Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: If z1 is assumed to be known, the ordinary least square (OLS) estimate for v1 is given byConsider the penalized least-squares (PLS) estimation that minimizes (5) where p λ (·) is a penalty function. For example, p λ (|v1j|) = λ|v1j| gives LASSO, gives ridge, and gives EN, where λ, λ1 and λ2 are tuning parameters. For the prediction the ridge-type penalty is effective and for sparse estimation the LASSO-type penalty is recommended, so that EN [19] has been recommended as a compromise between the ridge and LASSO methods. [7] proposed to use EN for sparse (SPCA), but it gives less sparse estimates than LASSO. [9] recently proposed the use of random-effect models to generate new penalty functions for sparse regression estimation. Suppose that v1jis a random variable such that (6) where θ is the dispersion parameter and u j follows the gamma distribution with a parameter w and densitysuch that E(u j ) = 1 and Var(u j ) = w. This model leads to a rather complex marginal distribution for v1j,characterized by parameter w and with densityThis model involves a computationally difficult integral, and its direct optimization is problematic due to the nonconvexity of -log f w, θ (v1j). To overcome these problems, first note that the random-effect model (6) can be written as (7) where τ j = u j θ and e j ~ N(0,1). This is the double hierarchical generalized linear model [20]. With the log link, we have an additive modelThis leads to the h-likelihood (HL) of [21] (8) whereand f θ (v1j|u j ) and f w (log u j ) are the density functions of u1j|u j and log u j respectively. Given (w, ϕ, θ), for the estimation of v1, [9] proposed to use the profile h-likelihoodwhere solves dh/du = 0. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses mathematical concepts related to estimation methods, such as ordinary least squares and penalized least squares, without any mention of bioinformatics or genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: ChARMView facilitates convenient discovery of these changes, focusing further experimental investigation. Figure 4Identifying functionally relevant genomic aberrations. A small amplification evident in the array CGH data breast cancer data (top) [18], and its effect on mRNA expression (bottom) [19] (chromosome 15 of breast tumor sample 709B shown). Full size imageA final unique characteristic of ChARMView is that its visualization and statistical tools are developed for general use, independent of data type and organism. Any dataset with features that can be associated with chromosomal position can be imported and analyzed with ChARMView. For instance, the software has been particularly useful in identification of aneuploidies based on gene expression datasets although array CGH is the typical experimental approach for probing genomic amplifications or deletions. ChARMView has also been used to identify spatially-correlated biases in gene expression that are unrelated to altered chromosome structure. Generally, our tool can be used to identify any region of non-randomness with respect to position in genomic data with inherent ordering. In addition to its usefulness for a variety of data types, ChARMView can be applied to a variety of organisms. By default, the system provides chromosomal coordinates for Saccharomyces cerevisiae data with ORF identifiers and human data with Unigene identifiers. However, any data that can be mapped to a set of linear chromosomes can be imported and analyzed by ChARMView. Illustration of applicationWe have applied ChARMView to a number of array CGH and gene expression datasets, including data derived from both Saccharomyces cerevisiae and human experiments. Here we present an example application of our software to array CGH data from experimental evolution experiments in which eight strains of budding yeast were analyzed for chromosomal copy number changes after 100–500 generations of growth in glucose-limited chemostats [5]. Dunham et al. confirmed aneuploidy regions identified by array CGH through pulsed-field gel electrophoresis, thus creating a standard for assessing our results. Our method identified all 12 of the confirmed aneuploidies and two additional regions of bias. The novel regions identified by our method correspond to biases smaller than the ones identified by Dunham et al. [5] and may reflect aneuploidy present in a subset of cells in the population or may be due to a hybridization artifact. [SEP] Justify:

Output: The text corroborates the claim by discussing the capabilities of ChARMView, a bioinformatics tool that facilitates the discovery and analysis of genomic data, including detecting chromosomal aberrations and gene expression biases. It emphasizes that ChARMView can analyze datasets associated with chromosomal positions, illustrating its importance in analyzing biological data effectively.


Input: Task: pass the claim using the given text. Claim: Europa’s subsurface ocean may harbor conditions suitable for life. [SEP] Text: 즉, 2005년도에 한국해양연구원이 발표한 심해설계파 자료는(당초 해양수산부가 목적으로 하였던 심해설계파) 기준으로서 그 기능을 일부 상실한 것으로 판단된다(Hong, 2015). Fig. 7은 각 해역별로 설계파에 대한 파형경사(H1/3/Lo)의 연도별 변화를 나타낸 것이다. 파형경사는 1970년대까지는 0. 02~0. 025 정도로 변화가 없었으나 그 이후부터 증가하기 시작하여 최근에는 0. 03~0. 35로 약 50%까지 증가되는 현상을 보이고 있다. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses data related to marine design waves from 2005 and their changes over time without mentioning Europa, subsurface oceans, or the potential for life. Therefore, it does not support or contrast the claim.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: [9] showed that (9) with and the estimate of v1 can be found using the iterative weighted least squares (IWLS) by solving (10) using and λ = ϕ/θ. In random-effect model approach, the penalty function p λ (|v1j|) stems from a probabilistic model. As noted previously the proposed penalty p λ (|v1j|) is nonconvex. However, by expressing the model for p λ (|v1j|) hierarchically as (i) v1j|u j is normal and (ii) u j is gamma, both models can be fitted by convex GLM optimizations. Thus, the proposed IWLS algorithm overcomes the difficulties of a nonconvex optimization by solving two-interlinked convex optimizations [22]. Figure 1 shows HL penalties p λ (|v1j|) at w = 0, 2, and 30, and SCAD penalty at λ = 1. The form of the penalty changes from a quadratic shape (w = 0) for ridge regressions to a cusped form (w = 2) for LASSO and then to an unbounded form (w > 2) at the origin. In the case of w > 2, it allows an infinite gain at zero. Bell-shaped penalties have been proposed for better prediction (e. g. , [23]), and cusped ones for simultaneous variable selection and estimation as in LASSO [6] or SCAD [24]. Until now, however, only finite penalties have been investigated. [9] proposed to use the unbounded penalty with w = 30, which we shall call the HL method. They illustrated the advantage of using this unbounded penalty to enhance sparse coefficient estimation. Singularities in LASSO and SCAD occur as the penalty functions have no derivatives at the origin. However, both penalties have |p λ (0)| < ∞ and while the new unbounded penalty has |p λ (0)| < ∞ and. Figure 1HL penalty functions associated with the ridge ( w = 0), LASSO ( w = 2), SCAD and when w = 30. Full size imageIn general, the minimizer of the penalized least-squares (5) can be found using the IWLS (10) with. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on discussing specific statistical methods and models, such as IWLS and various penalty functions in relation to optimization, without addressing bioinformatics or its importance in genomic analysis.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Figure 6B illustrates this capability on chromosome 17 array CGH profiles of three breast tumor samples (123B, 309A, and BC-A) from [18]. An amplicon frequently associated with breast tumors includes the ERBB2 oncogene at 17q12. While breakpoints identified in individual tumors vary, recent studies have identified a group of 7 genes surrounding the ERBB2 locus that are commonly amplified, including 2, MLN64, 2, GRB7, ZNFN1A3, and EST 48582 [22, 23]. ChARMView's amplification predictions for the three tumor profiles shown include 15, 18, and 13 probes respectively, all of which span the 7-gene region previously identified. All predictions shown in Figure 6 have Bonferroni-corrected p-values less than. 05 for both mean and sign significance tests. Complete lists of predicted breakpoints for both chromosome 1 and chromosome 17 amplicons are included in Table 2. Table 2 Examples of predicted breakpoints in breast tumor aCGH case study. Listing of Unigene IDs corresponding to predicted breakpoints for ChARMView results pictured in Figures 6A and 6B. ID and gene name are the first and last markers included in the predicted amplification. All results listed have Bonferroni-corrected less than p-values of. 05 for both mean and sign significance tests. Full size tablecan be downloaded at and run on virtually any platform if the J2SE Environment version 1. 4. 2 or greater is present. A brief overview of the primary features of the software follows. Loading dataChARMView accepts all types of data from any organism provided that the features can be ordered on a set of linear chromosomes. Input files must be tab-delimited, specifically in the commonly-used. pcl format. Chromosome labels and position must be included in the input file unless the organism type is Saccharomyces cerevisiae or human with ORF or Unigene identifiers, which ChARMView is able to order without coordinates. Viewing dataFigure 5 shows a typical ChARMView screenshot upon loading data and statistical analysis. The data display is zoomable and selectable with mouse-overs for identification of experiments and individual genes. Zoom features include standard single-click magnification, zoom to rectangle, and zoom reset (fit to screen) capabilities. [SEP] Justify:

Output: The text supports the claim by detailing the functionality of ChARMView software, which is a bioinformatics tool designed for the analysis of genomic data such as array CGH profiles in breast tumors. By discussing features like data loading and statistical analysis, the text illustrates the importance of bioinformatics tools in analyzing complex genomic information.


