Input: Claim: All bacteria are harmful to human health. [SEP] Text: Skip to Content eLife home page your research Search by keyword or author Reset form my search to 10. 7554/eLife. 39233 Open access Copyright information annotations (there are currently 0 annotations on this page). Share 50,545 views 2,243 downloads 128 9233. Copy to clipboard Download BibTeX Download. RIS 10. 7554/eLife. 39233. 001 reinhardtii is a single-celled green alga found in temperate soil habitats (Figure 1). It has proven to be such a powerful model for dissecting fundamental processes in biology that investigators have dubbed it the 'green yeast' (Goodenough, 1992; Rochaix, 1995). Ehrenberg described the genus Chlamydomonas in 1833, and Dangeard the species C. reinhardtii in 1888 (Harris et al. , 2009). Chlamydomonas was found suitable for genetic studies in the early 20th century (Harris, 2001), while the development of C. reinhardtii as a model organism dates to the 1950s when the first mutants were generated (Harris, 2009). Figure 1 Download asset Open asset Structure of a vegetative Chlamydomonas reinhardtii cell. This cell has a 5-10 µm diameter (Gallaher et al. , 2015). The two anterior cilia possess a 9+2 microtubule structure characteristic of motile cilia of eukaryotes. The cilia are critical for mating processes and confer motility to the cell (Harris, 2001). A single cup-shaped chloroplast occupies a large proportion of the cell's volume. This organelle houses the machinery for oxygenic photosynthesis and contains the pyrenoid, a structure in which Rubisco is concentrated; the pyrenoid is a component of the carbon concentrating mechanism (CCM) which functions to concentrate inorganic carbon in the cell against a concentration gradient (Mackinder et al. , 2016). Close to the cell equator, at the edge of the chloroplast, is the eyespot. This primordial visual system allows the cells to orient their swimming toward or away from the light (phototaxis). Under hypoosmotic conditions, the cytoplasmic water content is maintained by pumping water out of the cell through contractile vacuoles positioned at the cell’s anterior (Komsic-Buchmann et al. , 2014).

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The proportion of genes rejected within each category are plotted for the different methods. Before we compare the methods, it is important to note that the reading of these plots may be different from traditional power plots. Normally when looking at a power plot, the higher the power the better. In our case, however, this is not true for all categories. Remember that our goal is to focus on a particular subset of the non-null space - genes that are differentially expressed in at least 4 studies. Therefore, all non-null genes are not created equal. In particular, rejections of genes in categories 4 through 7 are desirable, whereas rejections of genes in categories 1 through 3 are undesirable. Rejections of genes in category 0 are of course considered false discoveries. As expected, regardless of the method, the proportion of genes rejected within a category increases from 0 to close to 1 as the number of studies that the genes are differentially expressed in increases from 0 to 7 (out of 7). Fisher’s method, being a very powerful method for testing H S1, has the highest proportion of rejections for every category from 1 to 7. Stouffer’s method also has the highest proportion of rejections when compared to the corresponding weighted versions and the rOP method. However, as mentioned earlier, rejections in categories 0 to 3 are considered undesirable when testing H S4. Both the WOP methods and the rOP method reject much smaller proportions of genes in categories 1 to 3 compared to the original Fisher’s or Stouffer’s method. Less rejections in categories 1 to 3 come at the expense of less power for categories 4 to 7, which is true for both WOP and rOP methods, but particularly so for the rOP method. For the binomial weighted WOP methods, the rejections for categories 1 to 3 are much lower compared to Fisher’s or Stouffer’s method, but the power for categories 4 to 7 gradually increases to catch up with Fisher’s or Stouffer’s. When it comes to categories 6 and 7, especially category 7, the binomial weighted WOP methods have virtually the same power as Fisher’s and Stouffer’s. On the other hand, the rOP method has the lowest power for categories 6 and 7, which are the genes that are differentially expressed in all or almost all the studies.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: was designed to facilitate the easy construction of protein family databases and to assist the analysis of sequence-structure-function relationships. The automated annotation transfer guided by biochemical knowledge results in a consistent annotation of the data integrated by the data warehouse system. We have applied the system to systematically compare various protein families to infer substrate specificities and to guide the engineering of optimized enzymes. Availability and was developed on Linux (3). Scripts for the back- and front-end were developed in Perl. The relational DWARF database can be accessed by the SQL interface located at For the Database the web interface created by DWARF is accessible at As in the previous release, it can be browsed by superfamilies and homologous families. BLAST searches can be performed against all sequence entries. Links are available to consistently annotated multisequence alignments, phylogenetic trees and structure data sets. 7. 