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

Output: corroborate


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

Output: pass


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

Output: pass


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

Output: corroborate


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

Output: pass


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

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: From our results it seems clear that the penalty approach alone is not able to yield sufficiently sparse PCA for high-dimensional genomic data. Additionally we also need the shrinkage on the singular values of the data matrix. In simulation studies we show that the proposed methods outperform existing methods both in estimation and model selection. Hence we believe that the new SPCA methods are promising tools for high-dimensional data analyses. For future works, it will be of interest to apply super-sparse technique in this paper to locally-linear methods of dimensionality reduction (e. g. [13]]), partial-least squares (PLS) regression and classification methods (e. g. [14]), or other high-throughput data analysis method where dimensionality reduction is used (e. g. [15]). algorithm for PCAStandard algorithms for SVD (e. g. [16]) give the PCA loadings, but if p is large and we only want to obtain a few singular vectors, the computation to obtain the whole set of singular vectors may be impractical. Furthermore, with these algorithms it is not obvious how to impose sparsity on the loadings. [10] described a algorithm that works like a power method ([17], p. 523) for obtaining the largest eigenvalue of a matrix and its associated eigenvector. algorithm computes only a singular vector at a time, so it is efficient if we only want to extract a few singular vectors. Also the steps are recognizable in regression terms, so the algorithm is immediately amenable to random-effect modification as needed to obtain the various SPCA methods proposed in this paper. First we review the ordinary algorithm: Set the initial value of z1 as the first column of X, then 1. Find 2. Normalize 3. Find z 1: z 1 ← X T v 1 4. Repeat steps 1 to 3 until convergence. To obtain the second-largest singular value, first compute residual then apply the algorithm above by replacing X by X2. Sparse PCA via random-effect modelsTo impose sparseness on the PCA loadings we first introduce the regression framework into step 1 of the algorithm. j as the j th column of X, following [18] we havewhere v1jis the j th element of the p × 1 vector v1 (the first loading vector), and ∊ j is an error term.

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: pass


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


Input: Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: [302] using isolated perfused dog brains all found that the influx of glucose measured using tracers could exceed the net flux by two to threefold. This is a direct, experimental demonstration that there is efflux across the blood–brain barrier that can be as large as two-thirds of the influx. This would of course be less under conditions of increased metabolic demand. Glucose distributes rapidly between intracellular and extracellular water within the parenchyma and thus its volume of distribution is close to the total aqueous volume, which is VD = 0. 77 mL g−1 [310,311,312,313,314,315]. Footnote 14 Pfeuffer et al. [316] used diffusion weighted NMR to distinguish between intracellular and extracellular glucose and found that only 19% of the glucose in the parenchyma was extracellular which is in agreement with the fraction of water that is extracellular. These observations imply that glucose transport across the membranes of astrocytes and neurons is rapid compared to the rate of metabolism. When glucose concentrations in plasma are near 6 mM, the average concentration of glucose in brain water is roughly 1. 3 mM (see Sect. 5. 3. 2). Even two fold changes in the concentration in brain water have little effect on the cerebral metabolic rate of glucose, CMRglc, because these concentrations are substantially greater than the Km for phosphorylation of glucose by hexokinase (0. 04–0. 05 mM [317,318,319]) and hence hexokinase, the first step in glucose metabolism, remains nearly saturated (compare e. g. [313]). It is unclear why the passive glucose transport at the blood–brain barrier is mediated by a carrier rather than by a pore. Pores have the advantage that they do not undergo any large conformation changes during transport of each substrate. Hence they are capable of high turnover numbers, which would seem to be an advantage. On the other hand carriers allow more complicated coupling of transport between different solutes and it is possible that during transport of a relative large solute like glucose, it is easier for a carrier than the “open hole” of a pore to prevent unwanted transfer of other solutes. (Water can probably get through both carriers and pores.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The other methods accept BED files containing information other than genome coordinates, but only RGmatch and ChIPSeeker return the additional columns in the output file. Association resolutionA unique feature of RGmatch is its ability to report associations at the exon, transcript, or gene level. and Seq2pathway only report associations at the gene level, whereas HOMER and ChIPSeeker can report associations at the gene or transcript level. Location of the region2pathway, and ChIPSeeker report the area of the gene where the region overlaps for each association. nor CisGenome return this information. 2pathway, and GREAT let users modify the basic parameters (related to the maximum distance) used to associate a region to a gene. HOMER, on the contrary, always associates the region to a gene no matter how far it is. RGmatch and ChIPSeeker also allow the user to modify the length of some gene areas, as well as the priorities for annotating the association with the gene area. In addition, RGmatch offers a flexible definition of the association rules, while this is not possible in HOMER or Seq2pathway. Supported speciesand ChIPseeker work with any organism as long as the user provides the GTF annotation file. However, the annotations must be converted to Txobjects for ChIPseeker to function. 2pathway, and CisGenome only work with the species list they provide; at the moment, GREAT and Seq2pathway support four species assemblies each, (both support hg19, mm9, and mm10, plusdanRer7andhg38 in GREAT and Seq2pathway, respectively), and CisGenome supports 12 different species. OutputAll of the algorithms compared return a tabulated file containing the region-gene associations and some additional information. Only RGmatch and ChIPSeeker preserve the original columns in the BED file when more than the three mandatory columns containing the genomic positions are provided (e. g. coverage, quality, p-values, etc. may also be included in the region BED file). RGmatch also allows the user to choose the gene identifier to be reported among all the identifiers in the GTF file. and ChIPseeker, the user can choose between gene and transcript IDs, CisGenome reports the gene ID, and GREAT returns gene names.

