Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Structure sectionThe protein structure section is adapted from the PDB data model [20] to describe experimentally determined protein structures as well as theoretical models. Each monomer is stored as a single structure entry (Structures). The table PDB holds the information on the source of the structure entries. Data on atoms, heteroatoms, coordinates and connectivities is stored in Atoms,Hetatms, Coords and Connectivities, respectively. The sequence corresponding to each structure data set is stored as separate sequence entry. Thus structure-derived properties like secondary structure information (DSSP) are mapped on the sequence and consistently annotated structure data sets are generated. This approach also overcomes the problem of sequence length inconsistencies caused by unresolved residues. ArchitectureThe data warehouse was developed on Linux (3) and is built as a server-client architecture. Scripts for the back-end and query interface were developed in Perl. For the web-based query interface the Apache web server [21] is used. Since the query interface was build as a set of CGI (common gateway interface) scripts, each Internet browser can be used as client software. The usage of the standard Perl CGI modules [22] ensures a high compatibility between scripts and Internet browsers. Some tasks (highlighting or selection of features) have been developed in Javascript. Besides the web-based query interface, the access on SQL level allows to query the underlying data directly. Thus, assisted by the position-specific sequence description, complex queries can be easily performed. Data loadersFor the population of the DWARF data tables, data loaders were developed to parse, transform, and load information form the original source data sets. The data loaders are separated into two main categories: (1) sequence specific data loaders for information derived from GenBank [17] and (2) a data loader for protein structure information on coordinates derived from ExPDB [18] and secondary structure information calculated by DSSP [23]. The sequence specific data loaders extract selected information from the GenBank data sets. These data sets have been represented in the. 1 format, but since recently also XML-formatted GenBank data sets are available. Data loaders for either the ASN. 1 or the XML Genpept format are provided in DWARF. [SEP] Justify:

Output: The text is largely focused on technical aspects of protein structure data and the infrastructure for querying and handling data in bioinformatics, without directly addressing the importance of bioinformatics in analyzing genomic data. Therefore, it does not corroborate or contrast the claim; instead, it is irrelevant to it.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: MethodsInformation theory provides a principled mathematical tool to quantify the amount of information flowing through a channel connecting a pair of nodes that are modeled by random variables. To process multimodal genomic data, we model SNPs by discrete random variables, and describe the gene expression levels by continuous random variables. A channel in the transcriptional mapping indicates that there is a transcription mechanism between the two linked nodes. The flux of transcriptional information between the two nodes is measured by mutual information. This evaluates the degree of their mutual dependence, e. g. , revealing how likely it is that a gene is regulated by a SNP; a measure that includes statistical noise and microarray error. Computations of transcriptional informationWe consider the information between a SNP-gene pair. Since the expressions are described by continuous variables, the common way of expression processing is to quantize them. However, when the sizes of quantization bins are large, the results may deviate from the true mutual information values. In contrast, when the bin sizes are small the computational time increases significantly. We avoid these quantization problems by deriving a closed-form of the mutual information between discrete and continuous variables. j be a discrete variable modeling a SNP with probability mass function p(x j ), and Y m be a continuous variable modeling the expression of a gene with probability density function f(y m ). We begin by discretizing Y m as Y m Δ with bins of size Δ. Then, the mutual information (denoted by MI) between a discrete genetic variable X j and continuous transcript variable Y m is approximated by a discrete case: (1)where H(X j ) denotes the entropy of X j and x jk denotes the k-th configuration of X j. When taking an infinitesimal bin size, the mutual information MI becomes: (2)m is a log-normal variable, it has a closed-form, [14], (3)where σ2 m denotes the variance of Y m. Similarly, its entropy conditional on X j = x jk is (4)where σ2 mjk denotes the variance of Y m conditional on x jk. Substituting equations 3 and 4 into MI 2 leads to the following formula (5)Note that the mean values do not play a role in the definition of the entropy of normal variables. Hence, computing MI merely relies on the marginal and conditional variances of Y m. [SEP] Justify:

Output: The text corroborates the claim by discussing methods in bioinformatics that quantify genetic information flow, specifically focusing on the mutual information between SNPs and gene expression. It details the mathematical modeling and processing of genomic data, indicating the significance of bioinformatics in understanding genetic regulation and interactions, which align with the claim that bioinformatics is crucial for genomic data analysis.


