Input: Task: contrast the claim using the given text. Claim: Astrobiology is fully understood and does not require further research. [SEP] Text: Our hypothesis, if correct, has considerable experimental and clinical implications; for optimal beneficial outcomes for the treatment of ailments across the body, from the brain to the musculoskeletal system and from the microbiome to the heart, scientists in the laboratory and medical and allied health professionals in the clinic should apply photobiomodulation soon after meals. Our hypothesis awaits experimental and clinical validation. We thank Fonds de dotation Clinatec and for supporting our work. We also thank (Wellred Inc. , Australia), (UCL, UK), and (London, UK) for proofreading the first version of the manuscript. C-Editors: T-Editor: Jia Y1. (2014) Metabolic determinants of cancer cell sensitivity to glucose limitation and biguanides. Nature 508:108–112. 2. Bonifacio M, Benfato ID, de M, de de F, de (2022) Effects of photobiomodulation on glucose homeostasis and morphometric parameters in pancreatic islets of diabetic mice. Sci 37:1799–1809. 3. Castro KMR, de (2020) Can photobiomodulation therapy (PBMT) control blood glucose levels and alter muscle glycogen synthesis? . J B 207:111877. 4. (2011) The role of mitochondria in Drosophila aging. 46:331–334. 5. (2004) Why do cancers have high aerobic glycolysis? . Cancer 4:891–899. 6. (2021) Photobiomodulation therapy ameliorates hyperglycemia and insulin resistance by activating cytochrome c oxidase-mediated protein kinase B in muscle. Aging (Albany NY) 13:10015–10033. 7. Hamblin MR (2016) Shining light on the head:photobiomodulation for brain disorders. BBA Clin 6:113–124. 8. (2016) Turning on lights to stop neurodegeneration:the potential of near infrared light therapy in Alzheimer's and Parkinson's disease. 9:500. 9. Judge A, Dodd MS (2020) Metabolism. 64:607–647. 10. (2021) Physiology glucose metabolism. In:StatPearls [Internet] (FL) StatPearls Publishing. 11. (2018). [SEP] Justify:

Output: The text contrasts the claim as it discusses ongoing hypotheses and the need for further experimental and clinical validation in fields related to health and therapy. This implies that there are still uncertainties and knowledge gaps that require additional research, which contradicts the claim that astrobiology is fully understood.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The high intensities and large variation in this area can skew the scaling and data mapping schemes employed in analysis algorithms. This area can be graphically selected, and cropping can be performed across a group of samples based on those parameters. Figure 4 depicts the use of the cropping tool. Mass filterWhen a user is looking for bio-markers and meaningful differences between samples, the user will often identify a few mass values that make up some interesting compound. The mass filter tool allows users to select a unique mass or set of masses from the mass spectrum display. Total ion count data for active TIC visualizations is then re-acquired using only the selected masses. The color mapping is then rescaled to match the new range of values across all of the TIC displays, and the TIC displays are refreshed. This allows a user to quickly determine differences among samples for a selected set of mass values. While other applications provide mass filtering via dialog options, none currently support real-time updating of the mass filter in an interactive manner. Mass values can quickly be added to or removed from the filter as the data sets are being interactively explored for differences in a unique set of mass values. Volume visualizationVolume rendering is a well-established method for depicting the embedded structures in a three-dimensional scalar field. The most interesting feature of volume rendering is that important regions can be enhanced while distracting details can be hidden by adjusting their transparency. By incorporating non-photorealistic rendering into volume visualization, volume illustration [13] has proven to be very effective in feature-oriented visualization. The exploration of the mass spectral data can benefit from applying volume visualization in three ways. First, visualizing one dataset with the assistance of a multi-dimensional transfer function gives the user a global picture on the intensity distribution and possible peak patterns. Second, the user can freely select subregions to investigate the local mass spectra. The determination is either interactively specified, or automatically found by comparing the mass spectra in a small-sized window with the mass spectra of known metabolites stored in a database. It also facilitates comparing the spectra of different potential bio-markers. Figure 6 compares a selected region of four samples. Third, by globally or locally comparing the mass spectra of a set of sample data with volume visualization techniques, the user's attention can be quickly directed towards the most interesting features, easing the task of finding or verifying the differences among samplesFigure 6Volume visualization. [SEP] Justify:

Output: The text does not provide any information regarding the importance of bioinformatics for analyzing genomic data. Instead, it discusses various techniques and tools related to mass filtering, visualization, and comparative analysis of mass spectral data, which are more focused on specific methods rather than the overarching role of bioinformatics in genomic analysis.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: in through your institution and BioinformaticsThis issuesearch term and Applied to Genomics Editor's -and Journals on Books on and Applied to Genomics Editor's -and and and BioinformaticsThis issuesearch term Menu 10. 1093/nargab/lqaa101 Published: 11 December 2020 10. 1093/nargab/lqaa101 Select format. ris (Mendeley, Papers, Zotero). enw (EndNote). bibtex (BibTex). txt (Medlars, RefWorks) Download citation and BioinformaticsThis issuesearch term and BioinformaticsThis issuesearch term important goal in molecular biology is to quantify both the patterns across a genomic sequence and the relationship between phenotype and underlying sequence. We propose a multivariate tensor-based orthogonal polynomial approach to characterize nucleotides or amino acids in a given sequence and map corresponding phenotypes onto the sequence space. We have applied this method to a previously published case of small transcription activating RNAs. Covariance patterns along the sequence showcased strong correlations between nucleotides at the ends of the sequence. However, when the phenotype is projected onto the sequence space, this pattern does not emerge. When doing second order analysis and quantifying the functional relationship between the phenotype and pairs of sites along the sequence, we identified sites with high regressions spread across the sequence, indicating potential intramolecular binding. In addition to quantifying interactions between different parts of a sequence, the method quantifies sequence–phenotype interactions at first and higher order levels. We discuss the strengths and constraints of the method and compare it to computational methods such as machine learning approaches. An accompanying command line tool to compute these polynomials is provided. We show proof of concept of this approach and demonstrate its potential application to other biological systems. to the inherent complexity in biological systems, much of the analysis that aims to study systems, such as sequence structure and function in genomics, has been largely based on computational methods. Advancements in sequencing technology and the rise in the availability of genomic data has led to the development of novel computational tools that seek to determine the relationship between underlying sequence and the resulting function or phenotype. These methods aim to predict protein function given sequence and structure information, identify novel and potential DNA binding motifs, including transcription factor binding sites and determine RNA secondary structure based on the underlying sequence (1–4). [SEP] Justify:

Output: The text corroborates the claim by discussing how bioinformatics plays a significant role in molecular biology, specifically in quantifying relationships between genomic sequences and phenotypes. It mentions that advancements in sequencing technology have led to the development of computational tools to analyze genomic data, emphasizing bioinformatics' crucial role in understanding sequence structure, predicting protein functions, and determining RNA secondary structures.


Input: Task: contrast the claim using the given text. Claim: Quantum mechanics can be completely understood through classical physics. [SEP] Text: That problem seemed resolved with the advent of Einstein’s Theory (SRT). SRT was believed to capture the true meaning of MED concerning the behavior of light signals, and SRT was certainly an endorsement of Lorentz transformation, so SRT was believed to offer the one possible revision of NM that could make mechanics fully consistent with MED. But meanwhile, new phenomena were being discovered at the micro scale of physics, and they often seemed inexplicable with any known theory, whether NM, SRT, or MED. These were phenomena suggesting quantization of light, quantized atomic states, atomic, molecular and crystal structures, radioactivity, etc. So at almost the same time as one problem seemed to be resolved, other problems were emerging. Since the earlier situation between NM and MED had demanded that Physics allow two seemingly discordant theories to co-exist until some good argument could replace one of them, the situation then presented by the new phenomena being discovered naturally invited the development of another potentially discordant theory: (QM). The discovery of the photoelectric effect, and the introduction of the idea of the photon, initiated QM. Almost immediately, QM was developed to handle the Hydrogen atom, and the ground state thereof, the stability of which was thought to be impossible with MED. Accepting that apparent incompatibility with MED, and even embracing it, researchers moved on to excited states, to other atoms, then to molecules, and reactions, and to all the rest of the complexity that today makes up modern (QC). Also, experimenters got into sub-atomic elementary p2. Maxwell’s electrodynamics and QM’s photons It often seems that MED, a theory largely about spatially extended EM fields, has little in common with QM, a theory largely about discrete material systems and the discrete photons that they emit and absorb. Photons are imagined to be the opposite of spatially extended; i. e. , localized, like the matter p2. 1. Waveform development The first step in the life history of a photon is its development from a spatially localized energy bundle that is emitted from a source into a spatially extended waveform that travels through space. To help think about this problem, it is useful to recall some phenomenology familiar from physics at a more macroscopic scale. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of classical physics (such as Newtonian Mechanics (NM), Special Relativity Theory (SRT), and the Lorentz transformation) in explaining new phenomena discovered at the micro scale, which led to the development of quantum mechanics (QM). It emphasizes that classical theories could not fully address issues like quantization of light and atomic states, indicating that quantum mechanics represents a necessary advance beyond classical physics for understanding these complex behaviors.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: This set of samples consists of twenty-nine samples obtained from mice. Ten each were extracted from the body and yolk, while nine were extracted from the head. In each case, half of the samples were exposed to alcohol and half were not. Because alcohol is a depressant and slows down metabolic processes, it is expected that differences between the two samples should show up in the metabolomics data. The first step taken is to convert the cdf files into raw data files. Since these particular files have very high background levels, the file size drops from about 1. 3GB down to 677MB. Compressing the files further reduced the size down to about 245MB. Finally, the samples were cropped to remove regions that were empty or oversaturated with solvent. It was also shown that background level alone occupies mass values above 600, so this area was cropped. The resulting files averaged about 60MB, which can be read from networked file servers in a reasonable amount of time. The first step after converting the data sets is to view the total ion count image from each one. Since each is displayed using a global color scale, quick visual comparison can identify unusual data samples. At this point, one is basically trying to obtain an overview of the data, and look for obvious outliers. For each pair of alcohol and control means, a difference volume was calculated. Mean/standard deviation color mappings were created to help visualize the samples. There is a single red peak that indicates an area of higher concentration in the alcohol samples, but several green peaks can be noticed, standing out against the green hued background that is caused by the higher background level present in the control samples. These areas are also indicated by the mean/standard deviation color mapping in Figure 7. A researcher searching for bio-markers could then target these areas specifically for further analysis. Figure 7Alcohol mean colorized by comparing with the mean and standard deviation of the control samples. The red area indicates an unusual concentration in the alcohol samples. Full size imageThe linked views included in the system allow a user to investigate the mass spectra in real time. This form of focus+context can be used to form hypothesis about what masses or compounds are contributing the observed differences. Figure 8 demonstrates this feature. Without spending too much time working with these samples, a quick effort can be made using this system to look for meaningful differences between the groups. Using visual inspection, samples with missing or blurry peaks can be eliminated. [SEP] Justify:

Output: The text provides a detailed account of sample extraction and analysis techniques but does not explicitly discuss bioinformatics or its importance in analyzing genomic data. Therefore, it is irrelevant to the claim regarding the significance of bioinformatics.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: While computational methods, such as machine learning (ML) models, provide powerful ways to extract patterns in complex data, they lack the clarity of interpretation that comes with purely mathematical approaches. In this work, we present a method to do analytical mathematics with sequences that integrates well with computational methods and that has the potential to reveal new results and complement those given by purely computational or ML approaches. Current methods that aim to connect sequence information to output phenotype largely utilize ML tools, such as deep learning, to quantify the role of each site in a sequence in the resulting phenotypes. Some examples of this include deep learning tools that aim to predict protein structure given the underlying amino acid sequence and others that are designed to predict the effect of each site in a pre-mRNA sequence on mRNA splicing (5,6). Although these methods seem promising and do reveal substantial biological insights, they come with caveats such as the requirement of large training datasets and ‘the incorporation of irrelevant features’ into the deep learning model as in the case of SpliceAI (6). Here, we describe a mathematical method using multivariate tensor-based orthogonal polynomials to convert sequence information into vectors and build orthogonal polynomials with the aim of quantifying the effect of the sequence states on the resulting phenotype (7). Our method proposes an entirely novel way to work with nucleotides in a genomic sequence. Elements in a sequence (nucleotides, amino acids, etc. ) are not meaningfully described by scalar values (i. e. by a single number) because they are distinct types of things that are not well-characterized in this way (7). In general, instead of representing nucleotides with individual numbers, current methods utilize kmers, or words of length k, to represent a set of nucleotide bases. Here, a 1-mer is the same as a single site in a sequence. The use of kmers is ubiquitous as a bioinformatics tool and they’re used widely in many bioinformatics applications such as genome assembly and compressing genomic sequence data (8,9). In addition, kmers can be utilized to look for associations between different sequences and any associated traits in GWAS studies (10). Another widely used representation is encoding each nucleotide as a one-hot encoded array that is fed into ML algorithms. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of bioinformatics tools such as kmers in various applications, including genome assembly and GWAS studies. It highlights the significance of computational methods, particularly in extracting biological insights from genomic sequences, underscoring that these methodologies form a crucial part of analyzing and understanding genomic data.


Input: Task: contrast the claim using the given text. Claim: Quantum mechanics can be completely understood through classical physics. [SEP] Text: One phenomenon very well known for light modeled as EM waves is the spreading transverse to the propagation direction known as of ‘diffraction’. Diffraction is the result of some sort of limitation transverse to the propagation direction. Historically, the limitation has been due to a finite aperture through which the light propagates. The light spreads out from the aperture, more-so the smaller the aperture is. In the photon model discussed here, the limitation is softer than an aperture edge, but a limitation nevertheless: it is the finite spread of the Gaussian waveform in the two directions transverse to the propagation direction. The more narrow the Gaussian peak is, the more spread there will be. The closest familiar analog for longitudinal spreading is known as ‘dispersion’. This word refers to the ‘blurring’ effect that any frequency dependence the propagation speed through the medium entails. For example, a signal pulse in a medium looses its sharp edges because those sharp edges imply superposition of many different wavelengths, and hence different frequencies, which the medium may affect differently. In Earth’s atmosphere, or ocean, square waves can turn to blob waves because of dispersion. Let us begin a scenario with a single pulse in E. Let it have a Gaussian profile along the propagation direction, say x with E ∝ exp ( − x 2 ). We can apply Maxwell’s equations, and watch what happens. is the so-called ‘generating function’ for the infinite set of Hermite polynomials, all of which have very regularly spaced zero crossings. What happens is that the single pulse in E (an even function) generates a double pulse in B (an odd function), which in turn generates a triple pulse in E (another even function), and so on; that is, all the derivatives in play generate successively higher-order Hermite polynomials multiplying the Gaussian. Meanwhile, all the E × B Poynting vectors in play support general spreading of the Gaussian. With each step, the emergent functions look more and more like wavelets, and the individual peaks in the wavelets stay about the same width as more of them accrue, so the wavelength for the emergent wavelet becomes more and more defined. Figure 1 illustrates this behavior at the stage where E has developed five peaks (four zero crossings). [SEP] Justify:

Output: The text contrasts the claim by discussing phenomena related to light diffraction and the limitations of classical physics in fully capturing such behaviors. It delves into concepts like Gaussian waveforms, dispersion, and Maxwell’s equations, which highlight the shortcomings of classical physics in explaining quantum mechanical phenomena. This suggests that while classical physics provides useful analogs, it cannot wholly account for the complexities of quantum mechanics.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Averages for the remaining samples in each group can then be computed. From the healthy group,multiple samples can be chosen and used for computation of the mean. While the variations present in these samples make conclusions hard to draw, researchers found such visual exploration techniques to be valuable. Particularly, researchers were able to visually inspect data and rule it out quickly, rather than spending days of analysis time trying to detect features and differences that may be completely obscured. Figure 8Linked views allow the mass spectra to be explored in real-time as the user interacts with the TIC image. Full size imageThree-dimensional mass spectrum explorationWe can also explore data in a three-dimensional visual representation to depict its properties such as the location, the uncertainty and the weight. This representation uses the (RT1, RT2, MS) triple coordinate space, and is modeled with a cylinder whose height and radius are proportional to the weight and uncertainty respectively, and whose appearance is encoded as a color-mapped texture representing its mass spectrum, as seen in Figure 9. The visual pattern of each peak is fixed during the exploration. The user can freely navigate in this three-dimensional space, visually check the mass spectrum of specific bio-marker candidates, and locate, explore and compare the properties of the selected metabolites of interest. Figure 9Exploring differences in canine samples. Interactive exploration of the 3500 detected peaks in two samples. The user interactively filters the bio-markers by adjusting parameters, (a) is the result (1300 peaks) by changing the thresholds from (0. 01, 0. 001) to (0. 008,0. 004) respectively (see the left top snapshot). (b) is the result (198 peaks) by interactively specifying a region in the region operating characteristics plane (see the left top snapshot). (c-d) Comparative exploration of 198 peaks in two samples. For instance, the bio-markers indicated with red arrows exhibit different mass spectra, which can be inspected by comparing their numerical values, color map views, and plot views provided in our system (see the views in the dashed red rectangle). Full size imageOur visualization technique compactly displays the mass spectra in a three-dimensional space, thereby easing the understanding and comparison. [SEP] Justify:

Output: The text does not address bioinformatics directly or mention its importance in analyzing genomic data. Instead, it discusses methods for visualizing and exploring data, particularly in relation to mass spectrometry, without linking these practices explicitly to bioinformatics or genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Thus, the study of both the positive and negative impacts of covariation on molecular function can provide insight into the nucleotides involved in important intra- and intermolecular interactions. Importantly, while the Watson-Crick base pairing interactions depicted in Figure 2 are rather easy to computationally predict, there are many other types of interactions occurring within structured RNAs that are less simple to predict. Studies providing insight into the functional importance of sequence covariation may assist in improving the predictions of these other types of structural elements in the future. Figure 2. Importance of covariation for secondary structure. (A) It shows a case where there exists favorable intramolecular interaction (top panel) and an unfavorable intermolecular interaction with another transcript (bottom panel). (B) Here, the same original sequence has 3 nt mutated which gives rise to an unfavorable intramolecular interaction (top panel) but a favorable intermolecular interaction with the other transcript (bottom panel). (C) It shows mutually exclusive structures with competing intramolecular and intermolecular interactions when there is a mutation (denoted in blue) that covaries with the nucleotides denoted in red. Open in new tabDownload slide of orthogonal polynomials Given a set of or protein sequences, along with corresponding phenotypic data, our method consists of building tensor-valued first and higher order polynomials and projecting the phenotypic data onto this polynomial space (see for detailed explanation and proofs). Note that we assume that the sequences we are working with are of good sequencing quality and depth. Thus, before applying this method to sequence data, the user will have to perform general bioinformatic pre-processing steps to produce the final set of sequences to be analyzed. To apply our methods to the STAR system, we first converted each site in each sequence of the 99 sequences of STAR and target RNAs into a vector as depicted in Figure 3. This is similar to one-hot encoding an array which is a first step when feeding sequence data to an ML algorithm (11). For each type of RNA, each sequence had a corresponding experimentally derived OFF/ON fluorescence value which served as the numerical phenotype that we later project onto the polynomial space. Figure 3. Example sequences with corresponding phenotypic values. Example sequences with corresponding phenotypic values. [SEP] Justify:

Output: The text corroborates the claim by emphasizing the importance of bioinformatics in processing and analyzing genomic data. It mentions 'bioinformatic pre-processing steps' necessary to prepare sequence data for analysis, indicating that bioinformatics techniques are essential for deriving meaningful insights from genomic information.


