowly move through the entire range of mass values. Multiple samples can be visualized, and as the mass filter is updated the researchers can very quickly visually identify cases in which a unique mass has an unusual abundance in some samples. Currently, a version of this system is deployed for use on the at Purdue University, volume visualization tools have also proven to be effective for comparing the selected region of different samples.

Output: corroborate


Input: Claim: Genetic engineering can produce organisms that never mutate. [SEP] Text: It is absolutely critical that these enzymes are not found in humans or animals; otherwise consumption of these GM crops would be catastrophic. Fortunately, the global distribution of Bt crops is similar to the distribution of herbicide-resistant crops. Bt corn is the second most common GM crop, representing 14 percent of the global area of GM crops in nine countries [11]. Genetically modified organisms versus genetic editing At this point,Cas9 enters the conversation, because biochemist and 2020 Nobel laureate Jennifer Doudna, professor in the molecular and cell biology departments at the University of California, Berkeley, and one of the discoverers of theCas9 technology, makes a distinction between GMOs and gene-edited organisms [13]. European governments have made a similar distinction as they struggle to fit genetic editing into their current regulatory framework, which was designed for GMOs. Doudna stated: Conventional GMOs contain foreign genes randomly inserted into the genome; these genes produce novel proteins that give the organism a beneficial trait it did not previously possess. Gene-edited organisms, by contrast, contain tiny alterations to existing genes that give the organism a beneficial trait by tweaking the levels of proteins that were already there to begin with—without adding any foreign DNA. In this respect, gene-edited organisms are often no different than those organisms produced by mutation-inducing chemicals and radiation. Furthermore, scientists have used methods to avoid leaving any traces of in plant genome once the gene-editing task is complete [14]. But members of the public clearly neither are educated enough in molecular genetics nor have the patience for the fine distinctions of scientists; they feel that “the gene-edited crops are nothing but hidden GMOs and that scientists are trying to sneak them into grocery stores through the back door ” [15]. has been used to edit genes in rice and oranges to make them resistant to different bacterial species [16]. For potatoes, corn, and soybeans, genes have been edited so that they have resistance to herbicides. Additionally, Doudna noted that potatoes have been edited to inactivate a gene that produces sugars such as glucose and fructose (produced during cold storage preservation and converted to acrylamide, a neurotoxin, during cooking); soybeans have been edited to generate seeds with less fatty acids; and mushrooms have been edited to prevent premature spoiling. The distinction between GMOs and gene-edited organisms seems specious; there is one common fact: humans are genetically modifying nonhuman species.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: 4, POLD1, COTL1, AXIN2, WIF1 and SLC2A1 (previous symbol GLUT1) are shown to be effective on classification of lung cancer. There are studies in the literature supporting the relatedness of these genes to lung cancer. POLD1 is studied in [46]. In [47] and [48], COTL-1 was proposed to be a biomarker or a therapeutic target for small cell lung cancer (SCLC) patients. In [49], AXIN2 was found to play a major role in modulating lung cancer risk. It was shown that WIF1 had the potential as a methylation biomarker in the diagnosis of non small cell lung cancer (NSCLC) [50]. In [51], it is reported that lung squamous cell carcinoma, a major subtype of NSCLC, exhibits remarkably elevated glucose transporter GLUT1 expression. Since there is literature evidence for the disease-relatedness of a subset of the most effective genes in classification using NN model with BM25-tf-rf, the other most effective genes that are not studied yet might also have causal roles in cancer development. According to these evidences, NN trained with the BM25-tf-rf representation of the mutations in the VCF files, can also be used for the purpose of finding new candidate genes for cancer types. ConclusionComplex genomic diseases are caused by changes in DNA that alter cell behavior. The impact level of each mutation may be different for various diseases. In order to model this diversity, being inspired from the document representation techniques in the information retrieval domain, we proposed different mutation based statistical genomic data representation schemes. We utilized VCF files, which contain mutation information in the DNA, for the classification of cancer types as a case study. Cancer, in general, results from a combination of several genomic alterations, which can be addressed in variant calls data. We evaluated the performance of the proposed data representation schemes with a wide range of machine learning algorithms. Our experiment results showed that BM25-tf-rf based representation is more successful at modeling VCF data compared to the binary, tf-idf and C-score based representation schemes. Each cancer type may develop as a result of different gene mutations. The supervised weighting approach of tf-rf successfully reflects this class-mutation relationship.

Output: corroborate


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: For instance, Figure 6 reveals some differences between the samples with and without breast cancer in the bottom left portion. Based on the visualization results, the researchers were able to locate the position and check the detailed mass spectrum with the three-dimensional representation. However, we have found that visualizing a large-sized dataset is likely too cluttered to find significant differences easily. In the future, we expect to explore more powerful data visualization toolkits such as spectra-dependent transfer functions to address this problem. These approaches can easily be incorporated into the present visualization framework. Conclusions and future workOur system provides several benefits to researchers. We have found that the ability to visualize multiple samples and explore the data interactively greatly adds to one's understanding, particularly for users who were previously unfamiliar with the data. Sample visualization allows researchers to quickly validate some level of quality and consistency in their data. Using visual analysis and exploration can help researchers identify differences and bio-markers more quickly than the traditional, purely analytic approaches. Finally, we have found that being able to visually analyze the data increases the level of confidence in the results obtained. As our system was evaluated, we also received several suggestion for future work. Plans for future work involve producing output that can be used by other tools. For example, we could allow the user to select a single peak (or what appears to be a single peak) from a TIC image. This could be used to reconstruct a one-dimensional chromatogram for that region, and input that data into other existing tools that would then perform peak deconvolution (if necessary) and identification. Finally, many of these features could benefit from incorporating gradient based value mapping into the display. This was applied to GCxGC datasets in [14], and could be very effective when used with the types of comparative visualization techniques provided by this system. AbbreviationsGC × GC-MS: Gas chromatography × - Data RT1: 1 RT2: 2 TIC: : : Least Squares. 120. 10. 1016/j. chemolab. 2003. 12. 0092011. 53. CAS 58. 10. 1016/j. chroma. 2005. 11. 074262. 10. 1016/j. chroma. 2007. 05. 06156. 10. 1016/S0021-9673(02)01498-X215. 6. 446. 454. 122. 10.