10. 1093/nar/gkh141685. 10. 1093/protein/10. 6. 677855. 10. 1002/prot. 20013128. 40. 10. 1016/S0168-9525(97)01325-5162. 51. CAS PubMed 171. 115. 10. 1006/cbmr. 1994. 1011271. 10. 1093/bioinformatics/17. 3. 262272. 10. 1093/nar/28. 1. 270165. 10. 1093/protein/13. 3. 1539. 10. 1093/nar/gkh039235. 10. 2174/1389203003381405211. 321. 10. 1093/nar/gkg0156. 10. 1093/nar/gkh045112. 10. 1016/S0923-2508(00)00121-2242. 10. 1093/nar/28. 1. 2352637. 10. 1002/bip. 3602212113402. 10. 1093/nar/25. 17. 33894680. 277. 10. 1016/S0168-9525(00)02024-22003. Res 2003, 31(1):365–370. 10. 1093/nar/gkg095347. 10. 1093/nar/gkg04040. 10.

Output: corroborate


Input: Claim: All bacteria are harmful to human health. [SEP] Text: 03 division-1 genome-1, corresponding to one mutation every 30 generations (Gallaher et al. , 2015). In addition, removal of C. reinhardtii from its natural environment, including cultivation in the laboratory or cryopreservation, may unintentionally select for specific traits. For example, C. reinhardtii is often grown on medium containing ammonium as a nitrogen source, which allowed for the evolution of mutants (nit1, nit2) unable to utilize nitrate (Harris, 2009; Gallaher et al. , 2015). For these reasons, the isolates domesticated for decades in the laboratory may only loosely correspond to wild C. reinhardtii strains. Furthermore, we do not know if the laboratory strains are still capable of surviving in the wild. To examine the ecological significance of laboratory findings, it will be important to isolate additional wild C. reinhardtii strains and characterize their behavior both in the field and in culture. spp. (not identified at the species level) occur widely in temperate, subtropical and tropical soils (Starks et al. , 1981), confirmed C. reinhardtii has only been found in temperate soils in and Japan (Pröschold et al. , 2005; Nakada et al. , 2010). It occurs in cultivated fields but appears absent from many other habitats, suggesting it prefers nutrient-rich, disturbed soils (Sack et al. , 1994). Most contemporary laboratory strains have emanated from a single soil isolate collected in 1945 (Box 1). Light typically penetrates only millimeters into the soil, depending on factors such as the soil structure and moisture content (Tester and Morris, 1987; Ciani et al. , 2005). Therefore, photosynthetic microbes are generally most abundant in the upper few millimeters where they can harvest light energy, although in some instances they can be present in soil layers where there is essentially no light (Metting, 1981). Chlamydomonas spp. are even present in biological soil crusts where they help stabilize the surface of drylands, contribute to primary production and potentially act as pioneer species (Büdel et al. , 2009). All unambiguously identified C. reinhardtii isolates were collected from soil habitats (T. Pröschold, personal communication), yet Chlamydomonas spp.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The half-binomial weighted WOP methods have the lowest rejection rates for categories 1 to 3, even lower than the rOP method, but their power surpasses the power of the rOP method when it comes to categories 6 and 7. Figure 2Comparison of power. Power comparison for the different methods. The x-axis denotes the 8 categories of genes, categorized by the number of studies that the genes are differentially expressed in. There are 1650 genes in the category 0 (no differential expression in any studies). The rest of the categories contain 50 genes each. For each category, the proportion of genes found significant within that category are plotted for each method. Full size imageTo better look at the trade off between rejection rates for categories 0 to 3 versus categories 4 to 7, we plotted the ROC curves for the methods, as seen in Figure 3. Since our goal is to test for H S4, we treat rejections in categories 0 to 3 to be false positives, while rejections in categories 4 to 7 are considered true positives. From the ROC curves we can clearly see that in terms of the trade off between true and false positives the binomial weighted WOP methods beat the original Fisher’s or Stouffer’s method, while the half-binomial weighted WOP methods beat the rOP method. We also gain better insight into the comparison between the binomial and half-binomial weighting schemes. The half-binomial weighting scheme is relatively more conservative, with relatively higher true positive rates at very low false positive rates. On the other hand, the binomial weighting scheme achieves higher power when slightly higher false positive rates are allowed. Figure 3ROC curves. ROC curves for the different methods. Rejections of genes differentially expressed in less than 4 studies are considered false positives. Rejections of genes differentially expressed in 4 or more studies are consider true positives. Full size imageIn summary, the binomial weighted WOP methods show improvement over the original Fisher’s or Stouffer’s method for testing differential expression in a majority of studies, with lower rejection rates for genes that are differentially expressed in a small number of studies, and just as high power for genes that are differentially expressed in almost all studies.