Input: Task: contrast the claim using the given text. Claim: Astrobiology explores the potential for life beyond Earth. [SEP] Text: , and Vick-Majors2014; Boetius et al. , Reference Boetius, Anesio, Deming, Mikucki and Rapp2015). However, the dissolved organic carbon input derived from overlying ice alone is calculated to be insufficient to support the reproductive growth of the microbial community inferred from cell concentrations in accretion ice above the lake (Christner et al. , Reference Christner, Royston-and Priscu2006). This strongly suggests that an alternative source of carbon is required to fuel life in subglacial lakes. For example, there is a large pool of dissolved organic carbon available, which probably results from the chemolithoautotrophic activity of ammonium oxidizing archaea and sulphide or iron oxidizing bacteria (Christner et al. , and Vick-Majors2014; Achberger et al. , and Vick Majors2016). The gene sequences reported from accretion ice above are a striking indicator of active chemosynthetic processes and furthermore may indicate associated metazoans (Bulat et al. , Reference Bulat, Alekhina, Blot, Petit, de and Lukin2004; Shtarkman et al. , 'Elia, Morris and Rogers2013); it should be noted that Christner et al. (and Vick-Majors2014) did not confirm the presence of any eukaryotic sequences, which however may be the result of a limited set of primers used in their study. Based on the sequence data (Shtarkman et al. , 'Elia, Morris and Rogers2013), appears to harbour a mixture of autotrophs and heterotrophs capable of performing nitrogen fixation, as well as carbon fixation, and nutrient recycling. Sequences found closest to those of psychrophiles and thermophiles indicate chemosynthesis driven by hydrothermal activity. Hydrothermal vent activity may afford sustained physico-chemical conditions within the lake environment that allow for chemosynthetic primary production capable of sustaining a complex food web (Rogers et al. , --Llodra, Reid, Roterman, Sweeting, Thatje and Zwirglmaier2012). Hydrothermal activity is known from both (Crane et al. , Reference Crane, Hecker and Golubev1991; Tiercelin et al. [SEP] Justify:

Output: The text contrasts the claim by discussing specific microbial communities and their chemosynthetic processes in subglacial lakes on Earth, focusing on the environmental conditions necessary for sustaining these communities. This emphasis on life in extreme conditions on Earth does not support the broader exploration of life beyond Earth, which is the primary focus of astrobiology.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The derivative for and HL penalties are summarized in Table 7. When v1j= 0, then and the j th element of W λ is not defined. [9] employed a perturbed random-effect estimate for a small positive δ = 10-8. Then, is always defined. As long as δ is small, the diagonal elements of W λ,δ are close to those of W λ and the resulting estimates are nearly identical to those of the original IWLS (10). In this paper, we report = 0 when < 0. 00005. Table 7 The derivatives of the penalty functions. Full size tableOther methods for sparse principal component analysis[7] also exploit the regression property of PCA in order to obtain sparse loadings. They proposed an alternating minimization algorithm to minimize the criterion (11) for deriving the first sparse loading vector v1. Given θ, this optimization problem becomes a naive elastic net problem for v1. Given v1 θ can updated from SVD of XTX v1. These two steps are repeated until v1 converges. Following [25], (11) is different from our objective function (5) even when we use the same penalty function. In fact, (5) is very close to the objective function of [26], but we put the normalization constraint of the loading inside iterated procedure so that it could make a different result. In this paper, we used the function spca() in the R-package elasticnet for the EN method in the simulation studies. Condition-number constraint for SPCAAs shown in the previous examples, the SPCA approaches above may not produce sufficient sparsity. For the moment suppose n ≥ p; the case where n <p can be dealt with by transposing the data; see the note below. From (2) we have the eigenvalue decomposition of the sample covariance matrix aswhere Λ = diag(l1,. . . , l p and for i = 1,. . . , p is the eigenvalues of S X in non-increasing order (l 1 ≥ … ≥ l p ≥ 0). Let the p × 1 random vectors x1, …, x n be rows of X that have zero mean vector and true covariance matrix Σ with the non-increasing eigenvalues, λ1 ≥. . . ≥ λ p. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses mathematical modeling and optimization methods related to sparse principal component analysis, without any mention of bioinformatics or its role in analyzing genomic data.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: When one or more gene or probe data points are selected, identifiers and associated annotation are displayed in the "Results" tab, which appears adjacent to the display panel. This allows users to select regions of interest on the display panel and retrieve lists of genes or probes within these regions. Additionally, any number of experiments may be viewed simultaneously by toggling the corresponding checkboxes in the "Experiment Options" tab, also adjacent to the display panel. Analyzing dataChARMView supports two different modes of analysis. The first employs the automated edge-finding algorithm discussed in Myers et al. [16] followed by statistical analysis. The second mode is for testing user-selected regions of data and only evaluates the statistical significance of the chosen region. Both methods of analysis rely on two tests of statistical significance: a mean-based permutation test, and a one-sample sign test. Details of both tests are discussed above and in Myers et al. [16]. P-values for these tests are reported for all regions found by the automated approach or selected by the user. Figure 5 displays a typical view of statistical results for a single experiment. Note that the red and green rectangles below the data correspond to regions of predicted aberration. The p-value cutoff at which results of the statistical analysis appear in the display panel can be adjusted by applying p-value filters provided in the "Prediction Options" tab adjacent to the display panel. A p-value filter consists of a logical combination of the mean permutation test and/or the one-sample sign test and real-valued cutoffs for each test. These combinations specify how the selected p-value cutoffs will be used to deem statistical significance. For instance, one possible p-value filter is "" with Sign p-value cutoff of 0. 001 and Mean p-value cutoff of 0. 01, which will result in only predictions with both Bonferroni corrected sign p-values of less than. 001 and mean p-values of. 01 being displayed. corrected p-value is obtained by multiplying the raw p-value from both significance tests by the number of regions tested for that chromosome. Another possibility is to apply "Sign OR Mean Tests", which results in a prediction being displayed if at least one of these criteria is met at the specified significance level. While we recommend the "" option for general use, other combinations may be useful under certain circumstances. [SEP] Justify:

Output: The text corroborates the claim by outlining the various methods and processes involved in analyzing genomic data, such as the automated edge-finding algorithm and statistical analysis. It discusses how selecting gene or probe data points leads to the display of identifiers and associated annotations, which is fundamental in bioinformatics. The mention of multiple modes of analysis and statistical significance testing emphasizes the role of bioinformatics in genomic data analysis.


Input: Task: contrast the claim using the given text. Claim: Panspermia is a hypothesis about the transfer of life between planets. [SEP] Text: While it is not possible to completely discount the possibility of life producing some of these signatures, it strains one’s credulity less to explain this evidence in terms of abiotic processes that currently have the balance of evidence in their favor. 2. 5 evidence for extraterrestrial life may soon come from the analysis of exoplanet atmospheres – the identification of certain nonequilibrium gases such as oxygen would strongly argue for life as the most plausible explanation. The detection of seasonal plumes of methane in Mars’ atmosphere has ignited debate on the possibility of chemoautotrophic methanogens surviving there (Krashnopolsky et al. , 2004), perhaps in habitats similar to those discovered in the high Arctic (Grasby and Londry, 2007). This interpretation is not without controversy, as there are differing theories for the methane’s origin (Fiebig et al. , 2007). In a similar vein, infrared spectroscopy has also been suggested to identify characteristic chemical bonds in the Europan ice sheets that may indicate biological activity (Dalton et al. , 2003). Outside of our solar system, the ambitious aims to track the presence of gases such as CO2, H2O, CH4, and O2 in exoplanets (Cockell et al. , 2009). The analysis of the spectrum of a newly discovered exoplanet, a so-called hot Jupiter, has revealed the presence of all the previously mentioned gases in its atmosphere, with the exception of oxygen (Swain et al. , 2009). While the presence of methane in large gas giants is not regarded as evidence of life in our own solar system or elsewhere, the recent discovery of several rocky planets dubbed “super Earths” (Charbonneau et al. , 2009) gives hope that we will soon discover a rocky planet with an atmosphere indicative of active biology. Currently, no one line of evidence has offered conclusive proof of extraterrestrial life, let alone panspermia, with controversies that have erupted over fossil evidence exposing our relative lack of understanding about these topics. Clearly, there is mounting evidence that life may survive in the far reaches of space, but the strategy of many proponents of panspermia to make sweeping conclusions from ambiguous evidence is untenable. More cautious authors note that evidence for life such as geochemical biosignatures may well be cryptic (Cockell et al. [SEP] Justify:

Output: The text contrasts the claim by highlighting that, while there is evidence suggesting that life may survive in space, the proponents of panspermia often draw sweeping conclusions from ambiguous evidence. It states that no conclusive proof of extraterrestrial life exists, nor is there definitive support for panspermia, revealing skepticism about the hypothesis. The text implies that alternative explanations for observed phenomena are often preferred, thus challenging the notion that panspermia is a widely accepted theory.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: algorithm for SPCA and we replace step 1 in the algorithm bywhere is defined in (9). we also apply this modified step, but replace X by X* defined in (14). Tuning parameter selectionTo complete the proposed algorithm we need to estimate the tuning parameters θ and λ = ϕ/θ in (9) and (10), respectively. First we note that from (7), marginally, v1 has mean zero and variance θ, so we use where is the estimated first loading vector from ordinary PCA and is the sample mean of. We use K-fold cross-validation for λ. Following [30], we select λ which maximizes the test sample variancewhere is the estimated loadings from the k th training sets (the whole data without the k th validation set) and SX[k]is the sample variance based on the k th validation set. For the numerical studies in Section we use K = 5. 0106. 10. 1073/pnas. 97. 18. 101010011. 1–11. 10. 1186/gb-2002-3-3-research0011 9. 10. 1093/bioinformatics/bti343104. full_text547. 10. 1198/1061860032148288. 286. 10. 1198/106186006X1134301360. 10. 1198/016214501753382273228. 10. 1002/cem. 1180020306693. 10. 1198/jasa. 2009. 012129. 10. 1038/755562326. 10. 1126/science. 290. 5500. 232333. 10. 2202/1544-6115. 10751800. 10. 1093/bioinformatics/btm2511971. 1985. 573. 320. 10. 1111/j. 1467-9868. 2005. 00503. x185. 678. 2006. 319. 10. 2307/2285814138. 10. 1007/BF03178906534. 10. 1093/biostatistics/kxp0081034. 10. 1016/j. jmva. 2007. 06. 007175. 10. 2307/2528966411. 10. [SEP] Justify:

Output: The text does not address the claim regarding the importance of bioinformatics in analyzing genomic data. Instead, it discusses algorithmic details related to parameter selection in a statistical context, which is irrelevant to the broader assertion about bioinformatics.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: MethodsConceptual method for confirming known breakpointsConfirming the existence of known breakpoints by using a dictionary of reads is conceptually simple and straightforward on the assumption that the exact breakpoint positions are precisely known. Namely, it is only necessary to confirm the existence of read fragments going through the breakpoints. This confirmation can be done immediately by using a query given by a concatenation of genomic subsequences that are to be concatenated by rearrangement. More specifically, let G =. . a|b. . c|d. . be a genomic sequence with a pair of known breakpoints (indicated by "|"), where a, b, c and d denote subsequences adjacent to the breakpoints, and ". . . "s denote the remaining sequences. For simplicity, assume that there are no other breakpoints. The known breakpoints can relate to four different rearrangements corresponding to four different concatenations, namely, ad, ac', cb, and d'b, or their reverse complements, d'a', ca', b'c', and b'd, where a' denotes the reverse complement of a and so on. Existence of these concatenations can be checked immediately by using exact matching with backward search [4] (essentially, by using so-called LF functions [2] or rank functions [18]). The chosen length of the adjacent subsequence should be large enough so as to avoid ambiguous false detection. In practice, however, most of known breakpoints are not given in terms of exact base positions but in terms of approximate positions relative to nearby genes or exons. In fact, even if similar disease-related fused genes are found in patients with the same cancer subtype, the precise breakpoint positions differ from patient to patient [13]. It is therefore necessary to make a number of queries according to a varying combination of possible precise breakpoint positions. Specifically, ad differs according to the chosen ends of a and chosen heads of d. Moreover, as is often the case, if an unknown short sequence is intervened between the breakpoints in the rearrangement, for example, asd instead of ad for some unknown short sequence s, it is necessary to make a number of queries, asd, according to varieties of the intervening s. Thus, it is not practical to make a large number of queries directly corresponding to all possible cases. [SEP] Justify:

Output: The text corroborates the claim by discussing methods in bioinformatics that facilitate the confirmation of known breakpoints in genomic sequences. It highlights the necessity of making queries and using genomic subsequences for verifying the existence of breakpoints, which underscores the role of bioinformatics in analyzing genomic data. The text details processes and approaches specifically related to genomic analysis, affirming the importance of bioinformatics in this context.


Input: Task: contrast the claim using the given text. Claim: Panspermia is a hypothesis about the transfer of life between planets. [SEP] Text: As with comets, asteroids may have been more amenable to life early in the solar system’s history, based on the possibility of liquid water (Wilson et al. , 1999). Nonetheless, it seems unlikely that life could currently persist in asteroids despite the discovery that asteroids still contain water (Campins et al. , 2010), simply because the current temperatures would be too low to support life. Nonetheless, extracts of meteorites have been shown to be amenable to algal growth, with dissolved inorganic salts supporting cell division (Mautner, 2002). Along with the literature on cometary life, suggestions have been raised that diatoms may inhabit these bodies (Hoover et al. , 1999). This is unlikely for a number of reasons. Aside from the previously mentioned criticisms of life in comets, eukaryotic cytoskeletons are unlikely to develop normally in microgravity (Papaseit et al. , 2000) (a testable proposition: Gordon et al. , 2007), and diatoms are also a recent arrival in the geological record (Sims et al. , 2006), which makes their appearance in bodies that originated many billions of years previously extremely unlikely. Other published accounts have attempted to draw a connection between “red rain” events and panspermia (Louis and Kumar, 2006), though no evidence to the authors’ knowledge was presented to substantiate this claim. We agree with Bar-Nun et al. (Bar-Nun et al. , 1981), who in reference to the theories of the cosmic abundance of life opine that “…we feel that it is not warranted to extrapolate from these observations to the existence of interstellar or cometary life. ” Citing inexplicable events such as “red rain” or ambiguous data as “proof” of panspermia will likely only be convincing to the ardent supporters of these hypotheses. Similarly, speculation about how extremophile organisms may thrive in extraterrestrial habitats cannot be taken as proof of life’s ability to colonize the universe – there may be unknown factors that prevent life from easily adapting to new environments that we are only just beginning to discover. 5 Question 4: Progress or Affect the Origin of Life Debate? 5. [SEP] Justify:

Output: The text contrasts the claim by discussing the various challenges to the panspermia hypothesis, such as the unlikelihood of life persisting in asteroids and the absence of substantial evidence to support claims relating to panspermia, particularly in relation to phenomena like 'red rain'. The authors express skepticism about extrapolating observations to infer the existence of life on other celestial bodies, suggesting that the notion of panspermia lacks sufficient evidence to be deemed credible.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Precision: fraction of retrieved results that are relevant to a particular query: . Sensitivity (or recall): fraction of relevant results that were retrieved: . Accuracy: fraction of true correct results: . F-scores: weighted harmonic means of the precision and the sensitivity: . We calculated the traditional F1 (balanced F-score), as well as F2 (emphasis on sensitivity) and F0. 5 (emphasis on precision), which gives three separate aspects for F-scores. Metrics based on true negative counts, such as the specificity or Matthew's Correlation Coefficient, were not considered as they are not relevant to this benchmark. Hardware configurations and performance metrics On the server side, the data warehouses were installed on the four hardware configurations (see ‘Impact of hardware’ section). On the client side, the benchmark was conducted with the latest version of the web browser on an ordinary laptop featuring an 2 Duo P8800 (2 cores @2. 66 GHz) and 8 GB of memory. The laptop was used to render and display the web pages generated by the frameworks to answer the queries. It was connected to the servers using a connection to minimize network delays. Performance measurements were recorded using the integrated within Chrome. The browser's cache was cleared between each query. For each hardware configuration, we recorded how long it took the warehouse to answer each query and generate and display the response page to the end user. Toward that end, we timed the onload JavaScript event, which is triggered immediately after a page has completely loaded, allowing an accurate measurement to the millisecond of the answer time. When appropriate, we also considered asynchronous requests delays. When multiple pages were needed to fully answer a query, the total answer time was defined as the sum of the loading times of all the individual pages. We calculated the following metrics based on the raw performance measurements. Query-development time: time to design a query for the warehouse, i. e. translate the query from natural language into a suitable form for the warehouse;Running-time: time for the warehouse to produce the output;Answer-time improvement (versus database): time to design and run the queries for a warehouse, compared with raw SQL queries for the normalized database;Answer-time improvement (versus manual): time to design and run the queries for a warehouse, compared with a manual approach using existing resources available on the web. [SEP] Justify:

Output: The text does not address the claim since it primarily discusses metrics and performance measurements related to querying data warehouses, without mentioning bioinformatics or its significance in analyzing genomic data.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Overview of the proposed method in practical useTo exploit the dictionary of reads in practice, it is important to make a limited number of effective queries that are likely to extract useful information from the dictionary. In particular, effective queries should be chosen from genomic regions where breakpoints are likely to exist. Additionally, breakpoint-containing queries should be avoided because they have a combinatorially increasing and unaffordable number of variations. Accordingly, the proposed method is composed of the following three steps. 1 Predict breakpoint regions that are likely to contain breakpoints and be joined by rearrangement. 2 Scan the breakpoint regions with a sliding window from right to left, collect all the read fragments exactly matching the window, and subsequently collect all possible leftward extensions of the fragments. 3 Identify breakpoints as branching points among the extensions. The dictionary of reads is effectively used in the second step. The most time-consuming steps are the last two steps; therefore, it is important to reduce the size of the breakpoint regions as much as possible. The first step further consists of the following three sub-steps:1a Collect discordant pairs by using a standard paired-end mapping tool. 1b Get rough breakpoint regions by means of clustering the discordant pairs. 1c Narrow down the breakpoint region in each cluster on the basis of a detailed analysis of the distribution of discordant pairs. The first two sub-steps are basically similar to known methods in previous studies [14–17], while the third sub-step is first introduced in this study. These sub-steps and the following main steps are described in the following subsections. Collection of discordant pairsThe paired-end short reads are mapped onto a reference genome by using a standard paired-end mapping tool, BWA [4]. Discordant pairs are extracted from the results in the SAM (sequence alignment/map) format [19] by reference to bitwise flags indicating whether paired-end mappings are accordant or discordant. Only unambiguous mapping results with Phred-scaled mapping-quality score not less than 30 are employed. Two-dimensional clustering of discordant pairsA global coordinate system is defined on the whole reference genome sequence. [SEP] Justify:

Output: The text describes a method for predicting and analyzing genomic breakpoints, highlighting steps that utilize bioinformatics techniques such as standard paired-end mapping tools. This supports the claim by demonstrating the importance of bioinformatics in efficiently extracting useful information from genomic data.


Input: Task: contrast the claim using the given text. Claim: Fossils provide insights into the evolutionary history of life. [SEP] Text: Since there is a lack of fossils that would be useful for calibration of the tree within the family, we used a 37 Ma for Rhinolophus-Hipposideros split (37 Myr, [43, 65]) to calibrate the tree (since the age of the fossil represents the minimum date of the fossil lineage occurrence, we provided estimates of minimum dates of divergences based on ML branch lengths). Geographic arrangements of parsimony networks were used to visualize phylogeographic pattern among haplotypes (Figure 4). We visualize the branches with minimum of mutational steps within clades revealed by tree building methods. The shortest connection between clades clades I and II and clades III and IV was displayed to indicate phylogeographic breaks within traditional R. hardwickii and R. muscatellum. Table 3 P-distances (lower triangle) and corrected distances (GTR model, upper triangle) among haplotypesFull size table 192. 10. 1038/35003188. 1436. 10. 1073/pnas. 022477199. 584. 10. 1126/science. 1105113. 120. 330. 10. 1644/1545-1542(2004)085<0321:>2. 0. CO;2. 1886. 10. 1093/molbev/msi180. 43. 24. 552. 241. 121. 529. Third 124. 4. Batfauna of Syria: distribution, systematics, ecology. Zool Bohem. 2006, 70: 1-329. 1946. 1951, London: (Natural History) 329. 10. 1016/S0169-5347(01)02145-0. 973. 10. 1644/1545-1542(2001)082<0960:>2. 0. CO;2. 662. 10. 1644/06-MAMM-F-038R2. 1. 297. 10. 3161/1733-5329(2006)8[277:]2. 0. CO;2. 10. 1186/1742-9994-4-4. 102. 238. 155. 225. 189. 188. 90. 190. 45. 188. 10. 1016/S0016-6995(06)80050-1. 683. 48. 43. 376. 154. [SEP] Justify:

Output: The text contrasts the claim by suggesting a lack of useful fossils for calibration of evolutionary trees. It indicates that the absence of appropriate fossils impedes the analysis of the evolutionary history, which goes against the idea that fossils generally provide insights into evolutionary processes.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Development, documentation and support metrics All warehouses required some efforts to get them up and running and offer customization options to some degree. We thus evaluated the warehouses from a developer's perspective. We also considered the quality of the documentation and the support provided by the systems’ authors. Time to install: Time to install the warehouse framework on a server, including all dependencies;Dependencies: Number of software on which the warehouses rely on to function. Fewer dependencies usually facilitate the update process;Time to configure: Time to configure the warehouse so that all data sources are properly integrated and presented to the end-users. This metric is critical in laboratories with limited IT support;Time to build: Time to build the system once it is properly configured. This aspect is important in laboratories that need to frequently update their warehouse as new data becomes available;APIs: Variety of computer languages that may be used to programmatically interact with the data warehouses;Documentation comprehensiveness: Description of all the parameters and options that may be defined by developers to set the system up and end users to customize their workspace;Documentation clarity: Difficulty to understand the documentation, in particular for non-computer-scientists;Support: Evaluation of the support provided by the warehouse authors;Customization options: Flexibility of the warehouse so that it may be customized to match the users’ needs, without editing the source code. This aspect of warehousing is mostly useful for laboratories with specific needs and significant IT support;Open-source: Open-source warehouses allow developers to edit the source code of software to tailor all aspects of the system to their specific needs. Like customization options, an open-source warehouse framework is mostly useful for larger laboratories with specific needs. Statistical analysis The significance of the differences of grades between the four warehouses was demonstrated using repeated-measures one-way ANOVA. The null-hypothesis is that the mean grade of the warehouses is identical, and we used 95% confidence intervals (α = 0. 05). The weight of each criterion was accounted for by repeating the criterion's grade as necessary. We did not assume an equal variability of the differences (sphericity) and used the Geisser-Greenhouse correction instead (ε = 0. 9218). We obtained the F-ratio (F = 68. 91) and P-value (P < 0. 0001). We further analyzed the results with a corrected multiple comparisons post test (Tukey's test). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various metrics related to data warehousing and the evaluation of these systems, without mentioning bioinformatics or genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Namely, DNA sequences from all chromosomes are concatenated into a single DNA sequence, G, with a punctuation symbol "$" in between them; and each base position in each chromosome is specified by a global coordinate indicating the position in the concatenated sequence. Thus, all chromosomal positions are mutually comparable according to the global coordinates. Each discordant pair of reads is represented by a point, (x, y), in a two-dimensional region, G×G, where x and y are the global coordinates of the mapped positions of the first bases of the reads, and, for disambiguation, x < y is assumed because x and y can be swapped otherwise. Thus, discordant pairs are identified with the corresponding points in G × G. The discordant pairs are scattered very sparsely as a whole in G × G because it is vast, and those associated with the same breakpoint are clustered together within a distance of the insert length. Therefore, it is easy and straightforward to extract such clusters. As an example of extraction procedure, sort the points (discordant pairs) according to the first coordinates, and classify them into groups so that the differences between the first coordinates in the same (different) groups are less (larger) than the insert length; subsequently, sort the points in each group according to the second coordinates, and further classify them into subgroups so that the differences between the second coordinates in the same (different) subgroups are less (larger) than the insert length; repeat such classification several times, and obtain the desired clusters. As for matched tumor and normal samples, clusters of discordant pairs associated with somatic breakpoints, simply referred to as somatic clusters here-after, exclusively consist of those from the tumor sample, while clusters of discordant pairs associated with germline breakpoints, simply referred to as germline clusters, are mixtures of those from both tumor and normal samples. Therefore, clusters are judged to be somatic if the rate of discordant pairs from the tumor is high, e. g. , 90% or more, and judged to be germline otherwise. Prediction of breakpoint regionsBreakpoints are also represented by points in G × G in a natural way. [SEP] Justify:

Output: The text corroborates the claim by detailing a specific bioinformatics technique used to analyze DNA sequences and identify discordant pairs. By describing methods for concatenating DNA sequences and extracting clusters from mapped positions, it highlights the importance of bioinformatics in handling and interpreting complex genomic data.


Input: Task: pass the claim using the given text. Claim: Paleoenvironments help us understand past geological changes. [SEP] Text: Empathy is also often confused with sympathy, which is the feeling of sadness or pity you may feel if someone else is sad, whereas empathy involves a broader understanding of others. Like any other skill, to be used successfully, empathy needs to be practised and developed over time. When used correctly, empathy provides a way to access the minds of others, helping us to understand their thoughts and actions, and enabling us to interact with them. Although empathy plays an important role in connecting with others, more seems to be needed for full understanding. To some people, ‘understanding’ means knowing why someone else is feeling a certain way, while for others, it requires a shared personal experience. “For example, we struggle to empathise with people who commit hideous crimes,” explains Thomas. “In consequence, we can fail to understand them, even though we might know their motives. ” Empathy and understanding, therefore, appear to be heavily linked, but full understanding likely requires more than just empathy. ? Thomas is a member of a research group investigating this connection between empathy and interpersonal understanding. Philosophers research a topic by checking reality (known as ‘phenomena’) against a specific concept. “We are concerned with conceptual problems and try to develop terminology related to these topics that is clear and unambiguous,” says Thomas. For example, Thomas may ask whether ‘emotional contagion’, such as our ability to pick up the mood of a room full of people, is really a form of empathy. “Does this phenomenon fall under the concept of empathy? ” Thomas is not only concerned with theoretical concepts. “We want to use findings from developmental psychology to reveal the reality of empathy,” he says. Hopefully, this will lead him to an empirically informed theory of empathy in relation to interpersonal understanding. “We want to explain how empathy works. ” today’s world, it often seems like we are getting worse at understanding each other. Thomas believes that psychological mechanisms such as biases and poor emotion regulation are hindering our performance of empathy. And our increasing interaction through technology is not helping, as we lose so many social cues such as tone of voice and body language when we do not communicate face-to-face. When no personal connection exists, it is hard to ‘read’ someone, and so therefore harder to empathise with them. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses empathy, interpersonal understanding, and psychological mechanisms, but does not mention paleoenvironments or geological changes. Therefore, it does not provide any support, contrast, or relevance to the claim about paleoenvironments.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: We obtained the following mean differences δ and adjusted P-values: BioMart versus BioXRT: δ = 1. 176, P < 0. 0001BioMart versus InterMine: δ = −0. 6622, P = 0. 0011BioMart versus PathwayTools: δ = −0. 7973, P < 0. 0001BioXRT versus InterMine: δ = −1. 938, P < 0. 0001BioXRT versus PathwayTools: δ = −1. 973, P < 0. 0001InterMine versus PathwayTools: δ = −0. 1351, P = 0. 8594The significance of the difference of the performance gain for each hardware upgrade was demonstrated using an unpaired two-tailed T-test. We obtained T = 6. 155 and P = 0. 0254. quality One of the major goals of a data warehouse is to provide search tools to facilitate the retrieval of data for a specific query. Therefore, it is important to evaluate the query capabilities of the systems. To quantify these capabilities, we selected 14 typical queries from genomics research (Table 2). Table 2: Open in new tab List of queries used for the benchmark ID. Query. Aggregation. Algebra. Graph. Integration. Sequence. Q01Return the starting and ending positions of all exons on gene fgenesh1_pg. C_scaffold_6000333 in A. niger. ✓Q02Return the chemical reactions within pathway for Inositol phosphate metabolism✓Q03Find all the cellular components of all gene products involved in protein biosynthesis in T. reesei. ✓Q04Return the total number of gene entries in P. chrysosporium that are assigned EC1. 9. 3. 1 (cytochrome-c oxidase)✓✓Q05Give the description from the of multicopper oxidase (type 1) proteins (IPR001117) in T. reesei. ✓Q06Obtain upstream sequences for gene estExt_Genewise1. C_43297 in S. thermophile (within 10 000 bp). ✓✓Q07Show the genes, transcripts and exons located between locations 10 K and 20 K on chromosome II of A. niger. ✓✓Q08Find pathways in U. [SEP] Justify:

Output: The text does not directly address the importance of bioinformatics in analyzing genomic data. Instead, it presents statistical results and describes querying capabilities related to genomic data without explicitly linking them to the claim.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Full size imageThe reference genome sequence around one of the projected regions, say, region A, is scanned from right to left, and a query is taken as a genomic subsequence of length MLU+(x) + α with the left end at x, where MLU+ (x) denotes MLU at x in the positive (right-ward) direction, and α is a small positive constant (Figure 3(b)). All of the read fragments exactly matching the query are collected from the dictionary of reads by exact matching with backward search [1]. Similarly, all of the possible leftward extensions of the collected read fragments are obtained by exact matching with backward search (by recursive examinations of all four possible extensions with A, C, G, and T at each extended base position) (Figure 3(c)). The extensions are performed until the length of extension reaches a fixed value, e. g. , 20. Generally, the extensions are diverse as a result of SNPs, breakpoints, sequencing errors, and other events. In particular, when some of the extensions contain breakpoints joining regions A and B, they can be detected by aligning them with the reference genome sequences in each of regions A and B (Figure 3(d)). Detection of breakpoints from the extensionsThe extensions can be aligned efficiently with the regions A and B by a very fast dynamic programming algorithm based on bit-level parallelism [20]. Since the minimum edit distance attained at the optimal local alignment can be computed much faster than the optimal local alignment, which requires backtracking computations, it is advantageous to detect breakpoints only from the minimum edit distances. Figure 4 intuitively illustrates this detection. Figure 4Detection of a breakpoint in a breakpoint region. (a) Optimal alignment of an extension with region A (B) changes while query position, x, scans region A from right to left. (b) Minimum edit distances given by the optimal alignment are plotted against scanning position x; a precise breakpoint position is indicated. Full size imageWhile the left end of the query, x, scans from right to left a neighborhood of region A, the minimum edit distances between extensions (from the query in region A) and region A (B) are computed. In the simplest case where there is only a unique extension with a single breakpoint, they are given by the length of unaligned part of the extension in the optimal alignment (Figure 4(a)). [SEP] Justify:

Output: The text supports the claim by detailing a complex procedure of analyzing genomic data through the alignment of read fragments to a reference genome, demonstrating the importance of specific bioinformatics methods such as dynamic programming algorithms and minimum edit distance calculations in detecting genomic features such as breakpoints. These analytical techniques directly exemplify the role of bioinformatics in managing and interpreting genomic information.


