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

Output: The text contrasts the claim by discussing the transport of glucose across the blood-brain barrier, indicating that there can be significant transport (two to threefold) and efflux, which suggests that the barrier does not completely prevent substances like glucose from entering the brain. It implies that while the blood-brain barrier selectively facilitates the transport of certain solutes, it may not fully protect the brain from potential harmful substances.


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

Output: The text discusses specific functionalities of various bioinformatics tools and software (RGmatch, ChIPSeeker, etc.) in terms of processing genomic data, but it does not explicitly support or refute the claim regarding the overall importance of bioinformatics for analyzing genomic data. Therefore, it is deemed irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: ✓✓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). [SEP] Justify:

Output: The text corroborates the claim by listing various queries related to analyzing genomic data, including tasks such as finding genes, pathways, and molecular functions in different species. These tasks demonstrate the application of bioinformatics techniques to manage and analyze genomic information, which supports the idea that bioinformatics is essential for such analyses.


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

Output: The text contrasts the claim by discussing how soluble Aβ can cross the blood-brain barrier, suggesting that rather than exclusively protecting the brain from harmful substances, the blood-brain barrier can also allow certain substances, including Aβ, to pass through. This implies that the barrier's role is more complex than simply protecting the brain, as it facilitates the transport of certain proteins, which challenges the notion of it being an unyielding barrier against all harmful substances.


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

Output: The text discusses various methods and tools used in genomic analysis, specifically RGmatch, HOMER, CisGenome, and others, but does not mention bioinformatics as a field or its importance in analyzing genomic data. Therefore, it doesn't provide evidence that supports or contradicts the claim about the necessity of bioinformatics in genomic analyses.


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

Output: The text corroborates the claim by illustrating various queries and operations focused on genomic data analysis in different biological contexts. It discusses methods to retrieve and evaluate genomic information, such as gene sequences and pathways, which highlights the importance of bioinformatics in processing and understanding complex genomic data.


Input: Task: contrast the claim using the given text. Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: The 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. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities of transport mechanisms across the blood-brain barrier, particularly in relation to amyloid-beta (Aβ). While it acknowledges that p-glycoprotein (Pgp) may play a role in the efflux of Aβ from the brain, it also highlights that various transport mechanisms result in both inward and outward fluxes. This implies that the blood-brain barrier does not function strictly as a protective barrier but rather allows for the transport of substances in and out of the brain, questioning the extent of its protective capabilities against harmful substances.


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

Output: The text does not directly address the claim regarding the importance of bioinformatics in the analysis of genomic data. Instead, it discusses specific methods (RGmatch and CisGenome) and their performance in associating genomic regions with genes. While this information pertains to bioinformatics tools, it does not explicitly establish their crucial role in analyzing genomic data.


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

Output: The text describes RGmatch, a bioinformatics tool designed to match genomic regions to proximal genes, indicating that bioinformatics plays an essential role in integrating and analyzing genomic data. By detailing how RGmatch operates and the specific metrics it calculates, the text supports the claim that bioinformatics is crucial for such analyses.


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

Output: The text contrasts with the claim as it discusses the mechanisms of solute movement in relation to the blood-brain barrier and highlights the complexities and controversies regarding its role. It mentions that soluble Aβ (amyloid beta) is eliminated by a combination of mechanisms including metabolism and perivascular efflux, implying that the blood-brain barrier may not solely serve to protect the brain from harmful substances but also allows for certain movements that may involve harmful components. This suggests that the blood-brain barrier's function might be more complex than simply protecting the brain.


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

Output: The text does not explicitly address the role of bioinformatics in analyzing genomic data. Instead, it discusses the definitions and comparisons of regional annotations used by RGmatch and HOMER, along with algorithm efficiency comparisons. Thus, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: 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. [SEP] Justify:

Output: The text discusses the process of analyzing genomic data through the association of genes and regions, such as the 'TSS' and other genomic annotations. By detailing the algorithms and settings used for gene region associations, the text supports the claim that bioinformatics plays a crucial role in the analysis of genomic data.


Input: Task: contrast the claim using the given text. Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: 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. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that suggests the movements of substances through the blood-brain barrier are not as protective as implied. It discusses the discovery of specific transporters and how substantial evidence indicates that solute entry to the brain may not be entirely restricted, showing that there is slow efflux and entry processes that could allow harmful substances to affect brain parenchyma. The mention of alternative routes like the 'glymphatic pathway' for the circulation of CSF and waste products further complicates the assertion that the blood-brain barrier serves solely to protect the brain from harmful substances.


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

Output: The text appears to list various tools and methods related to bioinformatics, such as 'Bowtie', 'SOAPsnp', and 'GATK', but it does not directly address or support the claim about the importance of bioinformatics in analyzing genomic data. Thus, it is considered irrelevant.