Input: Task: contrast the claim using the given text. Claim: Herbal remedies can replace all pharmaceuticals. [SEP] Text: k or good to kill Liſe 12 the with in water and take a bout one Spoonfull at a time — then wet the Same in Salt water from the Sea to Rub all over the Body, for Eich 13Maſter w[illegible][guess: or]t good for one Sort of headake 14 the and the of willow rt thetake wil rt firſt — one ^Spoon^ good for kings Evil — 15 Page 2r & Poiſon Iviy w16 A Leaf good Boil 17 Maſter over witch Rt 18 A Rt for fits Pound the Rt and Soke in water about half an hour 4 Rts will do[illegible][guess: e] 19 herb take it the Leaves ^&^ th^r^ow them into hot water and Put them upon the wrſts hallow of the [illegible] feet and upon the forehead 20 Page 2v Indian fl[illegible]ax [illegible][guess: a] Rt Boil^d^ good for Bloody flux 21 Indian hamp good for to Draw anything ^out^ of Sores 22 ^Soup^ good to 23 an herb good for worm^s^ 24 Poplar ^Rt^ and teeth good for Ruma^tick^ 25 S[illegible][guess: cen]ts good to make one vomit 26 Page 3r S[illegible][guess: cen]ts alſo good for humerous Sores 27 good for bruiſe 28 Winter-green and anothter herbe boild in 3 Quarts of water till it is Conſum’d to a pint and then take a jill honey to it good for 29 an herb good to make women bear high Sto[illegible][guess: r]k and 30 Page 3v wores Rt good to Draw young mens to Young women 31 a wead good to Reſtrain women from bearing Children — 32 Horſe Penroial and five fingers Leaves boild togather good for Fever-Ague 33 [illegible] An herbe good for bite 34 and Swamp penro^a^l penroial and water Criſſis Seal m[illegible][guess: o� [SEP] Justify:

Output: The text provides numerous instances of herbal remedies being used for specific ailments, suggesting their effectiveness. However, the claim that these remedies can replace all pharmaceuticals is not supported since the text does not imply that they are a complete substitute for all pharmaceutical treatments, which contrasts with the claim.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The protein structure-specific data loader is designed to parse the PDB formatted ExPDB coordinates data set and the DSSP secondary structure description, which is generated for each protein structure chain. Data populationUpon setting up a new protein family database, the initial step of data population consists in selecting seed sequences from classification databases like SCOP [13] or CATH [12], and sequences derived by keyword searches in primary sequence databases. These seed sequences are then used as a starting point for iterative BLAST [24] searches to achieve a complete coverage of the known sequence space for the protein family. searches in the GenBank [17] non-redundant protein database are performed by an automated data extraction system. From the start, seed sequences are assumed to represent a homologous family each. Then new hits are automatically assigned to one of the existing homologous families by a standalone BLAST search against all sequences already present in the data warehouse. For hits that are not sufficiently similar to an already existing entry, a new superfamily or homologous family is created. During the data population process the classification of sequences into homologous families and superfamilies is permanently controlled, new families are created, or homologous families are merged. The resulting GenBank hit entries are retrieved for further processing. Information on organism, original database entries, sequence features, mutations, and sequence are extracted and transformed into the DWARF data model using the sequence data loaders. For sequences that are linked to structure entries, header information and atom coordinates are extracted from ExPDB [18]. For each structure entry the secondary structure information is calculated using DSSP [23]. The protein family databases are updated regularly by an automated Perl script. To update a database, a BLAST search against the current version of the non-redundant sequence database GenBank for each sequence entry is performed. For a sequence entry referring to a new entry in the PDB, structure information is updated as well. New sequences and structure entries are classified into homologous families and superfamilies based on their sequence similarity. Web interfaceis designed as an in-house repository assisting local analysis rather than to be a public accessible resource, there are two interfaces: a browser available to the public and a set of data analysis tools available to the local curator. For public access a HTML-based WWW interface is provided [16], which lists all superfamilies and families stored in the data warehouse. [SEP] Justify:

Output: The text provides a detailed description of the processes involved in creating and maintaining a protein family database, including data extraction, sequence classification, and updates of structural information. However, it does not explicitly address the importance of bioinformatics in analyzing genomic data nor does it directly support or refute the claim. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Computing the TIM of a tremendous number of SNPs and transcripts is a time consuming task. We can utilize parallel computing to enhance computational efficiency. The computations of mutual information for a pair of values described in equation (5) do not rely on other variables. Hence, we can distribute the computations of mutual information over any number of computers. Figure 4 illustrates the distribution of the TIM computation task from a set of SNP and gene expression data. The TIM can be represented mathematically by a mutual information matrix. Each computer in the cluster is responsible for calculating a portion of the matrix elements. Once an element is computed, it is immediately used to determine if the pair of nodes is independent, and the mutual information is only recorded for dependent pairs. Finally, consolidation of all computed mutual information values results in a complete TIM. Figure 4The distributed computing system to construct a TIM from a set of SNP and gene expression data. Each computer in the cluster is responsible for calculating a portion of the TIM. Once the mutual information of a SNP-gene pair is computed, it is immediately used to determine if the pair is independent, and the mutual information is only recorded for dependent pairs. Finally, consolidation of all computed mutual information values generates a complete TIM. Full size imageClusteringVisualizing and understanding results from expression studies of the entire genome at once can be overwhelming. As a result, many researchers have used cluster analysis to distill the genomic data down to a more comprehensible level [26]. There are two main elements to any clustering strategy: (i) a similarity measure that can be used to determine which genes are most related to each other, and (ii) a procedure for joining similar genes into one single gene group, or cluster, that summarizes and preserves the statistical properties and information interactions of the constituent genes, while at the same time reducing the size of the network. In the TIM, linked SNP-gene pairs behave dependently, so we can use this statistical dependency as a similarity measure for cluster analysis. Within the TIM, a SNP linked by noiseless information channels to several transcripts provides evidence that these genes possess similar expression profiles. Conversely, a set of SNPs linked to the same gene share a similar genotypic pattern. Furthermore, a path (i. e. [SEP] Justify:

Output: The text corroborates the claim by detailing the importance of computational techniques in processing genomic data, specifically mentioning the use of parallel computing to enhance efficiency in the computation of mutual information from SNPs and gene expression data. It outlines how these calculations can help in understanding the statistical relationships and dependencies among genetic elements, thereby supporting the overall thesis that bioinformatics plays a vital role in analyzing genomic datasets.


Input: Task: contrast the claim using the given text. Claim: Herbal remedies can replace all pharmaceuticals. [SEP] Text: boild in about 3 Quarts of water till Conſumd to a Quart — good for Heart born 36 An herb good for green wound a Stalk with Buſhee top 37 another wead Somew[illegible]hat Like the other herb good for Cauſed by Cold with a So[illegible][guess: r]e heated Put upon the hinder part the Neck 38 Page 4v water take and boil and take the Rt after it is boil’d and Pound it fine to mLay upon f[illegible][guess: a]x Sore and [illegible]ce of the water of for it is good for it 39 Sweet flag good for 40 Augeet good for buſt take the Rt and Pound it in hot water and lett about a jill at once twice a Day 41 A little Sort of India^n^ flax for a Perſon to take 2 Days together that has Page 5r and then take Saſarfax and milk wead and Pound them together 2 thrids of the firſt and third of the Laſt 42 the Docter muſt kake an herb and Rub his hands with it wile he is Dreſſing the Patien 43 Muſk, with Some his fat and Furr wMixt togather good for Ear ake 44 Page 5v Wauhtouwox and Sap good for flme over the Eye 45 another herb and Saſſar‐fax heart and Speccle beans Good for 46 Herb good ^to^ heal brocken bones fingers and ^about the^ ^[below]foot^ 47 Budes and Small wiled but 2 third of the togeg — good for young Women whoſe Flowers are Stopt by weakneſs of Nature 48 Page 6r Dears horn [illegible] and Mut [illegible][guess: ly youbuck], but the horn ſhoud be gated mak about a jill and boil it in about 6 jills of water and Let it boil away half then the other into it goo for When their Monthly Sickneſs over flows 49 an herb boild in 2 gallon of water and boil it about half away and then Cool it. [SEP] Justify:

Output: The text lists several herbal remedies and their uses for various ailments, suggesting that these remedies are alternatives to pharmaceuticals rather than replacements for all pharmaceutical treatments. By detailing specific herbs and their medicinal properties ('good for green wound', 'good for heart', 'good for ear ache'), the text implies that while herbal remedies have uses, they do not encompass the entirety of medical treatment that pharmaceuticals provide, thus contrasting with the claim that they can replace all pharmaceuticals.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Full size tableThe GX-class consists of 21 superfamilies with known protein structures, where the oxyanion hole-forming residue X is structurally well conserved and is preceded by a strictly conserved glycine. Based on HMM profile-profile comparisons another six superfamilies (abH7, abH10, abH16, abH17, abH21, abH24) that contain no protein structure data were also assigned to the GX class. They also show significant similarity in the oxyanion hole region. The GX-class comprises the families abH7-abH26, abH31-abH37. The Y-class consist of 4 superfamilies with known protein structures, where the oxyanion hole is not formed by a backbone amide, but by the hydroxyl group of a tyrosine side chain, which is strictly conserved within the superfamilies. The Y-class comprises the families abH27-abH30. Database contentThe release 2. 3 (July 2006) of the Database contains 6138 sequence entries for 4322 proteins and thus covers the whole α/β-hydrolase fold. 36% of these proteins are marked as putative. For 41 protein entries, 167 experimentally determined protein structures comprising 574 structure data sets are available. The sequences were collected by performing single iteration BLAST searches (62 scoring matrix, 10-10 as E-value cutoff) for seed sequences against the non-redundant sequence database GenBank. The 4322 proteins were assigned to 37 superfamilies and 103 homologous families. The superfamily abH1 is the largest family and comprises 829 protein entries representing 19% of all Database protein entries. The three largest superfamilies abH1, abH8 and abH34 comprise 1694 protein entries representing 39% of the Database. The length of 93% of all α/β-hydrolase domains is between 181 and 615 amino acids. The minimal and maximal size of the α/β-hydrolase domain is defined by the lipase A from Bacillus subtilis (the smallest lipase known, 181 amino acids) and the cocaine esterase from Xanthomonas citri (615 amino acids), respectively. [SEP] Justify:

Output: The text does not directly address the claim about the importance of bioinformatics in analyzing genomic data. Instead, it provides detailed information on protein classifications and database contents, which does not relate to the claim. Therefore, the relationship is considered irrelevant.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Then the corresponding differentially methylated gene identities can be obtained from the list of DMPs. Minfi offers a comprehensive package to analyze Illumina 450K array data, where candidate regions are determined for DMR analysis and locally weighted scatterplot smoothing (LOESS) is adopted to smooth the methylation differences between groups within each determined region. It also can find long-range alterations such as identified hypomethylated blocks [23] based on “open sea” probes. A empirical Bayes moderated t-test is used in limma [24] when sample sizes are less than 10, in which case M-values should be used as they rely much more on Gaussianity assumption [25]. Generally, DMRs are detected by applying various statistical techniques such as Fisher’s exact test [26, 27], t-test [27], Wilcoxon rank sum test [28] or different regression models [29–31]. Biological interpretationThere is a long list of significant CpGs to be interpreted after differential methylation analysis. Using the Infinium annotation file, Illumina 450K probes are classified according to their relations to CGIs and to the closest annotated gene. Regarding their relation to CGIs, the probes are classified into four categories: sites located inside a CGI, sites located in the CGI shores (0-2k bp), sites located in the CGI shelves (2-4k bp) and sites located in the “open sea”. As regards their relation to annotated genes, the sites are categorized as inside the promoter, inside the 5’-UTR region, inside the gene body and inside the 3’-UTR region. Then the significant DMPs can be marked with their related genes. Performing gene set analysis is a popular way to understand the affected potential gene pathways. Although gene set analysis is well established in gene expression experimental, the research in methylation data is ongoing in different groups. 450K array, the numbers of CpGs associated with each gene ranges largely from 1 to 1299 [32]. Genes with larger numbers of probes are more likely to have significant differentially methylated CpGs [33]. [SEP] Justify:

Output: The text corroborates the claim by describing the use of bioinformatics tools such as Minfi for analyzing Illumina 450K array data and conducting complex statistical analyses on genomic data to identify differentially methylated genes. This indicates the importance of bioinformatics in extracting meaningful information from genomic data, supporting the assertion in the claim.


Input: Task: contrast the claim using the given text. Claim: All microbes can survive in extreme conditions indefinitely. [SEP] Text: Skip to content Menu AGU. org to by AGU for dropdown menu Space & & & & dropdown menu ’ Highlights Editors’ Eos en Español Eos 简体中文版 to for: ininto the Stratosphere? Searching for life on Mars starts at home, with a balloon launch. by Jo9 April 201512 April 2022 Share this:Click to print (Opens in new window)Click to email a link to a friend (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on LinkedIn (Opens in new window) Test tubes full of dilutions of haloarchaea floating in the stratosphere. After the payload is recovered, the attached pack of seeds will be sold to raise money for future student projects. Credit: Earth to Inflating a 2-meter-wide weather balloon is no easy task on a windy day, but these microbes were not going to launch themselves. As the balloon filled with helium, the morning’s launch crew—a mix of high schoolers and recent graduates from California’s High School—secured a test tube full of microbes and a GPS to it. Together, they released the balloon and watched it float over the mountains. The students—part of a club called Earth to Sky Calculus—are no strangers to launching weather balloons. They have conducted more than 75 such launches, with payloads equipped with instruments to measure temperatures, pressures, and radiation levels in the stratosphere. Amelia Yarborough, 18 (left), and Jamie Shultz, 17 (right), secure the balloon while Jordan Herbst, 16 (bottom), fills it with helium. Credit: This particular launch, one of three planned for the first weeks of April, had a more biological purpose—to send microbes to the upper reaches of Earth’s atmosphere. The goal: to see how high-stress environments affect the organisms, a finding that could help reveal how life on other planets might survive extreme conditions. This newsletter rocks. Get the most fascinating science news stories of the week in your inbox every Friday. Sign up now The microbes in question, a salt-loving organism called haloarchaea, can survive extreme temperatures, air pressures, and radiation levels. [SEP] Justify:

Output: The text contrasts the claim by suggesting that not all microbes, but specifically haloarchaea, can survive certain extreme conditions. The text states that the purpose of the balloon launch is to investigate how 'high-stress environments affect the organisms,' implying that their survival may depend on various factors rather than being indefinite for all microbes.


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

Output: The text is irrelevant to the claim as it focuses on the classification and characteristics of protein entries and various classes of α/β-hydrolases without mentioning bioinformatics or its role in analyzing genomic data.


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

Output: The text corroborates the claim by explaining the role of bioinformatics in analyzing genomic data and managing -omics data, such as mRNA and protein expression. It specifically mentions how researchers can annotate genes to ontology entries, which facilitates understanding gene functions in diseases, highlighting the importance of integrated analysis in bioinformatics.