Input: Task: pass the claim using the given text. Claim: Neurogenesis occurs in specific brain regions throughout life. [SEP] Text: Bender RA, Dubé C, Gonzalez-. Mossy fiber plasticity and enhanced hippocampal excitability, without hippocampal cell loss or altered neurogenesis, in an animal model of prolonged febrile seizures. Hippocampus 2003; 13: 399–412. CrossrefPubMed110. Ben-Ari Y, Holmes GL. Effects of seizures on developmental processes in the immature brain. 2006; 5: 1055–1063. CrossrefPubMed111. . Cognitive dysfunction after experimental febrile seizures. 2009; 1: 167–177. Crossref112. Dubé. Epileptogenesis provoked by prolonged experimental febrile seizures: Mechanisms and biomarkers. J Neurosci 2010; 30: 7484–7494. CrossrefPubMed113. . The role of inflammation in epilepsy. Neurol 2011; 7: 31–40. CrossrefPubMed114. . Blockade of Ca2+-permeable AMPA/kainate channels decreases oxygen-glucose deprivation-induced Zn2+ accumulation and neuronal loss in hippocampal pyramidal neurons. J Neurosci 2002; 22: 1273–1279. CrossrefPubMed115. Waldbaum S, Patel M. Mitochondria, oxidative stress, and temporal lobe epilepsy. 2010; 88: 23–45. CrossrefPubMed116. Sullivan PG, Dubé. Mitochondrial uncoupling protein-2 protects the immature brain from excitotoxic neuronal death. 2003; 53: 711–717. CrossrefPubMed117. . The ketogenic diet increases mitochondrial uncoupling protein levels and activity. 2004; 55: 576–580. CrossrefPubMed118. . Interictal spikes in developing rats cause long-standing cognitive deficits. 2010; 39: 362–371. CrossrefPubMed119. é SL. Seizure mechanisms and vulnerability in the developing brain. In: Epilepsy: A Comprehensive Textbook. Vol 1. 2nd ed. (eds. ) Philadelphia: & Wilkins, 2008: 469–480. 120. Dreifuss FE. Cognitive function-victim of disease or hostage to treatment? Epilepsia 1992; 33(suppl): S7–S12. CrossrefPubMed121. . [SEP] Justify:

Output: The text is largely a series of references to studies and does not provide information directly related to neurogenesis or its occurrence in specific brain regions throughout life. It discusses various studies related to epilepsy and cognitive dysfunction but does not address the claim directly or provide corroborative or contrasting information.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: maydis that involve cellulose, and find related pathways and the name of the molecular entities linking the pathways. ✓Q09Get the translated protein sequence of the shortest exon of gene fgenesh1_pg. C_scaffold_1000115 in A. niger. ✓✓Q10Get the reactions and pathways in S. thermophile where Thiamine diphosphate is a reactant or a product. ✓Q11How many enzymes from S. thermophile belong to the Starch and sucrose metabolism pathway? ✓Q12Return the GO terms associated with gene UM00020 in U. maydis. ✓Q13Return the molecular functions (from GO) of the best homolog (BLAST_score > 50) to the protein from P. chrysosporium with sequence: >phchr_7266✓✓✓Q14Find all the paths from Galactinol to D-Galactose in the galactose metabolism pathway that does not go through manninotriose. ✓✓Number of queries in aspect:34455ID. Query. Aggregation. Algebra. Graph. Integration. Sequence. Q01Return the starting and ending positions of all exons on gene fgenesh1_pg. C_scaffold_6000333 in A. niger. ✓Q02Return the chemical reactions within pathway for Inositol phosphate metabolism✓Q03Find all the cellular components of all gene products involved in protein biosynthesis in T. reesei. ✓Q04Return the total number of gene entries in P. chrysosporium that are assigned EC1. 9. 3. 1 (cytochrome-c oxidase)✓✓Q05Give the description from the of multicopper oxidase (type 1) proteins (IPR001117) in T. reesei. ✓Q06Obtain upstream sequences for gene estExt_Genewise1. C_43297 in S. thermophile (within 10 000 bp). ✓✓Q07Show the genes, transcripts and exons located between locations 10 K and 20 K on chromosome II of A. niger. ✓✓Q08Find pathways in U. [SEP] Justify:

Output: The text does not directly mention bioinformatics or its importance in analyzing genomic data. Instead, it lists various queries and tasks involved in genomic analysis without contextualizing them in relation to 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: From our results it seems clear that the penalty approach alone is not able to yield sufficiently sparse PCA for high-dimensional genomic data. Additionally we also need the shrinkage on the singular values of the data matrix. In simulation studies we show that the proposed methods outperform existing methods both in estimation and model selection. Hence we believe that the new SPCA methods are promising tools for high-dimensional data analyses. For future works, it will be of interest to apply super-sparse technique in this paper to locally-linear methods of dimensionality reduction (e. g. [13]]), partial-least squares (PLS) regression and classification methods (e. g. [14]), or other high-throughput data analysis method where dimensionality reduction is used (e. g. [15]). algorithm for PCAStandard algorithms for SVD (e. g. [16]) give the PCA loadings, but if p is large and we only want to obtain a few singular vectors, the computation to obtain the whole set of singular vectors may be impractical. Furthermore, with these algorithms it is not obvious how to impose sparsity on the loadings. [10] described a algorithm that works like a power method ([17], p. 523) for obtaining the largest eigenvalue of a matrix and its associated eigenvector. algorithm computes only a singular vector at a time, so it is efficient if we only want to extract a few singular vectors. Also the steps are recognizable in regression terms, so the algorithm is immediately amenable to random-effect modification as needed to obtain the various SPCA methods proposed in this paper. First we review the ordinary algorithm: Set the initial value of z1 as the first column of X, then 1. Find 2. Normalize 3. Find z 1: z 1 ← X T v 1 4. Repeat steps 1 to 3 until convergence. To obtain the second-largest singular value, first compute residual then apply the algorithm above by replacing X by X2. Sparse PCA via random-effect modelsTo impose sparseness on the PCA loadings we first introduce the regression framework into step 1 of the algorithm. j as the j th column of X, following [18] we havewhere v1jis the j th element of the p × 1 vector v1 (the first loading vector), and ∊ j is an error term. [SEP] Justify:

Output: The text supports the claim by discussing methods like Sparse Principal Component Analysis (SPCA) that are specifically designed for efficient analysis of high-dimensional genomic data. It mentions that the proposed methods are promising tools for high-dimensional data analyses, which indicates the importance of bioinformatics techniques in processing and analyzing genomic data.


Input: Task: contrast the claim using the given text. Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: Thus there must be mechanisms for eliminating themFull size image Polar substances like sugars, amino-acids, and many foreign molecules can cross the blood–brain barrier rapidly only if there are specific mechanisms provided (see Sect. 4). Indeed the blood–brain barrier has very low permeability to those polar substances that are unable to be carried by specific transporters. By contrast lipid soluble substances that are small (MW < ~ 600) and so able to cross cell membranes unaided are more likely to be able to cross the blood–brain barrier into the brain. However even some of these are denied entry by specific efflux mechanisms that transport them back to blood from the endothelial cells, e. g. by ABC efflux transporters, notably p-glycoprotein (Pgp), and breast-cancer resistance protein (BCRP), or by metabolism within the cells, e. g. by monoamine oxidase (MAO). Much is known and has been written about how substances enter the brain, about how others are prevented from doing this, and about the importance of the blood–brain barrier for delivery of drugs to the brain. Reviews include those dealing with glucose, water, and inorganic ions [2,3,4, 41]; those considering amino acids [4, 42,43,44]; and those concerned with a wide variety of other substances [20, 30, 32, 36, 38, 45,46,47,48,49,50,51]. However, much less has been investigated and/or written about how substances are eliminated from the brain. As indicated in Fig. 3 though there are numerous mechanisms for reducing entry of unwanted substances, it is equally important to have some means of expelling unwanted substances including those that have gained entry and those that have been formed within the brain (see Fig. 4). The rate of elimination is important for all substances that can enter and leave the brain because it determines the concentrations that can be achieved for any rate of entry. In the case of administered drugs, the rate of elimination also determines how long concentrations will persist between or after doses. Fig. 4Elimination of unwanted substances can be either by efflux alone or it can be by metabolism followed by efflux of the metabolitesFull size image Elimination thus plays a key role in maintenance of the status quo in the brain. [SEP] Justify:

Output: The text contrasts the claim by discussing the mechanisms by which substances, especially those that could be harmful, can cross the blood-brain barrier and emphasizes that while there are ways to limit entry of unwanted substances, it also highlights the importance of eliminating substances that have entered the brain. It describes both the challenges of unwanted substance entry and the elimination processes, indicating a more complex relationship than a simple protective role.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: maydis that involve cellulose, and find related pathways and the name of the molecular entities linking the pathways. ✓Q09Get the translated protein sequence of the shortest exon of gene fgenesh1_pg. C_scaffold_1000115 in A. niger. ✓✓Q10Get the reactions and pathways in S. thermophile where Thiamine diphosphate is a reactant or a product. ✓Q11How many enzymes from S. thermophile belong to the Starch and sucrose metabolism pathway? ✓Q12Return the GO terms associated with gene UM00020 in U. maydis. ✓Q13Return the molecular functions (from GO) of the best homolog (BLAST_score > 50) to the protein from P. chrysosporium with sequence: >phchr_7266✓✓✓Q14Find all the paths from Galactinol to D-Galactose in the galactose metabolism pathway that does not go through manninotriose. ✓✓Number of queries in aspect:34455The queries cover five major aspects of data mining in biology: aggregation, algebra, graph and networks, integration and sequence handling. Table 2: Open in new tab List of queries used for the benchmark ID. Query. Aggregation. Algebra. Graph. Integration. Sequence. Q01Return the starting and ending positions of all exons on gene fgenesh1_pg. C_scaffold_6000333 in A. niger. ✓Q02Return the chemical reactions within pathway for Inositol phosphate metabolism✓Q03Find all the cellular components of all gene products involved in protein biosynthesis in T. reesei. ✓Q04Return the total number of gene entries in P. chrysosporium that are assigned EC1. 9. 3. 1 (cytochrome-c oxidase)✓✓Q05Give the description from the of multicopper oxidase (type 1) proteins (IPR001117) in T. reesei. ✓Q06Obtain upstream sequences for gene estExt_Genewise1. C_43297 in S. thermophile (within 10 000 bp). [SEP] Justify:

Output: The text discusses various queries related to biological data mining, including retrieving protein sequences, metabolic pathways, and gene information. However, it does not specifically mention bioinformatics or its role 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: in through your institution in BioinformaticsThis issue search term to publish with us in and Self-on Books on to publish with us in and Self-in BioinformaticsThis issue BooksJournalsEnter search term Menu 10. 1093/bib/bbt031 Published: 14 May 2013 10. 1093/bib/bbt031 Select format. ris (Mendeley, Papers, Zotero). enw (EndNote). bibtex (BibTex). txt (Medlars, RefWorks) Download citation in BioinformaticsThis issue search term in BioinformaticsThis issue BooksJournalsEnter search term facilitate the integration and querying of genomics data, a number of generic data warehousing frameworks have been developed. They differ in their design and capabilities, as well as their intended audience. We provide a comprehensive and quantitative review of those genomic data warehousing frameworks in the context of large-scale systems biology. We reviewed in detail four genomic data warehouses (and PathwayTools) freely available to the academic community. We quantified 20 aspects of the warehouses, covering the accuracy of their responses, their computational requirements and development efforts. Performance of the warehouses was evaluated under various hardware configurations to help laboratories optimize hardware expenses. Each aspect of the benchmark may be dynamically weighted by scientists using our online tool BenchDW ( to build custom warehouse profiles and tailor our results to their specific needs. benchmark, large-scale genomics, systems biology, data warehousing, data integration sciences techniques made significant improvements during the past decades, resulting in huge amounts of data collected during the years by the scientific community. Whole-genome sequencing is becoming exponentially cheaper and faster [1, 2], and devices such as the MiSequencer (Illumina), the 454GS Junior (Roche) or the (Life Technologies) are expected to sequence an entire human genome in a day for $1000 [3]; large-scale genomics studies are thus accessible to most laboratories, and not confined anymore to large consortia only. Personal omics data can be integrated with existing knowledge bases to evaluate disease risks [4] and represents a tremendous potential in all areas of drug discovery and personalized medicine. Unfortunately, biological data present numerous challenges, from the lack of standard formats to data inconsistencies resulting from experimental data variations [5]. [SEP] Justify:

Output: The text corroborates the claim by explaining that bioinformatics facilitates the integration and querying of genomic data and highlights the development of genomic data warehousing frameworks that aid in analyzing vast amounts of biological data. This indicates the significance of bioinformatics in handling and interpreting genomic information.


Input: Task: pass the claim using the given text. Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: The principles involved in balancing inputs and outputs and what is meant by “clearance” are both considered more fully in Sect. 6. The relationship between rates of elimination, clearances, permeability-area products, volumes of distribution and half-lives together with the units used are described in 3. 2). The exact ways in which perivascular influx and efflux of solutes and water take place have been controversial as considered in some detail in Sect. 3. Tarasoff-Conway et al. [52] have addressed the issue of perivascular clearance with particular regard to one particular solute, amyloid-β. Brinker et al. [53], Hladky and Barrand [41], Simon and Iliff [39], Coles et al. [1], Abbott et al. [40], and Benveniste et al. [54] have summarized the evidence concerning perivascular transport from various perspectives. Transport across the blood–brain barrier is the dominant mechanism for removal of water and CO2 from brain parenchyma (for discussion and 4. 2). Certain larger solutes, e. g. insulin [59], transferrin [60, 61] and β-amyloid [62], may be transported across the blood–brain barrier by transcytosis [36, 63, 64] (see Sect. 4. 3). Many of the transporters found at the blood–brain barrier are capable of mediating not only efflux but also influx and have been studied more thoroughly from this standpoint. Other transporters, e. g. the ABC efflux pumps that are present in the luminal membranes of the endothelial cells (see Sect. 4. 2. 1), transfer many exogenous substances in an outward direction from endothelial cells to blood fuelled by the energy derived from ATP hydrolysis. This outward movement serves to decrease blood-to-brain influx as substances that enter the endothelial cells (or even just the luminal membranes of the cells) are returned to blood before they enter the brain proper. ABC transporters may also promote brain-to-blood efflux if there is some means for the substances to enter the endothelial cells across the abluminal membranes (see Sects. 4. 2. 1 and 4. 2. 2). 3 Perivascular pathways3. [SEP] Justify:

Output: The text is largely focused on the mechanisms of solute transport across the blood-brain barrier and discusses concepts like clearance and efflux/influx of various substances. While it includes information about transporters that influence the movement of substances, it does not explicitly support or contrast the claim that 'the blood-brain barrier protects the brain from harmful substances.' Thus, the text is considered irrelevant to the claim.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Open in new tabDownload slideBeyond the ability of the warehouses to answer those fourteen queries, our goal was to identify the types of queries that can be answered by the warehouses so that we could establish a more detailed profile of the warehouses query capabilities. The queries we selected thus cover five major aspects of data retrieval for genomics research: aggregation, algebra, graph, data integration and sequence handling (Table 2). The profiles show that most warehouses were able to integrate data (Figure 1b). They also highlight the superiority of PathwayTools and InterMine for sequence-based queries and algebra and reveal the deficiencies of BioMart and BioXRT in aggregation and algebra. facilitate the comparison between systems and determine the strengths and weaknesses of each system, we assigned a grade to the warehouse for each criterion (Table 3). Grades range from 1 to 4, where a higher grade indicates better results. The grades were summarized to establish a profile for each warehouse (Figure 2). The differences between grades are statistically significant (< 0. 0001, see ‘Methods’ section for details). We calculated an overall grade for each warehouse as a weighted average of the intermediate grades. With default weights, representative of the typical data warehousing needs and constraints in a genomics laboratory with limited IT resources, PathwayTools and InterMine obtained similar overall grades (Tukey's post-test P = 0. 86) and significantly outperformed BioMart and BioXRT (P < 0. 0001), whereas BioMart achieved a higher mean grade than BioXRT (P < 0. 0001). Figure 2:Performance offered by the hardware configurations compared to their cost. Performance and costs are relative to those of the fastest machine. Costs were obtained in September 2012 from major online retailers. The area of each point is proportional to the ratio performance/cost and indicated in the circle. SSD-based configurations are represented with a darker shade. Inset: Performance gain of obtained with faster storage (SSD) compared with the gain obtained with a faster CPU, relative to their cost. Error bars indicate the range of values (P = 0. 0254). Open in new tabDownload slide Impact of hardware Data warehouses are typically installed on a remote server and are queried through a web-based user interface, effectively enabling the warehouse to be accessed anywhere. [SEP] Justify:

Output: The text does not address the claim directly and primarily discusses the capabilities of data warehouses in genomics research. While it mentions aspects relevant to data retrieval and integration in the context of genomic data, it does not provide evidence or a clear stance on the importance of bioinformatics specifically. Therefore, 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: As a result, the rapidly growing number of genomics databases, created at various places worldwide [6], serves primarily as data storage with simple query interfaces designed for specific tasks. Although successful data integration is one of the keys to successful bioinformatics research, current databases are not readily amenable to complex system-wide research that requires the integration of a large number of these disparate data sources. There is a need for a paradigm shift, from single organism databases to more complex warehousing systems that accommodate the wealth of omics data from numerous organisms and provide effective mining tools for comparative genomics and system-wide queries. In an effort to overcome those challenges and facilitate the integration of genomics data, a number of generic frameworks have been developed during the past few years [7–17]. Those frameworks were primarily intended for biologists to integrate omics data from a variety of sources and to perform comparative analyses or protein function inferences. They also share the common goal of encouraging the dissemination of scientific data and collaborations among researchers worldwide. Those data warehousing systems differ in their design and capabilities, as well as their intended audience. For example, a system may provide a generic and easy-to-setup solution, whereas another system may allow biologists to tailor the warehouse to their specific needs, at the cost of a more tedious installation. A system may provide simple text fields to query the underlying database, whereas another system may provide more sophisticated search tools designed for advanced users. The goal of our review is to provide a comprehensive and unbiased evaluation of those frameworks for genomics data warehousing. We aimed at providing a broad assessment of the frameworks in the context of comparative genomics rather than evaluating the warehouses as a single organism database. We quantified 20 aspects of the warehouses (see Table 3), from their configuration times to the complexity of query design and their output quality. All raw and computed measurements are available online ( for all through BenchDW [18], a user-friendly web-based tool that may be used to easily visualize and inspect all aspects of the benchmark. Each aspect of the benchmark may be dynamically weighted using BenchDW to build custom warehouse profiles and tailor our results to the users’ specific needs. selected the biological data warehousing frameworks that were publicly available and free for the academic research community. We did not consider systems in early stages of development such as Fluxion ( or systems not available any more [9]. [SEP] Justify:

Output: The text corroborates the claim by discussing the importance of data integration and the development of frameworks for integrating genomic data. It highlights the need for more complex warehousing systems to accommodate and analyze the vast amount of omics data from various organisms, which aligns with the idea that bioinformatics plays a crucial role in the analysis of genomic data.