Output: pass


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

Output: corroborate


Input: Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: 7. 1. 1 Evidence for transport of soluble Aβ across the blood–brain barrierThe ways in which soluble Aβ can be transported across the blood–brain barrier have been investigated by several different groups. Shibata et al. [62] were the first to propose that Aβ could cross the blood–brain barrier by transcytosis mediated by low density lipoprotein receptor related protein (LRP1). This they said could account for the loss of 125I-Aβ1-40 from the brain that they observed. In support of their proposal they found that the loss of total 125I from the brain was reduced by antibodies against LRP1, by receptor (LRP1) associated protein (RAP), which interferes with binding of all known substrates to LRP1, and by absence of apoE seen in knockout mice. (ApoE affects the interaction of Aβ with LRP1). In addition the elimination process appeared to be saturable with Km of 15 nM. All of these observations are consistent with the idea that the elimination of soluble 125I-Aβ1-40 is primarily efflux across the blood–brain barrier and is via an LRP1-dependent process. However it should be kept in mind that demonstrating the importance of LRP1 is not the same as demonstrating elimination via the blood–brain barrier because LRP1 is also present on neurons, astrocytes and vascular smooth muscle cells where it can mediate endocytosis of Aβ leading to its metabolism inside the cells [448, 456, 459] (see [146] for further discussion). Further results supporting the involvement of efflux have been reported by Bell et al. [429], who found that the rate constant for elimination of Aβ1-42 was about half that for Aβ1-40 and also in other papers by Deane, Zhao, Nelson, Zlokovic and coworkers [452, 454, 460]. Results from several other groups also support the idea that efflux of soluble Aβ does occur at the blood–brain barrier and that LRP1 is involved in this elimination. Jaeger et al. [461] showed that antisense oligonucleotides against LRP-1 substantially decreased the loss of Aβ1-42 after intraparenchymal injection. Pflanzner et al.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: All the methods except CisGenome report the distance between the gene and the region. and Seq2pathway return the area of the gene overlapped by the region. The gene area definitions are similar forand RGmatch, or at least they can be made almost equivalent by tuning the RGmatch parameters. However, the column containing the gene area in the HOMER and ChIPseeker outputs also contains additional information so this column cannot be directly used in further analyses where a categorical classification of the gene areas is needed (see output examples in Additional file 1). Another unique feature of RGmatch and Seq2pathway is that if a region can be associated with two or more overlapping genes, all of them are reported as different rows in the output file, while the other methods only provide one associated gene in these cases. Quantitative comparisonTo quantitatively assess the functionality of our approach, we compared RGmatch to HOMER and CisGenome using a public set of genomic regions. We discarded GREAT and Seq2pathway from the comparison because they follow a completely different approach to associate chromatin regions, meaning that the results are not directly comparable. We also decided not to include ChIPseeker because it is very similar to HOMER. The public set of genomic regions, containing 2638 regions, comes from a human ChIP-Seq experiment, and was downloaded from the Archive (SRA) with accession number GSE55727. The annotation (GTF file) was downloaded from Ensembl GRCh37. 75. In order to make the outputs comparable between the methods, the RGmatch report was performed at the gene aggregation level, the maximum distance for reporting associations was set to 1000 kbp to allow at least one association per region, the promoter length was set to 0, and the TSS area was set to 1kbp. The rest of the parameters were left at their default values. We used the default parameters for HOMER. To run CisGenome, first the GTF was converted to refFlat format using the gtfToGenePred tool from the University of Institute, and then the BED file was converted to COD format using the file_bed2cod tool provided by CisGenome. CisGenome was then run setting the distance limits to 1000 kbp and leaving the rest of the parameters at their default values.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: ✓✓Q07Show the genes, transcripts and exons located between locations 10 K and 20 K on chromosome II of A. niger. ✓✓Q08Find pathways in U. maydis that involve cellulose, and find related pathways and the name of the molecular entities linking the pathways. ✓Q09Get the translated protein sequence of the shortest exon of gene fgenesh1_pg. C_scaffold_1000115 in A. niger. ✓✓Q10Get the reactions and pathways in S. thermophile where Thiamine diphosphate is a reactant or a product. ✓Q11How many enzymes from S. thermophile belong to the Starch and sucrose metabolism pathway? ✓Q12Return the GO terms associated with gene UM00020 in U. maydis. ✓Q13Return the molecular functions (from GO) of the best homolog (BLAST_score > 50) to the protein from P. chrysosporium with sequence: >phchr_7266✓✓✓Q14Find all the paths from Galactinol to D-Galactose in the galactose metabolism pathway that does not go through manninotriose. ✓✓Number of queries in aspect:34455The queries cover five major aspects of data mining in biology: aggregation, algebra, graph and networks, integration and sequence handling. To quantify the quality of an answer to a particular query, we compared the output of each warehouse to our manually curated gold standard and computed six metrics from raw true positive, false positive and false negative counts. F-scores, defined as the weighted harmonic mean of the precision and the sensitivity, are widely used in information retrieval to provide a single measurement for the overall accuracy of a system. The F1-scores achieved by the warehouses (Figure 1a) indicate that InterMine and PathwayTools produce more accurate results than BioMart and BioXRT. Other output-quality metrics show similar results (Table 3). Figure 1:Accuracy of the warehouses on a benchmark of 14 queries. (a) F1-scores achieved by the data warehouses; (b) F1-scores of the warehouses for five types of queries.