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

Output: The text corroborates the claim by detailing how RGmatch, a tool designed for matching genomic regions and genes, plays a significant role in bioinformatics. It specifically mentions that RGmatch promotes integration into broader analysis pipelines and facilitates analysis in multi-omics experiments involving gene expression and different types of chromatin features. This highlights the importance of bioinformatics tools in genomic data analysis.


Input: Task: contrast the claim using the given text. Claim: The blood-brain barrier protects the brain from harmful substances. [SEP] Text: 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]. [SEP] Justify:

Output: The text contrasts the claim by discussing various transport mechanisms relating to substances crossing the blood-brain barrier. It indicates that substances like transferrin can bind and be transported across the barrier, suggesting that the barrier does not completely prevent substances, specifically low-affinity antibodies and transferrin, from entering the brain. The mention of transcytosis and the fact that 'relatively little holo-transferrin is in fact exocytosed on the brain side' implies that the barrier may allow some substances to bypass its protective role.


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

Output: The text does not directly address the claim regarding the importance of bioinformatics in analyzing genomic data. Instead, it lists various bioinformatics tools and their applications without explicitly stating their significance or impact on genomic data analysis.


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

Output: The text corroborates the claim by discussing the significant role of bioinformatics in analyzing large-scale genomic data, specifically mentioning how existing tools are necessary to handle an increasing amount of next-generation sequencing data. It highlights key issues, such as the challenges in scalability and processing efficiency, which bioinformatics frameworks like Sparkhit aim to address. The text demonstrates that bioinformatics is pivotal for interpreting vast datasets, which is essential for advancements in life sciences.


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

Output: The text provided is a series of fragmented citations and references that do not engage with the claim about the blood-brain barrier. Therefore, it is irrelevant to the claim and does not support or contrast it in any way.


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

Output: The text does not directly address or support the claim about the importance of bioinformatics in genomic data analysis. Instead, it discusses technical aspects of data processing, such as the architecture of a Spark cluster and the challenges associated with downloading large genomic files. While it mentions tools used for profiling and mapping genomic data, it does not explicitly state that bioinformatics is crucial, nor does it link these processes to the broader significance of bioinformatics in analyzing genomic data.


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

Output: The text corroborates the claim by discussing the importance of bioinformatics tools and methods for handling large-scale data processing in genomics applications. It emphasizes the need for bioinformatics tools that are compatible with cloud infrastructure, which are essential for analyzing genomic data effectively, as highlighted when stating, 'we need both easy-access to large computational infrastructure and bioinformatics tools that are compatible and scalable on such platforms.' Furthermore, it mentions various tools used in genomics applications, reinforcing the critical role of bioinformatics in this field.


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

Output: The text contrasts the claim by discussing the permeability of substances across the blood-brain barrier, specifically indicating that the elimination rates of certain substances (sucrose and inulin) are less than the total rate constant, suggesting that alternative mechanisms may account for their elimination. This implies that the blood-brain barrier may not act as an absolute protective barrier but instead allows for certain substances to pass, challenging the notion of its protective function.


