Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Acetoin is an overflow metabolite of the formation of pyruvate from lactic acid that takes place instead of pyruvate oxidation through the TCA cycle in the case that ethanol (repressing the TCA cycle and hence preventing catabolic breakdown of acetic acid) is oxidized to acetic acid (Fig. 2). As a result, biomass formation from lactic acid accelerates, for instance through the amino acid supply from pyruvate (Adler et al. 2014). The oxidation of the non-volatile lactic acid contributes to the acidity (pH-lowering effect) and flavour (less acidic taste, buttery flavour) of the cured cocoa beans. As for the growth of LAB, the growth of AAB is sometimes considered unnecessary for cocoa fermentation, as too high acetic acid concentrations may cause too acidic beans and as they have no significant direct impact, compared to yeasts, on the production of desirable volatile organic compounds in both beans and chocolates (Ho, Zhao and Fleet 2015; Ho, Fleet and Zhao 2018). However, the acetic acid produced by AAB is indispensable for cocoa bean curing (Schwan and Wheals 2004; Misnawi 2008; Kongor et al. 2016; and Weckx 2016a,b; Muñoz et al. 2019). The minimization of the role of LAB and AAB by Ho and collaborators has been based on the investigation of controlled cocoa fermentation processes, carried out in small plastic boxes, in the presence and absence of appropriate antibiotics to investigate the role of individual microbial groups, in particular yeasts (Ho, Zhao and Fleet 2014, 2015; Ho, Fleet and Zhao 2018). However, during these small-scale plastic box fermentations, the growth of LAB and AAB was not prevented completely and neither could the effect of increased concentrations of reducing sugars, amino acids and pyrazines be explained by favoring the growth of yeasts only. Although part of the acetic acid evaporates, the highest temperatures in the fermenting cocoa pulp-bean mass are reached if further overoxidation of acetic acid, and also of lactic acid (and mannitol), into carbon dioxide and water takes place by the prevailing AAB (Fig. 2; Schwan and Wheals 2004; and Weckx 2016b; Figueroa-Hernández et al. 2019; Muñoz et al. 2019).

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Users can select any displayed prediction, which results in the genes or probes and associated annotation in that particular region to be displayed in the "Results" tab adjacent to the display panel (Figure 5). Exporting resultsPublication quality images can be exported in multiple formats at any stage of the visualization. This includes images of exclusively raw data, results of statistical analysis, or combinations of these. In addition, predictions resulting from automated or manual statistical analysis can be exported in tab-delimited text form with the associated gene or probe identifiers and corresponding p-values. A p-value filter similar to that described in "Analyzing data" can be applied to all exported results to allow full user control over which predictions are included. Finally, lists of genes or probes for any object selected on the display panel can also be exported to text files to facilitate immediate analysis of regions identified by manual inspection. Command-line usageChARMView can also be used in command-line mode to make automated predictions of amplification or deletions. This command-line feature can be used by invoking ChARMView as follows:java -Xmx300m -jar ChARM. jar inputFile <input-file> outputFile <output-file> organismType <organism-type> meanPvalCutoff <mean-pvalue-cutoff> signPvalCutoff <sign-pvalue-cutoff> sigTestType <significance-test-type> The possible organism types, which determine reference chromosomal coordinates, are: 1, Saccharomyces cerevisiae; 2, human; 3, other (user-provided coordinates). Possible significance test options include: 1, mean AND sign tests; 2-mean OR sign tests; 3, mean test only; 4, sign test only. When run in command-line mode, ChARMView outputs all predicted regions of amplification and deletion meeting the specified significance level.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Mining the data it contains makes it possible to identify which transition patterns could be used to uniquely identify any set of proteins in the proteome. This allows for the design of targeted proteomic experiments, where the experimenter defines a priori which proteins they wish to detect and using the atlas, find which specific transitions should be scanned for. Targeted approaches can monitor at low mass/charge (m/z) levels, and have been shown to detect protein concentrations at low copy number [43]. As SRM can be used on complex tissues, a minimum of separation chemistry is needed. This means that experiments can more accurately detect smaller amounts of protein in complex samples. Mining this data requires the use of a number of integration strategies and information theoretic approaches to connect the peptide data with information from genomic sources (e. g. TCGA), disease literature (e. g. ), and pathways (e. g. IntAct [44], MINT [45]). Providing visual tools that access this integrated data allows for refinement of biomarker or transition target lists from many thousands to the tens or hundreds that are detectable. The purpose is typically to identify peptides that will be suitable biomarkers for a specific disease or disease sub type. The mining tasks are rarely initiated without prior knowledge, instead they are typically initiated either through associations with other measurement types or through the literature information. As with the genomic visualizations, those developed for exploring the PeptideAtlas are linked and interactive to allow for greater detail to be provided at the appropriately zoomed level. mspecLINE [46] (see Figure 4) is the first level mining tool which combines knowledge about human disease from with empirical data about the detectable human proteome from spectral libraries. It associates diseases with proteins by calculating the semantic distance, based on the co-occurrence of disease and protein terms in the bibliographic database, between annotated terms from a controlled biomedical vocabulary. This association allows for the exploration of relationships between human diseases and parts of the proteome that are detectable using current instrumentation. Given a disease, the tool will find proteins and peptides from PeptideAtlas that may be associated, and display relevant literature information from. These associations can be visually explored, and the results directly exported to the experiment design pipeline ATAQS [47] or explored at subsequent levels of visualization with additional data. Figure 4mspecLINE Tool.