Output: corroborate


Input: Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: The role of p-glycoprotein (Pgp) has been considered in many studies [243, 473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490] that indicate that it does play a role, but there have also been studies suggesting that it does not [491,492,493,494]. P-glycoprotein is present in the luminal membranes of the endothelial cells (see Sect. 4. 2. 1). With LRP1 mediating entry of Aβ into the endothelial cells from ISF, an obvious role to suggest for p-glycoprotein is Aβ efflux to plasma. Another function of p-glycoprotein may be to return to plasma some of the Aβ brought into the cells by RAGE [423, 495, 496]. However the intervening steps between endocytosis mediated by either LRP1 or RAGE and efflux by p-glycoprotein remain to be established. The overall net flux of Aβ across the blood–brain barrier is thus seen to be the resultant of a number of transport mechanisms mediating both inward and outward fluxes. The use of complexing agents in plasma to reduce Aβ flux from blood-to-brain is one strategy being tried to reduce Aβ accumulation in the brain. 5. 7. 1. 2 Evidence for Aβ elimination via perivascular routesThe perivascular route has also been considered as a likely pathway for elimination of Aβ peptides from the brain. In initial studies, following exogenous Aβ introduction into the brain, aggregates were first found along the external boundaries of arterial walls [497, 498] (see also [499, 500]) but at later times were seen throughout the smooth muscle layer of the arteries ([497], see also [501]). The results from these initial studies are consistent with the idea that growth of the deposits starts occurring adjacent to an efflux route for Aβ along the outside of the arteries, i. e. an extramural periarterial route. Subsequent studies followed the routes of exit from the parenchyma of fluorescent dextran. This was used as a non-metabolizable marker for substances of the size of Aβ.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Regions corresponding to chromosomes X and Y were removed from the BED file used for all of the algorithms because CisGenome does not take them into account, which left a total of 2592 regions. Each of the final 2592 regions was associated with a single gene by HOMER and CisGenome. RGmatch returned 3406 associations due to overlapping and quasi-overlapping genes. The percentage of common associations reported by the three methods was high (Fig. 4). Almost 100 % of the associations called by RGmatch were also reported by HOMER and/or CisGenome. However, RGmatch reported 739 associations that were not called by the other two methods. Most of them (731) were due to the fact that RGmatch can associate regions to two different genes, so one of the two genes is reported by the other two methods, but the second gene is only reported by RGmatch. The reason for the remaining 8 associations, that were exclusively detected by RGmatch, was because RGmatch associated the region to the closest gene (which was downstream), while HOMER associated it to a more distal gene in an upstream area. There is no clear reason why CisGenome returned a different association for these cases. The associations that were common to RGmatch and only one of the other two methods were generally also due to RGmatch associating the region to two overlapping (or quasi-overlapping) genes whereas HOMER reported one of the two associations and CisGenome reported the other. Fig. 4Venn diagram showing the number of region-gene associations obtained with the and CisGenome methodsFull size image We also observed that, in some cases where the methods returned different results, the associated region was far away from the genes. RGmatch associated the region to the closest gene, even if the region was downstream from the gene. In these cases, CisGenome tends to associate the region to a gene with an upstream annotation (even if it is not the closest gene), while HOMER either does the same or chooses a downstream annotation but to the second closest gene. RGmatch and HOMER also report the area of the gene where the region overlaps. However, the definition of the gene areas reported by these two methods is not exactly the same.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: RGmatch: matching genomic regions to proximal genes in omics data integration 15 Proceedings of for Integration and Analysis 2015 access Published: 22 November 2016 RGmatch: matching genomic regions to proximal genes in omics data integration Pedro Furió-Tarí1, Ana Conesa1,2 & Sonia Tarazona1,3 BMC Bioinformatics 1. Area: Area of the gene (or transcript) where the region falls. Distance: Distance from the TSS or TTS to the midpoint of the region. When the region overlaps a gene, the distance reported is 0. PercRegion: Percentage of the region that overlaps the area of the gene reported. PercArea: Percentage of the reported area overlapped by the region. If the input BED file had more columns than the three mandatory ones, these columns are attached in the output file after the PercArea column. The associations rendered by RGmatchat the three different aggregation levels for the two examples shown in Fig. 2, according to the rules described and using the default parameters, are shown in Table 1, and to illustrate how the algorithm works some of them are also described below. Table 1 Table showing the results at the exon level for the example shown in Fig. 2 Full size table Region 1 (1_3400_3700) from Fig. 2a overlaps Gene 1 and Gene 2. Gene 1 has two different transcripts. If we report at the exon level, RGmatch returns all the areas of the different genes overlapped by the region. In this example, Region 1 overlaps the entire ‘TSS’ (100 %) and part of the ‘1st_exon’ (5. 94 %) of both transcripts of Gene 1, and part of the ‘gene_body’ and ‘intron’ areas of Gene 2. RGmatch reports the different overlap percentages, except for introns (for which it returns a -1 result). Of the total length of Region 1, 66 % overlaps the ‘TSS’ of Gene 1 (for both transcripts) and the ‘intron’ of Gene 2. According to the previously described rules, given that this percentage is higher than the 50 % set as the threshold, these areas will be returned when reporting at the transcript level.