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

Output: The text primarily discusses technical methods related to downloading and processing genomic data but does not address the importance of bioinformatics in analyzing the data itself. As such, it does not support or contradict the claim directly.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: , 2010), on the other hand, directly use Hadoop to invoke existing sequence aligner [Bowtie (Langmead, 2010)] and SNP caller [SOAPsnp (Li et al. , 2008) or GATK (McKenna et al. , 2010)] for sequence mapping and genotyping on large datasets. The three tools have successfully reduced the run times for mapping, genotyping and gene expression quantification. Yet, their data preprocessing step introduces a heavy overhead and their options for handling distributed data are limited. The more recent framework addresses these weaknesses with its unique data sharing primitive, called resilient distributed dataset (RDD) (Zaharia et al. , 2012). RDD offers a ‘cache’ function to store distributed data in the memory across computers on a cluster, thus, avoiding run time overhead of iterative data I/O (input and output). Moreover, Spark has more build-in functions for RDD to facilitate methods implementation and data handling via its application programming interface (API). These advantages make Spark suitable for large scale genomic data analysis. Nevertheless, existing Spark based bioinformatics tools have their own limitations. For instance, SparkBWA (Abuin et al. , 2016) adopts the same idea of Crossbow by using Spark to invoke the BWA (Li and Durbin, 2009) aligner. However, it does not provide data preprocessing functions for large amounts of compressed sequencing data. Thus, manually decompressing the sequencing data introduces a significant run time overhead. Instead of directly invoking existing aligners, MetaSpark (Zhou et al. , 2017) re-implemented a fragment recruitment algorithm (Rusch et al. , 2007). It has the same ‘seed and extend’ pipeline as Cloudburst, but implemented its algorithm on top of Spark. Thus, it also introduces large data shuffling in the reduce step that impacts its run time performance. Moreover, it requires a self-defined input format rather than the standard Fastq and Fasta format, which introduces an overhead to manually covert large Fastq files (Table 1). Table 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. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of various bioinformatics tools and frameworks, such as Spark and Hadoop, which are specifically designed for large-scale genomic data analysis. It highlights how these tools facilitate methods implementation and data handling for genomic data, thereby reinforcing the importance of bioinformatics in analyzing genomic data efficiently.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: Skip to Content eLife home page your research Search by keyword or author Reset form my search to 10. 7554/eLife. 43551 Open access Copyright information Version of RecordJune 4, 2019 Accepted ManuscriptMay 20, 2019 Open annotations (there are currently 0 annotations on this page). 5,205 views 692 downloads 42 3551. Copy to clipboard Download BibTeX Download. RIS Full text Figures and data Peer review Side by side Peer review process Decision letter Author response Peer review process This 10. 7554/eLife. 43551. 023 Author response [Editors’ note: the author responses to the first round of peer review follow. ] Reviewer #1: […] Although for the most part the results are convincing from the technical perspective, the interpretation completely depends on the authors' claim that port entries occurring shortly after an operant response are an index of reward expectancy. The authors support this claim by showing, in an instrumental task identical to the one used in the PIT experiments, that cues presented after the operant response that signal reward delivery (pellet drop) increase the probability of a port entry. However, this unsurprising result is entirely consistent with an alternative interpretation that the authors have apparently not considered. That is that port entry is simply the next step after the lever response in a chunked skilled action sequence. Such sequences are most likely a form of stimulus-response habit in which the stimulus is the previous action. In addition, because pellet drop occurs after the lever press and predicts reward, it could also participate in S-R habit learning. In this scenario, a lever press followed by pellet drop would simply activate the response (port entry) more strongly than either stimulus alone. Supporting this interpretation, the authors show that port entry after lever pressing is strongly resistant to outcome devaluation – a hallmark of S-R habits. It is therefore perplexing that the authors use this behavior as an index of reward expectancy – a component of goal-directed and cognitive forms of behavior, which should be sensitive to devaluation precisely because the value of the expected reward is reduced. The authors go on to show that during the PIT test, not only does the CS+ increase the probability of the operant response relative to control periods (e. g. , CS-), but it also increases the probability that any individual operant response will be followed by a port entry. [SEP] Justify:

Output: The text contrasts the claim by presenting an alternative interpretation that questions the reliance on operant response for reward expectancy. The reviewer suggests that the observed behavior, port entry following a lever press, could be more indicative of a stimulus-response habit rather than a direct measure of reward expectancy. This implies that while dopamine may play a role, the relationship is more complex and possibly not solely about reward processing and motivation, as evidenced by the statement that port entry is 'strongly resistant to outcome devaluation,' indicating a habitual response rather than a motivational one.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: For SAM and VCF files, each line is an independent unit that contains its corresponding information (mapping records or genotypes). Thus, when loading these files, each line is read and stored as an element of an RDD. However, a fastq file stores its basic information in a four-line unit, where each line is an essential part of a sequencing read. When loading fastq files into Spark RDD, a filter step is applied to check each four-line unit before sending it out to be processed. Both loading and saving run in parallel on each partition of the RDD (the sequencing data) with a default size of 256 MB per partition. Sparkhit can also directly load and save data from and to both HDFS and Amazon S3 by using the HDFS and the S3 URL scheme. 2. 5 Fragment recruitment and short-read mapping implementations of metagenomic studies, fragment recruitment is a key step to understand the genome structure, evolution, phylogenetic diversity and gene functions of biological samples (Rusch et al. , 2007). As a special case of read mapping, fragment recruitment supports more mismatches during the alignment. Thus, the computational complexity for fragment recruitment can be higher than for standard short read mapping. We implemented a fast and sensitive fragment recruitment tool, called Sparkhit-recruiter. Sparkhit-recruiter extends the Fr-hit (Niu et al. , 2011) pipeline and is implemented natively on top of the Apache Spark. The pipeline consists of four steps: (i) building reference index, (ii) searching candidate blocks, (iii) block filtering and (iv) banded alignment (Fig. 1C). When building the reference index, we use a K-mer hash table to store each K-mer’s location on the reference genome (Fig. 1C in blue color). Once the index is built, we extract the K-mers from sequencing reads (Fig. 1C in red color) and search against the reference hash table for exact matches. The matched K-mers will be placed on the genome as seeds to extend candidate blocks. The block filtering step incorporates a q-Gram threshold to remove badly matched blocks, therefore, improves run time performance. After filtering, banded alignment is applied to give a final mapping result. We also implemented a short-read aligner, called Sparkhit-mapper (Fig. 1B and D). [SEP] Justify:

Output: The text does not directly support or contradict the claim about the importance of bioinformatics in analyzing genomic data. Instead, it provides a technical description of specific file formats and methodologies used within the field of bioinformatics without addressing its overall significance.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: , 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 toolsWe present Sparkhit, an open source computational framework that is easy to use on a local cluster or on the cloud (Fig. 1). Sparkhit is built on top of the platform, integrates a series of analytical tools and methods for various genomic applications: (i) We have natively implemented a metagenomic fragment recruitment tool and a short-read mapping tool (Sparkhit-recruiter and Sparkhit-mapper). The short-read mapper implements the pigeonhole principle to report the best hit of a sequencing read. Whereas the fragment recruitment tool implements the q-Gram algorithm to allow more mismatches during the alignment, so that extra information is provided for the metagenomic analysis; (ii) For using external software on Sparkhit, we built a general tool wrapper (Sparkhit-piper) to invoke and parallelize existing executables, biocontainers [e. g. Docker containers (Merkel, 2014)] and scripts; (iii) For downstream data mining, we integrated and extended Spark’s machine learning library. All methods and tools are programmed and implemented in a new MapReduce model extended by Spark, where parallelization is optimized (load balanced) and supervised (fault tolerance). Fig. 1. A distributed computational framework for large scale genomic analysis. (A) Architecture of a Spark cluster deployed on the Amazon cloud. The yellow boxes represent Amazon EC2 instances that are virtualized into Spark master/worker nodes. (B) Distributed implementation of Sparkhit-recruiter. [SEP] Justify:

Output: The text supports the claim by detailing the functionalities of Sparkhit, which includes various analytical tools and methods specifically designed for genomic applications. It emphasizes that Sparkhit integrates tools for mapping, gene expression profiling, and metagenomic fragment recruitment, highlighting the importance of bioinformatics in the analysis of complex genomic data.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: They further show that chemogenetic inhibition of VTA dopamine neurons or terminals in the accumbens during the PIT test reduces the CS-induced increase in operant responding while leaving intact the probability of port entry after an operant response. The authors conclude that although a CS-triggered increase in reward expectancy drives a component of the PIT effect (those operant responses followed by port entry), an additional component is not driven by expectancy (operant responses not followed by port entry), and dopamine contributes to PIT via the latter mechanism, likely via enhancing cue-triggered motivation. To accept this interpretation, one must accept the assertion that operant-entry sequences are the result of reward expectancy to a greater extent than operant responses occurring without a subsequent entry. As described above, this claim is suspect. It is certainly interesting that inhibition of dopamine neurons has a greater effect on single operants than operant-entry sequences, but there are alternative explanations. For instance, the authors' devaluation experiment suggests that operant actions that are not followed by port entry are sensitive to devaluation and are therefore likely controlled by action-outcome goal-directed associations. In contrast, the post-devaluation increase in frequency of operant-entry sequences suggests that operant-entry behavior is at least partially controlled by stimulus-response habit associations. In this scenario, dopamine facilitates the influence of a Pavlovian CS+ on goal-directed behavior but not habit behavior. Under the assumption that reward expectancy is more important for goal-directed than habit behaviors, this is roughly the opposite of the authors' conclusion. What the authors may have found is something perhaps even more intriguing: that CSs in the PIT test can influence habit-based behaviors at least as strongly as goal-directed behaviors. Of course, one might expect the neural mechanism of such effects to be different (as an interaction between Pavlovian and action value representations is possible only with goal-directed behaviors), and it is therefore informative that dopamine contributes only to facilitation of goal-directed behaviors. In summary, the authors have found something intriguing in the resistance of operant response-port entry sequences to inhibition of dopamine neurons and dopamine terminals in the accumbens. However, their interpretation is not adequately supported by their data, some of which in fact contradict their conclusions. We agree that our original focus on reward expectancy was confusing. [SEP] Justify:

Output: The text contrasts the claim by questioning the authors' conclusion that dopamine is crucial for reward expectancy driving behavior. It suggests that operant responses may be more influenced by action-outcome associations rather than solely by dopamine activity. Additionally, it argues that dopamine's facilitation of behaviors is not as clear-cut and highlights that dopamine may contribute less to habit behaviors, thereby complicating the notion of its role in motivation and reward processing.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: It adopts the same pipeline of Sparkhit-recruiter, but uses a more strict pigeonhole principle for the filtering step. Compared to the q-gram filter implementation in Sparkhit-recruiter, the pigeonhole principle allows fewer mismatches on the sequence so that less identical blocks are filtered and high similarity candidate blocks are preserved (see Supplementary file 1 Method-1. 5. 5). In this case, less candidate blocks are sent to the next step for banded alignment, resulting in much faster runtime than Sparkhit-recruiter. To implement the pipelines in a distributed fashion, we split the mapping processes into two parts: building the reference index and querying sequencing reads (Fig. 1C, D, blue and red dashed boxes). When starting a Sparkhit-recruiter job, the reference index is, firstly, built on the driver node (usually the master node), where the main Spark program runs. Once the reference index is built, the driver program executes a ‘broadcast’ command to ship one copy of the index to each worker node shared by all local querying tasks. On the worker nodes, sequencing data chunks are loaded from HDFS to a Spark RDD. Each partition of the RDD is, then, independently queried to the broadcasted reference index as a ‘map’ step of the MapReduce pipeline. In the end, a ‘reduce’ step summarizes the mapping result (Fig. 1B, see also Supplementary file S1 Method-1. 5). 2. 6 Parallelizing external tools using be flexible for different kinds of analyses, we built a general tool wrapper called Sparkhit-piper. It extends Spark’s ‘pipe’ function to invoke existing tools for analyses like sequence mapping, taxonomic profiling, gene expression quantification and genotyping (Fig. 1E). We use Spark RDD to split and distribute NGS data across cluster nodes. Then, distributed datasets are sent to the invoked tool via a standard input (stdin) stream. The tool processes input data in a batch and sends back the result to another RDD via a standard output (stdout) stream. In this case, the Spark RDD splits and distributes NGS data, while external tools carry out correspond computations. Sparkhit-piper is intuitive and flexible for users to parallelize their own scripts or tools directly without modifying their codes. [SEP] Justify:

Output: The text discusses the technical aspects of a specific computational pipeline (Sparkhit-recruiter and Sparkhit-piper) used for processing sequencing data and does not directly address the claim about the importance of bioinformatics in genomic data analysis. While it implies a connection through the mention of sequence mapping and other analyses, it fails to explicitly support or refute the claim, making it irrelevant.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The reference index, illustrated in blue dashed box, is built on a driver node and broadcasted to each worker node. Sequencing reads, illustrated in Red dashes, are loaded into an RDD and queried to the broadcasted reference index in parallel as a ‘Map’ step. A ‘Reduce’ step is followed to summarize the mapping result. (C) Pipeline of Sparkhit-recruiter. The reference genome, illustrated in bold blue dash, is extracted and built into a K-mer hash table. The sequencing read, illustrated in bold red dash, will be searched against the reference hash table for exact matches. A smaller Kmer is used to apply the q-Gram filter. (D) Pipeline of Sparkhit-mapper. It is similar to (C), but uses the pigeonhole principle. (E, H) Using external tools and Docker containers for different analyses. Genomic data is loaded into an RDD and distributed across worker nodes. Each partition of the RDD is sent to external tools to be processed independently. (F) Different modules of the machine learning library. Colored dots denote vectors of either genotypes or gene expressions. (G) Parallel decompression. A Bzip2 file is split into blocks and stored in three worker nodes. Each block is decompressed independently (Color version of this figure is available at Bioinformatics online. )Open in new tabDownload slideOur benchmarks on Sparkhit demonstrated its high scalability. In comparison, Sparkhit ran 18–32 times faster than Crossbow on data preprocessing and Sparkhit-recruiter ran 92–157 time faster than MetaSpark on fragment recruitment (Figs 2 and 3). Utilizing a powerful compute cloud, Sparkhit can quickly analyze large amounts of genomic data (Fig. 4). Our use case presents a 21 h’ pay-as-you-go’ cloud application that analyzed 100 TB genomic data from 3 genome projects and a transcriptomics study (Wyatt et al. , 2014). The analysis on the Project (HMP), associated public ‘big data’ with private datasets, demonstrates how Sparkhit can be widely applied in different genomic studies (Supplementary file S2, Figs S1, S2 and S3). Thus, our framework enables the broader community to engage genomic investigations by leveraging cloud computing resources. Fig. 2. Performance benchmarks for Sparkhit. [SEP] Justify:

Output: The text corroborates the claim by detailing how tools like Sparkhit are utilized for analyzing large amounts of genomic data efficiently. It explains the processes involved in mapping sequencing reads against a reference genome and highlights the performance benchmarks demonstrating the speed of data processing using bioinformatics methods. This indicates that bioinformatics techniques are indeed essential for analyzing genomic data.