Output: corroborate


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Line 305: "…needed for PMF generation in aerobic…" Line 414: "previous results"; does this refer to results in this paper or a prior publication? If a prior publication, it should be cited here. Author response Essential revisions: 1) The methods and results of metabolite analyses are not sufficiently described. Further explanation is required if conclusions are to be made about yield, fermentation, and physiology. We thank the Editor and Reviewers for raising these important concerns. We revised the Materials and methods and Results sections to more comprehensively describe the methods and results of the metabolite analyses. Within the Results, we included new data (Figure 5 and Supplement file 1) showing the levels of formate and pyruvate under extracellular electron transfer and open circuit conditions. We also clarify that we included standards for a number of metabolites, but that our measurements show that these compounds were absent or sufficiently low to not be within the range of detection. Second, we also now provide a more complete picture which allows us to make quantitative conclusions about yield, fermentation, and physiology during EET in L. plantarum. Our new data on formate and pyruvate levels allowed us to track carbon and electrons at ~80% and ~85%, respectively. With this information, we show that EET increases the yield of ATP and fermentation products per mole of mannitol (Ymannitol, Yfermentation in Table 1) by ~1. 75x. This result indicates that EET enables more efficient energy conservation and greater mannitol fermentation in L. plantarum. The better accounting of electrons reveals that L. plantarum uses an ~2:1 ratio of endogenous to exogeneous electron acceptors during EET. Interestingly, the yield of biomass per ATP and per mannitol (YATP, Ymannitol) decreases by ~1. 5x, showing that while EET triggers faster, more efficient energy conservation, these catabolic processes are also less coupled to anabolic processes. Thus, the revised manuscript provides an improved, multifaceted view of this hybrid metabolism. 2) Some claims are overstated and need to be revised or, better yet, backed up with more experimental data.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Namely, when x0 and y0 are breakpoints originally apart from each other on G and are joined together by rearrangement, the pair of breakpoints is represented by point (x0, y0), where, for disambiguation, x0 < y0 is again assumed. Thus, breakpoints are identified with the corresponding points in G × G. When discordant pairs associated with the same breakpoint are clustered together, the breakpoint is within the insert length in G × G from the cluster. Therefore, the cluster of discordant pairs roughly indicates the breakpoint region. Furthermore, the region can be reduced drastically on the basis of detailed analysis of the relationship between the distribution of breakpoints and that of discordant pairs. Forward relationship -- from breakpoints to discordant pairsAssume that discordant pairs (x1, y1), (x2, y2),. . , (x p y p ) are associated with breakpoint (x0, y0). As explained earlier, in disregard of possible short intervening sequences, there are four essentially different rearrangements associated with breakpoint (x0, y0). They correspond to four different conditions on the ordering of coordinates: x i < x0 < y0 < y i x0 < x i < y0 < y i x i < x0 < y i < y0, and x0 < x i < y i < y0 for 1 ≤ i ≤ p. Here, only the first case is considered, since other cases can be treated similarly. Then, the discordant pairs are distributed in a belt in G×G extended in the diagonal direction and located in the anti-diagonal direction from the breakpoint (Figure 1). Figure 1Distribution of discordant pairs associated with a breakpoint. For each breakpoint, associated discordant pairs are distributed in a belt extended in the diagonal direction (at an angle of 45 degrees) located in the anti-diagonal direction (at an angle of 135 degrees) from the breakpoint. This corresponds to one of the four different conditions on the ordering of coordinates; namely, x i < x0 < y0 < y i for 1 ≤ i ≤ p. As for three remaining conditions, relative positions of the associated discordant pairs are rotated 90, 180, or 270 degrees around the breakpoint.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The mspecLINE tool [46] enables the mining of associations between literature information regarding specific diseases and observed peptide spectra. The resulting peptide lists can then be used to generate transition lists for new experiments. The user starts exploration from a specific disease and then all proteins associated with that disease are then discovered. Associations are discovered using an information theory based measure called Distance. The evidence for the associations, and identified proteotypic peptides can then be retrieved or displayed in Cytoscape. Full size imageThe next visualization, called CircAtlas (see Figure 5), provides a high level view of observed proteomic data in PeptideAtlas overlaid with genomic information and the concordance between multiple feature types. Again using the circular view of the genome (as in the Figure 2), observed peptides and protein information is overlaid on chromosomal locations. The zoomed in "track" view of a specific chromosome provides more detailed information regarding the observed data and feature annotations. Finally, this data can be integrated with pathway information and TCGA analytical data to provide a Cytoscape [48] network view (see Figure 6) that provides clinically relevant information about possible disease biomarkers. Figure 5Visualizing PeptideAtlas. The PeptideAtlas can be explored through literature and disease associations (Figure 4) as well as through gene centered views. The genome visualization on the left allows a user to mine observed spectra based on chromosome location, and drill down can be undertaken by selecting a location of interest and viewing available genomic and proteomic annotations. The user starts exploration of the repository through the main genome browser to find genes of interest, information about relationships between genes can be displayed in the center of the viewer. Information about the protein products of the genes, relating to information stored in PeptideAtlas, is shown in the concentric rings. The display on the right provides further details about the protein products, including detectability. Full size imageFigure 6Cytoscape detectable proteins. Using Cytoscape, PeptideAtlas information can be overlaid on TCGA data in a network context, allowing users to locate potential biomarkers within the context of a given cancer network. In this instance genes that have been identified as having being important in cancer progression, through changes in gene dosage, are shown. The diamonds show loci of the genes, and the circles show the genes themselves.

