Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 1722. BackgroundOMICs technologies allow the comprehensive and parallel measurement of multiple molecular events (e. g. , DNA modifications, RNA transcription and protein translation) in the same samples. Exploiting such complex and rich data is needed in the frame of systems biology for building global models able to explain complex phenotypes. In order to get useful information, the data must first be mined in search of relevant subsets of features, but classical feature selection methods can potentially fail as they classically test a feature at the time, not considering their potential interactions. Likewise, single-data layers (views) analysed separately could provide incomplete and fragmented information. On the contrary, multi-view leaning approaches take into account the different views simultaneously to reconstruct the underlying structure of the data. They can be benchmarked on real and synthetic datasets. A common problem with real datasets is that they are not fully understood and well annotated, whereas the synthetic data, although under full control, may be too simplistic to efficiently simulate the complex regulatory interactions among the molecules. Different approaches for simulating biological data have been proposed. A first method consists in generating synthetic data with multivariate distributions similar to those observed on the real datasets [1-3]. New data can be generated using models that incorporate phenotypic variation, additive and multiplicative noise, transcriptional activity or inactivity, and/or block-correlation structures. An alternative method focuses on generating data from synthetic transcriptional regulatory networks (TRNs). The main idea is to generate regulatory networks that include different types of biological interactions and produce biologically plausible synthetic gene expression data. An important point of these simulation methods is the computational technique used to quantitatively model the network interactions. A common technique for this purpose is based on solving a set of ordinary differential equations (ODEs) that explicitly model the variation of concentration of gene products. In [4-7], different models for the definition of the interactions are proposed. In [5,6], interaction networks are sampled from existing ones. Starting from a given real network and a seed node of the network, a new network is constructed by sampling the modules of the real network. The main drawback of this method is that the number of possible networks that can be generated is limited by the size of the original network used for sampling. In [4], network topologies are generated based on different theoretical random network models. [SEP] Justify:

Output: The text corroborates the claim by discussing the importance of OMICs technologies and multi-view learning approaches in analyzing complex biological data. It highlights the necessity of mining data for relevant subsets of features and emphasizes the challenges involved in handling genomic data, thereby underlining the role of bioinformatics in deriving meaningful insights from such data. The entire narrative points to the essentiality of computational methods to make effective use of genomic information, supporting the claim that bioinformatics is crucial for this analysis.


Input: Task: contrast the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: 2014,2017; Ramos et al. 2014; Ho, Zhao and Fleet 2015; Ho, Fleet and Zhao 2018; Mota-Gutierrez et al. 2018; Romanens et al. 2018, 2019; Lee et al. 2019). Mannitol production is caused by the use of fructose as alternative external electron acceptor, which results in a higher competitiveness due to extra ATP production by converting acetyl phosphate into acetic acid instead of ethanol formation (Zaunmüller et al. 2006; Gänzle 2015; von Wright and Axelsson 2019). This lowers the ratio of the concentrations of lactic acid to acetic acid, which contributes to a lower acidity of the cured beans (Lefeber et al. 2010, 2012). As lactic acid, such as the volatile acetic acid, may penetrate into the beans during the fermentation process, it may yield too acidic beans for chocolate production. Moreover, because of its non-volatility, lactic acid cannot be removed easily during bean drying and the roasting and conching steps of chocolate manufacturing. Hence, the growth of LAB is sometimes considered as undesirable or even unnecessary for the fermentation process. Whereas the former concern is related to the acidic defects that may occur (lactic acid and acetic acid contribute fresh acid and vinegar-like acid notes, respectively), the latter is mainly based on the fact that LAB do not contribute to desirable ester formation, which is necessary for the floral and fruity aromas of the beans (Ho, Zhao and Fleet 2015; Meersman et al. 2015; Miguel et al. 2017; Ho, Fleet and Zhao 2018; John et al. 2019). Besides those of the yeasts, the metabolic contributions of the LAB should nevertheless be considered as key to a successful cocoa fermentation process, encompassing their early growth on fructose, their citric acid conversion, as well as their production of lactic acid, acetic acid and mannitol, when growth on both glucose and fructose occurs (Lefeber et al. 2010, 2011b; Adler et al. 2013, 2014; and Weckx 2016a,b). [SEP] Justify:

Output: The text contrasts the claim by discussing the negative aspects of lactic acid bacteria (LAB) in the fermentation process, stating that their growth is sometimes considered 'undesirable or even unnecessary.' It highlights how LAB can lead to 'acidic defects' and do not contribute to 'desirable ester formation,' necessary for the floral and fruity aromas of the beans. Therefore, the text suggests that while LAB may play a role in cocoa fermentation, their presence is not always beneficial and can even be seen as detrimental.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Conversely other visualizations, such as the structural variation visualization, are less easy to immediately understand and so general acceptance is limited. In our experience, easy to understand visualizations are those that are used widely, as the researchers themselves typically seek to apply the visualization to other data sets. The metaphors that are familiar are frequently those that are popularly and generically used in visualization (e. g. parallel coordinates) or those that are commonly used in a specific domain (e. g. pathway diagrams, circular genome plots). ConclusionsBiology is a big data science with the added complexity that there is no clear understanding as to how the data may be used. Supporting the scale of data that is being generated has led to the development of a number of large scale repositories, and the need to provide visualization tools to mine this data. This paper discusses work that has been undertaken to provide such visual mining tools, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. We have developed a number of bespoke visual tools, but have preferred to adopt more commonly used designs. In this paper we have discussed a number of visualizations, including: an interactive Circos viewer with context sensitive zooming provided through the track viewer, which shows genomic features and their interactions; Parallel Coordinates, which is used to show and analyze comparisons of genes that are disruptions in different cancers; a table based view, for exploring proteins which are associated with specific diseases and are detectable; and a gene rearrangement viewer, which shows the complexity of localized rearrangements that have been identified through anomalies in read-pairs. We have found that delivery using web technologies is preferable, both due to the low admin requirements and the diverse community using the data. These visualizations each have advantages and disadvantages. The macro views, such as Circos and the parallel coordinates, are relatively easy to understand and use as they provide a high level overview. The nested views, such as the track and gene rearrangement views tend to be more specialized and therefore require some level of learning. Interactivity suffers with the high level views due to the number of items being displayed, this is especially true due to the limitations in web based delivery. Where possible principles of information visualization have been adopted which have been used extensively elsewhere to visualize biological data (e. g. [SEP] Justify:

Output: The text does not directly address the importance of bioinformatics for analyzing genomic data. Instead, it discusses various visualization tools and methods used to present biological data, including genomic features and their interactions. While these visualizations may play a role in analyzing data, the text does not provide evidence or arguments that bioinformatics itself is crucial for this analysis.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Methods for visual mining of genomic and proteomic data atlases access Published: 23 April 2012 Methods for visual mining of genomic and proteomic data atlases John Boyle1, Richard Kreisberg1, Ryan Bressler1 & …Sarah Killcoyne1 Show authors BMC Bioinformatics 13. HYDIN aberrations [34, 35] are known to cause Hydrocephalus (water on the brain), and so the disruption of this gene in the brain produces a aberration that induces a survival physiological change. DNAH3 produces a Dynein protein and has been shown to be over expressed in ovarian cancer, under expressed in GBM [36] and also to be important in APC mutation based carcinogenesis in colon adenocarcinoma [37]. The OR2L13 olfactory gene is one without obvious function, however it is one of the main 44 recurrently mutated genes in this disease [38]. Figure 1b shows a second query, where the selection tool is used to identify all genes that show a high level of structural aberration across all three cancers. All the genes have been identified by others as being important in cancer and generally appear on multiple gene lists as complied by the gene ranker tool [39]. The three genes that score lowest on this tool are PKHD1 [40] which is known to be involved in colorectal adenocarcinoma, DYNA9 which is involved in cilia transduction signals related to tumorgenesis important in Hedgehog and Wnt pathways [41], and SYNE1 which has recently been implicated in GBM [42]. SYNE1 is followed through the linked tools in Figures 2 and 3 to show the types of information that can be discovered and visualized. Full size imageFigure 2Single cancer view. The genome visualization provides a high level (macro) view to show similarities and differences across samples within a single cancer (Ovarian Cancer). This visualization is based around Circos [33], and allows for the display of high level aggregated features as concentric circles, with connecting arches showing identified associations, in this case common translocations. The concentric rings show, in this instance, information about the genes and karyotypes, and can include experiment information (e. g. identified mutations, methylation, expression). The associations are calculated, and in this instance show genes that have similar levels of disruptions. [SEP] Justify:

Output: The text corroborates the claim by discussing methods of visual mining for genomic and proteomic data. It emphasizes the use of genome visualization techniques to analyze structural aberrations across different cancers and the importance of various genes in cancer research. The mention of tools that aggregate and visualize genomic data supports the assertion that bioinformatics plays a critical role in analyzing such data.


Input: Task: pass the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Although lactic acid itself may not play the same role as acetic acid in modulating bean pH, peptides and flavanols, the effects of citric acid conversion in the pulp (avoiding citric acid transfer into the beans) and reduction of fructose to mannitol (possibly diffusing into the beans) can result into other processes that contribute to the cocoa flavour than those caused by acetic acid. This refers in particular to the conversion of amino acids in the pulp (contributing to cocoa flavour production as yeasts do) and the provision of a sweet and cool taste due to mannitol accumulation (Lefeber et al. 2010, 2011b; and Weckx 2016a,b; John et al. 2020). As is the case for acetic acid, lactic acid may display a flavanol-protective effect, i. e. reducing polyphenol degradation (Evina et al. 2016; John et al. 2019). Desirable volatile organic compounds contributing to the cocoa flavour produced by LAB encompass pyruvate metabolites (diacetyl and acetoin; buttery/creamy notes) and amino acid conversion metabolites, such as the aldehydes 2-methylbutanal and 3-methylbutanal (malty and chocolate notes) produced from isoleucine and leucine, respectively, as well as benzaldehyde (almond and bitter notes) produced from phenylalanine (see below). The exhaustion of the energy sources glucose and fructose, a continuous increase of the ethanol, lactic acid and acetic acid concentrations, and a rise in temperature of the fermenting cocoa pulp-bean mass eventually cause a decline of the LAB counts and a further decline of the yeast counts (Schwan and Wheals 2004; and Weckx 2016b). The LAB species diversity recovered from fermenting cocoa pulp-bean mass is rather restricted. Fructophilic species that occur in the early stages of the cocoa fermentation process encompass Fructobacillus pseudoficulneus, Fructobacillus tropeaoli and Leuconostoc pseudomesenteroides, as well as Lactobacillus plantarum, the latter species being reclassified as Lactiplantibacillus plantarum (Ardhana and Fleet 2003; Camu et al. 2007, 2008a; Gálvez et al. [SEP] Justify:

Output: The text does not directly corroborate or contrast with the claim about lactic acid bacteria (LAB) being important for fermentation. Instead, it discusses various aspects of fermentation in the context of cocoa, mentioning LAB species and their metabolites, but does not provide a definitive argument regarding their importance in fermentation processes. Hence, it is considered irrelevant to the specific claim.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Further laboratory experiments are required to further evaluate these predictions. Figure 5 shows a screenshot of our application upon finishing automated statistical analysis of one of these experiments. Figure 5Screenshot of ChARMView applied to S. cerevisiae array CGH data. Screenshot of ChARMView analysis of S. cerevisiae molecular evolution experiments data from Dunham et al [5]. The right panel displays array CGH data arranged in the order of chromosomal position and amplification (red) and deletion (green) predictions. The left panel displays information for the selected region, including gene names and values and statistics for the selected amplification prediction. Full size imageWe also present two specific instances from an array CGH breast cancer study where ChARMView can be used to visualize and accurately predict breakpoints of known amplifications. Figure 6A illustrates the results of ChARMView's automated statistical analysis on chromosome 1 array CGH profiles of three different breast tumor samples (110B, 112B, 122A) from [18]. The entire q arm of chromosome 1 is known to frequently amplified in breast cancer (typically observed in approximately 50–60% of tumors [20, 21]). Thus, we expect the amplications here to begin at or near the centromeric end of the q arm. ChARMView predicts breakpoints 3, 1, and 0 probes from the centromeric end of the q arm for samples 110B, 112B, and 122A respectively. Figure 6Predicted amplifications and deletions on breast cancer array CGH data. ChARMView automated predictions on three breast tumor array CGH profiles (110B, 112B, 122A) from chromosome 1 and three profiles (123B, 309A, and BC-A) from chromosome 17 of [18]. The predicted chromosome 1 breakpoints (identified by arrows in Figure 6A) are 3, 1, and 0 probes from the centromere. The predicted chromosome 17 amplification common to all three profiles (identified by arrows in Figure 6B) includes 7 genes known to be typically amplified with the ERBB2 locus. All visible predictions have Bonferroni-corrected p-values less than. 05 for both mean and sign significance tests. 2 for a complete list of breakpoint predictions for each of the results pictured. Full size imageChARMView can also be used to accurately find much smaller regions of amplification or deletion and the associated breakpoints. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the application of a specific tool, ChARMView, for visualizing and predicting genomic data in the context of breast cancer studies. While it mentions the analysis of genomic data, it does not explicitly state the importance of bioinformatics as a whole in this process.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Selection of information allows for drill down into the related data sets, and filters can be applied which allow for control over the amount of information displayed. Full size imageFigure 3Individual sample comparator. The individual sample comparison tool allows for sets of patients to be explored. It shows disruptions at the gene or sub chromosome level and shows the complexity of gene disruptions between patients and normal/disease pairs. Using the cancer comparison (Figure 1) and genome focused (Figure 2) views, regions or genes of interest can be mined from hundreds of samples and then smaller sets of samples can be visually compared. In this instance on the right hand side are cancer samples, and on the left hand side are the matched normal tissues. The visualization displays the level of rearrangement at the chosen loci. The rearrangements can be complex and involve multiple crossovers or translations across different loci. To accommodate such complexity a nested layout procedure is used, where the main x-axis shows the scaffold chromosome, and the graph that is drawn directly from this shows represents how the rearrangement has resulted in connections between new non-contiguous portions of the chromosome (the thickness of the connecting curves gives an indication as to the portion of reads that show this level of structural variation). For complex multi-site rearrangements this branching procedure is repeated using nested graphs. The amount of disruption, and degree of gene fusion or similar, can then be visually compared. Color coding is used to show different chromosomes, and coverage information is displayed below the x-axis. The system is interactive so selecting on different loci will allow for further exploration filters can also be applied to change the patients being viewed. Full size imageMillions of spectraThe PeptideAtlas repository contains thousands of experiments and is designed to provide a compendium of the likelihood of a given peptide being detected on mass spectrometers. The repository contains information from thousands of mass spectrometry experiments (millions of spectra) across numerous species, tissues and disease conditions. The goals in providing this repository are both to annotate genomic information with observable peptides, and to provide an integrated view on a given proteome so it can be used as the basis of Monitoring (SRM) experiments. PeptideAtlas can be used to identify representative (proteotypic) peptides that are unique to an individual protein. [SEP] Justify:

Output: The text corroborates the claim by detailing the complex processes involved in analyzing genomic data using bioinformatics tools. It discusses how information selection allows for in-depth exploration of genomic data, and emphasizes tools for comparing individual samples, visualizing gene disruptions, and analyzing data sets from numerous experiments. Furthermore, it describes repositories like PeptideAtlas that integrate genomic information with proteomic data, highlighting the importance of bioinformatics in extracting insights from such complex datasets.


Input: Task: contrast the claim using the given text. 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). [SEP] Justify:

Output: The text contrasts the claim by discussing the diminishing role of lactic acid bacteria (LAB) in cocoa fermentation. It highlights that acetic acid bacteria (AAB) are deemed more crucial in that context, as their growth can lead to undesirable acidity levels in cocoa beans and that LAB's contributions are minimized when controlled cocoa fermentation processes are analyzed. Specifically, the text states that the growth of AAB is sometimes considered unnecessary compared to yeasts in terms of producing desirable volatile organic compounds, which indicates that LAB may not play a significant role, thus undermining the importance claimed.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not address the significance of bioinformatics in analyzing genomic data. Instead, it describes functionalities related to a specific software tool (ChARMView) for performing predictions and exporting results. While it mentions analysis-related features, it does not explicitly support or refute the claim about the overall importance of bioinformatics.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by highlighting the importance of bioinformatics in mining data for analyzing proteomic experiments. It emphasizes how integration strategies are required to connect peptide data with genomic sources, thereby illustrating bioinformatics' role in refining biomarker detection related to diseases. The mention of using integrated data to improve the analysis and understanding of genomic information supports the assertion that bioinformatics is crucial for such analyses.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text contrasts the claim by providing a more complex understanding of Lactic acid bacteria (specifically L. plantarum) and its metabolic processes. While it discusses energy conservation and fermentation yield, it indicates through various results that the energy processes are less coupled to anabolic processes, which might suggest limitations in the fermentation efficiency or reliability of these bacteria alone in beneficial fermentation outcomes. The revised data showing 'while EET triggers faster, more efficient energy conservation, these catabolic processes are also less coupled to anabolic processes' implies potential concerns about relying solely on lactic acid bacteria for fermentation.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not provide any information regarding the importance of bioinformatics in analyzing genomic data. Instead, it focuses on specific technical details related to breakpoints and discordant pairs in genomic analysis without making a statement on the overall significance of bioinformatics.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text discusses various tools and methods used within bioinformatics, such as the mspecLINE tool and Cytoscape, highlighting their roles in analyzing genomic and proteomic data. It emphasizes the integration of peptide and protein information with genomic data, which supports the claim that bioinformatics is crucial for analyzing genomic data.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not address the importance of lactic acid bacteria in fermentation processes. Instead, it discusses various aspects of electron transfer and carbon tracking during fermentation without mentioning lactic acid bacteria specifically. Therefore, it does not corroborate or contrast the claim, leading to the conclusion that it is irrelevant.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not address the importance of bioinformatics for analyzing genomic data. Instead, it discusses the statistical distribution of insert lengths and breakpoints in the context of DNA samples and does not relate to the claim.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by emphasizing the importance of visualizations in uncovering meaningful information from large-scale genomic and proteomics data repositories. It points out that due to the high volumes, complexity, and heterogeneity of such data, simply mirroring the data is inadequate, which underscores the necessity of advanced analytical tools in bioinformatics. Moreover, it discusses how visualizations must adapt to meet the evolving demands of research, further highlighting the crucial role of bioinformatics in the analysis process.


Input: Task: contrast the claim using the given text. 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). [SEP] Justify:

Output: The text contrasts the claim by indicating that while lactic acid bacteria (LAB) have distinct metabolic strategies compared to other bacteria like Listeria monocytogenes, it suggests that the findings regarding iron reductase activities associated with certain proteins in LAB could be seen as new. The mention of a different metabolic strategy implies that LAB might not be directly comparable to other species in terms of fermentation processes or their significance, which challenges the broader assertion of their importance in fermentation.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses mathematical transformations and predictions related to discordant pairs and breakpoints in data analysis but does not mention bioinformatics or its importance in analyzing genomic data.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not directly address the claim about bioinformatics being crucial for analyzing genomic data. Instead, it discusses the development of tools and frameworks for data visualization and management in a broader context, which may indirectly relate to bioinformatics but does not explicitly corroborate or contrast with the claim.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text discusses the significance of alcoholic fermentation in yeast, specifically S. cerevisiae, and contrasts this with lactic acid fermentation in bacteria. However, the focus is predominantly on yeast fermentation and its mechanisms rather than directly addressing the claim about the importance of lactic acid bacteria for fermentation processes. Therefore, the text is largely irrelevant to the claim.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses technical aspects of identifying breakpoints in genomic data but does not address the importance of bioinformatics as a discipline in analyzing that data. It lacks any reference to bioinformatics, making it difficult to establish any connection.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text supports the claim by outlining various tools and methods for analyzing genomic data, specifically mentioning how the mspecLINE tool can mine literature associations linked to genomic information and how data from experiments like RNASeq can be analyzed and visualized in specific tools like Cytoscape. This indicates the importance of bioinformatics in processing and interpreting genomic data.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text contrasts the claim by expressing skepticism regarding the specific contributions of lactic acid bacteria in the context of indigenous fermentation. It states that any lactic acid bacteria found in an indigenous fermentation do not necessarily function as probiotics, arguing that the assumption of their importance is 'unfounded and speculative.' Additionally, it criticizes the broadening of the probiotic definition and notes that there is a lack of evidence supporting their role in the fermentation process, stating, 'The whole probiotics angle of this work needs to be softened if not omitted.' Thus, it suggests that the role of lactic acid bacteria may not be as crucial as implied in the claim.