Input: Task: contrast the claim using the given text. Claim: Quantum mechanics can be completely understood through classical physics. [SEP] Text: Series 1 is the original input Gaussian function, Series 2 is the Gaussian after the overall spreading has developed to this point, and Series 3 is the wavelet that has emerged in the process; i. e. the spread-out Gaussian times the fourth-order Hermit polynomial generated. Figure 1. A wavelet develops when an EM pulse is acted upon by Maxwell’s equations. What we have so far is only one eighth of the story needed to fully represent a photon: development from a pulse into a waveform. We have told the story for one pulse in E. If we would match that with another pulse in B we would have overall propagation along with waveform development. That would bring us to one quarter of the whole story of the photon. If we would match that with two more pulses, E and B pointing at 90 ° in space from the first pair and coming ‘alive’ a quarter cycle out of phase with the first pair, we would have the circular polarization characteristic of photons, but we would still have just half the story. So let us move on, and seek the other half. 2. 2. Waveform regression The remaining half of the story of the photon is about waveform regression. How does this complex structure of four Hermite polynomials multiplied by their generating Gaussian unwind, and go back to being a set of four pulses, so that it can be absorbed into a receiver? Again, let us refer to some similar but more familiar phenomenology: A third phenomenon possible for light modeled as EM waves is ‘focusing’. This is what we have optical lenses and shaped mirrors for. It works somewhat contrary to transverse spreading, gathering incident energy into a smaller area transverse to the propagation direction. Of course we don’t have any lenses or mirrors in the photon model, but we shall find a mechanism that produces a similar effect. A fourth phenomenon possible for light modeled as EM waves is ‘pulse restoration’. This is what transmission lines have ‘repeater stations’ for. A communication signal degraded by dispersion can be reconstituted when passed through an intelligent filter. Of course we don’t have any filters in a photon model, but we shall find a mechanism that produces a similar effect. The ‘similar effect’ comes from the imposition of boundary conditions in the longitudinal direction. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities of photon behavior and waveforms, indicating that understanding photons requires more than classical physics concepts. It elaborates on phenomena such as waveform regression and intricate interactions modeled by Maxwell's equations, emphasizing that the nature of light involves advanced topics beyond classical physics alone.


Input: Task: pass the claim using the given text. 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 1. the interacting proteins have homologs that interact, 2. the interacting proteins each have a Pfam domain found to interact with the other in PDB and, 3. the interacting proteins have at least one identical GO annotation. We used protein-protein interaction data from the Database of (DIP) [27] (July 2004 release) and IntAct [28] (September 2004 release). We prove our hypothesis first in yeast by estimating likelihood ratios for high-throughput interactions based on the number of known true positives and false positives using Bayesian network approaches. Based on these results, we estimate the number of true positives in the high-throughput data sets of S. cerevisiae, C. elegans, D. melanogaster and H. sapiens. The results can be searched at and downloaded from our website [29]. ResultsCalculating the reliability of each genomic featureWe used protein-protein interactions from high-throughput data sets for yeast as our test set to calculate the reliability of each genomic feature (see Methods). Of these 12,674 interactions, we chose a set of 3464 interactions as our gold standard – 1479 as the positive gold standard and 1985 interactions as the negative gold standard (see Methods). Our goal was to maximize the interactions identified in the gold positive set (high sensitivity) and at the same time minimize the number of interactions identified in the gold negative set (high specificity). In these interactions, we identified all those that had homologous interactions. The true positives (TP) were those interactions with homologs that were in the gold positive set, and the false positives (FP) were those that were in the gold negative set. Using these values, we calculated the likelihood ratio (L) for the genomic feature of 'homologous interactions' (see Methods). Similarly, we calculated the likelihood ratios for the other two genomic features – interacting proteins with at least one identical GO annotation and interacting proteins having one of 2 Pfam domains known to interact in PDB. We also calculated the likelihood ratio for the absence of genomic features. Figure 1 shows the likelihood ratios calculated. Likelihood ratio (L) expresses the reliability of each genomic feature. > 1 indicates the ability of the genomic feature to identify more true positives than false positives. As seen in Figure 1, all the genomic features have L values greater than 1. [SEP] Justify:

Output: The text does not address the significance of bioinformatics in the analysis of genomic data. Instead, it discusses methods of evaluating protein-protein interactions and statistical measures related to genomic features but does not specifically mention bioinformatics or its role in genomic data analysis. Hence, it is deemed irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: (A) It shows the sampling distribution of absolute values of the largest regressions of the phenotype onto combination of two sites. Notable regressions are those at the tail end of the distribution, and are shown in red (>35 000). (B) Relationship between distances across STAR sites and absolute values of the largest regressions with 14 site pairs with high regressions shown in red. (C) To further zoom into the 14 pairs of sites with notable regressions, as depicted in (A) and (B), curves are drawn between the interacting nucleotide at one site and the corresponding nucleotide at another site. This indicates a more global structure across the STAR sequence, instead of a case in which regressions of the phenotype onto pairs of sites in the 5′ region of the sequence are greater than those in the 3′ region. Open in new tabDownload slideOut of the 14 site pairs shown in Figure 9, there are four pairs that are between sites in the 5′ and 3′ regions. This potentially supports the hypothesis mentioned earlier which states that possible binding between these parts of the sequence would allow the hairpin region of the RNA to stay intact (by not binding with the hairpin) and pursue its function of transcription termination of the downstream gene. However, in addition to this long-range interaction, there are a cluster of strongly interacting sites in the middle portion of the sequence, between sites 11 and 20. This includes site 17 which has the most interactions (a ‘C’ at site 17 interacting with five other sites). There exists large values for regressions of the phenotype onto these site pairs (see S2 for values). This suggests that there might exist some level of potential intramolecular binding in the middle section of the sequence that was not picked up by the software when the initial sequences were being designed. this work, we propose a novel mathematical tool to describe sites along biological sequences as vectors and quantify sequence–function relationships by projecting phenotypes onto the sequence space. Given a set of sequences and corresponding phenotypic data for each sequence, tensor-based orthogonal polynomials can be constructed based on the actual variation in sequences. The regression of phenotypes onto these polynomials can quantify not only the effects of different nucleotides at individual sites, but also the effect on phenotype of combinations of nucleotides at different sites. [SEP] Justify:

Output: The text corroborates the claim by discussing the complexities involved in analyzing genomic data through mathematical tools and regressions. Specifically, it describes how phenotypes can be quantified based on variations in biological sequences and emphasizes the critical role of a 'novel mathematical tool' for understanding the sequence-function relationships in genomic studies. This highlights the importance of bioinformatics in processing and interpreting genomic data.


Input: Task: contrast the claim using the given text. Claim: Quantum mechanics can be completely understood through classical physics. [SEP] Text: pulse that was used to describe the waveform development part of the scenario was somewhat unrealistic in that its tails extended to infinity. There is no way that a localized source could emit an energy pulse whose tails would extend to infinity. It is somewhat more realistic to imagine the equivalent of a mirror at the source, and another mirror at the eventual receiver, to confine the waveform like a wave in a box, with zero amplitude at the surface of each mirror and everywhere beyond. With such boundary conditions imposed, the analytic functions involved in the model are no longer the simple Gaussian and the simple Hermite polynomials that it generates. Now we have not one, but three, Gaussians, the extra two being needed to cancel the first one at the two boundaries. Correspondingly, we always have at least three (actually six) Hermite polynomials alive at any given time. That is a loss of mathematical simplicity. But there is a gain of conceptual simplicity. It is easy to envision that the propagation scenario has some symmetry about its mid point. The waveform will spread until its central peak is halfway between the source and the receiver. After that, the mirror at the receiver will be more significant than the mirror at the source, causing the waveform to start ‘piling up’ near the receiver, and eventually end up as a pulse near the receiver, similar to the pulse originally launched near the source. This ‘regressing waveform’ is somewhat reminiscent of ‘advanced’ solutions to Maxwell’s equations going backwards in time. These were introduced many times in the early 20th century, but particularly popularized in the mid 20th century by Wheeler and Feynman (1945 and 1949). Wheeler and Feynman were looking to time symmetry as the basis for an electromagnetic generalization of instantaneous (Newtonian) gravitational interaction. There are important differences between the regressing waveforms introduced above and the Wheeler-Feynman advanced solutions: 1) Wheeler and Feynman were looking at interactions between essentially point sources and receivers, and so had to be looking at spherically expanding retarded solutions and spherically contracting advanced solutions, not at essentially one-dimensional expanding and contracting wavelets. 2) The Wheeler-Feynman expansion or contraction is related to the spherical area of a wave front, not the waveform in the radial propagation direction. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities involved in waveform development, implying that classical physics cannot fully capture the intricacies of quantum mechanics. The mention of advanced solutions to Maxwell’s equations and the differences between those and quantum waveforms highlights the limitations of classical interpretations in explaining quantum phenomena.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Thus, including the interactions supported by the presence of all 3 genomic features (d+g+h) in the results gives a sensitivity of 12. 3% and a specificity of 99. 4%. On further including interactions supported by interacting Pfam domains and similar GO annotations (d+g), the sensitivity rises to 14. 5% and the specificity marginally decreases to 99. 3%. As interactions supported by each individual feature or other combinations of features that have an L > 1, are included in the results, the sensitivity increases at the cost of specificity. Thus our method predicts interactions, which are supported by at least one of the 3 genomic features, to be true with a sensitivity of 89. 7% and a specificity of 62. 8%. Figure 2ROC curve for the combination of genomic features using 10-fold cross validations. The dotted line shows the empirical ROC curve, while the solid line shows the fitted ROC curve (obtained using ). Each point on the ROC curve corresponds to sensitivity and specificity for one or a combination of more than one genomic features. d: interacting Pfam domains; g: similar GO annotations; h: homologous interactions; none: no genomic features. More than one genomic features are indicated by listing the features separated by a '+' sign. Full size imagePredicting true interactions in all high-throughput data setsWe used our method to assign L values to all interactions in three other high-throughput data sets for C. elegans, D. melanogaster and H. sapiens [7–9]. We also assigned L values to the interactions in yeast high-throughput data sets [3–6] that were not part of the gold standard. We predicted all interactions with L > 1 as true interactions. Table 5 shows the distribution of the predicted true interactions across different L values for each species. Figure 3 shows the percentage of interactions predicted as true in the high-throughput data sets of each species. Table 5 Number of interactions in different ranges of likelihood ratios for high-throughput data sets of various speciesFull size tableFigure 3Percentage of interactions predicted true across different high-throughput data sets. Full size imageAuthors of the high-throughput data sets usually assign a confidence level to interactions. [SEP] Justify:

Output: The text does not directly address the claim regarding the importance of bioinformatics in analyzing genomic data. Instead, it discusses specific methods for predicting interactions based on genomic features and reports sensitivity and specificity metrics without elaborating on the role of bioinformatics. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: To show proof of concept, this method was applied to a case of synthetic RNA regulatory sequences, described in previously published work (19), that were experimentally constructed with the goal of identifying how sequence structure and design motifs affect RNA regulatory activity. This method has some fundamental parallels with ML methods, such as neural networks, in that they share the very first step of one-hot encoding nucleotides and converting them to vectors/arrays. Figure 10 shows the similarities and differences between the two approaches. In the ML approach, sequence and corresponding phenotype data are fed into an algorithm (such as a deep neural net) after which the algorithm is trained to learn the relationship between the phenotype and the sequence (see (25) for example). In the mathematical approach, the sequence data is used to build a space within which we can conduct further mathematical analysis such as mapping phenotypes onto the sequence space. We argue that doing such mathematical analysis has some advantages because, here, the meaning of the values produced through this approach is clear because they were derived through the mathematics whereas when these results are derived from black box approaches, it is more difficult to interpret them. For example, in the analytical approach, we have a clear understanding of what a projection of a variable onto the polynomial space is, whereas this would be difficult to grasp in the traditional deep learning approaches. Figure 10. Comparison of analytical and ML approaches. Overview of similarities and differences between an ML approach and an analytical mathematics approach such as the method described here. The two share similar first steps and are ultimately aiming to understand sequence–phenotype relationships. Open in new tabDownload slideOne other advantage that this approach has over other ML approaches is that once the sequence space is constructed (which is the part that requires most of the computational resources), one can easily project any corresponding phenotypes onto this space. In the ML approach, on the other hand, the algorithm would have to be re-trained entirely if one wanted to map a different phenotype to the underlying sequence. Furthermore, these two mappings would not be easily comparable as each phenotype and underlying sequence would have been trained differently. However, a hybrid approach that combines the two methods could be implemented. For example, it is possible to approximate some types of orthogonal polynomials using neural networks as has been done before (26,27). [SEP] Justify:

Output: The text supports the claim by discussing the application of analysis methods, such as the mathematical and machine learning (ML) approaches, to understand sequence–phenotype relationships. This underscores the importance of bioinformatics techniques in analyzing genomic data by highlighting how these methods help interpret relationships within genetic sequences.


Input: Task: contrast the claim using the given text. Claim: Quantum mechanics can be completely understood through classical physics. [SEP] Text: 3) A lengthy discussion of the paradox of advanced actions is necessitated in the Wheeler-Feynman work, whereas the ‘regressing’ solutions introduced here are not in fact ‘advanced’ at all; they are just regressing, in real time, in the propagation direction. What we have here is quite different though. There are no differential equations running backwards in time; there is just ‘piling up’ of a solution to differential equations in response to a boundary condition. 2. 3. The photon model in terms of EM fields Taken together, the waveform development followed by the waveform regression suggest a photon model in terms of EM fields that exhibits continuous evolution: it goes from a state of pulse-like localization near its source, to a state of wave-like extension in space during its travel, and then back to a state of pulse-like localization near its receiver. Observe that with this photon model, ‘light in flight’ develops its wavelength only during its flight. It doesn’t have it to start with, and it gives it up at the end. So light at emission, or reception, has a position, but no wavelength, whereas light in flight has a wavelength, but no position. Thus the model expresses a ‘wave-p3. EM signals as photons Every neutral atom contains at least two p3. 1. Standard formulae for scalar and vector potentials Expressed in Gaussian units, the Liénard-Wiechert (LW) scalar and vector potentials at position r and time t are Φ ( r t ) = e [ 1 κ R ] retarded and A ( r t ) = e [ β → κ R ] retarded E1 where κ = 1 − n ⋅ β → β → is source velocity normalized by c and n = R R (a unit vector), and R = r source ( t − R c ) − r (an implicit definition for the terminology ‘retarded’). [SEP] Justify:

Output: The text contrasts the claim by discussing advanced concepts in quantum mechanics, such as the photon model and the propagation of light, which are inherently different from classical physics principles. The mention of ‘regressing solutions’ and the detailed analysis involving EM fields indicates that quantum mechanics involves complexities that cannot be fully encapsulated by classical physics.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Surprisingly, evidence from interacting Pfam domains (d) performs better than that of the combination of the other two features (g+h), highlighting the importance of the incorporation of structural evidence. Due to the absence of information about non-interacting proteins, we prepared our gold negative set from proteins that have different subcellular localizations. However, some interactions are transient with interacting proteins residing in the same sub-cellular compartment for only a small fraction of their life time. As a result, some of the interactions in the gold negative set are actually true. Thus, the specificity of our method is probably higher than 62. 9%. Using the evidence of the three genomic features, we predicted the number of true interactions in various high-throughput data sets. Our prediction of 56. 3% true interactions in yeast high-throughput data sets is in conformance with the previous estimates of the number of false positives in these data sets [1, 11]. However, yeast Y2H [3, 4] and Co-IP data sets [5, 6] show very different numbers of true positives independently – 37% and 73% respectively (data not shown). The D. melanogaster data set [8] shows a very low rate of true positives. One reason could be that this experiment was performed using most of the predicted transcripts in the D. melanogaster genome, including biologically irrelevant ones. The C. elegans data set [7] includes a higher percentage of true positives than D. melanogaster, perhaps because the experiment was performed only on a restricted set of predicted proteins related to multi-cellular functions. The H. sapiens data set [9] shows the highest number of true positives at 67. 9%. This data set was obtained from a study that focused on the identification of putative protein complexes in the TNF-α/NF-κB signal transduction pathway using Co-IP [9]. There are two possible reasons for the high number of true positives. Firstly, the choice of proteins from a specific signal transduction pathway precludes many random interactions between proteins of unrelated functionality. Secondly, the Co-IP approach to the identification of protein complexes, and thus interactions, is known to have a low false positive rate of around 20% [1, 9], in comparison to Y2H approaches. [SEP] Justify:

Output: The text does not directly address the claim regarding the importance of bioinformatics in analyzing genomic data. Instead, it focuses on specific genomic features, protein interactions, and the evaluation of true positive rates from various high-throughput data sets, without mentioning bioinformatics techniques or their relevance to genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: This presents an exciting opportunity to use novel computational and mathematical design approaches to inform experimental data and use the results to refine the design approach. While direct Watson–Crick binding of the STAR to the target is a critical component of the STAR function, the function of this molecule will also likely be impacted by its potential to form secondary structures in the absence of the target RNA. Some level of secondary structure could be beneficial, perhaps by providing the STAR a level of stability from spontaneous degradation or nuclease-mediated degradation whereas other structures may negatively impact its stability for the same reasons. Additionally, a high amount of structural potential may create too much intramolecular binding and not allow for the intermolecular binding between the STAR and the target. However, for other reasons that might not be easily predicted, a certain level of STAR structure might be beneficial in the intermolecular binding. conclusion, our methods provide a mathematical tool to find patterns in sequence data and to quantify the effect of the corresponding phenotype on the underlying sequence structure. Using this vector-based orthogonal polynomial approach, we can not only look at global patterns of sequence structure but can also identify the nucleotide state at each given site and how this affects phenotype at first and higher order levels. Implementing this approach can be thought of as a two-step process. First, we analyze the existing covariance structure in the sequence data to see how different parts of the sequence might be correlated with one another (5). Second, we use the observed patterns of covariance to construct an orthogonal sequence space, into which we can project another variable to see which, if any, properties of the sequence predict the functional variable (Figures 7–9). In the case of the STAR sequence, the covariance analysis showed that a group of adjacent nucleotides near the 5′ end of the sequence were correlated with other nucleotides spread throughout the rest of the sequence. To see if this correlation corresponds to functional interactions between those nucleotides, we projected the experimental STAR fluorescence data into the orthogonal sequence space. This analysis identified a number of pairs of nucleotides that interact in their effect on fluorescence (Figure 9). However, these were not the pairs of nucleotides that were correlated with one another (Figure 5). The method thus allows us to tease apart structural properties of a sequence and functional interactions between different elements of that sequence. [SEP] Justify:

Output: The text corroborates the claim by detailing methods for analyzing genomic sequence data through mathematical tools and computational approaches. It specifically mentions covariance analysis and vector-based orthogonal polynomial approaches that are essential for identifying patterns in sequence data, emphasizing the importance of computational strategies in understanding genomic information. This aligns with the claim that bioinformatics is crucial for genomic data analysis.