Output: corroborate


Input: Claim: Genetic engineering can produce organisms that never mutate. [SEP] Text: To date, GM and gene-edited crops have not caused any major medical issues [17]. But what are the potential long-term evolutionary biological changes for humans, animals, and plants? Environmental scientist in the Department of at the University of California, Berkeley, noted the emphasis on genes and their effects as separate from the environmental context. She wrote: In what scholar calls the “god-trick,” we thought of genetics as the key to scientific mastery of nature, as if there was no context, no agency in the object, no imperfection in human knowledge. Molecular science somehow licensed us to treat genes as separate from ecology and bodies. Now we fathoming intricate interactions between genes and environments, and ecosystems whose changes aren’t smooth or predictable, but that bristle with threshold effects and emergent properties. We’ve come to appreciate the inseparability of nature and culture in complex systems [18]. (italics added) In short, molecular scientists consumed by “correcting” nature forget about the complex bioenvironmental interactions. There is now fear that after a particular genetic modification, modified genes might spread to wild relatives through a process called introgression, leading to biodiversity loss and extinction (gene drive mechanisms in the wrong hands might do the same) [19]. Also, there is a good chance that the engineered gene could trigger an allergic reaction based on the genetics of the individual consuming a product derived from it. Despite these concerns, some beneficial human proteins have been produced in genetically engineered plants.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: (MSC). 2018. . (HGNC). 2018. Jones KS. A statistical interpretation of term specificity and its application in retrieval. J Doc. 1972; 28:11–21. 35. 1. Manage. 2000; 36:779–808. 30. 2015. égas. TensorFlow: Large-Learning on Heterogeneous Systems. Software available from tensorflow. org. 2015. 2018. érrez-Enríquez S, Santamariña á J, Janatova M, Zemánková -Fernández van. Brca1 and brca2 5 noncoding region variants identified in breast cancer patients alter promoter activity and protein binding. In: Human Mutation. 111 NJ 07030-5774 Hoboken: Wiley Periodicals, Inc: 2018. 2018. 109. Res Treat. 2009; 124:27–38. 18. 111. 66. 13. 1767. 8. 2017. 1. J Proteome Res. 2011; 10 1:269–76. 87. 18. 67. PubMed Pub4. 3242. We would like to thank Yıldız, Tunga Güngör and Ethem Alpaydın for their precious time and comments on our study. We would also like to thank Hamdi Erkut, an MS student in our department, for downloading the selected files from TCGA system [26] and Rıza Özçelik, an MS student in our department, for annotating the downloaded files with [27]. publication cost of this 3242. The funding body did not play any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Availability of data and materials The dataset is available at Author informationAuthors and AffiliationsDepartment of Computer Engineering, Boğaziçi University, İstanbul, TurkeyN. Özlem ÖZCAN ŞİMŞEK, Arzucan ÖZGÜR & Fikret GÜ. Özlem ÖZCAN ŞİMŞEKView author publicationsYou can also search for this author in PubMed 4.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The absence of any genomic feature to support the interaction results in L < 1. This indicates that in the absence of any support from the selected genomic features, the interaction is more likely to be a false one. domains in the interacting proteins gives the highest L showing that interactions with evidence from structural data have the highest reliability. This is followed by the L values of similar GO annotations for interacting proteins and the presence of homologous interactions respectively. Figure 1Likelihood ratios for genomic features. Full size imageUsing naïve to combine the evidence of genomic featuresWe used naïve Bayesian networks to combine the evidence of each genomic feature for a particular interaction. Since naïve Bayesian networks require that the genomic features be conditionally independent of each other, we calculated the Pearson's correlation coefficient for a pair of genomic features to ascertain their independence (see Methods). We then combined the evidence of each interaction by simply multiplying the L values of each genomic feature found for the interaction. Thus, to each interaction in the gold set of 3464 interactions, we assigned an L value based on the genomic features it had. value greater than 1 represents higher posterior odds of an interaction being true than prior odds (see Methods). Hence all interactions with an L value greater than 1 were predicted as true. Table 4 shows the L values obtained for each possible combination of genomic features supporting an interaction. For instance, an interaction that is supported by the presence of all 3 genomic features has the highest L value, thus having the highest probability of being true. Table 1 Yeast high-throughput data setsFull size tableTable 2 Sources of Positive yeast protein interaction dataFull size tableTable 3 Correlation coefficients of the genomic features for 100 random interactionsFull size tableTable 4 Likelihood ratio, sensitivity and specificity for the combination of different genomic featuresFull size tableAssessing the accuracy of the predictions in yeastTo assess the accuracy of our method, we identified the number of predicted true interactions in the gold positive set and those in the gold negative set respectively. We conducted 10-fold cross-validation on the limited set of yeast high-throughput interactions to calculate the sensitivity and specificity of the method. Figure 2 shows the receiver operating characteristic (ROC) curve for our method. Each point on the ROC curve denotes the sensitivity and specificity obtained on the inclusion of interactions with a lower L value. A particular L value is associated with a specific combination of the 3 genomic features (Table 4).