Output: corroborate


Input: Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: In the extramural proposal movements of solutes parallel to the vessel occur primarily in a perivascular space with lower resistance to flow. They also move in and out of the wall by a combination of diffusion and convection, shown as the red trajectories. As discussed in Sect. 3. 1 the nature of the extramural pathway is still controversial including whether it is a space one side or the other of the pial cells or alternatively the pial and glial basement membranes themselves. endo endothelium, s. m smooth muscle, BM basement membrane. Pial cells and pial basement membrane(s) are shown together because they are very thinFull size image The importance of the perivascular route for Aβ elimination may be not so much that it removes Aβ from the parenchyma but rather that it delivers Aβ into the vessel walls of arterioles and arteries. Cerebral amyloid angiopathy is often seen before formation of senile plaques within the parenchyma (see e. g. [508]) and the damage to the arterioles and arteries may have secondary consequences for the well-being of parenchymal cells, either by effects on blood flow or via local inflammation [509,510,511]. 5. 7. 2 Relative importance of metabolism, blood–brain barrier transport and the perivascular route for elimination of soluble AβAttempts have been made to estimate the proportions of soluble Aβ removed from the brain by metabolism, by transport across the blood–brain barrier, and by perivascular efflux. It is possible to get an estimate of perivascular elimination alone using inulin. When this was done in mice, Shibata et al. [62] found that the half-time for the elimination of 125I-Aβ1-40 was much shorter than could be explained by elimination by the perivascular route, with calculated rate constants of 0. 027 min−1 and 0. 0029 min−1 respectively (see Table 5). As they had concluded that metabolism played little part, the faster, non-perivascular elimination was held to be transfer across the blood–brain barrier. Bell et al. [429] (see 42. It is interesting to note that Xie et al. [128] found the half-lives for both Aβ and inulin to be different when the mice were asleep as compared to when they were awake.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: HOMER defines their ‘promoter-TSS’ as the region comprising −1kbp to +100 bp and the ‘TTS’ as from −100 bp to +1kbp. In order to cover the same areas, we defined our ‘TSS’ area as −1kbp to −1 bp and removed the ‘promoter’ area. This way, HOMER’s TSS area was equivalent to ours plus the first 100 bps from our ‘1st_exon’ area, and our ‘Downstream’ area was equivalent to Homer’s TTS and Intergenic area, etc. (see all the equivalences in Table 3). Table 3 Equivalences between the gene areas defined by RGmatch and size table Table 4 shows the number of associations reported by HOMER and RGmatch with equivalent annotations for the region location (in green), accounting for the vast majority (more than 95 % of the reported associations). Associations where the gene area did not agree are indicated in red. Discrepancies are due to regions overlapping several areas of the gene. In such cases, the true location of the region in the gene is unclear. chooses the area overlapping the midpoint of the region, the RGmatch annotation is based on the overlap percentage and on the priorities chosen by the user, allowing them to fine-tune the association results depending on their analysis goals. Table 4 Annotations for the region location within the gene returned by RGmatch (columns) and HOMER (rows)Full size table In summary, the association results from RGmatch are comparable to the results provided by other methods. Nevertheless, RGmatch is more flexible than other approaches because it allows the rules used to compute the associations, and annotate them with the region location within the gene, to be defined by the user. Moreover, it returns all the possible associations when the region overlaps more than one gene (overlapping or quasi-overlapping genes), and the output is easier for the user to understand and re-use. To check the efficiency of the algorithms, we compared the computation time and memory used when running the algorithms on the full human ChIP-seq example (2638 regions, including the X and Y chromosomes) with the human reference genome annotation GTF file.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: In the gene-level report, both Gene1 and Gene2 are associated with Region 1 (overlapping genes). For Gene1, the association is annotated to ‘TSS’ since both transcripts had the same annotation. Region 3 from Fig. 2b overlaps Gene 3 and Gene 4, and has a percentage of overlap of 33. 28, 33. 61, 11. 65, and 21. 46 % with the ‘TSS’, ‘promoter’, ‘1st_exon’, and ‘intron’ regions of Gene 3, respectively. When reporting at the transcript or gene aggregation levels, since these overlap percentages do not exceed 50 % in any case, we have to look at the percentage of each gene area overlapped by the region. Two different areas (‘TSS’ and ‘1st_exon’) are completely overlapped with a percentage higher than 90 %, and so they are tied. In this case the algorithm returns the area with the highest percentage of the region overlapping it, which corresponds to the TSS (33. 28 %). The same procedure also has to be applied to Gene 4, this process results in the same TSS annotation. Therefore, Region 3 will have two associated genes reported with the ‘TSS’ annotation (quasi-overlapping genes). RGmatch provides many configuration options and the user can modify the priorities and rules followed to associate a region with a gene area. The following arguments can be optionally set by the user: Report: Argument to select the aggregation level for the report. By default, it is set to ‘exon’ and all possible associations to all the different areas of a gene or genes where the region overlaps will be reported. When it is set to ‘transcript’ or ‘gene’ the rules explained above are applied. Distance: By default, a region will be associated with a gene if it is closer than 10 kbp. TSS: Area starting at the transcription start site of a gene and finishing t bp upstream from that point. By default, t = 200. TTS: Intergenic area starting at the transcription termination site of a gene with a length of s bp. By default, s = 0, so this area is not considered unless this parameter is modified by the user.