Output: corroborate


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: We also acknowledge that EET could involve other as-yet unidentified respiratory components (lines 478-485 and 504-5) and that we cannot exclude ATP generation by oxidative phosphorylation (lines 482-485). Reviewer #1 (Recommendations for the authors): 1) The only weakness: The methods and results of metabolite analyses are not sufficiently described. This requires explanation if conclusions are to be made about yield, fermentation, and physiology. The paper provides strong evidence that electron diversion to external acceptors is an electron sink. However, while quantitative conclusions are made about fermentation, only ~30% of the mannitol can be accounted for in the EET condition that is the focus of this paper. Put another way, the paper is about where electrons go, but the majority of the electrons are missing. Clarify if this strain can excrete products such as sugar polymers, sugars, pyruvate, formate, glycerol, acetoin, or butanediol. State if standards for these were included in the product analysis (an RI detector can pick up nearly all of these). Explain why formate isn't present (was it not measured? ), as this would allow determination of flux through PFL vs. a pyruvate excretion, a carbon sink of acetoin, or butanediol. Could missing carbon be due to excretion of a partially oxidized sugar like fructose (which would also show up on an RI detector), or accumulation of storage polysaccharide? We thank the Reviewer for the Reviewer’s strong endorsement of our manuscript and clear recommendations for improvements. In the revised manuscript, we provide additional data showing where ~80% of the carbon goes under EET conditions (1) and where the majority of the carbon goes under open circuit conditions. With this more complete information, our balances account for between 77-96% of the electrons (see Supplement file 1). These new data more completely support our conclusion that EET increases fermentation product yield, metabolic flux, and environmental acidification. A) Address these questions in the section around line 299; where fermentation products are mentioned as being present or absent. What was looked for, but not found (lactate yes, succinate no), vs which were not looked for, (pyruvate? Formate? Acetoin, etc). This makes it clear that carbon is not missing, just that the methods were unable to detect it.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Full size imageIn particular, when the inserts are obtained by sonication of a DNA sample, the distribution of the insert length is well-approximated by a Gaussian distribution, N (L, σ), where L and σ denote the mean and standard deviation. Then, discordant pair (x, y) associated with given breakpoint (x0, y0) is distributed such that1 ℓ = (x0 - x) + (y - y0) is distributed according to N (L, σ). 2 x0 - x is uniformly distributed in interval (0, ℓ) on the condition that (x0 − x) + (y − y0) = ℓ. The first condition refers to the distribution of insert length, and the second condition reflects an ideal condition that there is no sonication bias around the breakpoint. Distribution of breakpoints is represented by vector x= (x ij ) indexed by i, j ∈ G with i < j, where x ij = 1 if a breakpoint exists at (i, j) ∈ G × G, and x ij = 0 otherwise. Then, distribution of the discordant pairs associated with these breakpoints is given by y = Ax, where Ais a stochastic matrix determined by the distribution of the insert length. More precisely, given a discordant pair, the probability that it is found at (i, j) ∈ G × G is given by y ijn, a component of y = (y ij ) divided by the number of breakpoints, n. Arepresents a linear transformation of a distribution of breakpoints into a distribution of discordant pairs. In particular, when the insert length is distributed according to a Gaussian distribution, it is given by a composition of three basic linear transformations:A= G σ U L T L (1) where G σ represents a two-dimensional Gaussian diffusion with standard deviation σ, U L represents a one-dimensional uniform diffusion in finite length ±L/ 2 in the diagonal direction, and T L represents a parallel translation in the upper-left anti-diagonal direction by distance L/ 2 These basic linear transformations are mutually commutative. In practice, these linear transformations are discretized so that they work on grid points in G × G.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Information from PeptideAtlas, relating to if it is known if the corresponding protein is detectable, is overlaid. Full size imageResultsOne of the important aspects of this work is that the tools must support active research, where the data sets are continually growing and often changing in scale and complexity [49]. This means that the requirements continually change, and this must be factored into the design of visual analytic tools and the associated software technology choice [50, 51]. In most cases information visualizations require a costly investment in terms of expertise, user feedback, and developer time. Such investment is beyond most research groups, who must put tools in place quickly, and so often a simple, minimal functionality approach needs to be adopted. Instead of focusing on the best solution, we have found that visualizations must be used together, as each visualization has specific strengths in terms of: ability to work with different sizes of data (e. g. responsiveness); portraying generic aspects of information so that they can be used with multiple data sources; ease of use and understanding; and their suitability for specific tasks (e. g. feature identification). In addition to good and scalable design, we have found that there are three important facets to the development of visualizations for large scale data repositories which allow for suitable functionality to be put in place: 1. Rapid development and deployment of visualizations, which allows for the development of tools to suit specific tasks through the use of software technologies. 2. Nested task specific views, which allows for the adoption of best information visualization practices without dramatically increasing development time. 3. Common understandable metaphors, which allows for acceptance as intuitive understanding minimizes learning time. Rapid development and deployment of visualizationsVisualization is a crucial mechanism for discovering meaningful information from research data. The high volumes, complexity and heterogeneity of the proteomics and genomic data repositories means that representations that simply mirror the data are not appropriate. As research is not a static process, but rather an ongoing dynamic investigative endeavour, the visualizations must be able to deal with highly diverse and continually changing types of information. This means that it is difficult, or in many cases impossible, to provide one visualization that suits all users and usage. Instead, it is more effective to adopt a task based approach, where visualizations are provided for specific tasks (or analysis). If required, common visualizations can then be developed and refined.

Output: corroborate


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: plantarum per unit protein and compare this value to reported values for Geobacter sulfurreducens and Shewanella oneidensis MR-1 (lines 140-147). Line 152 – predicted to encode The text was revised to state the predicted protein encoded by pplA. Line 182 – predicted to encode The text was revised to state the predicted protein encoded by ndh2. Line 183 – under the condition tested. The text was revised to incorporate the Reviewer’s suggestion. Line 200 – isn't this already known from the Listeria work? Prior work in ListeriaListeria monocytogenes (Light et al. , 2018) showed that iron reductase activity was associated with the presence of PplA and Ndh2. However, as a respiratory species, L. monocytogenes has a metabolic strategy that is distinct from lactic acid bacteria. Thus the finding that iron reduction is associated with PplA and Ndh2 in lactic acid bacteria is new. Additionally, our work shows for the first time in any bacterium that ndh2 and pplA are upregulated during growth in the presence of DHNA and iron. Line 206 – unclear what 'extracellular redox potential' is, unclear to me if any differences are significant in Figure 3B. I guess this is a measurement of Fe(II) Fe(III) in the medium? Extracellular redox potential is also referred to as oxidation-reduction potential (ORP), which is the ratio of all oxidative to reductive components in the environment (Killeen et al. , 2018 – Am. J. Enol. Vitic. ). The text has been revised to provide a definition of ORP and this additional background to highlight its importance (lines 213-217). Significant differences in ORP were found between the wild-type and mutant strains. Figure 3B was revised to now at which time points there ORP values differed between the wild-type and mutant L. plantarum strains. is correct in that iron (II) iron (III) complexes influence ORP, but there are many other drivers of ORP reduction in the MRS medium, such as LAB consumption of oxygen with NADH oxidase (Zotta et al. , 2017 – J. App. Micro) (see also response to Reviewer #1).