Output: corroborate


Input: Claim: Extraterrestrial signals can be easily detected from Earth. [SEP] Text: Skip to main content Get published My account Search all SpringerOpen 10. 2 kbroadcasts of an artificial signal broadcast from ground stations, propagated in the plasmasphere, and detected using an automatic detection method we developed. We study the propagation patterns of the Omega signals to understand the propagation characteristics that are strongly affected by plasmaspheric electron density and the ambient magnetic field. We show the unique propagation patterns of the Omega 10. 2 kHz signal when it was broadcast from two high–middle-latitude stations. We use about eight years of data captured by the Poynting flux analyzer subsystem on board the Akebono satellite from October 1989 to September 1997. We demonstrate that the signals broadcast from almost the same latitude (in geomagnetic coordinates) propagated differently depending on the geographic latitude. We also study propagation characteristics as a function of local time, season, and solar activity. signal tended to propagate farther on the nightside than on the dayside and was more widely distributed during winter than during summer. When solar activity was at maximum, the Omega signal propagated at a lower intensity level. In contrast, when solar activity was at minimum, the Omega signal propagated at a higher intensity and farther from the transmitter station. . IntroductionThe Earth’s plasmasphere is located in the inner part of the magnetosphere and is mainly filled with cold plasma. Very low frequency (VLF) waves such as whistlers (Carpenter 1963) and Omega signals propagate in the magnetosphere as whistler mode waves and are strongly affected by the electron density profile. The major species of ions in the plasmasphere are protons, helium ions, and oxygen ions. The composition ratio of these ions plays an important role in the propagation effect of VLF waves such as the subprotonospheric whistler and the magnestospherically reflected whistler (Kimura 1966). Hence, it is very important to clarify the spatial distribution of electron density as well as the ion constituents in the magnetosphere to understand the propagation characteristics of VLF waves. In other words, the propagation characteristics of the whistler mode wave in the VLF range could be an important clue to study the electron density profile in the magnetosphere and the impact of these waves on the wider system. Several studies had previously been conducted to determine the electron density profile around the Earth.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: 3, and its activation has been linked to cardiovascular disease. A study has recently found up-regulation of 1 in pediatric ALL samples [16], most likely due to methylation of 1[17]. With reference to the TIM shown in Figure 1, it is not surprising that 1 has a number of cis-eQTLs on chromosome 21q21, confirming that its regulatory mechanisms are due to SNPs residing in its neighborhood [17]. However, the trans-genomic regulation of this gene by SNPs has not been previously investigated. The TIM further shows that gene 1 also has strong association with SNPs on cytobands q11 and q22 in chromosome 21, in addition to its known association with SNPs on cytoband q21. We have also found multiple SNPs on q11 and q22 with strong linkage to 1 – these indicate that the q11 and q22 regions of chromosome 21 are candidate trans-eQTLs. A number of genes exist in the same cluster of 1, including, for example, 4, DSCAM, and RIPK4. All of these genes have similar cis- and trans-loci; they share 70% of linked SNPs in common. In this cluster, 1 and GART are known cancer biomarkers in ALL [16, 19]. The involvement of PCP4 in osteogenesis explains that abnormal bone marrow production leads to leukemia [20]. DSCAM and RIPK4 have known relations to Down’s syndrome [21]; since the association of Down's syndrome and leukemia has been documented for over 70 years [22], it is not surprising that DSCAM and RIPK4 are also grouped in this cluster. Causal regulatory analysisFigure 2 displays the causal networks computed from the cluster containing the gene 1. These networks explain cis-trans regulatory mechanisms. For example, RIPK4 is a gene located at cytoband q22. 3, but there are 5 distant SNPs in q11. 2 (shown as blue in the figure) affecting its expression. CYYR1, located at chromosome 21q21. 1, is a recently discovered gene [23]. It is know that it encodes a cysteine and tyrosine-rich protein, but its functional role is still under investigation, although a recent study found a correlation with neuroendocrine tumors [24].

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Those interactions that are either reconfirmed experimentally or have a high probability of being true based on some statistical method are deemed as high confidence with the rest being low confidence interactions. We tested the overlap between our predicted true interactions and the high and low confidence data sets given by the authors. As seen in Figure 4, more high-confidence interactions are predicted as true in all data sets, except in H. sapiens [9]. For instance, 52. 8% of the high confidence interactions in yeast are predicted to be true by our method, as opposed to 27. 9% of the low confidence interactions. Figure 4Percentage of interactions predicted true in high and low confidence interactions across different high-throughput data sets. Full size imageSome validated predictionsFigure 5 shows two instances where our method predicts low confidence interactions to be true. Figure 5A gives the interactions between the proteins ps, mub, bl and aret. These proteins have all been recently shown to co-regulate the alternative splicing of Dscam exon 4 in D. melanogaster [30]. Figure 5B shows the interactions between the Lsm proteins in the mRNA degradation process in yeast that were predicted to be true by our method. These interactions were later confirmed by similar ones in the human mRNA degradation process [31]. Figure 5Some low confidence interactions predicted to be true by our method and confirmed by other publications. ratio for each interaction is indicated. Interactions with a Likelihood ratio greater than 100 are shown with a solid line, while those with a Likelihood ratio less than 10 are shown with a dashed line. (A) Interactions between proteins co-regulating the alternative splicing of Dscam exon 4 in D. menalogaster. (B) Interactions between proteins in the Lsm1-7 complex in S. cerevisiae confirmed by similar interactions found in H. sapiens. Full size imageDiscussionWe present here a method to identify the true interactions in high-throughput protein-protein interaction data sets using a combination of three genomic features. We used the likelihood ratio (L) to evaluate the accuracy and reliability of each genomic feature. We combined the evidence from each genomic feature using naïve Bayesian networks. Our method gives a sensitivity of 89. 7% which is higher than any of the other methods used so far. Our method also has a good specificity at 62. 9%.