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

Output: The text contrasts the claim by discussing specific experiments that test the survivability of microbes under extreme conditions, such as the stratosphere, which subject them to severe temperature drops and high cosmic radiation. This suggests that while some microbes can survive these conditions for a time, it does not assert that they can do so indefinitely. The mention of potential mutation and the limited resourcing and environment they face indicates that there may be limits to their survivability, negating the idea that all microbes can survive extreme conditions indefinitely.


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

Output: The text does not directly address the importance of bioinformatics for analyzing genomic data. Instead, it describes various data representation models and specific methodologies like binary mutation models and C-score based mutation models without emphasizing the role of bioinformatics in the analysis process. Therefore, it is considered irrelevant to the claim.


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

Output: The text corroborates the claim by discussing various methods for analyzing large genomic datasets, specifically highlighting the use of bioinformatics tools like minfi and meffil. It mentions the performance evaluation of these tools for handling large datasets and addressing technical issues in data analysis, thereby demonstrating the crucial role of bioinformatics in genomic data analysis.


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

Output: The text contrasts the claim by implying that while certain microbes, like haloarchaea, may survive under extreme conditions briefly, their survival is not guaranteed indefinitely. S. DasSarma mentions that modeling in the lab has limited value and that it is essential to understand survival in real-world scenarios. Additionally, the mention of experimenting with conditions similar to those on Mars suggests uncertainties about long-term survival.


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

Output: The text does not specifically address bioinformatics or its role in analyzing genomic data. Instead, it discusses parameter tuning in machine learning algorithms, classification results, and various performance measures related to data representation models. As such, the content is not relevant to the claim.


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

Output: The text corroborates the claim by discussing tools and methods relevant to the analysis of complex data, specifically mentioning 'OmicsVis,' an interactive tool for visually analyzing metabolomics data, which relates to bioinformatics. The focus on visualization and data exploration tools suggests the importance of bioinformatics methods in handling genomic and other types of biological data. It highlights the effectiveness of systems designed for analyzing differences across samples, which is a key component of bioinformatics in genomic studies.


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

Output: The text contrasts the claim by emphasizing the specific conditions under which certain microbes, specifically haloarchaea, can survive—such as being encapsulated within salt crystals and in seasonal salty flows. It does not support the notion that all microbes have the same capability to survive in extreme conditions indefinitely.


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

Output: The text does not address the claim about bioinformatics being crucial for analyzing genomic data. Instead, it discusses the performance of a specific algorithm (BM25-tf-rf) for cancer type classification without relating it to the broader importance of bioinformatics in genomic data analysis.


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

Output: The text corroborates the claim by discussing techniques related to analyzing data using different modes of intensity and difference in sample comparisons, which are indicative of bioinformatics tools and methodologies. It explains how standard deviation calculations and color mapping are utilized to aid in visualizing and interpreting genomic data effectively, which underlines the importance of bioinformatics in data analysis.


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

Output: The text contrasts the claim by discussing the modifications made to plants and animals through genetic engineering, highlighting that these organisms are engineered to be resistant to diseases and herbicides rather than suggesting they will never mutate. Furthermore, it raises concerns about the long-term effects of such modifications, implying that these engineered organisms may still undergo mutations or have unforeseen consequences.


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

Output: The text does not mention bioinformatics or its role in analyzing genomic data. Instead, it focuses on the classification of cancer types and the performance of various mutation representation models without directly linking to the importance of bioinformatics in genomic analysis.


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

Output: The text supports the claim by detailing methods in bioinformatics, such as volume visualization and mass spectrum exploration, which are essential for analyzing differences between healthy and cancer samples. It explains how visual representation of data aids in studying bio-markers and differentiating samples, highlighting the role of bioinformatics in genomic data analysis.