Input: Task: contrast the claim using the given text. Claim: Quantum mechanics can be completely understood through classical physics. [SEP] Text: The LW fields obtained from those potentials are then E ( x t ) = e [ ( n − β → ) ( 1 − β 2 ) κ 3 R 2 + n c κ 3 R × ( ( n − β → ) × d β → d t ) ] retarded and B ( r t ) = n retarded × E ( r t ) E2 The LW fields have some interesting properties. The 1 R fields are radiation fields, and they make a Poynting vector (energy flow per unit area per unit time) that lies along n retarded : P = c 4 π E radiative × B radiative = c 4 π E radiative × ( n retarded × E radiative ) = c 4 π ( E radiative ) 2 n retarded E3 But the 1 R 2 fields are Coulomb-Ampère fields, and the Coulomb field does not lie along n retarded as one might naively expect; instead, it lies along ( n − β → ) retarded. Assume that β → does not change much over the total field propagation time, in which case ( n − β → ) retarded is virtually indistinguishable from n present. So then the Coulomb field and the radiation are arriving to the observer from different directions. One can feel moved to check this surprising result. Fortunately, one can look up the original sources, obtain translations if necessary, and verify the original algebra. There is no problem with the algebra. There are also numerous re-derivations that use more modern techniques involving the Dirac delta function and the Heaviside step function. These are ‘generalized’ functions of some parameter that, when driven to infinity, produces an infinite pulse or a unit step. One can study these re-derivations too. One finds various re-orderings of the mathematical operators ‘differentiate’, ‘integrate’, and ‘go to parameter limit’. These re-orderings are dodgy because the generalized functions lack the mathematical property of uniform convergence, so these operations don’t necessarily commute; it is possible to change the result by changing operation order. But even so, such findings do not change the fact that the original LW derivations, although pedestrian, were correct. [SEP] Justify:

Output: The text contrasts the claim by discussing the differences between LW fields, radiation fields, and Coulomb fields and their respective behaviors. It emphasizes that the original derivations were correct but involves complex mathematical interpretations that suggest a divergence from classical physics concepts. This implies that classical physics may not fully encompass the complexities involved in quantum mechanics, indicating a limitation to understanding quantum mechanics solely through classical physics.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: This is also reflected in the prediction of a larger number (73%) of true positive interactions in the Co-IP data sets of yeast[5, 6] by our method. We also studied the overlap of the predicted true interactions with the high-confidence and low-confidence interactions as given by the respective authors. Though the number of interactions predicted in high-confidence data sets is higher, 17–28% of the interactions in low-confidence data sets are also predicted to be true, except in the H. sapiens data set. This shows that some low-confidence interactions can be biologically relevant. When compared to other data sets, the predicted true interactions in H. sapiens data set show a much higher overlap (68. 7%) with the low confidence interactions. This is because the high confidence data set given by Bouwmeester et al. primarily focuses on interactions, novel or otherwise, that are most likely to be a part of the signalling cascade triggered by TNF-α [9]. Hence, interactions of proteins that are also part of other systems, like the cell cycle, are not included in this high confidence data set. These include interactions of nucleasome assembly proteins and MCM proteins, among others. Other interactions which have been filtered out include those of frequently copurified proteins like the Proteins. In order to limit their interaction map to the TNF-α/NF-κB signal transduction pathway, the authors have chosen a very stringent statistical criterion to identify the interactions of proteins that are expressed well above their normal levels on being triggered by TNF-α [9]. As a result, the high confidence data set as given by Bouwmeester et al. forms only 10% of the total interactions identified in their study, while our method predicts a large number of low confidence interactions to be true. We were also able to confirm several of the low confidence interactions, that were predicted as true, in literature using iHOP [33]. In fact, most of the interactions of the Lsm proteins, shown in Figure 5B, are found in iHOP. Several interactions from the human dataset are also found in iHOP and the [34]. [SEP] Justify:

Output: The text does not address the importance of bioinformatics in analyzing genomic data. Instead, it discusses interaction predictions in various datasets without linking this analysis specifically to bioinformatics methods or the overall significance of bioinformatics in genomics.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Given structural data, our approach can be utilized to yield insight into potential non-canonical base pairing interactions for different RNA families. And when combined with existing tools such as it can identify these interactions and thus allow for the construction of candidate sequences that can be used for downstream experimental analyses. The limitations of this method largely lie with the computational power and memory required to compute polynomials for sequences with a large number of sites for which higher than second order analyses are sought. The proof of concept shown here consists of 99 sequences that are 40 sites long. However, a large number of sequences with a smaller number of sites might be more optimal for this method. A further area of investigation is potentially implementing a hybrid approach that utilizes a deep neural network to identify sites along the sequence that are functionally more important and then using just these sites as inputs to the tensor-based orthogonal polynomial approach described here. The existing program and the resulting command line tool is written in the Python programming language, however, efforts to optimize and parallelize the program using other languages such as C++ or Rust is one of the current objectives. While we have given proof of concept of this approach using an example of regulatory RNAs, this method can be applied to other questions that aim to understand sequence–phenotype interactions such as transcription factor binding sites and how their sequence composition gives rise to different TF binding energies along with applications in synthetic biology that aim to understand the relationships between the underlying RNA sequence and corresponding secondary structure (25,28). are available at. authors would like to thank Dhruba, for their insightful comments and ideas. We would like to thank the authors of the paper, including James Chappell, for doing the initial work that we used to apply our methods to and for stimulating discussions and comments. We would like to acknowledge the program, the TTU Department of Biohub for their support throughout the time this work was being done. of Biological Sciences, University; (Center for the Integration of STEM Education & Research) Program. Conflict of interest statement. None declared. 1. Lee D. , Redfern O. , Orengo C. Predicting protein function from sequence and structure. Nat. Rev. Mol. Cell. Biol. 2007; 8:995–1005. 2. Kel A. , Goessling E. , Reuter I. , Cheremushkin E. [SEP] Justify:

Output: The text corroborates the claim by describing how bioinformatics approaches can yield insights into RNA families and sequence interactions. It discusses the use of computational tools to analyze structural data and identify candidate sequences for experimental analyses, highlighting the importance of computational methods in understanding genomic data. The mention of employing a hybrid approach with deep neural networks for better analysis further supports the claim about the significance of bioinformatics in analyzing genomic data.


Input: Task: contrast the claim using the given text. Claim: Quantum mechanics can be completely understood through classical physics. [SEP] Text: If a problem exists with this LW result, then there is really only one place where it can arise: in the initial assumption; namely, that electromagnetic fields propagate like bullets shot at speed c. But this is the very same assumption that Einstein later formalized as his (1905, 1907). He just called them “signals” rather than “fields”. The LW idea of bullets shot at speed c is the foundation for Theory (SRT). (Indeed, SRT offers one of the modern ways to re-derive the LW results. ) But SRT is also the foundation for Theory (GRT). SRT and GRT together make one of the two great pillars of 20th century Physics: (RT). So questioning the LW assumption is not just questioning the LW results; it is questioning the founding assumption of SRT, and so threatening this whole pillar of 20th century theory. Many people have just accepted that this is just ‘the way things are’ with classical field theory, and with SRT, and with all of relativity theory as well. But what if one wanted to describe the same scenarios in a thoroughly modern way, with photons instead of radiation fields, and virtual photons instead of Coulomb-Ampère fields? Could anyone really accept the idea that the real photons and the virtual photons created by the same space-time event would arrive at a detector from different directions? But one needn’t accept any such thing, given the photon model in terms of Maxwell fields developed in Sect. 2. In short, since we have a model for photons in terms of fields, we should be able to reverse engineer a model for fields in terms of photons. So what does the photon model developed in Sect. 2 imply? Observe that the developing wavelet can move at speed c relative to the source, and the regressing wavelet can move at speed c relative to the receiver. Applying this idea can help to modify the LW results appropriately. 3. 2. Updated formulae for scalar and vector potentials Recall that with the photon model developed in terms of Maxwell fields in Sect. 2, the life history of the photon has a symmetry point in the middle. Before the mid point of the propagation scenario, the waveform is developing, and after the mid point of the propagation scenario the waveform is regressing. That makes the mid point very important. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of classical physics in fully explaining phenomena that are described by quantum mechanics. It critiques the foundational assumptions of classical field theory and special relativity (SRT), indicating that reliance on such classical views hampers a proper understanding of quantum mechanics and the behavior of photons. The statement 'many people have just accepted that this is just ‘the way things are’ with classical field theory' suggests that classical physics may overlook essential aspects and complexities inherent in quantum phenomena.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Among others, these include the low confidence interactions of the C-Rel proto-oncogene with itself, NK-κB p105 subunit, NF-κB p100/p49 subunits, Heat shock cognate 71 kDa protein and NF-κB beta inhibitor. This further reiterates the biological relevance of a large number of low confidence interactions. ConclusionIn this study, we show that a combination of genomic features that includes sequence, structure and annotation information, can be used to identify true interactions from high-throughput protein-protein interaction data sets. We use likelihood ratios to assess the reliability of each genomic feature and combine their evidence using naïve Bayesian networks. We provide a likelihood ratio for each predicted true interaction based on the evidence that supports it. Our method has a high sensitivity and a good specificity. The results of our study are available on our website [29]for search and download. MethodsYeast high-throughput data setsTable 1 shows the number and type of interactions from the 4 yeast high-throughput data sets used. Data inferred from mass spectrometry of coimmunoprecipitated complexes (Co-IP) is converted to binary interactions using the spoke model (the spoke model has been previously shown to be more reliable than the matrix model [18, 35]). Gold standard data setsThe gold standard positive data set consisted of: 1. all physical interactions from MIPS [36] yeast two-hybrid data (excluding interactions from Uetz et al. [3] and Ito et al. [4]), 2. MIPS complexes data (excluding complexes from Gavin et al. [5] and Ho et al. [6]), 3. small-scale yeast-two hybrid experimental data from DIP and IntAct and, 4. interactions found in more than one high-throughput data sets. Table 2 shows the number and type of interactions from each data set. For this study, the gold standard positives are limited to those found in the yeast high-throughput data sets i. e. 1479 interactions, instead of all possible gold standard positives. This is because the aim is to identify the true protein-protein interactions in high-throughput data setsin yeast, as opposed to predicting all true protein-protein interactions in yeast. The gold standard negative data set is derived from protein localization data in yeast cells [37]. [SEP] Justify:

Output: The text does not directly address the claim about the importance of bioinformatics in analyzing genomic data. Instead, it discusses specific methodologies for identifying protein-protein interactions from yeast high-throughput data sets, which may utilize bioinformatics techniques but does not explicitly mention the role of bioinformatics itself in genomic analysis. Therefore, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Characterizing all interactions in the Stiffler et al. [22] and Chen et al. [23] datasets gave us a matrix, X, with 2306 observations (2019 from mouse, 147 from C. elegans, and 140 from D. melanogaster PDZ domains) and 154 variables (67 residue positions, r1,. . . ,r67, in the alignment of the PDZ domains were "gap-free", and each position was coded with two tciz numbers: 67·2 = 134 variables; the peptides contained 10 residues each: 10·2 = 20 variables). = (X, H(X)) be mean centered and scaled to unit variance, where H(X) denotes the second order interaction terms of the variables in X. H thus has 2306 rows and 11935 columns. Let the column vector y = (y1,. . . ,y2306) be the response variable, where y i = 1 represent that the PDZ domain and the ligand interact and y i = 0 represents that they do not interact. We assumed, with support from the literature [37] and the data from Stiffler et al. [22], that some PDZ domains are more promiscuous binders than others. This resulted in a correlated error structure when modeling the data. This is an assumption violation in standard generalized linear models, but could be accommodated for in our Bayesian setting by allowing parameter estimates to vary among PDZ domains. Thus, let ϕ = 1,. . . ,96 denote the 96 PDZ domains in the data from Stiffler et al. [22] and Chen et al. [23] (82 from mouse, 7 from C. elegans, and 7 from D. melanogaster). The PDZ domain residues r1,. . . ,r67 and the peptide residues p1,. . . ,r10 are associated with the integers c1,. . . ,c77, where the c-values represent the number of contacts its corresponding residue makes with a ligand, as estimated by SAS by analyzing all solved 3D structures of PDZ domain-peptide complexes available in the Bank [25] (PDB) (Fig. 4). We construct the row vectors (5) where H(c X ) denotes the second order interaction terms of c X. [SEP] Justify:

Output: The text corroborates the claim by discussing the analysis of complex datasets derived from genomic contexts, specifically focusing on interactions within PDZ domains and their ligands using statistical models. The reference to Bayesian settings and the analysis of 3D structures indicate the use of bioinformatics methods to analyze genomic data, thus supporting the importance of bioinformatics in such analyses.


Input: Task: contrast the claim using the given text. Claim: Quantum mechanics can be completely understood through classical physics. [SEP] Text: So far as the receiver is concerned, nothing that happened before the midpoint affects the signal he receives. The source position and velocity information he receives is determined, not by the specification ‘retarded’, but rather by the specification ‘half retarded’. With this new specification, the scalar and vector potentials become: Φ ( r t ) = e [ 1 κ R ] half retarded and A ( r t ) = e [ β → κ R ] half retarded E4 The fields become: E ( r t ) = e [ ( n − β → ) ( 1 − β 2 ) κ 3 R 2 + n c κ 3 R × ( ( n − β → ) × d β → d t ) ] half retarded E5 and B ( r t ) = n half retarded × E ( r t ) E6 vector P ( r t ) becomes: c 4 π E radiative × B radiative = c 4 π E radiative × ( n half retarded × E radiative ) = c 4 π ( E radiative ) 2 n half retarded E7 Observe that now the direction of the Coulomb field is ( n − β → ) half retarded ≈ ( n present ) half retarded ≜ n half retarded and the direction of the Poynting vector is n half retarded too. So now, the Coulomb field and the Poynting vector are reconciled to the same direction. That is the first big gift from the photon model in terms of EM fields given in Sect. 2. And the gifts of photon model in terms of EM fields go well beyond this rather arcane problem about field direction. The photon model in terms of EM fields eliminates the central mystery of Einstein’s SRT: having just one light speed relative to however many different observers there may be. This is complexity at the level of ‘multiplicity’, much more daunting than the complexity at the level of the mere ‘duality’ that is found in modern QM. Advertisement 4. EM fields within atoms An noted in Sect. 2, atoms were the really big problem for Maxwell’s EMT. Now armed with some new information about EMT, it is appropriate the revisit the problem about atoms. We turn again to Hydrogen. From Sect. [SEP] Justify:

Output: The text contrasts the claim by discussing complexities in electromagnetic fields and the photon model, which suggests that the understanding of atomic behavior and phenomena in quantum mechanics extends beyond classical physics. Phrases like 'complexity at the level of ‘multiplicity’' indicate that the nuances of quantum mechanics cannot be solely captured by classical physical laws, thereby undermining the claim that quantum mechanics can be completely explained through classical physics.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Proteins that do not exist in the same sub-cellular compartment are assumed to be non-interacting since the majority of the interactions occur between proteins in the same sub-cellular compartment [19, 38, 39]. As with the gold standard positive, the gold standard negative data set is also limited to those interactions found in the yeast high-throughput data sets i. e. 1985. Genomic featuresIn order to predict true interactions, we identified those that have at least one of the following genomic features based on: 1. Homologous interactions – Using our HINT database [21], we identified all interactions from high-throughput data sets that had homologous interactions, including orthologous or paralogous interactions. An interaction is deemed as homologous to a given interaction when each of its interacting proteins has homologs that are found to interact in DIP or IntAct. Homologs of interacting proteins are identified by HINT using PSIBlast with 5 iterations and an E-value cut-off of 10-8. 2. GO annotations – Using data from the GO database [24], we identified all interactions from high-throughput data sets where the interacting proteins shared at least one GO term, since interacting proteins generally share a common function [39]. 3. domains – We identified interactions in high-throughput data sets, where each of the interacting proteins had one of the two Pfam domains that were found to interact in PDB structures by the 3did database [23]. Correlation between genomic featuresThe correlation between each genomic feature was calculated using Pearson's correlation coefficient for 100 random interactions from the high-throughput data sets. The significance of each correlation coefficient was tested using a t-test with 98 degrees of freedom. Table 3 shows the correlation coefficients, t values and the probability. All the genomic features were found to be independent of each other. Bayesian networksBayesian networks can be used to combine evidence from different sources and calculate the posterior odds of an event based on prior evidence [25]. The relation between the posterior odds and prior odds of finding a true interaction is given by Bayes' rule as follows:Oposterior = L(g1, g2, g3,. . . , gN) Oprior, (1)where g1, g2, g3,. . . . . [SEP] Justify:

Output: The text does not directly address bioinformatics or its role in analyzing genomic data. Instead, it discusses protein interactions, genomic features, and statistical methods like Pearson's correlation coefficient and Bayesian networks, which are related to data analysis but do not explicitly mention bioinformatics as a crucial method or field for this task.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The orthologous region of human chromosome 5 (map 2) has been aligned with the bovine map, with the gene homology relationships represented by lines coloured according to type of homology; in this case the user configured the human map to label only those genes homologous to cattle genes. The alignment clearly shows an apparent inversion in the distal portion of cow chromosome 20 relative to the human gene order, with Ensembl classifying the majority of homology relationships as 1:1 orthologies. Note that the ‘Status Message’ panel records the current threshold used for filtering homologies (controlled from the ‘Options’ menu). The same region of map 1 was used to identify and import the corresponding region from a more recent annotated version of the bovine genome from Ensembl release 70, which used the UMD3. 1 genome assembly. This map (3) has been aligned alongside the Btau_4. 0 assembly with the genes sharing stable Ensembl identifiers linked in purple (again the UMD3. 1 map has been configured to only label genes related to those present on the Btau_4. 0 assembly). We can clearly see that the inversion has been ‘corrected’ in the more recent assembly build, to match the human gene order. Interestingly the human gene RANBP17 is shown to be (1:N) orthology linked to two cattle genes bounding the potential ‘inversion’ or assembly error. The full gene description which is reported (not shown) when clicking the ‘novel’ bovine gene 000000034824 categorizes this marker as a processed pseudogene, which may indicate a possible source of the ambiguity. Figure 2 illustrates some of the interactive features available in ArkMAP, as described in its legend. Of particular note is how the selection of a region of a map leads to the presentation of a context-dependent menu of possible actions that may be performed using the chosen map coordinates and the encompassed markers. Figure 3 illustrates how ArkMAP may be used to integrate personal mapping data, with data curated by the ArkDB and Ensembl resources. In this case a local map (1) has been created positioning a salmon QTL marker on a linkage map of chromosome 26. [SEP] Justify:

Output: The text supports the claim by describing a complex analysis involving genomic data. It details the alignment of human and bovine chromosome maps, highlighting gene homology relationships, and explains how bioinformatics tools like ArkMAP are utilized to integrate and interpret genetic information. This indicates that bioinformatics plays a significant role in studying and visualizing genomic data.