Output: corroborate


Input: Claim: Extraterrestrial signals can be easily detected from Earth. [SEP] Text: 2016) and the datasets used for the statistical study, in which we investigated the Omega signals broadcast from the high–middle-latitude stations in Norway and North Dakota. Next, we show the results of propagation characteristics depending on the MLT, season, and solar activity. Finally, discussion and conclusions of the present study are described. Data processing and automatic detection methodsAs was also mentioned in the previous section, the Omega signal had been broadcast based on the specific pattern of frequency and time duration assigned to each transmission station. Each station had its own unique transmission pattern every 10 s with four common frequencies and one unique frequency. In the present paper, we analyzed the PFX data when the center frequency of the PFX was fixed at 10. 2 kHz, which was one of the common frequencies of the Omega signal. The PFX data are stored as waveforms using the common data format (CDF) file format. We have already developed a method in the Java programming language to detect the presence of Omega signals measured by the PFX and then analyze them comprehensively (Suarjaya et al. 2016). The flow of the data processing is described in the paper by Suarjaya et al. (2016) in more detail, but we briefly outline the process as follows. In the automatic detection process, we first performed a FFT analysis of the PFX waveform data to detect the signal around 10. 2 kHz. When the monochromatic signal at 10. 2 kHz was detected in the PFX data, we determined the station that broadcast the signal, referring to the unique frequency pattern for each of the eight different Omega stations. For this process, it was first necessary to synchronize the Omega time with UTC time because the Omega system did not have leap-second correction. Next, we calculated the delay time of the signal by subtracting the raise time of the signal from the transmission time. The raise time is the arrival time of the Omega signal at 10. 2 kHz, as determined by detecting the sudden increase in signal intensity at the center frequency (10. 2 kHz), compared with those of surrounding frequencies (Suarjaya et al. 2016). As a next step, the signal intensity was determined when the average intensity at the center frequency was large enough to compare with those of surrounding frequency and ambient noise. Some error detection and correction were also applied to produce reliable results. The detection method developed by Suarjaya et al.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: We provide an example to illustrate mutual information between continuous and discrete variables. Figure 3(a) shows an example where expression level of gene Y is modulated by a SNP X. The distribution of Y alone is a Gaussian with entropy H(Y)= 2. 61. When conditional on SNP X, the gene Y is a bimodal Gaussian whose mutual information with SNP X is H(Y:X)=0. 57. In contrast, Figure 3(b) shows the other example where gene Y and SNP X are independent. Although gene Y follows a Gaussian distribution and its entropy is the same as the preceding example, its distribution conditional on SNP X remains unimodal and its mutual information with SNP X is H(Y:X)=0. Figure 3Illustration of mutual information between discrete and continuous variables. (a) The expression level of gene Y is modulated by a SNP X. The distribution of Y alone is a Gaussian with entropy H(Y)= 2. 61. When conditional on SNP X, the gene Y is a bimodal Gaussian whose mutual information with SNP X is H(Y:X)=0. 57. (b) The gene Y and SNP X are independent. Although gene Y follows a Gaussian distribution and its entropy is the same as the entropy in (a), its distribution conditional on SNP X remains a unimodal Gaussian and its mutual information with SNP X is H(Y:X)=0. Full size imageIdentification of system noiseIn a noiseless environment, transcriptional relations exist between SNP-gene pairs with mutual information greater than zero. When microarray noise and error take place, the noise level ε needs to be derived from the available data, and the mutual information above ε is deemed statistically significant. We determined the noise level ε using permutation test [10]. Ideally, we would randomly permute all the SNP and gene expression data to compute mutual information of all SNP-gene pairs, and then repeat this procedure multiple times to identify ε. However, a huge number of SNPs and genes make this impractical with the full dataset. We surmount this difficulty by sampling one tenth of SNPs and one tenth of genes and running the permutation test with a limit of 30 random permutations. Parallelization of TIM computationModern microarray technologies can assay hundreds of thousands of SNPs or transcripts on a single chip.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: We chose the three genomic features to maximize the inclusion of all aspects of information about the interactions. Structure information was incorporated through Pfam domains found to interact in PDB structures in the 3did database. As would be expected, this feature has the highest accuracy and reliability as shown by its high L value (Figure 1). As seen in the ROC curve (Figure 2), this genomic feature gives the lowest number of false positives (high specificity). However, the number of true positives (sensitivity) is limited by the small number of complex structures in PDB that can be used to identify interacting Pfam domains. The sensitivity will significantly improve as the number of structures in PDB increases. Database annotations were included through the use of GO annotations of the interacting proteins. This feature shows the second highest reliability (Figure 1). It is also able to identify the maximum number of true positives. Indeed, Lin et al. have recently shown that GO annotations are the dominant contributors in predicting protein-protein interactions [32]. As the number of annotated proteins increases, this method promises to be useful in filtering interaction data. Sequence information was included in the form of homologous interactions found using the HINT database. Homologous interactions do not give the reliability expected (Figure 1), perhaps because they are not limited to orthologous or paralogous interactions. However, it is the only feature that does not require any protein annotations and is useful in identifying true interactions of un-annotated or hypothetical proteins. Methods based on network topology [17, 18] are also independent of protein annotations and would be a useful addition to the genomic features. However, we have not considered it in the current study. Though evidence from each feature can independently predict an interaction to be true, a combination of 2 or more features performs better (Table 4). For instance, the combination of interacting Pfam domains and similar GO annotations (d+g) or interacting Pfam domains and homologous interactions (d+h), has a higher L value than either of the features independently. Both combinations increase the sensitivity without much compromise in the specificity. Similarly, a combination of similar GO annotations and homologous interactions (g+h), predicts an interaction to be true with a higher probability than each feature independently. This combination too adds to the sensitivity with only a slight decrease in the specificity.