Output: corroborate


Input: Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: Ca2+, Mg2+ and some vitamins and hormones the major route of entry is secretion by the choroid plexuses followed by distribution around the brain in CSF. However, the discovery of specific transporters for many substances at the blood–brain barrier and the finding, based largely on the work of Cserr and associates [82, 83, 126, 525], that efflux of solutes from the parenchyma to CSF was slow with half-lives of many hours led to the view that entry of most substances to the parenchyma was via the blood–brain barrier. There were early suggestions that movements of large molecules between the parenchyma and CSF could be faster than seen in Cserr’s work. The first of these was the work of Wagner [68] followed by Rennels et al. [69, 135] on entry and exit of horseradish peroxidase. Within an hour of being added to CSF, it could be seen outlining blood vessels deep in the parenchyma. Somewhat later Shibata et al. [62] found that the half-life of inulin was shorter than expected and Groothuis et al. [131] found that when rats were anaesthetized with barbiturates the half-life for sucrose was as Cserr had seen with albumin, but when animals were either awake or anaesthetized with ketamine/zylazine the half-life was much shorter (see Table 1). The recent explosion of interest in perivascular routes for delivery to and removal of substances from the parenchyma stems largely from observations made using two photon fluorescence microscopy and magnetic resonance imaging [25, 526]. These led to the proposal that there is a “glymphatic pathway” through which CSF flows into and within the parenchyma propelling “the waste products of metabolism into the paravenous space” [136]. As discussed in this review there is convincing evidence for perivascular routes of access to the parenchyma from CSF and also exit from the parenchyma to CSF and/or lymph. However, the balance of available evidence does not support a “glymphatic pathway” for flow through the parenchyma.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: , 2009)HadoopInvoking external utilitiesMapping and genotypingBowtie and SOAPsnp (Li et al. , 2008)Halvade (Decap et al. , 2015)HadoopInvoking external utilitiesMappingBWA and GATK (McKenna et al. , 2010)Myrna (Langmead et al. , 2010)HadoopInvoking external utilitiesMapping and gene expression profilingBowtie (Langmead, 2010)CS-(Chen et al. , 2015)Sparkre-implementationMappingBurrows-Wheeler transformSparkSW (Zhao et al. , 2015)Sparkre-implementationMappingSmith-WatermanSparkBWA (Abuin et al. , 2016)SparkInvoking external utilitiesMappingBWA (Li and Durbin, 2009)MetaSpark (Zhou et al. , 2017)Sparkre-implementationFragment recruitmentSeed and extendSparkhitSparkBoth native and externalFragment recruitment, mapping, genotyping, taxonomy profiling and gene expression profilingSeed and extend and a collection of toolsTools. Platform. Implementation. Application. Methods and tools. Cloudburst (Schatz, 2009)Hadoopre-implementationMappingSeed and extendCrossbow (Langmead et al. , 2009)HadoopInvoking external utilitiesMapping and genotypingBowtie and SOAPsnp (Li et al. , 2008)Halvade (Decap et al. , 2015)HadoopInvoking external utilitiesMappingBWA and GATK (McKenna et al. , 2010)Myrna (Langmead et al. , 2010)HadoopInvoking external utilitiesMapping and gene expression profilingBowtie (Langmead, 2010)CS-(Chen et al. , 2015)Sparkre-implementationMappingBurrows-Wheeler transformSparkSW (Zhao et al. , 2015)Sparkre-implementationMappingSmith-WatermanSparkBWA (Abuin et al. , 2016)SparkInvoking external utilitiesMappingBWA (Li and Durbin, 2009)MetaSpark (Zhou et al.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: RGmatch took 32 s to obtain the results and required 1 GB of RAM memory. In contrast, HOMER took 1 min and 30 s and required up to 3 GB of RAM. CisGenome was almost instantaneous, since some prior extra work had been performed. These calculations were performed on an Intel(R) Xeon(R) CPU E3-1225 V2 @ 3. 20GHz machine. RGmatch has been designed in order to check only the proximal annotations for each region. This implies that it is highly scalable despite having a large number of regions. In our tests, RGmatch obtained results in 15 s using a file with ~25,000 regions, 50 s with ~200,000 regions and 122 s with ~600,000 regions in a 2. 4 GCore i5. The slowest step is the internal ordering of the regions and annotations, but the association step is really straightforward. ConclusionsAs sequencing technologies evolve and studies that integrate gene expression with chromatin features become more common, the need to associate genomic regions to genes in order to understand regulatory mechanisms has increased. Although there are a number of publicly-available tools to perform this task, most of them have limitations in terms of flexibility or usability. In this work, we present RGmatch, a user-friendly tool for matching genomic regions and genes (as well as transcripts or exons), which reports the area of the gene where the region overlaps. RGmatch supports all species as long as the user provides the GTF file with the reference genome annotation. The tool is a freely accessible Python script, which promotes integration into broader analysis pipelines. RGmatch is a valuable resource for facilitating analysis in multi-omics experiments involving gene expression and different types of chromatin features. The main advantages of RGmatch, when compared to the state-of-the-art methods, are its flexibility for the user to define its association rules, gene areas, gene identifiers to be reported, and priorities for the gene area annotation when the region overlaps different areas of the gene, as well as its ability to report associations at different aggregation levels. In addition, when a genomic region overlaps several genes, all the associations are returned. Hence RGmatch provides a biologically meaningful set of rules and parameters that can be tuned by users to adapt the associations to their p43. 67. 25. 22. 2414. 89. 501. 300. 3043. 3. 2015.