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 76. 44%) and f-score (76. 95%) are achieved with the BM25-tf-rf based data representation. ConclusionsAs a result of our experiments, the BM25-tf-rf scheme and the proposed neural network model is shown to be the best performing classification system for our case study of cancer type classification. This system is further utilized for causal gene analysis. Examples from the most effective genes that are used for decision making are found to be in the literature as target or causal genes. BackgroundComplex diseases with genetic components arise from different combinations of the mutations in DNA. With the help of the decreasing cost of sequencing technologies, large scale sequencing datasets are being curated. Machine learning methods can be helpful in analyzing the huge genome data. However, a suitable representation technique for the sequencing data is still a problem to be solved. In this paper, we propose using statistical methods from the field of information retrieval for the representation of mutation information in DNA. The proposed representation methods are evaluated for the task of cancer type classification. As stated in the 2017 report of for [1], cancer is the second among top leading causes of death. Cancer is a group of diseases and each cancer type is labeled by the primary area of the body where the cancer cells arise. Each cancer type in general has a different set of causal genes and the disease emerges from the combination of various mutations of these genes [2]. The treatment is planned according to the primary site. Late diagnosis prevents the application of treatments and often results in the loss of the patient. Accordingly, the unknown or wrong analysis of the primary site and late diagnosis are major problems for cancer patients. The use of genomic data for diagnosis might help both to recognize the disease in early stages and to accurately classify the primary site. Cancer classification has been primarily based on the morphological appearance of the tumor. Medical images such as magnetic resonance images (MRI) [3–5], X-ray and computed tomography (CT) images [6, 7], as well as histopathology images [8, 9] have been utilized for cancer diagnosis and classification. Medical images provide valuable information, especially about tumors, but they represent a restricted area. Therefore, there is a need for a more comprehensive data source. Another commonly used data type for cancer classification is gene expression data.

Output: corroborate


Input: Claim: All bacteria are harmful to human health. [SEP] Text: , 2007). In the laboratory, gametogenesis can be induced by nitrogen starvation (Treier et al. , 1989) in conjunction with specific light conditions; both signals may inform the cell of deteriorating environmental conditions (see below). The fusion of haploid gametes results in diploid zygotes that can develop over several days into highly resistant, dormant zygospores (Figure 2). When nitrogen is added back to the medium, the zygotes germinate in the light, undergo meiosis and typically release four haploid cells that resume vegetative growth (Harris, 2001). Dormant zygospores can remain viable in soil for many years (Harris, 2001) and survive freezing (Suzuki and Johnson, 2002), desiccation (Heimerl et al. , 2018) and probably other forms of harsh environmental conditions. This extraordinary resistance is associated with the multilayered cell wall of the zygospores, which contains a durable lipid polymer structurally similar to those found in million-year-old microfossils (described for Chlamydomonas monoica; Blokker et al. , 1999). Furthermore, sexual reproduction can increase the rate of adaptation of C. reinhardtii to new or changing environmental conditions, particularly if the population and genetic diversity within the population are large (Colegrave, 2002). Box 2. Outstanding questions about the natural history of C. reinhardtii What are the geographic origins of C. reinhardtii? What are its current geographic and vertical distributions? How do populations of C. reinhardtii quantitatively change over time and what factors impact these changes? What are the major mechanisms of C. reinhardtii dispersal? For example, are aquifers common routes for the transport of C. reinhardtii over long distances? What is the genetic variability within and between C. reinhardtii populations? What are the relationships among populations of the various Chlamydomonas species? Do specific pelagic strains of C. reinhardtii exist in lakes? If so, do they have major differences in their life histories, physiologies and genome sequences compared to soil-dwelling strains? What are the typical division rates of vegetative C. reinhardtii cells in the wild? How frequently does sexual reproduction occur in natural populations?