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Backward relationship -- from discordant pairs to breakpointsAn empirical distribution of discordant pairs found by analysis of short-read data is represented by vector y = (y ij ) indexed by i, j ∈ G with i < j, where y ij is the number of discordant pairs found at (i, j) ∈ G × G. Note that it is not normalized as a probability distribution. Since only a limited number of discordant pairs can be obtained from a finite amount of short-read data, the empirical distribution varies from sequencing to sequencing even for the same sample, and approximates to the unknown true distribution. In principle, the distribution of breakpoints can be obtained by solving the following equation:Ax=cy (2) where c is a normalizing constant. However, the equation can be solved more easily by a kind of conjugate gradient method. Namely, by multiplying both sides of the equation by A∗, the conjugate linear transformation (transposed matrix) of A, from the left, A * Ax=c A * y (3) is obtained. The obtained equation is conventionally referred to as normal equation. It is noteworthy that the composite transformation on the leftside is positive semi-definite and self-adjoint: x⋅ A * Ax=||Ax| | 2 ≥0 for any xand (A∗A)∗ = A∗A. Intuitively, A∗Ais a "blurring" transformation. In particular, when the insert length is distributed according to a Gaussian distribution, the composite transformation is given by A * A = ( G σ U L T L ) * G σ U L T L = G 2 σ U L 2 (4) This is a two-dimensional diffusion symmetric both in the diagonal and anti-diagonal directions. Prediction of breakpoint regions by using the conjugateThe breakpoint regions, where x ij is significantly large, are blurred by A∗Aand expanded to wider regions in which z ij where z ij is a component of z= A∗y, is significantly large. Therefore, a prediction of breakpoint regions is given by regions where z ij is significantly large. Since the blurrings occur locally, z= A∗yin each cluster can be computed independently.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: As data production and research are a constantly moving target, any tools provided to mine the data must be developed as quickly as the data is being produced. As creating entirely new tools is a time and developer intensive process, using rapid application development (RAD) techniques, appropriate visual analytics can be quickly provided. By adapting tools and using appropriate frameworks, rather than creating entirely new ones, it becomes practical to create views that are specific to a particular task or research direction. For the work discussed in this paper we adopted a number of standard software frameworks. A number of web-based frameworks for both data and visual development were used that enable both rapid application development and rapid data delivery. These include: the Source libraries for simple, standard data access using SQL database-like language; ExtJS and GXTJS (JavaScript frameworks) for quickly creating intuitive, web-based user interfaces; and web-based visual libraries such as Protovis [52]. Well-known and understood desktop tools such as Cytoscape are also adopted and integrated where appropriate. The use of existing libraries and visual tools has enabled the development of interactive, usable tools (see Figures 4, 5, and 6) within days or weeks rather than months or more. Nested task specific viewsWhile it is necessary to enable general exploration, researchers often need to explore the data from a particular starting point. Our visualizations are generally dedicated to a limited set of tasks however, so providing multiple levels of linked visualization for the data becomes necessary (e. g. cross-population, whole genome, chromosomal location). Information visualization has advocated common workflows and scopes for the development of visualizations which are useful when accessing massive data sets. The macro and micro view [53] ideas have more recently evolved into the information seeking mantra [54] workflow (overview, filter, data on demand). Such a workflow offers a practical approach to the delivery of visualizations, so that they can be accessed using desktop and web based tools. However, development of initial "overview" or macro visualizations is not straightforward with large research data sets, and depends largely on the task that is going to be undertaken. For this reason we have typically provide a number of macro views which can be used to start filtering or analyzing the data. In the PeptideAtlas project the visual tools provide three different "overview" starting points to explore the underlying data.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Significance of alcoholic fermentation for yeast Based on our study of the cross-kingdom microbial interaction in traditional sake making, the significance of alcoholic fermentation for the yeast S. cerevisiae was revisited. One molecule of glucose is broken down into two molecules of pyruvate through a series of reactions in glycolysis. Simultaneously, two molecules of NAD+ are converted to NADH, and two molecules of ADP are converted to ATP. The well-defined significance of glycolysis is undeniably to obtain energy from glucose; ATP is used for various intracellular tasks and converted back to ADP. The byproducts, NADH and pyruvate, are processed in different ways from species to species. In the yeast S. cerevisiae, pyruvate is decarboxylated into acetaldehyde, which is reduced to ethanol by the addition of electrons and protons from NADH, in a two-step reaction (Figure 3). Ethanol is produced to regenerate NAD+ to allow glycolysis to continue. In contrast, in the great majority of bacteria, as well as in animal cells in the absence of oxygen, lactic acid fermentation is widely used to regenerate NAD+ by reducing pyruvate to lactic acid in a single step. Accordingly, alcoholic fermentation requires more metabolic processes and produces a more toxic compound than lactic acid fermentation. It is reasonable to assume that the yeast S. cerevisiae dares to yield ethanol and emit it into the environment. The highly toxic ethanol can kill the other coexisting microorganisms. By acquiring a higher tolerance to ethanol than the other microorganisms, ethanol-producing yeast may increase its dominance in the environment. Thus, yeast may have evolved to yield and tolerate high concentrations of ethanol as the selfish behavior. To combat this, some bacteria may have evolved to inhibit alcohol fermentation of coexisting yeast and maintain microbial symbiosis. Such hypothesis may gain reliability through the study of microbial interactions as described in this review. Ethanol is not only a metabolic byproduct, but also a kind of tool for microbial interactions: the prototype pheromone or quorum sensing factor. Figure 3. The uniqueness of alcohol fermentation in comparison to lactic acid fermentation. Open in new tabDownload slide Closing remarks This minireview discussed the possible significance of switching the carbon metabolic mode of sake yeast by kimoto lactic acid bacteria.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Incidentally, A∗ycan be interpreted intuitively as follows. If there is only a single discordant pair associated with an unknown breakpoint, A∗yindicates a region where the breakpoint is likely to occur (Figure 2); more precisely, z ij a component of z = A∗y, is the likelihood that the breakpoint occurs at position (i, j) ∈ G × G. Thus, the breakpoint is most likely to occur at the maximum component of A∗y. If there are multiple discordant pairs associated with the same unknown breakpoint, the breakpoint is most likely to occur in the overlap among the regions indicated by the discordant pairs (Figure 2). Figure 2Conjugate transformation of an empirical distribution of discordant pairs. When discordant pairs are associated with an unknown breakpoint, each discordant pair indicates, through the conjugate transformation, a region where the breakpoint likely to exist. A prediction of breakpoint region is therefore given by a result of consensus among them at the maximum overlap. Full size imageCollecting relevant read fragments and their extensionsTo find breakpoints in the breakpoint regions, relevant read fragments that partially match with the regions are collected. If the matching length is too small, irrelevant read fragments are also collected with the desired ones. Therefore, the matching length is chosen to be somewhat (e. g. , by three bases) larger than MLU (minimum length for uniqueness). MLU is defined on the reference genome as the minimum length of the subsequence starting from a given position and extending in a given direction such that the subsequence appears only once in both strands of the genome [7]. MLUs over the whole reference genome are efficiently computed, and the results are compactly represented [7]. Figure 3 illustrates how the relevant read fragments and their subsequent extensions are collected. Regions A and B are the projections of a two-dimensional breakpoint region onto each dimension (Figure 3(a)). Figure 3Collecting partially matching read fragments and their extensions. (a) regions A and B (projections of a two-dimensional breakpoint region along each dimension); (b) read fragments exactly matching a query in region A; (c) leftward extensions from the read fragments; and (d) comparison of an extension with each of regions A and B.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Using the mspecLINE tool (see Figure 4) users can mine literature associations through a disease of interest (e. g. breast neoplasm) and find observed peptide spectra that are linked within the literature. These spectra can then be viewed in the context of the genome or directly viewed. Using the Peptidevisualization, spectra can also be searched by a gene of interest or chromosomal location (see Figure 5) then viewed or exported to other tools. Alternatively, data from other experiments (e. g RNASeq) or network inference analysis can be imported into Cytoscape, and then information about the suitability of the associated proteins to act as biomarkers can be overlaid (see Figure 6). Using data from TCGA analyses a separate set of macro visualizations provides users with several methods to search through the data. Starting from the (see Figure 1), a user can explore gene disruption rates across multiple diseases. This list of genes can then be used to mine a single cancer across multiple samples as within the genome visualization (see Figure 2). The genome visualization can then lead further to specific samples where the gene of interest can be compared across tens of patient samples with the disruptions annotated (see Figure 3). Common understandable metaphorsInformation visualization provides a means for the non-expert user to mine, interrogate and interact with information at a highly conceptual level. This conceptualization is through a mental model (or metaphor) of the data which is shared by both the developer of the visualization and the end-user. The majority of information visualizations are based around the idea of providing a metaphor which is easily and immediately understandable enables rich interactions with complex and diverse data sets. In the development of the visualizations discussed above it was found that immediate understanding, typically through the use of common metaphors or positive knowledge transfer, were an important facet of the success of the visualization in portraying information. Rapid understanding and user acceptance of a visualization is important, as it allows scientists to immediately understand what is being portrayed. For example, the Circos plot suffers due to problems associated with the use of atypical non-rectangular based interactions, making it more difficult to use standard mouse drag based operations. However, the familiarity of the metaphor means that people are willing to accept these limitations as they understand both the visual encoding of the information, and are familiar with the layout.