Output: corroborate


Input: Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: For instance for Oatp1a4 (Oatp2) expressed in Xenopus oocytes suspended in low bicarbonate solution, increased concentrations of a variety of solutes present inside the cells, including taurocholate, glutathione, and glutathione conjugates, stimulate influx of labelled taurocholate [621]. However, when Oatp1a4 is expressed in a HeLa cell line suspended in bicarbonate buffered solution, the influx of taurocholate seems to be coupled to efflux of bicarbonate [622]. This coupling with bicarbonate has been confirmed using a number of different Oatp transporters expressed in CHO cells [623]. Exchangers are able to perform secondary active transport by coupling the downhill transport of one solute to the uphill movement of the other. Thus the demonstration of uphill transport from brain to blood might correspond to abluminal secondary active transport into the endothelial cells driven by an outward gradient of something like glutarate or glutathione or to luminal primary active transport out of the endothelial cells via an ABC transporter or to both. For instance PAH may be taken up into the cells by secondary active transport via Oat3 and subsequently expelled from them by primary active transport via an ABC transporter, possibly MRP4 [560]. Further work is required to establish the interplay of the effects of the various transporters. TfR has been shown to be accessible to antibodies on both surfaces of the endothelial cells [280, 624]. This suggests that transferrin could bind to or dissociate from TfR on either side. Furthermore there is effective transfer across the blood–brain barrier of low-affinity antibodies to TfR [625], which strongly suggests that there can be transcytosis of substances bound to the TfR. In terms of one current conceptual model (see Figure 1 in [280]) there are two possible fates of transferrin and iron after endocytosis. In the first fate, holo-transferrin is exocytosed across the abluminal membrane as in the original hypothesis. However the current consensus is that relatively little holo-transferrin is in fact exocytosed on the brain side [262, 279,280,281,282, 626,627,628].

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: , 2017)Sparkre-implementationFragment recruitmentSeed and extendSparkhitSparkBoth native and externalFragment recruitment, mapping, genotyping, taxonomy profiling and gene expression profilingSeed and extend and a collection of toolsTable 1. Open in new tab Features of different cloud based bioinformatics tools Tools. Platform. Implementation. Application. Methods and tools. Cloudburst (Schatz, 2009)Hadoopre-implementationMappingSeed and extendCrossbow (Langmead et al. , 2009)HadoopInvoking external utilitiesMapping and genotypingBowtie and SOAPsnp (Li et al. , 2008)Halvade (Decap et al. , 2015)HadoopInvoking external utilitiesMappingBWA and GATK (McKenna et al. , 2010)Myrna (Langmead et al. , 2010)HadoopInvoking external utilitiesMapping and gene expression profilingBowtie (Langmead, 2010)CS-(Chen et al. , 2015)Sparkre-implementationMappingBurrows-Wheeler transformSparkSW (Zhao et al. , 2015)Sparkre-implementationMappingSmith-WatermanSparkBWA (Abuin et al. , 2016)SparkInvoking external utilitiesMappingBWA (Li and Durbin, 2009)MetaSpark (Zhou et al. , 2017)Sparkre-implementationFragment recruitmentSeed and extendSparkhitSparkBoth native and externalFragment recruitment, mapping, genotyping, taxonomy profiling and gene expression profilingSeed and extend and a collection of toolsTools. Platform. Implementation. Application. Methods and tools. Cloudburst (Schatz, 2009)Hadoopre-implementationMappingSeed and extendCrossbow (Langmead et al. , 2009)HadoopInvoking external utilitiesMapping and genotypingBowtie and SOAPsnp (Li et al. , 2008)Halvade (Decap et al. , 2015)HadoopInvoking external utilitiesMappingBWA and GATK (McKenna et al. , 2010)Myrna (Langmead et al.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: in through your institution Filter BioinformaticsThis issuesearch term Advance 10. 1093/bioinformatics/btx808 Published: 15 December 2017 10. 1093/bioinformatics/btx808 Select format. ris (Mendeley, Papers, Zotero). enw (EndNote). bibtex (BibTex). txt (Medlars, RefWorks) Download citation issuesearch term issuesearch term increasing amount of next-generation sequencing data poses a fundamental challenge on large scale genomic analytics. Existing tools use different distributed computational platforms to scale-out bioinformatics workloads. However, the scalability of these tools is not efficient. Moreover, they have heavy run time overheads when pre-processing large amounts of data. To address these limitations, we have developed Sparkhit: a distributed bioinformatics framework built on top of the platform. ResultsSparkhit integrates a variety of analytical methods. It is implemented in the Spark extended MapReduce model. It runs 92–157 times faster than MetaSpark on metagenomic fragment recruitment and 18–32 times faster than Crossbow on data pre-processing. We analyzed 100 terabytes of data across four genomic projects in the cloud in 21 h, which includes the run times of cluster deployment and data downloading. Furthermore, our application on the entire Project shotgun sequencing data was completed in 2 h, presenting an approach to easily associate large amounts of public datasets with reference data. Availability and implementationSparkhit is freely available at: informationSupplementary data are available at Bioinformatics online. 1 and transcriptome projects have provided unprecedented knowledge and insights for life science by interpreting large amounts of next-generation sequencing data (Consortium and Auton, 2015; Peterson et al. , 2009; R Genomes Project, 2014; Wyatt et al. , 2014). These genomic datasets, sequenced from thousands of samples, take days or weeks to analyze on private clusters (Bao et al. , 2014). For researchers with limited computational resources, processing terabytes (TB) of data remains a fundamental bottleneck. Although several existing tools have been developed and utilized on single computers (Bray et al. , 2016; Langmead and Salzberg, 2012; Li and Durbin, 2009; Li et al. , 2009, 2008; Niu et al.