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: On the other hand, the half-binomial weighted WOP methods are more robust versions of the rOP method, having similar properties to the rOP method, but even lower rejections rates for categories 1 to 3 and higher power for categories 6 and 7. In practice, the binomial weighting scheme is recommended if the user wishes to have a larger pool of significant genes, and when the control of false positives is relatively less important. For better false positive control, the half-binomial weighting scheme is recommended. We note that because of our hypothesis setup, false positives are not the same as type I error. A type I error would be rejecting a gene that is not differentially expressed in any studies. On the other hand, a false positive would be rejecting a gene that is differentially expressed in less than r studies. An application to meta-analysis of a set of stem cell studiesAs an application of the proposed methodology, we conduct meta-analysis on a set of microarray data studies from four stem cell papers: Chin et al. [21], Guenther et al. [22], Newman and Cooper [23] and Chin et al. [24]. We wish to find probesets that are differentially expressed between human induced pluripotent stem (hiPS) cells and human embryonic stem (hES) cells in the majority of studies. Some of the studies contain other samples such as human fibroblasts, but we only used samples from hiPS cells and hES cells. We included studies that had at least two samples for each group (hiPS and hES), giving us a total of 9 studies. All the studies used the Genome U133 Plus 2. 0 Array platform, which contains 54675 probesets. We directly used the data preprocessed by the original contributors and did not perform any additional normalization, except for taking the log for data that were not already on the log2 scale. We performed a two sample T-test between the hiPS cell samples and the hES cell samples to obtain the original p-values for differential expression for each probeset and each study. The hypothesis setting for the meta-analysis is H S5, i. e. we aim at testing the alternative that the probeset is differentially expressed in at least 5 out of the 9 studies.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: While a number of studies utilizing gene expression data have addressed the classification of cancer types [10–14], this type of data is highly sensitive to the microarray experiment setup and in general suffers from low accuracy and robustness [15]. In addition, due to the high dimensionality of gene expression data, gene selection methods are commonly applied prior to classification [16, 17]. The feature selection step may eliminate genes that in general have minor effects on disease generation while still being significant for the diagnosis of particular cancer types for some patients. The biotechnology improvements and the automation of sequencing systems have increased the speed and lowered the cost of human DNA sequencing, which enabled the usage of this data type for disease diagnosis. The variants or mutations in the DNA of an individual can be identified by comparing the DNA sequence of the individual to the DNA sequence of a reference genome maintained by [18] and stored in a variant call format (VCF) file [19]. In recent studies, the binary representations of mutation data obtained from sources of manually curated somatic mutation profiles have been utilized for cancer classification [20, 21]. However, binary representation is a limited way of describing the data. It highlights the genes with mutations, but treats them as equal. The distinction of common, rare and disease causing mutations is not expressed with the binary representation. Therefore, methods such as C-score from the (CADD) framework [22, 23] have been developed for weighting gene mutations. Recently, the sum of C-scores of gene mutations has been successfully used to cluster breast cancer patients and predict the stage of the disease [24]. In this paper, we propose adapting and using term weighting techniques (tf-idf, tf-rf and BM25) from the information retrieval field for weighting genes based on mutation information. As far as we know, these techniques have not been used on variant data before. The proposed gene weighting techniques are evaluated for the task of cancer type classification. Our results demonstrate that the best performing information retrieval based model (BM25-tf-rf) outperforms the C-score based approach. When the best performing classification model is analyzed, the most effective genes in the classification of certain cancer types are found to have been also proposed as causal or target genes in the previously published studies. These literature findings support the effectiveness of our representation models.