Input: Task: contrast the claim using the given text. Claim: Quantum mechanics can be completely understood through classical physics. [SEP] Text: 3 can infer that at least two processes go on inside the Hydrogen atom, and we shall discover shortly that there are actually three. Only one is familiar. The other two challenge familiar concepts of ‘conservation’ that originally grew out of Newtonian mechanics. But electromagnetism is not Newtonian mechanics. In electromagnetic problems, the concepts of momentum and energy ‘conservation’ have to include the momentum and energy of fields, as well as those of matter. Momentum and energy can both be exchanged between matter and fields. ‘Conservation’ applies only to the system overall, not to matter alone (nor to fields alone either). 4. 1. Energy loss due to far-field radiation The first process that occurs with the Hydrogen atom is the familiar energy loss from the atom due to far-field radiation. There will be a far-field power radiated (energy loss per unit time) of magnitude P radiated = ∫ 4 π | P | R 2 d Ω = ∫ 4 π c 4 π ( E radiative ) 2 R 2 d Ω = ∫ 4 π c 4 π e 2 c 2 κ 6 | n × ( ( n − β → ) × d β → d t ) | 2 d Ω E8 where Ω means ‘solid angle’. Because the full 4 π of solid angle captures opposing directions of n contributions to the integral from the vector β → visible in the integrand cancel out. Contributions to the integral that come from the dot product n ⋅ β → that is hidden in the κ 6 factor may not be zero at every moment, but they time-average to zero. So let us simplify the expression for far-field power radiated by setting β → to zero. [SEP] Justify:

Output: The text contrasts the claim by explaining how processes within the Hydrogen atom reveal limitations of classical physics concepts such as 'conservation'. It states that electromagnetism challenges familiar concepts from Newtonian mechanics and introduces concepts that are not valid under classical physics, suggesting that quantum mechanics cannot be fully understood through classical frameworks.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Specificity is defined as the ability to identify a true negative in a data set. where TP = number of true positives,TN = number of true negatives,FP = number of false positives,T = total number of positives,F = total number of negatives. The ROC curve is plotted with the Sensitivity on the Y-axis and (1-Specificity) on the X-axis. The smooth ROC curve is plotted using [41]. Cross-validationSince the training set (data set used to calculate the likelihood ratios) and the test set (data set used to calculate the sensitivity and specificity) are the same yeast high-throughput data set, we used 10-fold cross-validation to assess our predictions. We divided the positive and negative gold standards into 10 approximately equal sets. We used 9 of these to calculate likelihood ratios for each genomic feature. Then we identified the true positives and false positives in the remaining set using these likelihood ratios. We did this in turn, so that each of the 10 sets was a test set and the remaining 9 sets were training sets. We then summed the number of true positives and false positives across all the 10 test sets to obtain the Sensitivity and Specificity and plotted the ROC curve. 403. 10. 1038/nature750215. 10. 1038/35051615627. 10. 1038/350010094574. 10. 1073/pnas. 061034498147. 10. 1038/415141a183. 10. 1038/415180a543. 10. 1126/science. 10914031736. 10. 1126/science. 1090289105. 10. 1038/ncb1086299. 10. 1016/j. sbi. 2004. 05. 003923. 10. 1016/S0022-2836(03)00239-063. 10. 1186/gb-2004-5-9-r631118. 10. 1101/gr. 1774904356. 10. 1074/mcp. M100037-MCP200536. 10. 1016/S0168-9525(02)02763-4242. 10. 1093/nar/28. 1. 235763. 10. 1093/bioinformatics/btg07085. 10. [SEP] Justify:

Output: The text does not specifically address bioinformatics or its importance in analyzing genomic data. Instead, it discusses statistical concepts related to sensitivity and specificity, cross-validation techniques, and ROC curve plotting, which are methods used in data analysis but do not directly support or refute the claim about bioinformatics.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: In this paper, we have presented FastKmer, an efficient system for the extraction of k-mer statistics from large collection of genomic and meta-genomic sequences using arbitrary values of k. FastKmer succeeds in being, to the best of our knowledge, the fastest k-mer statistic distributed system to date, not only because it implements a clever algorithm for the extraction and the aggregation of k-mers, but even because it has been purposely engineered and tuned so to extract the most from the underlying Spark framework. This is especially the case of the different strategies that we developed for the distribution of the k-mers aggregation workload over a Spark cluster, and that can be used as well in more general Bioinformatics application scenarios. As a future direction, we observe that the internal architecture of FastKmer has been conceived so as to make it easy to integrate its workflow in more complex data processing pipelines. For instances, we cite the case of distributed alignment-free algorithms. These could use FastKmer as a sub-pipeline to extract the k-mers from each sequence of a collection for later comparison. AbbreviationsHDFS: Hadoop distributed file system LPS: Longest processing time MPI: Message-passing interface MPS: Multi-processor scheduling MSP: Minimum substring partitioning PVM: Parallel virtual machine RDD: Resilient distributed dataset YARN: Yet another resource negotiator 7. 83. 708. Berlin: Publishing: 2017. p. 53–64. 12. 2. CAS PubMed 1. 86. 46. 2016. p. 114–28. 30. 9. 42. 60. 842. 406. 1833. 3. Ed. O’Reilly; 2012. J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Commun ACM. 2008; 51(1):107–13. 26. 95. 11. 1999. 113. 2013. p. 5. 10. Washington, DC: : 2010. p. 1–10. 1. Cambridge: MIT press; 1999. Book 1994. Book 2011. 2015. Accessed 28 Nov 2018. . Pig latin: a not-so-foreign language for data processing. In: Proceedings of the 2008 international conference on Management of data. New York: ACM: 2008. p. 1099–110. 11. [SEP] Justify:

Output: The text corroborates the claim by discussing FastKmer, a system specifically designed for the extraction of k-mer statistics from large collections of genomic and meta-genomic sequences. The text notes that FastKmer is intended for bioinformatics applications and mentions its integration into data processing pipelines, which reinforces the importance of bioinformatics in genomic data analysis.


Input: Task: contrast the claim using the given text. Claim: Quantum mechanics can be completely understood through classical physics. [SEP] Text: We have: P radiated = e 2 4 π c ∫ 4 π | n × ( n × d β → d t ) | 2 d Ω E9 It evaluates to the well-known Larmor result: P radiated = e 2 4 π c ∫ − 1 1 d cos θ ∫ 0 2 π d φ | d β → d t | 2 sin 2 θ = e 2 2 c | d β → d t | 2 ∫ − 1 1 d cos θ ( 1 − cos 2 θ ) = e 2 2 c | d β → d t | 2 ( cos θ − 1 3 cos 3 θ ) | − 1 1 = 2 e 2 3 c | d β → d t | 2 E10 4. 2. Energy gain due to internal torquing The second process that occurs in the Hydrogen atom is a not previously noticed energy gain due to internal torquing. This process occurs because the Coulomb force within the atom is not central; it is along n half retarded and not along ( n − β → ) retarded ≈ n present. The power inflow to the electron is P torquing = T e Ω e where Ω e is the electron orbit frequency, and T e is the magnitude of the torque on the electron, given by T e = r e × F e where r e is the electron orbit radius, and F e is the tangential force on the electron. But that is not all. The proton also orbits at frequency Ω e and experiences its own torque, given by T p = r p × F p where r p is the proton orbit radius (tiny) and F p is the tangential force on the proton (huge), with the result that the magnitude T p is the same as T e. The total torque on the system is T = T e + T p = 2 T e. It is determined by the angle between r e and F e which is given by r p Ω e 2 c = ( m e m p ) r e Ω e 2 c. [SEP] Justify:

Output: The text contrasts the claim by detailing complex phenomena such as electron and proton torquing and Larmor radiation, which indicate a reliance on quantum mechanics, thereby suggesting that classical physics alone cannot fully explain these behaviors. The acknowledgment of electron dynamics and internal forces implies that quantum mechanics provides insights that classical physics cannot adequately cover.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: character(unrPositions[,1), nProbes = unrNProbes, dp = DisplayPars(color = col, mapColor = 'dodgerblue2'), displayProbesets = FALSE)> affyModel <- makeAnnotationTrack(start = unrPositions[,3], end = unrPositions[,4], feature = "gene_model", group = "ENSG00000009307", dp = DisplayPars(gene_model = "darkblue"))> gene = makeGene(id = 'ENSG00000009307', biomart = mart)> transcript = makeTranscript(id ='ENSG00000009307', biomart = mart)>legend = makeLegend(text = c('affyModel','Ensembl Gene', 'Ensembl Transcript'), fill = c('darkgreen','orange','cornflowerblue'), cex = 0. 5)In a second step, we use the RectangleOverlay class to create a highlighted region followed by the gdPlot function to produce the integrated plot. > rOverlay = makeRectangleOverlay(start = 115085100, end = 115086500, region = c(3,5), dp = DisplayPars(alpha =. 2, fill = "olivedrab1"))> gdPlot(list(title, exon, affyModel, gene, transcript, legend), minBase = 115061061, maxBase = 115102147, overlay = rOverlay)The plot generated in this second example is shown in Figure 2. Example III: Short read sequencing and tiling array dataIn the final example, we show how complex and diverse sets of data can be integrated to facilitate joint analysis and draw biological conclusions by presenting data from various published datasets on yeast. First, we construct a list where each gdObject represents either annotation or a publicly available dataset. We have plotted data from Ensembl, an Illumina sequencing dataset [9], Affymetrix tiling array data [10], nucleosome position data [11], and conservation data across 7 related species [12]. [SEP] Justify:

Output: The text primarily describes a technical implementation of various data analysis methods related to genomic data processing, such as generating plots and integrating multiple datasets. However, it does not explicitly discuss the importance or role of bioinformatics as a field in analyzing genomic data, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Moreover, this is difficult to estimate due to the limited size of the datasets available. Previous studies have addressed these issues in engineering and medical informatics applications [12, 13]. In this paper, three sampling techniques were implemented to assess the classification accuracy of ANNs in three genomic data analysis problems. It shows that in general there is variability among the three techniques. However, these experiments suggest that it is possible to achieve lower variance estimates for different numbers of train-test runs. Furthermore, one may identify conservative and optimistic accuracy predictors, whose overall estimates may be significantly different. This effect is more distinguishable in small-sample applications. For both expression datasets, the more conservative predictions were generated by the 50%–50% cross-validation and bootstrap methods. In the leukaemia classification problem, both methods require the same number of train-test experiments (1000) to significantly reduce the variance of their estimates. The estimates produced by such conservative classifiers were not significantly different. In the SRBCT classification task, the predictions generated by those methods were significantly different (t-tests, p < 0. 01). However, they needed a similar number of train-test experiments to provide low variance estimates. The most optimistic estimates were produced by the 95%–5% cross-validation and leave-one-out techniques. Their accuracy estimates were not significantly different in both data classification problems. The predicted accuracy of a classifier is generally proportional to the size of the training dataset. However, one of the reviewers of this paper has pointed out that the predicted accuracies for the leukaemia models increase little as the splits varies from 50%–50% trough 75%–25% to 95%–5%. The reviewer indicated that this may occur because some of the observations in the dataset may be considered as atypical, which has been reported by Golub and colleagues [14]. A larger dataset consisting of DNA splice-junction sequences was analysed in order to identify possible differences relating to the train-test data size factor. These classifiers were built on a more robust dataset. Therefore, the estimates generated by the sampling techniques are relatively similar. Like in the case of the expression data classification applications, the 50%–50% cross-validation and bootstrap methods produced the most conservative estimates. The most optimistic estimates were again provided by the 95%–5% cross-validation and leave-one-out techniques. [SEP] Justify:

Output: The text supports the claim by discussing the implementation of sampling techniques to analyze genomic data and assess classification accuracy of artificial neural networks (ANNs). It highlights the importance of bioinformatics methods, such as cross-validation and bootstrap techniques, in achieving more reliable estimates in genomic data classification tasks. The mention of the variability among these techniques and their effectiveness indicates the critical role that bioinformatics plays in interpreting genomic data.


Input: Task: contrast the claim using the given text. Claim: Quantum mechanics can be completely understood through classical physics. [SEP] Text: So torque T = ( m e m p ) ( r e Ω e c ) e 2 ( r e + r p ) and power received is P torqing = m e m p r e Ω e 2 c e 2 ( r e + r p ) = ( e 4 m p ) c ( r e + r p ) 3 E11 The existence of such a process is why the concept of ‘balance’ emerges: there can be a balance between gain of energy due to internal torquing and the inevitable loss of energy due to radiation. But we are not done with radiation yet. 4. 3. Extra radiation due to Thomas rotation The fact that the electron and the proton have such different masses, and orbit at such different radii, means that the EM forces within the atom are not only not central; they are not even balanced. This situation has another major implications: The system as a whole experiences a net force. That means the system center of mass (C of M) can move. This sort of effect does not occur in Newtonian mechanics due to the fact that Newtonian mechanics assumes infinite signal propagation speed. Looking in more detail, the unbalanced forces in the Hydrogen atom must cause the C of M of the whole atom to traverse its own circular orbit, on top of the orbits of the electron and proton individually. This is an additional source of accelerations, and hence of radiation. It evidently makes even worse the original problem of putative energy loss by radiation that prompted the development of QM. But on the other hand, the torque on the system is a candidate mechanism to compensate the rate of energy loss due to radiation, even if there is a lot more radiation than originally thought. . The details are worked out quantitatively as follows. First ask what the circulation can do to the radiation. Some 20 years after the advent of SRT, a relevant kinematic truth about systems traversing circular paths was uncovered by L. H. Thomas (1927), in connection with explaining the then anomalous magnetic moment of the electron: 1/2 its expected value. He showed that a coordinate frame attached to a p5. 5 × 10 − 9 E14 Compare that value to the accepted value r e + r p = 5. 28 × 10 − 9 cm. The match is fairly close, running just about 4% high. That means the concept of torque vs. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of classical physics in explaining atomic behavior, particularly in the context of torque and radiation, and highlights the differences between Newtonian mechanics and quantum mechanics. It mentions 'this sort of effect does not occur in Newtonian mechanics' and explains how quantum mechanics was developed in part to address 'putative energy loss by radiation that prompted the development of QM,' indicating that classical physics cannot fully account for the phenomena observed in quantum mechanics.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: > data("seqDataEx", package = "GenomeGraphs")> str = seqDataEx$david [,"strand"] == 1> biomart = useMart("ensembl", "scerevisiae_gene_ensembl")> pList = list("-" = makeGeneRegion(chromosome = "IV", start = 1300000, end = 1310000, strand = "-", biomart = biomart, dp = DisplayPars(plotId = TRUE, idRotation = 0, cex =. 5)), makeGenomeAxis(dp = DisplayPars(byValue = 1e3, size = 3)), "+" = makeGeneRegion(chromosome = "IV", start = 1300000, end = 1310000, strand = "+", biomart = biomart, dp = DisplayPars(plotId = TRUE, idRotation = 0, cex =. 5)), "Nagalakshmi" = makeBaseTrack(base = seqDataEx$snyder [, "location"], value = seqDataEx$snyder [, "counts"], dp = DisplayPars(lwd =. 3, color = "darkblue", ylim = c(0,300))), "David +" = makeGenericArray(probeStart = seqDataEx$david [str, "location"], intensity = seqDataEx$david [str, "expr", drop = FALSE], dp = DisplayPars(pointSize =. 5)), "David -" = makeGenericArray(probeStart = seqDataEx$david [! str, "location"], intensity = seqDataEx$david [! str, "expr", drop = FALSE], dp = DisplayPars(color = "darkgreen", pointSize =. 5)), "Lee" = makeBaseTrack(base = seqDataEx$nislow [, "location"], value = seqDataEx$nislow [, "evalue"], dp = DisplayPars(color="grey", lwd =. 25)), "Conservation" = makeBaseTrack(base = seqDataEx$conservation [, "location"], value = seqDataEx$conservation [, "score"], dp = DisplayPars(color="gold4", lwd =. [SEP] Justify:

Output: The text is irrelevant to the claim as it appears to be code related to specific bioinformatics operations but does not discuss the importance or role of bioinformatics in analyzing genomic data.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: in through your institution Filter BioinformaticsThis issueBioinformatics and search term Advance 10. 1093/bioinformatics/bty106 Published: 20 February 2018 10. 1093/bioinformatics/bty106 Select format. ris (Mendeley, Papers, Zotero). enw (EndNote). bibtex (BibTex). txt (Medlars, RefWorks) Download citation issueBioinformatics and search term issueBioinformatics and search term -CoGe browser is a web-based genome visualization utility that integrates the genome browser with the extensive CoGe genome database (currently containing over 30 000 genomes). In addition, the EPIC-CoGe browser boasts many additional features over basic JBrowse, including enhanced search capability and on-the-fly analyses for comparisons and analyses between all types of functional and diversity genomics data. There is no installation required and data (genome, annotation, functional genomic and diversity data) can be loaded by following a simple point and click wizard, or using a making the browser widely accessible and easy to use by researchers of all computational skill levels. In addition, EPIC-CoGe and data tracks are easily embedded in other websites and JBrowse instances. Availability and implementationEPIC-Cois freely available for use online through CoGe ( Source code (MIT open source) is available: informationSupplementary data are available at Bioinformatics online. 1 visualization is a useful means of inspecting content within the framework of a genome such as gene models, transcriptional information, or SNP data. JBrowse is a powerful browser-based genome viewer that handles large datasets with minimal resource requirements. It supports a wide variety of input files, allowing for fast and seamless data integration. The comparative genomics platform CoGe enables the rapid association of private and public experimental datasets with its large database of genomes (>33 000) to facilitate downstream analyses (Lyons and Freeling, 2008). Recognizing the need for an enhanced genome visualization experience when dealing with large and complex datasets, the Codeveloped an updated implementation of JBrowse (Buels et al. , 2016), which we call the EPIC-CoGe browser. Here we describe the new features associated with the EPIC-CoGe browser that will increase researcher’s ability to visualize genomic information and analyze data associated with those genomes. [SEP] Justify:

Output: The text corroborates the claim by discussing various bioinformatics tools, such as the EPIC-CoGe browser and JBrowse, which are designed for genome visualization and analysis of genomic data. It emphasizes the browser's capabilities in handling large datasets and facilitating the analysis of functional and diversity genomics data, thus highlighting the importance of bioinformatics in genomic analysis.