Output: corroborate


Input: Claim: Extraterrestrial signals can be easily detected from Earth. [SEP] Text: In the analyses, a dipole model was used for the geomagnetic field model and invariant latitude was referred from the “orbit datasets” provided by the Akebono project team. Regarding events in our analyses, we have “data” events and “detected” events, where a data event means data available whether detected or not, while detected events refer to events detected within the data of the Omega signal. General characteristics of Omega signal propagation from Norway and StationsFirst, we studied the signal intensity just above the transmission station to evaluate how widely and how far the Omega signals penetrated through the ionosphere over the station. For this purpose, we restricted the PFX data obtained below 640 km in altitude. Figure 2a shows the number of events from October 1989 to September 1997 used for the analysis just above the Norway station, which was located at latitude 55. 96° N and longitude 100. 72° E (geomagnetic coordinates). The number of events is indicated by the color scale given at the right of the panel, and white areas on the map indicate unavailability of PFX data. Figure 2b, c shows the averaged intensity of the signals in the magnetic and electric fields, respectively. Magnetic or electric signal intensity is indicated by the color scale given at the right of each panel. The gray color in Fig. 2b, c indicates that no Omega signal was detected, although the PFX was in operation and measuring at 10. 2 kHz. This is because the intensity was too low compared to the threshold level for signal detection. It is noted that the threshold level is not a fixed absolute value but was adaptively determined according to the level of background noise. The detailed algorithm used to determine the threshold level is described in Suarjaya et al. (2016). We divided the vertical axis into 20 bins of latitude (5° for each bin) and the horizontal axis into 20 bins of longitude (5° for each bin). The location of the station is shown on the map (indicated by a rectangle in each panel). It is also notable that the high-intensity area was located ~10° north of the transmitter station. The center of the penetration region was located approximately at latitude 65° N and longitude 97° E (geomagnetic coordinates).

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: , a sequence of linked SNPs and genes) in the TIM indicates that these linked nodes have dependent probability distributions, so the SNPs and genes on this path should belong to the same cluster. We can regard this cluster as the domain of a message passed from a SNP to a gene, and from the gene to another SNP again, in repeated relays. When there is a smooth channel, the message should arrive from the sender in perfect order; when the channel is clogged, the message may be misinterpreted or totally missing. In other words, SNPs and genes linked by transcriptional channels are highly likely to be involved in the same transcriptional mechanism. Isolated groups of linked SNPs and genes are considered clusters. Inference of causalityWe carry out the inference of transcriptional interactions using a Bayesian networks method which can handle mixed types of random variables [27, 28]. In a TIM, models containing clusters with no channels connecting each other signify independence between clusters, implying that the causal relations exist only within clusters but not across clusters. Hence, we can transform the TIM into a causal network by learning the optimal Bayesian networks within individual clusters. In our Bayesian network model, the causality between SNPs and genes always leads from SNPs to genes, but the complete causal inference is still complicated. First, the mapping of transcriptional information computes the (in)dependencies between SNP-gene pairs. For a disconnected pair in the TIM, there must be no causal link in the Bayesian network. For a connected pair, the causal relationship between the SNP and the gene is not necessarily from this SNP to the gene, the causal link might be through other SNPs and/or genes. Thus, the clustered TIM provides constraints on the optimal Bayesian network, leading to great gains in computational efficiency. Network abstractionA cluster can contain a large number of SNPs and genes, leading to difficulty in providing meaningful biological interpretation. To extract useful biological information, we apply a holistic approach to finding hidden relationships of the network. In order to capture global topologic properties, topological distances between all the nodes are calculated via Dijkstra’s algorithm. The largest connected graph, referred to here as the global topologic profile, is then examined in detail. We further calculate a scree plot [29] to determine the dimensionality of the data.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: , gN are genomic features of an interaction,Oprior = prior odds of an interactions being true,Oposterior = posterior odds of an interaction with N genomic features being true,L(g1, g2, g3,. . . , gN) = likelihood ratio of an interaction with genomic features. where P (true) = probability of an interaction being true. where P(true|g1, g2, g3,. . . , gN) = probability of an interaction with N genomic features being true. From equation (1), the likelihood ratio is,,where P (g1, g2, g3,. . . , gN |true) = probability of a true interaction having N genomic features. If the N genomic features, g1, g2, g3,. . . . . . , gN, are conditionally independent, then the resulting Bayesian network is called a naïve Bayesian network and its likelihood ratio can be given as the product of the likelihood ratios for each feature: where T = all true interactions (gold standard positives),F = all false interactions (gold standard negatives),TPi = number of true interactions in the high-throughput data set with the ith featureFPi = number of false interactions in the high-throughput data set with the ith featureFor any organism, L(g1, g2, g3,. . . , gN) > 1, results in Oposterior > Oprior. This is because, in equation (1), Oprior is a constant and depends on the number of interactions in any organism. Hence, Oposterior is directly proportional to L(g1, g2, g3,. . . , gN). Thus, the posterior odds of an interaction being true, if it has one or more genomic features, increases as L(g1, g2, g3,. . . , gN) increases i. e. larger the L(g1, g2, g3,. . . , gN), the higher are the odds of an interaction being true. ROC curve analysisA Characteristic (ROC) curve is a graphical representation of the accuracy of a test and expresses the trade-off between the sensitivity and the specificity of the test [40]. Sensitivity of a test is defined as the ability to identify a true positive in a data set.