Output: corroborate


Input: Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: 29. Amsterdam: Elsevier; 1968. p. 159–72. 77. 39. 91. 8. 39. 5. 340. 111. 311. 716. 40. 2005. 20. 3. 5629. 10. 68. 32. 76. 7680. 86. 51. 407. 26. 44. 1. Cambridge: Press; 1998. p. 188–97. 511. 25. 49. 94. 92. 305. 92. 70. 10. 14. 1979. 1996. 38. 8. 8. 5. 600. 99. 71. , Advances in pharmacologyLondon: Elsevier; 2014. p. 147–63. 31. 9. CAS PubMed 1. Cambridge: Press; 1998. p. 24–31. 13. 315. 9. CAS PubMed 44. 85. 86. 5024. 15. 84. 504. 501. 8635. 7679. 9. 2017. p. 105–27. 36. CAS PubMed 44. CAS PubMed 13. 76. 2. 64. 152. 2582. 81. 7. 5. 22. 9. 36. 94. 48. 85. 2018. Review: cerebral amyloid angiopathy, prion angiopathy, and the spectrum of protein elimination failure angiopathies (PEFA) in neurodegenerative disease with a focus on therapy. Neurobiol. 2013;39:593–611. 23. 9. 14. 14. 38. 26. 9. 41. 1434. 67. 9738. 2018. p. 1057816. . Magnetic resonance imaging provides evidence of glymphatic drainage from human brain to cervical lymph nodes. Sci Rep. 2018;8:7194. 65. 84. 8. 9. 40. 9. 6. 70. 52. 34. 73. 28. CAS PubMed 7. 2000. Exp Neurol. 1974;45:50–60. 103. , Handbook of experimental pharmacologyBerlin: Springer; 1992. p. 245–61. 56. 20. 32. 14. 63. 30. 94. 37. 46. 51. 64. 72. 42. 2017. p. 17–25. 2017. 103. 5793. 85. 53. 63. 51. 2. CAS 1943. 1967. 1952. 21.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Durations include pending for approval of EC2 spot request and waiting for SSH connection to each EC2 instance. service. (B) Run times for processing all WGS data from the Project. Mapping was carried out using Sparkhit-recruiter while profiling was carried out using Sparkhit invoked Kraken. (C) Run times for processing 15 TB BAM files of the 3000 Project. We uploaded the variant calling result to Amazon S3. (D) Run times for processing 5. 6 TB compressed sequencing data. Mapping was carried out using Sparkhit invoked BWA aligner. We uploaded the SAM files to Amazon S3. (E) Run times for processing 3. 2 TB RNA-seq data. Gene expression profiling is carried out using Sparkhit invoked Kallisto. (F) Fast access to genomic data on public repositories. Datasets of the Project, the 3000 Project and the 1000 are hosted in different regions on Amazon S3. Whereas the RNA-seq data of a prostate cancer transcriptomic study is stored on the ENA ftp serverOpen in new tabDownload slide 2 Materials and methods 2. 1 Spark cluster’s architecture and cloud deployment The architecture of Sparkhit is organized by a Spark cluster, which virtualizes each computer and forms a network consists of one or more master node(s) and worker nodes. To organize all virtual compute nodes, a master node is selected to supervise worker nodes, allocate resources and balance workloads, whereas worker nodes carry out assigned tasks (Fig. 1A). On such architecture, Spark extends and integrates the MapReduce model into RDD’s functions, where the ‘map’ step loads, processes and saves data in parallel on each worker node and the ‘reduce’ step sorts, shuffles and aggregates intermediate data. We used Spark-ec2 and BiBiGrid ( a Java-based tool developed in our research group, to easily setup Spark clusters on the Amazon cloud with one command line (see Supplementary file 1 Method-1. 2). 2. 2 Parallel data downloading Downloading and decompressing large genomic files are significant bottlenecks before the actual data analysis begins. Spark’s architecture can greatly benefit from high-performance networks during large data transfers.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: , 2011; Wood and Salzberg, 2014), most of the parallelizations on multi-computer networked clusters are done by manually splitting and distributing in smaller batches (Droop, 2016). To be compatible with a cluster, methods involving message passing and graph representation between computers must be re-implemented with higher level programming interfaces (Gropp et al. , 1996). To address this challenge, we need both easy-access to large computational infrastructure and bioinformatics tools that are compatible and scalable on such platforms. Cloud infrastructure and platform services (IaaS and PaaS) have been well established in the informatics discipline (Dean and Ghemawat, 2008; Schadt et al. , 2010; Shvachko et al. , 2010; Zaharia et al. , 2012). To fully exploit distributed cloud computing systems, methods and programs should be scalable, fault tolerant and platform independent. In genomics applications, there are several tools [e. g. ABySS (Simpson et al. , 2009) and Ray (Boisvert et al. , 2010)] using the message passing interface (MPI) for distributed implementations. However, programming on top of MPI has to tangle with thread synchronization and load balance. Moreover, there is no complete fault tolerance mechanism built inside of MPI. The conventional framework is designed to offer higher scalability and a supervised fault tolerance mechanism by providing a distributed data storage system called HDFS (Shvachko et al. , 2010) and a distributed computing engine, Hadoop MapReduce (Dean and Ghemawat, 2008). Cloudburst (Schatz, 2009) is a Hadoop based sequence mapping tool programmed in the MapReduce model. The common seed and extend alignment pipeline is split and implemented in ‘map’ and ‘reduce’ steps, where the ‘map’ step searches K-mer matches (as seeds) and the ‘reduce’ step extends the seeds to apply dynamic alignments. The limitation for such method is that the ‘reduce’ step introduces large data shuffling across cluster nodes that impacts its performance. Crossbow (Langmead et al. , 2009), Halvade (Decap et al. , 2015) and Myrna (Langmead et al.