Output: corroborate


Input: Claim: All bacteria are harmful to human health. [SEP] Text: reinhardtii not only orients itself with respect to light, but can also swim upward in complete darkness. This negative gravitaxis may facilitate orientation and movement of the cells at night or in the soil environment, potentially helping the alga locate areas with more favorable conditions of illumination following daybreak (Bean, 1977). Furthermore, vegetative cells are attracted to ammonium, nitrite and nitrate (Ermilova and Zalutskaya, 2014 and 10. 7554/eLife. 39233. 006 C. reinhardtii is also a prey for soil bacteria. The bacterium Pseudomonas protegens can surround and immobilize algal cells (Video 1); it secretes a cyclic lipopeptide that triggers an increase in calcium levels inside C. reinhardtii cells with subsequent deciliation (Aiyar et al. , 2017). This antagonistic interaction inhibits algal growth and probably leads to the death of most of the algal cells; the bacteria may acquire trace metals from the dying cells (Aiyar et al. , 2017). Furthermore, small molecules from C. reinhardtii activate quorum sensing in Pseudomonas aeruginosa (Rajamani et al. , 2008). It will be important to determine if algal cells also produce quorum-sensing mimics that influence P. protegens. Finally, while no viral pathogens of C. reinhardtii have been reported, it seems likely that they exist. The areas of algal-bacterial and algal-viral interactions are fertile for more probing basic research. Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg C. reinhardtii surrounded by the harmful bacteria Pseudomonas protegens (Aiyar et al. , 2017) Video credit: Prasad Aiyar,. Several beneficial interactions of C. reinhardtii have been described, including interactions with growth-promoting bacteria and even mutualism (e. g. Nikolaev et al. , 2008; Lörincz et al. , 2010; Kim et al. , 2014). These findings provide the basis for future studies that address regulatory mechanisms and identify specific compounds that impact the biology of C.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: We applied the proposed WOP methods, in particular the binomial and half-binomial weighted Fisher’s and Stouffer’s statistic to the p-values. The p-values for the WOP statistics are obtained by comparing the statistics to the corresponding numerical distributions. We also applied the original Fisher’s and Stouffer’s method, and the rOP method (in this case the 5th ordered p-value). The p-values for the rOP method are obtained via its theoretical distribution. To adjust for multiple testing, we applied the Benjamini-Hochberg (1995) procedure afterwards, controlling the false discovery rate at the 0. 05 level. The number of probesets significant for the meta-analysis using the different methods are summarized in Table 1. The original Fisher’s and Stouffer’s methods found the most number of significant probesets. It is likely that they picked out many probesets that are differentially expressed in a few, but less than half the studies. The half-binomial weighted WOP methods and the rOP method are relatively more conservative and focused. Figure 4 shows a Venn diagram of the probesets found significant by the binomial and half-binomial weighted Fisher’s method and the rOP method. We can see that the probesets detected by both the half-binomial weighted Fisher’s method and the rOP method are mostly detected by the binomial weighted Fisher’s method as well. However, there are still a number of unique probesets that are only detected by either the half-binomial weighted Fisher’s method or the rOP method. To get an idea of the types of probesets detected by only one of the three aforementioned methods, we randomly selected some of these probesets and plotted the ordered original p-values from the 9 studies for these probesets. As seen in Figure 5, probesets exclusively detected by the binomial weighted Fisher’s method tend to have very small values for the two or three smallest p-values, but relatively larger values starting the 5th ordered p-value. This shows that the binomial weighted Fisher’s method is more prone to influences by the smallest p-values, since it takes into account all the p-values in the statistics.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Our work brings the following contributions: 1 Term weighting methods from the field of information retrieval have been proposed for the representation of mutation information within genomic data. 2 A comparison of these data representation schemes for the task of cancer type classification has been performed using a wide range of machine learning methods. 