Output: corroborate


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: 2 (PDF) Download author's response letter - submitted May 5, 2014 Version 0. 1 (original submission) · Mar 7, 2014 · As you will see from the detailed comments provided by two expert reviewers, your study is of interest with respect to the microbial communities associated with chicha fermentation. Nevertheless, both reviewers identified a number of major shortcomings, such as those with respect to unsupported health claims, incomplete presentation of data (community composition), complete lack of analysis of the fungal (yeast) component, and lack of data from raw materials as potential source of inoculum. Reviewer 1 · Feb 13, 2014 Basic reporting As far as i can this 3. No proof of domestication nor co-evolution has been demonstrated at all. There also is far too much speculation on a purported link to probiotics, that angle should be softened or removed entirely. Additional comments This is fascinating work on an absolutely fascinating indigenous fermentation. I have several concerns. I find the assumption of a sole human contributor to the chicha fermentation microbiota likely, but unproven, given the substrate wasn't sampled pre fermentation to assess plant-borne bacteria and yeast. believe the relevance to "probiotics" is not supported here. To claim that any lactic acid bacteria showing up in an indigenous fermentation, even if they are of the same species of common commercial probiotics is unfounded and speculative. Moreover it lends to the broadening of the probiotic definition onto anything fermented. The whole probiotics angle of this work needs to be softened if not omitted. This is an interesting fermentation without that overt speculation. Minor concerns: ln49. typical wild ferment is to vague? which one? Cacao? Kimchi, Sauerkraut? this sentence is not correct as Leuconostoc do not start all wild ferments. need to be specific here. Authors need to find a fermentation roughly similar and discuss that. Cassava fermentations have been previously studied. ln 52. lacto-fermentation is not really used. fermentation by lactic acid bacteria is ln 86. The statement “living ferments contain viable probiotics” is not true. The use of the term “probiotic” to cover any microbe in a ferment is simply wrong. That some species of commercial probiotics are found in indigenous fermentations does not make them probiotics per se.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Specifically, when at most d % of the extension can be aligned somewhere in region A (B), they are given by (1 − d/100)e, where e is the extension length. They are functions of x (Figure 4(b)), more precisely, multivalued functions of x because of the multiplicity of extensions. They are thus hereafter referred to as a multivalued edit-distance function concerning region A and that concerning region B. When x, the left end of the query in region A, passes though a breakpoint, the edit-distance function changes drastically as shown in Figure 4(b). On the other hand, when breakpoints do not exist, such a drastic change never happens -- the multivalued editdistance function concerning region A remains zero, and that concerning region B remains large. Thus, breakpoints are detected from the multivalued editdistance function, and their precise positions are also obtained. To provide more confidence in the detection, similar analysis is performed once again with regions A and B swapped. As for the analysis of matched tumor and normal samples, breakpoints are somatic if such a drastic change is found only in the tumor sample but not in the normal sample. Computation of the BWT of short-read dataThe BWT of a large amount of short-read data, typically larger than 100 Gbp, is efficiently calculated by the BWT/WT algorithm [7], which basically follows the BCRext algorithm [6] with DNA sequences represented by wavelet trees [21].