Output: corroborate


Input: Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: 0001 min−1. Thirdly for sucrose and inulin the rate constants for elimination by transport across the blood–brain barrier are substantially less than the total rate constant for elimination, 0. 003 min−1, implying that some other mechanism accounts for most of the elimination. It is at present unclear whether or not mannitol is suitable as a marker for perivascular elimination. Preston et al. [531] pointed out a major difficulty that occurs in the measurement of very small permeabilities using radiotracers. If the sample of labelled substance contains small quantities of labelled impurities that are more permeable than the principal substance, the impurities will make a disproportionately large contribution to permeability measured by accumulation of the radiolabel. They showed that further purification by thin layer chromatography decreased the measured permeability for mannitol and sucrose (see Table 7). Miah et al. [532] have taken this one step further and have compared the uptake of radiolabel from a sample of 14C-sucrose with the uptake of 13C-sucrose measured by mass spectrometry. Their measurements using a sample of radiolabelled sucrose yielded one of the higher measured permeabilities, while that obtained using assay of sucrose yielded the lowest. It is thus plausible that perivascular elimination accounts for an even larger fraction of the elimination of sucrose than indicated by comparison of the average of the rate constants in the table with the total rate constant of elimination. The available evidence strongly supports the commonly held view that sucrose, inulin, albumin, and a number of dextrans are suitable markers for elimination from the brain parenchyma by perivascular convection. 1. 2 [156, 157, 533]; (3) n-octanol, with the water it contains in a partition experiment [161], may differ from the membrane core in how it interacts with hydrogen bonding groups;Footnote 24 (4) permeation may occur by pathways other than via the core of the membrane, e. g. transport for some of the substances considered may be via specific transporters (see Sect. 4. 2), by transcytosis (see Sect. 4. 3) or, particularly for small polar solutes, by a paracellular pathway (as mentioned in Sect. 4.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: In particular, when commencing a download task, files can be split into chunks and transferred from distributed storage system, such as Amazon simple storage service (S3), to each worker node. This parallel transfer method fully utilizes high network bandwidth of a distributed cluster (Fig. 1A). When downloading data from Amazon S3 to HDFS, we have used (distributed copy), a tool designed for large inter-cluster copying. For downloading data from Amazon S3 to a shared network file system (NFS), we used a Java-based tool developed in our research group called BiBiS3 ( BiBiS3 not only parallelizes downloading jobs to multiple computer nodes, but also applies multi-thread downloading on each computer node to fully exploit the capacities of every computer’s network connection (see 1 Method-1. 3). 2. 3 Parallel data decompression Most genomic datasets are compressed and stored on public repositories. To directly access and analyze the compressed data, we also improved the performance on data preprocessing by introducing parallel decompression after downloading the data. We developed a parallel decompression tool, Sparkhit-spadoop, that is built on top of the (Spark’s parallel decompression has a thread safety issue at 2. 0. 0 version) and included it in our toolkit. Since Bzip2 files are compressed in blocks (900 KB per block by default), a large Bzip2 compressed file with sequencing data can be decompressed in parallel. In particular, each block is an independent component that is delimited by a 48-bit pattern, which makes it possible to find the block boundaries. When block boundaries are found, parallel decompression can be applied to each block and processed by multiple CPUs, so that more compute cores are utilized. Then, we distributed all processes in a Hadoop MapReduce job that creates a ‘mapper’ for each chunk of the input HDFS data. Each ‘mapper’ loads a data chunk and commences a decompression process on the Bzip2 blocks in the chunk. (see Supplementary file 1 Method-1. 4) 2. 4 Data loading and saving RDD stores data chunks in line-based text format, where identifying entries requires finding line boundaries denoted by newline characters. After decompression, most NGS data is stored in line-based text files, e. g. fastq, SAM and VCF files.

Output: pass