Output: corroborate


Input: Claim: Extraterrestrial signals can be easily detected from Earth. [SEP] Text: The intensities at this point were approximately −265 dBT (magnetic field) and −120 dBV/m (electric field), where dBT and dBV/m are magnetic and electric intensity referenced to 1 T and 1 V/m, respectively. It was found that the region where the Omega signal penetrated the ionosphere covered a radius of ~4900 km from the center of the transmitter station, between approximately latitude 0°–90° N and longitude 60°–160° E (geomagnetic coordinates), although the penetration region was not always a complete circle. Fig. 2 a Duration of data processed in the magnetic and electric field below altitude 640 km. b Omega signal propagation for the Norway station in the magnetic field, and c the electric field. The rectangle indicates the location of the Norway station at latitude 55. 96° N and longitude 100. 72° EFull size image As the next step, we investigated the propagation characteristics of the Omega signal from the Norway station in a higher altitude region. In the analysis, we restricted the data measured to those between 10° to the east and 10° to the west (longitude) from the transmitter station, to illustrate the propagation of high-intensity signals within roughly 6 dB attenuation in the magnetic and electric fields compared to the center of the maximum penetration region in the meridian plane. Figure 3 shows the Omega signal propagation pattern broadcast by the Norway station from October 1989 to September 1997 in geomagnetic coordinates. The rectangle on the map indicates the location of the station (55. 96° N). We plotted the meridian map into 20 bins of altitude (631. 7 km for each bin) and 36 bins of latitude (5° for each bin). The highest altitude for which we had PFX data was approximately 10,500 km, while the lowest altitude was approximately 274 km. White areas on the map indicate unavailability of PFX data, especially around the equator where L < 2, while more data are available at higher altitude (above 6371 km) as shown in Fig. 3a. Figure 3b, c shows the averaged intensity of the signals in the magnetic and electric fields, respectively, indicated by a color scale given at the right of each panel. The gray color in Fig. 3b, c indicates no Omega signal was detected in that region although the PFX was in operation measuring at 10. 2 kHz. In Fig.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Considering the first three principal components is enough to capture 60% of the variability and thus they are used to project, and thereby topologically abstract, the global topologic profile. In order to examine the biological information encoded, the topologic profile’s connectivity matrix is used to construct a new visual representation of the network. In the study of pediatric ALL, Figure 2 shows the final abstract networks. 134. 10. 1093/hmg/ddn285577. 10. 1038/nrg2612549. 10. 1097/FPC. 0b013e3282fe1745747. 10. 1038/nature027971369. 10. 1038/nature042442004. , 75(1094–1105):: Searching for the regulators of human gene expression. Bioessays 2006, 28(10):968–972. 10. 1002/bies. 20466479. 10. 1007/s00335-005-0175-z1701. 10. 1371/journal. pgen. 00301622186. 10. 1073/pnas. 2203921979128. 242. 162. 10. 1038/msb. 2008. 42003. 38. 10. 1002/gcc. 20616589. 10. 1016/j. leukres. 2006. 10. 0077049. 10. 1200/JCO. 2005. 01. 49441380. 10. 1126/science. 11642661441. 10. 1182/blood-2003-12-4306150. 10. 1007/s11010-007-9652-x237. 10. 1093/hmg/7. 2. 22776. 10. 1016/0165-4608(87)90354-221. 2), encodes a cysteine and tyrosine-rich protein and defines a new family of highly conserved vertebrate-specific genes. Gene 2002, 290(1–2):141–151. 10. 1016/S0378-1119(02)00550-466. 10. 1186/1471-2407-7-665. 1501. 10. 1038/nbt1205-14991. 10. 1186/1471-2105-10-S9-S11106. 10.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: ResultsExample I: arrayCGH and exon array dataIn this first example, we illustrate how different genomic datasets can be visualized together in an integrated GenomeGraphs graphic. We use arrayCGH and Affymetrix exon array data and plot these together with genomic annotation from Ensembl. We first load the GenomeGraphs package and one of its example datasets. This dataset contains copy number data and segmented copy number data, as well as exon array data for a small genomic region. Once the data are loaded, a gdObject is created for each data type, namely a Segmentation object containing the copy number segments, a GenericArray object containing the raw copy number data, an Ideogram object representing the relevant chromosome we are plotting, a GenericArray object containing the exon array data, and a GenomeAxis object for the genomic coordinate axis. > library(GenomeGraphs)> data('exampleData', package='GenomeGraphs')> seg = makeSegmentation(value = segments, start = segStart, end = segEnd, dp = DisplayPars(color = 'dodgerblue2', lwd = 2, lty = 'dashed'))> copyNumber = makeGenericArray(intensity = cn, probeStart = probestart, segmentation = seg, dp = DisplayPars(size = 3, color = 'seagreen', type="dot"))> ideogram = makeIdeogram(chromosome = 3)> expression = makeGenericArray(intensity = intensity, probeStart = exonProbePos, dp = DisplayPars(color='darkred', type='point'))> genomeAxis = makeGenomeAxis(add53 = TRUE, add35 = TRUE)In a next step, genomic annotation information is retrieved on-line from Ensembl using the biomaRt package. We first connect to the Ensembl BioMart database and select the human (hsapiens) dataset. Then, we retrieve gene structures on the forward and reverse strands of the region we want to visualize.