Input: Task: contrast the claim using the given text. Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: Even the almost complete coverage of the endothelial cells by glial endfeet proposed by Mathiisen [149] leaves sufficient gaps (see Footnote 7). Thus normally it is the endothelial cells that are the site for the rate limiting steps in efflux across the blood–brain barrier. The current state of knowledge about the role of the endfeet was considered further in [4]. 4. 1 Passive, non-specific transfer across the blood–brain barrierThere are two possible routes for passive, non-specific transfer across the microvascular endothelial layer, through the cells or around them. The paracellular pathway is “blocked” by the presence of tight junctions but this pathway may still be the principal route for the passive fluxes of small solutes that are barred from the transcellular route by being too polar (mannitol, sucrose and inulin are considered in 5. 6 evidence for this includes the observation that the tracer fluxes of Na+ and Cl− are not affected by ouabain [152] or bumetanide [153], agents that specifically inhibit ion transporters known to be involved in transcellular fluxes of these ions. Almost all of the passive, non-selective permeability of the blood–brain barrier to molecules more lipophilic than mannitol is the result of their ability to diffuse across both the cell membranes and the interior of the endothelial cells. Strong indications that such a physical mechanism applies are the observations: that transport does not saturate, that it is not inhibited by competition by other transported substances, and that no specific inhibitors have been found. Small neutral substances that are able to enter and leave the brain parenchyma by this mechanism include water, methanol, ethanol, isopropanol, glycerol, ethylene glycol, urea and thiourea (see Fig. 8). Fig. 8(Data read from Figure 8 of [159])Plot of log(PS/mL g−1 min−1), versus log(Kn-octanol/water MW−1/2) for the substances indicated along the abscissa. PS is the product of permeability and surface area for the blood–brain barrier, Kn-octanol/water is the octanol/water partition coefficient and MW is the molecular weight of the substance. [SEP] Justify:

Output: The text contrasts the claim by explaining mechanisms of permeability in the blood-brain barrier, highlighting that there are gaps and pathways that allow substances to cross, including potentially harmful small solutes such as methanol, ethanol, and urea. This suggests that rather than fully protecting the brain, the blood-brain barrier allows for non-specific transfer of substances, which contradicts the idea that it solely protects against harmful substances.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: To assess the impact of the hardware configuration of the server on performance and evaluate how well the warehouses can scale up as more researchers use them, we installed the frameworks on an entry-level server (LP) with 8 GB of memory and an 2 Duo E6550 (2 cores @2. 33 GHz) and a more robust configuration (HP) featuring 64 GB of memory and a pair of E5–2620 (12 cores @2. 00 GHz). Furthermore, the relatively recent solid-state drive (SSD) technology is reputed to work well with database applications. On both machines, we thus installed the frameworks on a SSD (OCZ Vertex2) and a traditional magnetic hard disk (7200. 10) so that we could evaluate the practical benefits of SSDs compared with magnetic hard disks for data warehousing. Figure 3 compares the relative performance of the four different hardware configurations (HP-SSD, HP-HDD, LP-SSD and LP-HDD) to their relative cost [Costs were estimated in September 2012 based on prices from major online retailers in the (Amazon and Newegg)], using the fastest hardware configuration (HP-SSD) as a reference. HP-SSD is >28% faster than any other configuration. Surprisingly, the configuration with the slower CPU and the SSD (LP-SSD) outperforms the high-end machine equipped with traditional hard disks (HP-HDD), despite its substantially lower cost. The results also show that when the cost the of the machine used to run the warehouse is an important criteria, choosing an SSD in place of a traditional hard disk is 4. 61 times more beneficial (P = 0. 0254) than opting for a faster CPU (inset). Figure 3:Warehouse profiles. The profiles represent the average grade for each aspect of data warehousing. Customized profiles can be dynamically generated with our online assistant BenchDW based on user specified weights. Open in new tabDownload slide Table 3: Open in new tab Heat map highlighting the aggregated results of the benchmark For each category, the warehouses are assigned a grade on a scale from 1 to 4. Higher grades, associated with darker shades, indicate better results. The heat map highlights significant differences between warehouses (P < 0. 0001). The percentages indicate the relative importance of the category. [SEP] Justify:

Output: The text discusses the performance assessment of various hardware configurations for data warehousing and does not mention bioinformatics or its role in analyzing genomic data. 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 make results comparable and remove any bias solely owing to the input data, we filtered out data integration systems available as web services only and those that do not accept custom input data [8, 10, 12, 13, 15]. Although biological-specific database schemas may be useful to facilitate data integration, by themselves they do not provide user interfaces or query mechanisms and were therefore excluded from the benchmark [11, 19]. Similarly, we did not consider biology-specific Interfaces (API) such as BioPerl [20] or other Bio* projects [21–23]. Our criteria restricted the list of suitable data integration frameworks (see S1) to BioMart [16], BioXRT [7], InterMine [17] and PathwayTools [14]. Table 1 summarizes the main features of the frameworks. In each case, we used the latest stable version available, with the default settings and the default set of parsers/extensions when possible: we designed the benchmark to reflect the fact that most wet laboratories do not have the IT resources to implement new features or extensions but are likely to minimize development efforts and use the warehouses’ default configurations instead. Table 1: Open in new tab Overview of the biological data warehousing frameworks selected for the benchmark Grayed requirements are recommended but optional only. Table 1: Open in new tab Overview of the biological data warehousing frameworks selected for the benchmark Grayed requirements are recommended but optional only. BioMart (v0. 8 rc6) Unlike other systems presented in this benchmark, BioMart is a data federation framework that provides a unified user interface to multiple data sources that may be distributed worldwide. One of the main benefits of BioMart is its ability to integrate any existing data source, which is internally represented using their ‘reverse star’ schema [24] that is optimized for fast retrieval of large amounts of data. The software package also includes MartConfigurator, a user-friendly tool that facilitates the configuration of the web user interface and the definition of the relationships between data sources. It also provides REST/SOAP and Java APIs, as well as for semantic queries. BioMart has been successfully used by numerous laboratories and consortia to build integrated portals for cancer-related [25, 26], microarray [27] and gene expression [28] data. BioXRT (v1. [SEP] Justify:

Output: The text corroborates the claim by discussing BioMart, a data federation framework that offers a unified user interface to multiple data sources, which has been successfully utilized by numerous laboratories for integrating biological data such as cancer-related, microarray, and gene expression data. This illustrates the importance of bioinformatics tools in managing and analyzing genomic data.


Input: Task: contrast the claim using the given text. Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: The principal known transporters in the rat are shown. In human OAT3 is abluminal, while both OATP1A4 and OATP2B1 are present on both membranes. The ABC efflux pumps, P-gp, 4 and MRP5, are all localized to the luminal, plasma facing, membrane. and Oatp transporters are exchangers (see Footnote 12). Localizations from [180] and the 3. 2). There is ample further evidence (see the 4. 3 Efflux by transcytosisTranscytosis is much less prevalent across the endothelial cells of the blood–brain barrier than across those of peripheral capillaries [248,249,250,251]. Nevertheless both adsorptive mediated transcytosis (AMT) and receptor mediated transcytosis (RMT) are still likely to be important mechanisms for the transfer of some large substrates across the blood–brain barrier. The initial event in AMT is the adsorption of usually positive substrates onto the surfaces of caveolae, while that for RMT is binding of the substrate to specific receptors that are in or become incorporated into clathrin coated pits. In both cases at the blood–brain barrier this leads to endocytosis followed by delivery of a substantial fraction of the contents of the resulting vesicles to the opposite membrane for exit, possibly by exocytosis [49, 63, 252]. AMT is thought to account for much of the influx into the brain of histones [253], “cell penetrating peptides” [49, 251, 254], HIV [255, 256], and cargos conjugated to the lectin wheat germ agglutinin [257] and to underlie the increase in “generalized permeability” caused by protamine [258]. The downsides of AMT are that it is relatively non-selective for substrates [256] and that it occurs in many cells throughout the body. In addition there is little if any evidence that it occurs in the direction from brain to blood [257, 259]. While RMT also occurs throughout the body, transport by this mechanism depends on interaction of the substrates with specific receptors that may be found primarily in specific locations such as the blood–brain barrier. In addition there is evidence that RMT can occur in either direction, i. e. [SEP] Justify:

Output: The text contrasts the claim by discussing mechanisms such as adsorptive mediated transcytosis (AMT) and receptor mediated transcytosis (RMT) that allow the transfer of substances across the blood-brain barrier. It highlights that AMT is relatively non-selective for substrates, which suggests that rather than purely protecting the brain from harmful substances, the blood-brain barrier allows certain substances, including potentially harmful ones, to pass through via these mechanisms.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Promoter: Area starting one nucleotide after the predefined TSS area and extending up to p bp upstream from that point. By default, p = 1300. PercArea: Threshold for the percentage of the gene area overlapped by the region, used in the selection rules (see flowchart in Fig. 3). By default, this is set at 90 %. PercRegion: Threshold for the percentage of the region overlapping the gene area, used in the selection rules (see flowchart in Fig. 3). By default, this is set at 50 %. Rules: In case of ties after following the rules shown in Fig. 3, the algorithm will decide the area to annotate the association to according to a rank of priorities, by default this is: TSS, 1st__and. To modify these priorities, a string containing the eight disjoint areas must be introduced. Gene: Tag indicating which gene identifier from the GTF annotation file is to be reported. By default ‘gene_id’ is used. Transcript: Tag indicating which transcript identifier from the GTF annotation file is to be reported. By default ‘transcript_id’ is used. GTF: Mandatory input. GTF annotation file. Files compressed with gzip are also accepted. BED: Mandatory input. BED file with the set of genomic regions to be matched. Files compressed with gzip are also accepted. Output: Mandatory input. Full path and name of the file where the output will be written. Results and discussionIn order to show the functionalities and main advantages of RGmatch, we compared it to the other methods available: 2pathway, and ChIPseeker. Comparisons are difficult because, on the one hand, there is no gold-standard data set of true associations between the genomic regions and the genes and, on the other hand, the goal of the different methods is not always exactly the same. For instance, GREAT and Seq2pathway do not only return the closest gene but also other distal genes by following an approach that is completely different to the other methods. GREAT assigns a ‘regulatory domain’ for each gene, so if any region lies within the regulatory domain, it is assumed to regulate the gene. There are three options to define this regulatory domain. [SEP] Justify:

Output: The text describes various aspects of a specific bioinformatics tool and the parameters associated with it but does not directly address or support the claim about the importance of bioinformatics in analyzing genomic data. It is more focused on technical specifications rather than the broader significance of bioinformatics.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: 03) BioXRT was designed to allow biologists to publish their data on the Internet with only minimal knowledge of databases and web development. In particular, BioXRT is reputed an excellent choice for small and medium size laboratories, which need to publish their results and correlate them to data from other public sources. The system was implemented in Perl and allows researchers to convert spreadsheets to the internal XRT data schema into the underlying MySQL ( database. The XRT schema comprises four Cross-Referenced Tables, which describe data, their structure and their relationships. The Cross-(XRT) model is highly flexible and may be expanded as needed to accommodate unforeseen data complexities. BioXRT has been used in various projects, ranging from the annotation of the 7 [29] to the study of structural variation of chromosomes in autism spectrum disorder [30], thereby demonstrating the versatility and flexibility of the framework. InterMine (v1. 0b) InterMine is an open-source framework that features a user-friendly web interface. InterMine relies on a traditional ETL (Extract, Transform and Load) architecture and provides a core data model and a collection of parsers to load data from 28 typical data sources such as the [31], the of Genes and Genomes [32] or the Bank [33]. The core model and the set of parsers may be extended by end users to accommodate new types of data or define new relationships between data. The default user interface can be customized and enhanced with widgets and plugins such as the genome browser GBrowse [34], the interaction graph viewer Cytoscape [35] and gene expression heat maps. The core of InterMine, implemented in Java, translates the data model into a normalized database schema and loads the data into the underlying PostgreSQL ( database with optional pre/post-processing steps. InterMine also features Java, Perl, Python, Ruby and for programmatic access to the data and the implementation of automated workflows. InterMine has been successfully leveraged to build warehouses describing omics data from numerous organisms [36–38]. PathwayTools (v16. 0) PathwayTools is a systems biology suite that may be used to build organism-specific databases—or model-organism databases (MODs)—that integrate various omics data types, from genomes to metabolic pathways. Those databases may be visualized and published on the web. [SEP] Justify:

Output: The text corroborates the claim by discussing bioinformatics tools like BioXRT, InterMine, and PathwayTools, which are specifically designed to manage, analyze, and integrate various omics data types, including genomic data. For instance, the mention of PathwayTools highlights its capability to handle different omics data types and build organism-specific databases, reinforcing the idea that bioinformatics plays a crucial role in genomic data analysis.


Input: Task: contrast the claim using the given text. Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: from brain to blood as well as from blood to brain. AMT and RMT in the direction from blood to brain have been studied extensively as routes of entry to the brain for endogenous substrates, but even more in the context of mechanisms for drug delivery. These studies have been reviewed frequently [57, 64, 154, 249, 252, 260,261,262,263,264,265,266]. However, even so, the steps occurring after the initial endocytosis remain only partially understood [63, 249, 250, 262, 267, 268] including even the answer to the important question of whether the cargo is released within the cell or delivered to the far side by exocytosis. By contrast evidence for transport via transcytosis in the direction brain to blood has been reported for only a few systems including transport of amyloid-β peptides via interaction with LRP1 (low density lipoprotein receptor related protein 1) and LRP2 (low density lipoprotein receptor related protein 2) (see Sect. 5. 7), of IgG antibodies via interaction with an unidentified receptor [269,270,271,272,273,274,275] and of transferrin [60] via interaction with the transferrin receptor (TfR) [61] (see below). Transport of transferrin is closely related to transfer of iron. Iron in plasma and in brain extracellular fluid is present almost entirely complexed to transferrin i. e. as holo-transferrin. It has long been known that iron and transferrin enter the brain across the blood–brain barrier and it was originally hypothesised that they are transferred together by endocytosis followed by exocytosis, i. e. direct transcytosis, of holo-transferrin (see e. g. [61, 276]). Yet there have been arguments against this idea arising from dual labelling experiments showing that far more labelled iron than labelled transferrin accumulates in the brain, see e. g. [60, 277, 278]. In addition it has been argued that release of holo-transferrin from TfR is unlikely to occur as there needs to be prior dissociation of iron for release of transferrin from its receptor [279]. [SEP] Justify:

Output: The text contrasts the claim by discussing the mechanisms of transport across the blood-brain barrier, including the transport of various substances like iron and transferrin. It raises concerns about the understanding of the transport processes and the efficacy of the barrier in restricting substances by highlighting evidence and arguments questioning the initial hypotheses regarding endocytosis and transcytosis. The mention of 'arguments against' the idea of direct transfer through the blood-brain barrier implies that it does not fully protect the brain from certain substances.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The default option (the one we compared RGmatch to), called the ‘basal plus extension’, assigns a ‘basal regulatory region’ that extends 5 kbp upstream and 1 kbp downstream of the TSS, irrespective of the presence of any neighboring genes. Based on a similar approach, Seq2pathway takes the functional impact of coding and non-coding genes into account to make associations. In the following sections we provide both qualitative and quantitative comparisons based on the results obtained with a publicly available set of genomic regions. Qualitative comparison to the state of the art methodsIn this section, we highlight the characteristics of RGmatch that make it different from any of the other approaches (see a summary in Table 2), and which therefore support the need to make this novel tool available to the research community. Table 2 Comparison of the functionalities of the different algorithmsFull size table User-friendlyRGmatch and HOMER are easy-to-use command line algorithms that can be run locally on any computer and in any operating system provided Python or Perl interpreters are installed. GREAT is accessible via their website, which makes it user-friendly on any operating system, but it cannot be used locally. CisGenome can also be used in any operating system via command line and has a graphical interface, but only for Windows. On the contrary, ChIPseeker and Seq2pathway are both R packages that can be easily used if the R interpreter is installed. However, we had problems using Seq2pathway on the Linux platform because the association function did not work. Adaptable to pipelinesAll methods except GREAT, which is a web tool, can be easily integrated into any analysis pipeline. HOMER is a suite of tools, and the whole suite has to be installed for the method to work. As for all R packages, ChIPSeeker and Seq2pathway, can also be integrated into any analysis pipeline, although some additional scripting is required. In contrast, RGmatch can be directly used in any pipeline and does not require additional steps or modules to work. Input formatand ChIPSeeker take a BED file containing the regions to be associated as input. CisGenome and Seq2pathway require the BED file to be converted into their own formats. GREAT accepts a 3-column BED file. [SEP] Justify:

Output: The text discusses various bioinformatics tools such as RGmatch, Seq2pathway, and others, detailing their functionalities and how they can be integrated into analysis pipelines. However, it does not explicitly state the importance of bioinformatics in genomic data analysis or support the claim that it is crucial; therefore, it is considered irrelevant.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: PathwayTools includes a variety of predictors for operons [39], transport reactions [40] and the complete metabolic network [41] of an organism, including missing enzymes [42], leveraging the 1800+ manually curated pathways from MetaCyc [43] as a reference. The suite also features numerous visualization tools [44, 45], such as genome and pathway browsers, to facilitate the analysis and comparison of complete genomic data and metabolic networks, and includes Pathway/to facilitate the manual curation of MODs in a collaborative environment. It is also possible to share and/or download MODs that were made available by the community in the BioCyc database collection [43] that now comprises nearly 3000 single organism databases. data sets and gold standard The benchmark aims at comparing and assessing the capabilities of those existing frameworks for genomic data warehousing. It goes beyond single organism and basic annotation querying and is based on readily available data. The benchmark covers genomics data from five well-characterized fungal species: Aspergillus niger [46], Phanerochaete chrysosporium [47], Sporotrichum thermophile [46], Trichoderma reesei [48] and Ustilago maydis [Ustilago maydis Sequencing Project. of MIT and Harvard ( They were selected from the Institute database for the comprehensiveness of their annotations, which is essential to define the gold standard that will be used as a reference for the systematic unbiased evaluation and comparison of the output quality of the data warehouses. Annotations include metabolic pathways from of Genes and Genomes [32], InterPro signatures [49], classifications from the [50], semantic terms from the [51] and orthologs from Clusters of of proteins (COG) [52]. The input data were also used to build a normalized database, which was used to configure BioMart, as it requires an existing data source to run. Output quality metrics The output of each warehouse to the various queries was compared against our manually curated gold standard. For each query, we compared the output of the data warehouse to the manually curated gold standard answer and counted the number of true positives (tp), false positives (fp) and false negatives (fn). Those values were used to compute the following metrics that are widely used in information retrieval and machine learning to quantify the performance of a system. [SEP] Justify:

Output: The text corroborates the claim by describing tools and methodologies, such as PathwayTools and BioMart, which facilitate the analysis and comparison of genomic data and metabolic networks. It emphasizes the importance of these tools in handling complex genomic data and highlights the processes involved in analyzing annotations and output quality, supporting the notion that bioinformatics is crucial for such analyses.