Input: Task: pass the claim using the given text. 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]. [SEP] Justify:

Output: The text does not directly address the claim regarding the importance of bioinformatics in analyzing genomic data. Instead, it provides details about specific computational methods such as the multivalued edit-distance function and BWT calculation for short-read data without making a clear statement about bioinformatics’ overall role in genomic analysis.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by describing how bioinformatics tools and visualizations enable researchers to explore and analyze large-scale genomic data from projects like PeptideAtlas. It emphasizes the importance of providing task-specific views that facilitate targeted exploration of genomic information, which is essential for making sense of large datasets in genomic analysis.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text contrasts the claim by questioning the methodology and conclusions related to health properties linked to lactic acid bacteria within the fermentation processes discussed. It emphasizes that the authors' conclusions about the benefits of lactic acid bacteria, especially their health properties, are overly broad and not adequately supported by the study's findings or methodology. Furthermore, the critique mentions the lack of detailed analysis of the lactobacilli strains involved and argues that the health claims are unwarranted, thereby undermining their importance in the fermentation processes presented.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 1. 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. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses various statistical methods for model selection and estimation performance related to PCA and SPCA, without mentioning bioinformatics or its significance in genomic data analysis.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by providing details on the role of bioinformatics, specifically through the discussion of the ChARM algorithm and its application in analyzing genomic data, such as gene expression and array CGH data. It highlights that the statistical analysis conducted by ChARM is essential for detecting genomic regions, thus underscoring the importance of bioinformatics in analyzing and interpreting genomic data.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text contrasts the claim by questioning the beneficial properties of lactic acid bacteria in fermentation processes. It states that not every strain of lactic acid bacteria will necessarily have beneficial effects and highlights that many strains can actually be harmful, particularly pointing out that the study's methodology does not reliably identify beneficial strains. The author emphasizes that it is inappropriate to assume beneficial properties based merely on the presence of lactic acid bacteria in fermented foods, suggesting a lack of evidence to support the idea that all lactic acids are important for beneficial fermentation.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not address the claim directly and primarily discusses the analysis of microarray data using various statistical techniques like PCA and SPCA. While it entails aspects of data analysis relevant to genomics, it does not explicitly establish bioinformatics as crucial for such analyses.


Input: Task: corroborate the claim using the given text. 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). [SEP] Justify:

Output: The text corroborates the claim by emphasizing the integration of computational analysis with visualization in the context of genomic data. It explains how tools like ChARMView enable statistical analysis of gene expression datasets and help identify trends through visualization, which enhances analytical precision and understanding, thereby underlining the importance of bioinformatics in genomic analysis.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text discusses heat stress and cold shock responses in lactic acid bacteria (LAB), focusing on their physiological changes and the synthesis of stress proteins. However, it does not directly address the role of LAB in fermentation processes. Therefore, while it mentions LAB, it does not provide relevant information supporting or contrasting the claim regarding their importance specifically for fermentation.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not directly address the claim about bioinformatics being crucial for analyzing genomic data. Instead, it discusses various statistical methods, including PCA and its challenges related to genomic data analysis, without explicitly stating the importance of bioinformatics in that context. Thus, it is considered irrelevant.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by discussing various computational methods and statistical analyses used in genomic data analysis. It highlights the importance of simultaneously visualizing array CGH and gene expression data to understand how genomic alterations affect phenotypes, suggesting that bioinformatics plays a key role in deciphering complex biological questions. Furthermore, it explains how tools like ChARMView aid in visual and computational analysis, reinforcing the assertion that bioinformatics is essential for analyzing genomic data.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the design and engineering aspects of breakwaters and does not mention Europa, its subsurface ocean, or the potential for life, thus failing to provide any support or contrast to the claim.