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: context sensitive displays, multiple encoding of information using intrinsic and extrinsic properties, boundaries and brushing [18, 29, 55, 56]). However, practicalities due to the demands of research (i. e. short time scales, small development teams) means that good design always has to be weighed against rapidly delivering multiple visualizations with the required functionality. Visual exploration of the large-scale atlas data sets being produced by the PeptideAtlas projects and to support TCGA analysis allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing task specific views that allow a user to start from points of interest (from diseases to single genes) allows targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as small a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools which follow common metaphors where possible to assist in understanding. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of atlas level data. 658. 1068. 10. 1038/nature073851227. 10. 1016/j. imbio. 2011. 08. 004372. 10. 1146/annurev. genom. 2. 1. 3432659. 10. 1093/bioinformatics/btm4011998. 10. Concurrency: Practice & Experience; 1998. 2003. 2010. Book 2000. , 6(1): 2006. , 12(5): 2003. 2006. , 12(5): 2005. , 11(4): 1993. Book 2002. 49. Communications of the ACM; 2006. 2007. , 23(8): 2011. doi: 10. 1093/bib/bbr069 doi: 10. 1093/bib/bbr069 1987. , 2(4): 1. Computer; 1985. 25. ; 1996. 92. 10. 1093/bioinformatics/btg377444. 10. 1093/bioinformatics/bti1881213. 10.

Output: corroborate


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: (see comment above on overuse of probiotic aspect to this ms) ln 173. Were the samples really frozen at -20 immediately while on site? Really? Not put in on ice and transported? Ln 269. Brew mistress is likely not to be understood. Please define Ln 301-3. Again unnecessary (and in my view incorrect) over emphasis on health benefits. Figure 1 The fact that that strain which most closely matched the short 16S sequence came from a defined probiotic strain does not confirm that the L. acidiphilus or L. reuteri found in that fermentation (which should to be validated by a second method such as tareted QPCR) is a probiotic. This is a misconception by the authors. Saccharomyces cerevisiae is often isolated from the human intestinal tract, that doesn’t make it a probiotic. Figure 3. I think Figure 3 is too speculative. No analysis of the input microbiota on the substrate, nor analysis of the fungal populations have been provided. No detailed genomic analysis of the lactobacilli strains involved has been carried out, ergo no proof of domestication or co-evolution has been proven. This should be removed completely. Cite this review as (2014) #1 of "Local domestication of lactic acid bacteria via cassava beer fermentation (v0. 1)". PeerJ Reviewer 2 · Feb 9, 2014 Basic reporting No comments Experimental design See general comments Validity of the findings See general comments Additional comments This manuscript describes the investigation of chicha fermentations from two villages in Ecuador, demonstrating regional variation in these fermentations. The manuscript presents a fascinating narrative of how chicha fits in the lifestyle of the local people and I really like the goals of this study. However, I have several major concerns, namely with some of the broad-stroke conclusions made (especially regarding beneficial health properties) that do not reflect the results or methodology of the study. I think this study is a great addition to the food/fermentation microbiology literature, but think the health claims come off as rather heavy-handed, when this study really has not been executed to address the potential health properties of chicha. My foremost concern is that the microbial consituents of these ferments are linked to health throughout, whereas the methodology and design do not remotely handle health properties.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 1. To evaluate the performance in estimation of the first loading vector, following [11], we use the sine values of the angle between true loading and estimated loading as the measure of the closeness of two vectors, i. e. When v1 = dist (v1, ) = 0. The summary of estimation performance is given in Table 1. Generally SPCA methods have much better estimation than the ordinary PCA method. Among SPCA methods, the condition-number constrained SSPCA method with HL is generally the best, although the improvement over the unconstrained method is not substantial. The improvement performance of HL over LASSO and EN is small when n > p, but it is substantial when n <p and the underlying signal is not very strong ( = 0. 5). Table 1 Simulation results: estimationFull size tableTo evaluate the performance in variable selection, in Table 2 we report the percentage of selecting the true model (correctly identifying all of the true zero elements), the median number of correctly estimated zeroes divided by the number of true zeroes (true negatives) and incorrect zero estimates divided by the number of true nonzeroes (false negative). Because it does not produce zeroes, the ordinary PCA method never gets the true model and always gets 0 true negatives and 0 false negatives. The HL penalty outperforms the LASSO penalty and generally better than the EN penalty, particularly in identification of the true model. LASSO identifies fewer true negatives compared to HL. methods with the HL and LASSO penalties outperform the corresponding SPCA methods, but here again HL is better than LASSO and EN. The EN performs worst when n <p and the underlying signal is not very strong ( = 0. 5). Table 2 Simulation results: model selectionFull size tableFinally, as a measure of the prediction power, we compute the test sample variance,where is the estimated loading using data matrix and Xtest is the independent test data sets generated from (3) with same sample size n. The results are in Table 3. SPCA methods give better prediction power than the ordinary PCA. Except for the SSPCA method when n <p and the underlying signal is not very strong ( = 0. 5), six SPCA methods have similar prediction power.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Results and DiscussionMethodology: statistical analysisChARMView computational analysis automatically detects regions of non-random spatial bias and is appropriate for any genomic data associated with chromosomal coordinates. Statistical analysis is based on our algorithm ChARM (Region Miner), described in detail in [16]. ChARM identifies potential breakpoints by a differential filter followed by an accurate expectation-maximization approach. The statistical significance of each identified region is evaluated with a one-sample sign test and a permutation-based mean test. By their formulation, the significance tests are valid for any size segment, but do lose power with decreasing segment size. ChARM has been evaluated on gene expression and array CGH data: it is robust and accurate for regions as small as 4–5 probes, and sensitive enough to detect aneuploidies even in mixed populations of cells [16]. As a system for dynamic and real-time data analysis and visualization, ChARMView requires very fast statistical algorithms. However, the permutation-based test as originally described in Myers et al. [16] requires non-trivial computation since it involves performing several thousand permutations of the chromosome order. To speed up the mean permutation test for the software system, we have developed an accurate approximation that requires many fewer permutations. The original version of the test requires computing the mean of the region of interest and comparing this with the means of similar-sized segments in randomly permuted data. We have verified that means of typical chromosomal segments in array CGH and gene expression data can generally be reasonably approximated with a normal distribution. This is a generally well-accepted claim even for small groups (~10) unless the underlying population is extremely non-normal, which is typically not the case for log-transformed array CGH or gene expression data. The statistical significance of predicted aneuploidy region in ChARMView is obtained by computing means of 200 permutations of chromosome ordering of the actual data, estimating the parameters, and then integrating the tail of the underlying distribution beyond the observed value. Figure 1 illustrates the correlation between p-values generated from 10,000 random permutations and p-values obtained from a normal approximation whose parameters were estimated with only 200 permutations. This approximation yields the precision of several thousand permutations based on significantly less computation.