Input: Task: corroborate the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: This was partly due to our unconventional use of that term, which we did not intend to be synonymous with goal-directed control given evidence that reward expectancies can guide action selection in a manner that is independent of reward value during PIT performance (Colwill and Rescorla, 1990; Rescorla, 1994; Holland, 2004). But we now believe this framework for data interpretation was unclear and unnecessary. We have therefore omitted the term reward expectancy altogether, and have substantially revised the manuscript to focus on the broader topic of how dopamine contributes to the control of instrumental reward-seeking and reward-retrieval actions, while explicitly contrasting goal-directed (devaluation-sensitive) and habitual (devaluation-insensitive) accounts of behavior. From this perspective, we agree with the reviewer’s conclusion that press-contingent food-cup approaches were performed habitually, suggesting the use of a press-approach behavioral chain or action chunk. This interpretation is also compatible with our finding that reward-paired cues strongly increase production of press-approach chunks, given previous studies showing that habitual behavior is particularly sensitive to the motivational influence of such cues (Holland, 2004; Wiltgen et al. , 2012). As the reviewer notes, presses that were not followed by food-cup approach were particularly sensitive to reward devaluation. We have elaborated on our discussion of this issue (subsection “Inhibiting dopamine neurons spares the sensitivity of reward-seeking actions to reward devaluation”, last paragraph; Discussion, second paragraph, documents with colored text) and added a new analysis to help develop this finding (Figure 5D; Supplementary file 1G). However, we do not believe this is a defining feature of such actions. For instance, the variable-interval training protocol used in our PIT experiments is known to support habit formation. Thus, even discrete lever presses (without approach) performed during PIT tests were likely to have been insensitive to devaluation, had this been assessed. We suggest that such discrete lever presses are not exclusively aligned with either habitual or goal-directed control. Indeed, others have argued that repetitive lever pressing without subsequent food-cup approach may model OCD-like compulsive checking behavior, which we discuss in the last paragraph of the Discussion section. Reviewer #2: […] I have a series of issues with the manuscript: The terminology is very confusing, and the theoretical frame is globally difficult to grasp. [SEP] Justify:

Output: The text corroborates the claim by discussing how dopamine contributes to the control of reward-seeking and reward-retrieval actions. It emphasizes dopamine's role in instrumental behaviors and mentions how reward-paired cues influence habitual actions and motivation. The discussion of how habitual behavior is sensitive to motivational influences and the contrasts between goal-directed and habitual processing further support the significance of dopamine in reward processing.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: 2E). When scaled-out to more compute nodes, Sparkhit experienced slight slowdown after we increased the number of worker nodes to 60 (Fig. 2F). The slowdown is introduced by the overhead of building the reference index. However, since metagenomic fragment recruitment applications actively change reference genomes between different studies, the index building overhead is quite small and it runs much faster compared to other Burrows-Wheeler transform (BWT) based methods (Supplementary file S2, Fig. S5 and Discussion section). 3. 1. 3 Preprocessing comparison with preprocessing is a critical step for interpreting cloud stored public datasets. Manually decompressed and distributed large amounts of genomic data on a cluster introduce significant overheads before data analysis. Although, several existing cloud tools have provided preprocessing functions (Decap et al. , 2015; Langmead et al. , 2009; Schatz, 2009), their proprocessing speeds are limited by their non-parallel implementations. We have compared the run time performances on data preprocessing between Sparkhit and Crossbow. For 338 TB Bzip2 compressed data (Data-1), Sparkhit ran 18 to 32 times faster than Crossbow on 50 and 100 worker nodes (Fig. 2G). utilizes all vCPUs for parallel decompression, its run time performance almost doubled from 50 nodes to 100 nodes, whereas Crossbow had similar run times on both clusters (Supplementary file S2, Table S12). 3. 1. 4 Machine learning library benchmarking and run time performances on different clusters For modules of the machine learning library, we have compared their run time performances on both a private cluster and the Amazon EC2 cloud (Fig. 2I and J). For the private cluster, data was stored on magnetic disks of a shared network file system (NFS). Whereas on Amazon EC2, data was stored on an HDFS deployed on solid state disks (SSD). We ran every module on both 20 and 40 nodes Spark clusters using a 200 GB VCF file from the 1000 and compared their run times between a private cluster and Amazon EC2. Since each module opened 640 and 1280 I/O tasks (20 nodes and 40 nodes, each node has 32 cores) to read input data and write output result, the run time performance on the private cluster was significantly slower than on the Amazon EC2 cloud (Supplementary file S2, Table S14). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses technical details regarding the performance of Sparkhit and Crossbow in processing and analyzing genomic data, rather than addressing the importance of bioinformatics in analyzing genomic data.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: (A–D) Run time comparisons between different aligners. The comparisons were carried out across different sizes of input fastq files, different sizes of reference genomes and different numbers of worker nodes. (E) Run time performance of Sparkhit-recruiter for recruiting 100–1000 GB sequencing data to a 72 MB reference genome on a 30 nodes Spark cluster deployed on the Amazon EC2 cloud. Each node has 32 vCPUs. (F) Scaling performance of Sparkhit-recruiter. When increasing the number of worker nodes, the mean speed ups are measured by comparing their run times to the run time on 10 worker nodes. We recruited 1. 3 TB fastq files (Data-1) to a 72 MB reference genome (Ref-2) on the same cluster of (E). (G) Run time comparisons between Crossbow and Sparkhit for preprocessing 338 TB compressed fastq files on 50 and 100 worker nodes. (H) Comparing the recruited number of reads between Crossbow and Sparkhit-recruiter when mapping 1. 3 TB fastq files to a 72 MB reference genome. (I–J) Run times of the machine learning library on a private cluster and the Amazon EC2 cloud. All computations were performed on a 200 GB VCF file cached in the memory. (K) Run times for different iterations of the K means clustering. We ran iterations on the same VCF file from I, J, with data caching and non data caching. (L–M) Sensitivity and accuracy comparisons between mapping toolsOpen in new tabDownload slide Fig. 3. Comparisons between Sparkhit-recruiter and MetaSpark on metagenomic fragment recruitment. (A) Run times on recruiting simulated sequencing reads to 72 MB and 142 MB reference genomes. All tests were carried out on 10, 20 and 30 worker nodes Spark clusters. Each worker node has 16 vCPUs. Run times are presented in logarithmic scale base 2. (B) Numbers of recruited reads on recruiting 6 million simulated reads to 72 MB reference genome and 1 million simulated reads to 142 MB reference genomeOpen in new tabDownload slide Fig. 4. Large scale genomic data analyses on the cloud with Sparkhit. (A) Run time comparison between three auto-scaling tools for deploying a Spark cluster on the Amazon EC2 cloud. [SEP] Justify:

Output: The text corroborates the claim by providing insight into various methods and performances of tools used in bioinformatics for analyzing large genomic datasets. It discusses the comparisons between different aligners, run times for recruiting and mapping genomic data, and the scaling performance of computational resources, which all highlight the importance of bioinformatics in efficiently processing and analyzing genomic data.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: There are many concepts that are used to capture very simple behaviors, even in the Results section (reward seeking, exploration, reward expectancy, motivation, vigor etc. ). Could the authors make a set of simple predictions based on a simple theoretical frame and describe how they decide to operationalize the questions? There are places in the text when it is the case, but I'm afraid that there is a drift in the issues at stake between the Abstract and the Discussion. We acknowledge that our original framework was confusing. As noted in our comments to reviewer #1, we have thoroughly revised the manuscript to correct this problem. Also, as noted above, we have adopted unambiguous terms for behavior in the Results section and figures, and have operationalized more complex or ambiguous terms. The description of the methods is too superficial. It might be sufficient for specialists in the field (conditioning in rats) and/or people who know the work of this team, but not for a more general audience. Typically, including the contingencies and the timing of the reward schedules in the different tasks would help. We apologize for any omissions and have added to this part of the Materials and methods section to make sure that these and other important details are specified. I am having some trouble reconciling the conclusions of the authors and the data, as shown by the figure: Experiment 1: Again, the text is difficult to read but it seems to me that there are two things going on here: the operant behavior of these animals in the task is very 'habitual' (as opposed to goal-directed), since the rate of lever pressing decreases very little when reward is omitted (Figure 1D). In line with that, presumably, animals almost only try to collect the reward when they get a direct evidence (cue) that it has been delivered. So reward does not drive action and actions do not predict reward, and animals are left with Pavlovian processes to anticipate the reward. Is there more to it than that? If yes, it would require precise quantitative predictions and measures to demonstrate it. I would avoid the word 'exploration', unless the authors can demonstrate that animals try to get the reward by exploring another potential way to get the reward (e. g. another lever). Here, action seems to be driven much more by compulsion/ habits than exploration. We generally agree with and have adopted this habit-based interpretation, as noted in our comments to reviewer #1. [SEP] Justify:

Output: The text contrasts the claim by discussing how, despite the expectations around reward processing, the behavior observed in the experiments suggests that reward does not drive action. It indicates that the operant behavior in the task is habitual rather than goal-directed, suggesting that animals rely more on Pavlovian processes rather than motivation linked to dopamine-driven reward processing. This implies that dopamine's role may not be as crucial in the operationalization of behavior in the context described, contradicting the claim.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: These data were compressed and stored in different regions around the world on Amazon S3 and the Archive (ENA) ftp server (Fig. 4F). We rented 100 c3. 8xlarge Amazon EC2 instances (3200 cores, 6TB memory and 60TB disk space in total; see Supplementary file 1 Materials) with a total spot price around 38 dollars per hour. Sparkhit completed the entire process, including cluster deployment, data downloading, decompression and various data mining, in 21 h (the entire duration that the cloud provider charges) with a total cost less than 800 dollars (Fig. 4A–E). We started deploying a Spark cluster of 100 worker nodes in Ireland region using Spark-ec2 script, which is the slowest one among three cluster deployment tools (the worst scenario for users’ cloud budget). This step took 39 min and 54 s on the entire run time clock (Fig. 4A and Supplementary file S2, Table S3). 3. 2. 1 Processing all WGS data of the hosts 2. 3 TB of compressed metagenomic whole genome shotgun (WGS) data on Amazon S3 located in the Oregon region. The data enables comprehensive characterization of the human microbiome and serves as a metagenomic database for microbiome studies. To associate these public datasets with microbial reference genomes, we downloaded all WGS data to the Spark cluster located in the Ireland region. After decompression, we recruited all sequencing reads to a collection of 21 microbial reference genomes (72 MB total, defined as Ref-2) and summarized the fragment recruitment results. We also profiled the taxonomy abundance using Sparkhit invoked Kraken (Wood and Salzberg, 2014). All processes were completed in 1:34:52 h (Fig. 4B and Supplementary file S2, Table S4). 3. 2. 2 Genotyping on 3000 samples of the 3000 3000 Project hosts 200 TB of public data on Amazon S3 (Virginia region). This data enables large scale discovery of novel alleles for important rice phenotypes. Variant detection is a particularly expensive step in analyzing whole genome or exome sequencing data. To test the run time performance of Sparkhit on variant detection, we downloaded 15 TB BAM files to the Spark cluster. invoking Samtools-Mpileup (Li et al. , 2009), we genotyped 3000 rice samples and uploaded detected variants to Amazon S3. [SEP] Justify:

Output: The text does not directly address the claim about the importance of bioinformatics in analyzing genomic data. Instead, it focuses on the technical processes of data storage and computational procedures used during the analysis of genomic datasets on cloud platforms like Amazon S3 and Amazon EC2. While it mentions data processing steps and tools used, it lacks specific commentary on the role or significance of bioinformatics itself.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The implementation is based on Spark RDD’s ‘pipe’ function, where the RDD is able to send its data out of the JVM for processing in the operating system, like a Linux pipe operator (‘|’). code can be expressed as: JavaRDD < String> MapRDD = FastqRDD. pipe(param. tool + param. toolOptions); where ‘FastqRDD’ is the input RDD that stores sequence data and ‘MapRDD’ is the output RDD that stores mapping result. The ‘param. tool’ represents the full path of the tool executable while ‘param. toolOptions’ represents the corresponding tool parameters. Together, they assemble an external command that runs as an independent process on each partition of the input RDD. 2. 7 Machine learning library Downstream genomic data mining usually applies statistical tests and machine learning methods to analyze upstream results (gene abundance, taxonomic profile of different samples and genotypes of a certain cohort). We extended Spark’s machine learning library (Mllib) and integrated a variety of algorithms: (i) clustering, (ii) regression, (iii) chi-square test, (iv) correlation test and (v) dimensional reduction (Fig. 1F). These algorithms are implemented with more RDD functions and iterative processes (see Supplementary file 1 Method-1. 6). 3 Results 3. 1 General performance benchmarking for the following section we present a series of benchmarks for our framework (Fig. 2). 3. 1. 1 Run time comparison between different mappers We first measured the run times of all Sparkhit based mappers and compared them to Crossbow (Fig. 2A–D). Our toolkit ran faster than Crossbow across different numbers of worker nodes (30 and 50), different sizes of input data (1. 3 TB and 545 GB) and different sizes of reference genomes (36 MB, 72 MB and 142 MB, see Supplementary file 1 Materials and Methods). Although Sparkhit-recruiter was slower than other Sparkhit based mappers, it recruited many more reads than standard short-read mappers such as BWA (Fig. 2H). 3. 1. 2 Scaling performance of Sparkhit-recruiter Sparkhit-recruiter scaled linearly with the increasing amount of input data on a 30 worker nodes Spark cluster (Fig. [SEP] Justify:

Output: The text corroborates the claim by discussing the integration of machine learning methods and statistical tests used to analyze genomic data, highlighting the significance of bioinformatics in processing and deriving insights from genomic information. The mention of using Spark's machine learning library and various algorithms for downstream genomic data mining supports the importance of bioinformatics in analyzing genomic data.