Output: corroborate


Input: Claim: Extraterrestrial signals can be easily detected from Earth. [SEP] Text: signal that penetrated the ionosphere and propagated along the Earth’s magnetic field lines covered a radius of ~4700 km in the region around the transmitter station between approximately latitude 0°–90° N and longitude 80° W–20° E (geomagnetic), although the penetration region was not always a complete circle. A high-intensity area is visible just above the station transmitter at approximately −255 dBT in the magnetic field and approximately −110 dBV/m in the electric field. Compared to the Norway station, different regions of high-intensity signal are apparent, where the center of the penetration region is located at approximately latitude 55° N and longitude 34° W (geomagnetic), despite both stations being at the same latitude in geomagnetic coordinates (approximately 55. 9° N). Fig. 4 a Duration of data processed in the magnetic and electric fields below altitude 640 km. b Omega signal propagation for the station in the magnetic field and c in the electric field. The rectangle indicates the location of the station at latitude 55. 98° N and longitude 34. 83° WFull size image Figure 5 shows the Omega signal propagation pattern broadcast by the station from October 1989 to September 1997 in geomagnetic coordinates, with the same restrictions in longitude range (between 10° to the east and 10° to the west in longitude from the transmitter station) as already described in Fig. 3. The rectangle on the map indicates the location of the station (55. 98° N). White areas on the map indicate unavailability of PFX data because of the orbital condition of Akebono. The gray color in Fig. 5b, c indicates that no Omega signal was detected in that region. Compared to the Norway station, the high intensities of the Omega signal from the station propagated toward the lower-latitude region between 30° and 60° along the ambient magnetic field line, and the signal propagation reached the opposite hemisphere between 35° and 65° S latitudes. Similar to the case of the Norway station, the Omega signal in the electric field did not attenuate as much as in the magnetic field along a magnetic field line up to a higher altitude region and latitude range. Fig. 5 a Duration of data processed in the magnetic and electric fields with longitude restricted to ±10°. b Omega signal propagation for the station in the magnetic field and c in the electric field.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 0. 01), while such probes have a β-value of “NA” in *. txt files. It is worth mentioned that different strategies of assigning missing β-value are applied in different modules. In minfi, the β-values are assigned with “NAs” when both M and U intensities are zero, but an additional criteria is that “NAs” will be assigned if either M or U intensities don’t fluoresce above background. Second, there are 850 inbuilt control probes on the 450K array, such as bisulfite conversion I, bisulfite conversion II, extension, hybridization and negative (n=613), which can be used to evaluate the other probes’ intensities. Samples that can’t pass this quality control are excluded in further analysis. Third, probes on or Y should be filtered out to eliminate the impact of sex on differential methylated analysis in many studies. Fourth, Price ME et al. [17] reported that there were 4. 3% of Illumina 450K probes containing a known SNP at the targeted site. Such probes should cause problems in inter-sample analysis. Last but not the least, cross-reactive probes on the Illumina 450K array are identified depending on [18], which is particularly problematic because the β-value of such probes is more likely to represent a combination of multiple sites and not the level of initially targeted CpG sites. Within-array normalizationThis step includes background correction and color bias adjustment. Background correction methods have been developed by Triche et al. [19] such as Noob and Normexp, which are based on convolution models and use out-of-band (OOB) probes intensities to measure the background. The lumi R package provides two different methods for eliminating the background. The first one is based on the negative-control probes inbuilt the BeadChip and the second one estimates the background from the density modes of probes intensities. The second one would show bad performance when there are more than two density modes for some sample. The popular methylumi R package [20] proposed a color bias adjustment based on smooth quantile or shift-and-scaling normalization. Globally, these methods seem to improve the data quality in some cases [7].

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: > minbase = 180292097> maxbase = 180492096> mart = useMart('ensembl', dataset='hsapiens_gene_ensembl')> genesplus = makeGeneRegion(start = minbase, end = maxbase, strand = '+', chromosome = '3', biomart = mart)> genesmin = makeGeneRegion(start = minbase, end = maxbase, strand = '-', chromosome = '3', biomart = mart)In a last step, the gdPlot function is called to plot instances of gdObject that were created above. The objects are given to gdPlot as a list and the order in the list will determine the plotting order from top to bottom. A minimum and maximum base position are also given as arguments to restrict the visualization to this particular genomic region. The plot produced from this example is shown in Figure 1. Figure 1ArrayCGH and exon array data. The first track in this figure shows an ideogram of the human chromosome 3. The red marker highlights the plotted genomic region. The second track shows exon array data, where each data point corresponds to a probe measuring the expression level of an exon. The third track displays copy number data in green and segmented copy number data with dashed blue lines. Note the amplification which can be seen in both the copy number and exon array tracks, suggesting that the amplification event results in higher expression levels of the gene in this region. The bottom track shows the gene annotation data from Ensembl. Full size image> gdPlot(list(ideogram, expression, copyNumber, genesplus, genomeAxis, genesmin), minBase = minbase, maxBase = maxbase)Example II: Transcript isoforms and exon array dataIn a second example, we show how probe-level exon array data from the Affymetrix GeneChip® 1. 0 ST platform (data available from can be plotted along with gene models from Affymetrix as well as gene and transcript annotation from Ensembl. The data of the exon array are not plotted at the exact chromosomal location of the probes in order to clearly visualize alternative splicing events. Most of the exons are represented on the 1. 0 ST platform by four probes. The location of these four probes are equally spaced in the data plots.