Output: corroborate


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: In the abstract, in the introduction, in the discussion the health benefits of lactic acid bacteria are described and we are led to believe that the lactics detected in chicha must therefore be beneficial to human health. There are several issues with this: 1) Beneficial properties have been demonstrated only in specific strains of lactics. Not every species or strain will automatically have beneficial properties. In fact, many in foods can be detrimental to human health, e. g. , biogenic-amine producing lactic acid bacteria. 2) The methodology used cannot confidently identify species (see below), and thus no comparison should be made between the putative species detected and the probiotic characteristics of other strains in this same species. This is a large leap to conclusions. 3) None of the approaches used in this study relate in any way to human health properties, so even speculation as to the possible health benefits of lactics in these ferments is inappropriate in my opinion. This is the equivalent of automatically conferring probiotic properties to other foods simply based on the presence of lactic acid bacteria. Lactics are found in abundance in many different fermentations, almost all fermented foods in fact (traditional fermentations, at least), and are frequently spoilage agents. Does wine automatically become probiotic, simply because it has a high population of lactics active during the fermentation? All said, I agree that the lactics in these fermentations very well may have beneficial properties when consumed, but it is a very large jump to conclusions that any lactic may be probiotic (especially when the ID is dubious) and health discussions are not relevant in the context of the evidence provided. Second, there seems to be an assumption (e. g. , in Fig 3) that the dominant lactics are human-derived (with the implication that they are also beneficial following consumption), leading to a long-term co-evolution of microbial constituents in these fermentations with their human creators. However, this is not supported by the evidence provided. Lactics are common on plant material as well as in foods and host-associated environments, so could conceivably come from many sources. Sampling the raw materials, built environment, and saliva would have helped address this question (as would strain profiling techniques). Lacking evidence to the contrary, I would argue that the fermentation vessel and raw materials are the most likely sources of these lactics.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Table 3 Simulation results: predictionFull size tableAnalysis of NCI dataIn the analysis of microarray data it is often of interest to co-regulated genes, since they will point to some common involvement in molecular functions or biological processes or cellular states. PCA is a useful tool for such analyses [1–4]; since interpretation depends on comparing the relative sizes of the loading vectors, the sparse loadings in SPCA are much easier to interpret than ordinary PCA. Furthermore, the previous section also shows that SPCA has better estimation characteristics than the ordinary PCA. For illustrations we consider the so-called NCI-60 microarray data downloaded from the CellMiner program package, Institute Only n = 59 of the 60 human cancer cell lines were used in the analysis, as one of the cell lines had missing microarray information. The cell lines consist of 9 different cancers and were used by the Program of the U. S. Institute to screen > 100,000 compounds and natural products. The number of genes is p = 21,225. Figure 1 in the Additional file 1 gives the plots of the estimates of first loading of 21,225 variables (genes) from five PCA methods, and Table 4 shows the proportion of zero coefficients (< 0. 00005) of first loading vector. The ordinary PCA has almost all nonzero loadings (99%); to interpret the results, one must apply a threshold value on the coefficients, but it is not obvious how to choose the threshold. SPCA with LASSO penalty gives only 3% zero loadings, so it is not sparse; HL penalty give more sparse loadings (37. 5% zeroes), but the proportion of nonzero loadings are still quite large. SSPCA with LASSO penalty is slightly improves, with 5. 4% zeroes, but it is still far from sparsity with more than 20,000 nonzero loadings. with HL penalty gives the most sparse loadings, with only 6% nonzero loadings, so we have managed to force almost 20,000 loadings to zero. Table 4 Analyses of NCI data: number of zero loadingsFull size tableTo select the number of principal components, we use a permutation approach as follows. First, we randomly permute the expression values within each sample (row) of X to create permuted data X perm.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Completing a fully automated statistical analysis on a typical gene expression dataset (6000 genes over 16 chromosomes, measured in 16 experiments) requires approximately 7 seconds/experiment for a total of less than 2 minutes on a Pentium 4 3. 2 GHz desktop. ChARMView also allows users to manually select regions to test for statistical significance. Figure 1Gaussian approximation of the permutation-based p-value. ChARMView performs 200 permutations to assess statistical significance of predictions, then uses Gaussian approximation to estimate the p-value. The p-values based on 200 permutations and Gaussian approximation show. 9991 correlation to the p-values based on 10,000 permutations as computed by ChARM. Full size imageMethodology: visualization-based analysisThe most powerful aspect of ChARMView is integration of computational analysis with visualization. This combination of visualization and analysis enables users to view automated predictions of aneuploidies as well as analyze statistical significance of manually selected regions. Visualization is a critical complement to computational analysis as human perception can often identify subtle trends in the data that cannot be detected with purely computational methods. This is especially critical when comparing results of multiple experiments or experimental replicates, such as in cancer studies where researchers often search for recurring aneuploidies in a set of patients. ChARMView facilitates such discovery with visualization of multiple experimental replicates, experiments, and data types. The most common way to increase confidence in results of an experiment is to produce replicate microarray experiments. Data from such replicate experiments is usually averaged for computational analysis. However, viewing such replicates simultaneously is an effective approach to analysis, as people are often perceptive of subtle but repeated trends that are difficult to capture with a statistical test. This visualization-based approach does not make any assumptions, such as independence assumption of the typically used Fisher meta-analysis test [17]. Thus, aligning corresponding chromosomal data from several replicates of the same experiment typically allows the user to spot trends that might otherwise go unnoticed. Figure 2 illustrates this phenomenon with two replicates of the same array CGH experiment. Figure 2Simultaneous visualization of replicate experiments. A set of replicate array CGH experiments from Dunham et al. [5] displayed with ChARMView (chromosome 4 of CP1AB, replicates 1 and 2 shown).