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

Output: The text contrasts the claim by explaining the genetic engineering process used to modify rice for β-carotene production, indicating that the modified organisms can still face issues like insect resistance and adverse environmental effects. Moreover, the text implies that engineered crops can interact with insects and other organisms, leading to changes, thus suggesting that engineered organisms do not necessarily escape the potential for mutation.


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

Output: The text does not directly address or support the claim regarding the importance of bioinformatics in analyzing genomic data. Instead, it focuses on the specific methodologies used for gene classification in cancer prediction, including the use of heat maps and neural networks. There is no mention of bioinformatics as a field or its significance in the analysis of genomic data, making the text irrelevant to the claim.


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

Output: The text corroborates the claim by describing how a specific bioinformatics system enhances the analysis of mass spectra data. It emphasizes the advantages of visualizing multiple samples simultaneously, quickly filtering bio-marker candidates, and calculating mean, standard deviation, and differences among samples, thus aiding researchers in identifying significant differences in genomic or metabolomic data. Additionally, the discussion illustrates the efficiency and speed improvements over other systems, highlighting the importance of bioinformatics tools in processing and interpreting complex datasets.


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

Output: The text contrasts the claim by explaining that gene-edited organisms can be similar to those produced by natural mutations. It states that gene-edited organisms involve 'tiny alterations to existing genes' and are 'often no different than those organisms produced by mutation-inducing chemicals and radiation,' indicating that genetic engineering does not eliminate the potential for mutations to occur, thereby opposing the claim that such organisms would never mutate.


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

Output: The text corroborates the claim by discussing the use of various computational techniques, specifically mentioning how machine learning algorithms are utilized to classify cancer types based on genomic data. The passage highlights the importance of representing genomic mutations effectively to analyze complex diseases, implying that bioinformatics is essential for understanding and analyzing genomic data in a meaningful way.


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

Output: The text corroborates the claim by highlighting the importance of visual analysis and exploration techniques in research, which can expedite the identification of differences and bio-markers in datasets. It mentions that structures like 'our system' provide benefits to researchers by enhancing understanding and confidence in results, which implies an integration of bioinformatics in the analysis of data. Additionally, the text discusses the future directions of data visualization tools, suggesting that advancements in bioinformatics techniques are essential for improving the analysis of genomic data.


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

Output: The text contrasts the claim by highlighting the complex interactions between genes and their environments, suggesting that genetic modifications can have unpredictable effects and may not ensure stability or eliminate mutation. It discusses fears related to genetic modifications, such as the potential for engineered genes to spread to wild relatives, leading to biodiversity loss and adverse biological consequences.


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

Output: The text does not directly address the claim regarding the importance of bioinformatics in analyzing genomic data. It primarily contains references to various studies, acknowledgments, and author affiliations without discussing the role of bioinformatics in genomic analysis.


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

Output: The text corroborates the claim by detailing how genomic features are critical in determining the reliability of predicted interactions. It describes the process of using naïve Bayesian networks to analyze and combine evidence from multiple genomic features, indicating that these features significantly influence the accuracy of interaction predictions. Specifically, it states that the presence of genomic features directly correlates with the likelihood of interactions being true, thus underscoring the importance of bioinformatics in genomic data analysis.


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

Output: The text contrasts the claim by discussing the complexities involved in the propagation of signals, such as the influence of local conditions like electron density and solar activity, which affect the ability to detect signals. It mentions that signals propagate differently depending on geographic latitude, local time, and environmental factors, which suggests that detecting extraterrestrial signals is not as straightforward as the claim implies.


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

Output: The text does not address the claim directly. It discusses various genes, SNPs, and their associations, but it does not explicitly mention bioinformatics or its importance in the analysis of genomic data.


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

Output: The text corroborates the claim by discussing a method for identifying true interactions in high-throughput protein-protein interaction data sets, which relies on analyzing genomic features through statistical methods. The mention of a high sensitivity rate (89.7%) highlights the effectiveness of bioinformatics in analyzing genomic data and validating interaction predictions.