Output: corroborate


Input: Claim: Astrobiology is fully understood and does not require further research. [SEP] Text: © Author(s) 2024. This work is distributed under the Attribution 4. 0 License. © Author(s) 2024. This work is distributed under the Attribution 4. 0 License. Preprints AbstractAssetsDiscussionMetrics 20 Feb 2024 | 20 Feb 2024 Status: a revised version of this preprint is currently under review for the journal CP. Holocene land cover change in North America: continental trends, regional drivers, and implications for vegetation-atmosphere feedbacks Andria Dawson, John W. Williams, Marie-José Gaillard, Simon J. . . Gavin, Terri Lacourse, Thomas A. Minckley, Wyatt Oswald, Bryan Shuman, and Abstract. Land cover governs the biogeophysical and biogeochemical feedbacks between the land surface and atmosphere. Holocene vegetation-atmosphere interactions are of particular interest, both to understand the climate effects of intensifying human land use and as a possible explanation for the Holocene Conundrum, a widely studied mismatch between simulated and reconstructed temperatures. Progress has been limited by a lack of data-constrained, quantified, and consistently produced reconstructions of Holocene land cover change. As a contribution to the Changes (PAGES) LandCover6k Working Group, we present a new suite of land cover reconstructions with uncertainty for North America, based on a network of 1445 sedimentary pollen records and the pollen-vegetation model coupled with a Bayesian spatial model. These spatially comprehensive land cover maps are then used to determine the pattern and magnitude of land cover changes at continental to regional scales. afforestation in was driven by rising temperatures and deglaciation, and this afforestation likely amplified early Holocene warming via the albedo effect. A continental-scale mid-Holocene peak in summergreen trees and shrubs (8. 5 to 4 ka) is hypothesized to represent a positive and understudied feedback loop among insolation, temperature, and phenology seasonality. A last-millennium decrease in summergreen trees and shrubs with corresponding increases in open land likely was driven by a spatially varying combination of intensifying land use and neoglacial cooling. Land cover trends vary within and across regions, due to individualistic taxon-level responses to environmental change. Major species-level events, such as the mid-Holocene decline of eastern hemlock, may have altered regional climates.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Correction of the type biasThe type bias is the one that is most crucial to correct as it is the main source decreasing the data quality. There have been several efforts to develop methodologies to correct the probe type bias because of the differences between I. I probes are more stable and reproducible across different samples, most methods reduce the bias of Infinium II rather than probes. The first method is called peak-based correction (PBC) [7], which rescale the methylation values of Infinium II to the same modes for distribution of methylation values of Infinium I. But this method is sensitive to the shape of β-value density curves and is therefore less robust when the methylation density distribution does not exhibit well-defined peaks. Touleimat and Tost [8] developed a method called Normalization (SQN) based on an assumption that the β-values of CpGs form the same biological category should have the same density distribution. They found that the normalization result of using the “relation to CpG” annotation perfectly corrected the bias. Subset-quantile Normalization (SWAN) [10] was developed based on the assumption that the β-values distribution should be the same when the probes have the same number of CpGs. But SWAN also alters probe data, which increases technical variation, and does not seem to improve the data quality when applied to some datasets [21]. Quantile normalization (BMIQ) [9] method decomposes the density profile of I probes by fitting a beta-mixture model of three states: unmethylated (default β-value <0. 25), hemimethylated (default 0. 25 ≤β-value <0. 75) and fully methylated (default β-value ≥0. 75). Then it uses a quantile normalization to fit β-values distribution of Infinium II to the corresponding β-values distribution of Infinium I. This method does not depend on unceremonious choices of biological characteristics to be used to normalize data. Thus it seems more suitable than other methods. However, some points appear worse after BMIQ correction. Another method, called Regression on (RCP) [14], uses a quantile linear regression model of correlation between pairs of nearby and II probes that share the same genomic context to adjust the methylation levels of Infinium II probes.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Each exon is separated by a vertical line and the exons are linked to their genomic location by connecting lines. This visualization makes it easy to relate alternative exon usage, as observed in the exon array data, to known alternative transcript isoforms in Ensembl (Figure 2). The region highlighted in the plot shows the exon that is not expressed in the samples. To generate this plot, we first create the different subclasses of gdObject, namely: Title, ExonArray, Gene, Transcript, and Legend objects. In addition, we make a custom annotation track using the AnnotationTrack class. Figure 2Transcript isoforms and exon array data. Probe-level exon array data is plotted in the top graphic. The data of the exon array is intentionally not plotted on the exact chromosomal location of the probes in order to clearly visualize alternative splicing events. Each line in the top track represents a different sample. Usually, there are four probes per exon on the Affymetrix GeneChip® 1. 0 ST platform, vertical gray lines group these four probes belonging to the same exon together. The blue connecting lines map these exons to gene models as defined by Affymetrix (green) and Ensembl (orange). Transcript isoforms known for this gene are plotted in dark blue. The region highlighted in the plot by an RectangleOverlay object shows the exon that is not expressed in the samples. One can see that this is a known alternatively spliced exon as annotated by Ensembl. Full size image> data('unrData', package='GenomeGraphs')> title = makeTitle(text ='ENSG00000009307', color = 'darkred')> col = colorRampPalette(c('firebrick2','dodgerblue2'))(length(unrData[1,]))> exon = makeExonArray(intensity = unrData, probeStart = unrPositions[,3], probeEnd = unrPositions[,4], probeId = as.

Output: corroborate


Input: Claim: Astrobiology is fully understood and does not require further research. [SEP] Text: The substantial land-cover changes reconstructed here support the importance of biogeophysical vegetation feedbacks to Holocene climate dynamics. However, recent model experiments that invoke vegetation feedbacks to explain the may have overestimated the land cover forcing by replacing grasslands >30° N with forests; an ecosystem state that is not supported by these land cover reconstructions. reconstructions for North America, along with similar LandCover6k products now available for other continents, serve the Earth system modeling community by providing better-constrained land cover scenarios and benchmarks for model evaluation, ultimately making it possible to better understand the regional- to global-scale processes driving Holocene land cover dynamics. How to cite. Dawson, A. ,. , Lindstrom, J. , Anderson, R. S. , Brunelle, A. , Foster, D. , Gajewski, K. , Gavin, D. G. , Lacourse, T. , Minckley, T. A. , Oswald, W. , Shuman, B. , and Whitlock, C. : Holocene land cover change in North America: continental trends, regional drivers, and implications for vegetation-atmosphere feedbacks, Clim. Past Discuss. [preprint], in review, 2024. Received: 30 Jan 2024 – Discussion started: 20 Feb 2024 Publisher's note: remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors. Download & links Preprint (PDF, 3269 KB) Supplement (5860 KB) Download & links Preprint (3269 KB) Metadata XML Supplement (5860 KB). Williams, Marie-José Gaillard, Simon J. . . Gavin, Terri Lacourse, Thomas A. Minckley, Wyatt Oswald, Bryan Shuman, and Status: final response (author comments only) Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse Login for authors/editors Subscribe to comment alert RC1: 'Comment on cp-2024-6', #1, 04 Apr 2024 Please find my comments in the attached pdf. Citation: AC2: 'Reply on RC1', Andria Dawson, 16 May 2024 Thank you for your comments.

Output: contrast