Output: corroborate


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Heat stress response is characterized by the transient induction of general and specific proteins and by physiological changes. In every strain tested the involvement of Proteins (HSPs such as DnaK, GroEL and GroES during the heat stress was clear) [23-38]. The role of these stress proteins is complex; in fact, the bind substrate proteins in a transient non-covalent manner prevent premature folding and promote the attainment to the correct state in vivo. The resistance to heat stress is higher when the cells were previously exposed and adapted to this type of stress in the stationary phase, otherwise, when pre-adapted in exponential phase, the cells are more sensitive. In particular, the storage stability of the culture that was heat shocked after stationary phase was superior to that of culture heat shocked after log phase [34, 23, 30]. Cold shock response: It is very important to improve knowledge about LAB behavior in cold environment. In fact, during industrial processes, like in cheese ripening and refrigerated storage of fermented products, these microorganisms are subjected to different temperatures far below the optimal growth temperature. When LAB living cells are exposed to these cold environments, important physiological changes occur, such as decrease in membrane fluidity and stabilization of secondary structures of RNA and DNA, resulting in a reduced efficiency of translation, transcription and DNA replication. The response of microorganisms to these effects is termed cold-shock response during which a number of Proteins (CIPs) are synthesized. The roles of these proteins are at the levels of membrane fluidity, DNA supercoiling and transcription and translation. Few papers have described cold shock proteins and mechanisms in LAB, in particular they have focused on Lactococcus lactis and L. plantarum [39-42]. Kim et al. [39, 40] tested different LAB to evaluate cold shock effects on cryotolerance. Improved understanding of cold-shock-induced cryotolerance may contribute to the development of environmental conditions that allow improved viability/activity of frozen or freeze-dried commercial LAB starter cultures. The results showed that, as with heat stress, there is also an improvement of the viability of the tested strains as concerning the cryotolerance after a cold shock. The process of freezing appeared to have different effects on different LAB as well as different effects on strains within the same genus.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: We use the same number of top-ranking nonzero loadings as for the SSPCA, which are 1,260, 681 and 375 for the first 3 principal components, respectively. Out of these, the number of overlapping probes between the SSPCA and PCA are 462, 194 and 60. These overlaps are substantially more (up to 8 times more) than expected under random re-arrangement. However, there is sufficiently large number of distinct probes in the two methods, so the GO analyses could be different. The P-values from the SSPCA-based GO analyses are more significant than those from the ordinary PCA; this may be due to better estimation of the loadings, so that the SSPCA has better power than the ordinary PCA in revealing biologically-important grouping of genes. Discussions and ConclusionsPCA is one of the most important tools in multivariate statistics, where it has been used, for example, in data reduction or visualization of high-dimensional data. The emergence of ultra-high dimensional data such as in genomics, involving 10,000s of variables but with only a few samples has brought new opportunities for PCA applications. However, there are new challenges also, particularly on the interpretation of results. If we treat PCA quantities such as the loading vectors as parameter estimates, the large-p-small-n applications typically produce very noisy estimates. This is obvious since the loading vectors are a statistic derived from the sample covariance matrix, and the latter is not well estimated. It is well known that improved estimation can come by imposing constraints, and in this case sparsity constraint is natural. As PCA scores capture some underlying biological processes, we do not expect every gene in the genome to be involved. Out of possibly 30,000 genes we can expect only a small fraction, probably less than 1,000, to be involved in a cellular process. Hence sparsity constraint can help in reducing the number of loading parameters to estimate. Imposing statistical constraints can be achieved by applying a penalty approach as used by the ridge regression or the LASSO methods [6]. In this paper we have investigated a random-effect model approach using a gamma scale mixture, which leads to a class of penalties that includes the ridge and LASSO penalties as special cases. One significant property is that it can produce unbounded penalties on the origin, which leads to stronger constraints and more sparse estimates.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The region identified by the arrows is hard to distinguish from noise in either of the replicates when viewed separately, but is clearly a region of positive bias when the replicates are viewed together. This is confirmed by statistical analysis. Full size imageThe simultaneous display feature of ChARMView is also useful for visual analysis of computational prediction results for multiple experiments. This is an effective method for identifying common genomic aberrations in otherwise uncorrelated experiments or a characteristic aberration in a set of samples with a common phenotype. For example, a set of breast cancer samples [18, 19] can share the same bias in gene expression that corresponds to a predicted aneuploidy or a localized expression bias, as shown in Figure 3. Overlapping predictions serve as independent confirmations that the predicted aberration is real. Furthermore, results of such analysis of multiple samples can then be used to correlate specific chromosomal aberrations with phenotypic or clinical parameters. Figure 3Simultaneous visualization of multiple independent expression microarrays. Simultaneous visualization of overlapping significant expression biases in a set of four independent breast tumor samples from Sorlie et al. [19] (chromosome 6 of breast tumor expression profiles BC208A-BE, BC305A-BE, BC308B-BE, and BC111A-BE shown). Each red bar below the data indicates a predicted aberration identified independently on the corresponding experiment. Full size imageAs array CGH techniques become more widely applied, the generation of copy number data is rarely the end goal of biological studies. Instead, a key challenge is deciphering which parts of a karyotypic profile are responsible for particular phenotypes. While sophisticated statistical and computational methods will certainly be required to answer these questions, the most effective approaches will also need to harness the power of human visual perception. To address this issue, ChARMView can display and analyze both array CGH and gene expression microarray data and display these diverse data and predictions for corresponding chromosomes simultaneously. Simultaneous display of array CGH and gene expression data enables researchers to observe the effect that amplification or deletion of particular sequences of genomic DNA has on the abundance of mRNA transcripts (Figure 4). We have noted a number of cases where large amplifications or deletions result in no detectable change in gene expression. These regions may be less likely to cause a particular phenotype than aneuploidies that result in drastic changes in gene expression.

Output: corroborate


Input: Claim: Europa’s subsurface ocean may harbor conditions suitable for life. [SEP] Text: 하지만 최근 방파제 설계에 적용되는 각 항만의 심해설계 파고의 적용사례를 살펴보면 2002년 심해설계파 이후의 파랑내습 자료를 추가 삽입하여 심해설계파를 재추산함으로써 설계파가 상향되는 경향이 있고, 이로 인해 방파제의 마루높이가 크게는 약 25% 정도 높게 설계되는 결과를 가져오고 있다. 이러한 설계파고와 마루높이 상향적용 현상은 대안/턴키 설계뿐만 아니라 정부에서 시행하고 있는 설계용역에서도 재연되고 있다.

Output: pass


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: pass


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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).

Output: pass