3 The best performing representation and classification model are utilized for causal gene analysis. MethodsIn this section, we describe the proposed data representation models as well as their utilization with machine learning algorithms for cancer type classification. An overview of the developed system is shown in Fig. 1. The phenotypes occur as a result of DNA mutations. In our system, we take VCF files, which hold the DNA mutation information, as input. These gene mutations are weighted by using the proposed representation models. The genomic data representation vector is then processed with a wide range of machine learning algorithms for the task of cancer type classification. So, the VCF data constitute the set of observations and the genes, which are weighted based on mutation information, are the features in our classification model for learning the given cancer classes. The first output of the system is the prediction of the cancer type. The second output is the list of most effective genes in the classification process, which is obtained by analyzing the most accurate representation and classification model. Fig. 1Overview of the proposed systemFull size image DatasetFacts and Figures 2017 annual report [25] states the leading sites of cancer. According to this list and the common cancer types from [2], we created a dataset of ten cancer types, which are observed frequently and account for above half of the estimated cancer caused deaths. The list of selected cancer types and the sample counts for each cancer type are provided in Table 1. We downloaded a total of 7028 VCF files for ten cancer types from (TCGA) [26]. [27] is used for gene-based annotation of the VCF files. From the annotated files, we selected the exonic and intronic mutations as they include specific gene labels. This selection of mutation types resulted in 16,383 distinct genes. As a result, our dataset, named as BOUN10has 7028 samples with mutation information of 16,383 distinct genes and a class label for each sample representing the cancer type.

Output: corroborate


Input: Claim: All bacteria are harmful to human health. [SEP] Text: reinhardtii in nature. One compound synthesized by prokaryotes and used by many algae is vitamin B12. Although C. reinhardtii does not depend on vitamin B12 to grow, it can obtain the compound from bacteria and use it as a cofactor in a pathway for methionine biosynthesis that is thermal tolerant (Kazamia et al. , 2012; Xie et al. , 2013). Indeed, under elevated temperatures, B12-providing bacteria increase the fitness of the alga (Xie et al. , 2013). A mutualism was also observed between C. reinhardtii and S. cerevisiae in sealed microtiter plates, with the algae trading reduced nitrogen for CO2 (Hom and Murray, 2014). While the significance of these interactions may be uncertain, they, and many yet to be discovered, likely shape the ways in which C. reinhardtii navigates in a complex biosphere. C. reinhardtii has been studied in the laboratory for many decades, we do not know the extent to which results from the laboratory reflect growth, life cycle and behavior of this alga in nature (Box 2). As a model system, C. reinhardtii is almost exclusively grown as a pure culture, a situation almost never encountered in the 'wild'. Returning a laboratory strain of C. reinhardtii to its native habitat would reveal whether domestication caused it to lose its ability to survive within the dynamic fabric of nature. Molecular analyses of the reintroduced strain could also reveal changes in the cells' physiology that underlie the loss of fitness in natural habitats, as well as other changes potentially associated with its adaptation to laboratory conditions. Field surveys are often hampered by difficulties in assessing the metabolic state of the cells and in establishing key inter-organismal interactions. However, harnessing the full potential of meta-omics and single-cell technologies could provide a fuller appreciation of the physiological status of cells as they experience environmental fluctuations and the dominant interactions that shape the life of C. reinhardtii. Expanding this understanding will require time-resolved data on the geographical occurrence of C. reinhardtii in different habitats, its genetic potential and population genetics, and dissection of biotic and abiotic interactions.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The rOP method, which uses the 5th ordered p-value as the statistic, exclusively identifies probesets that are guaranteed to have relatively small p-values up to the 5th ordered p-value, but tend to have very large values starting the 6th ordered p-value. This shows the sensitivity of the rOP method to the particular value of r chosen. On the other hand, the half-binomial weighted Fisher’s method weights in the 5th through the 9th ordered p-values, and thus is able to identify probesets that have relatively small values through larger ordered p-values. In other words, probesets that have relatively small p-values for most of the studies can be exclusively identified by the half-binomial weighted method, even if the smallest p-values are not very small. Table 1 Number of significant probesets for the meta-analysis of stem cell studies by different methods Full size tableFigure 4Venn diagram for stem cell studies. Venn diagram for the probesets found significant by the binomial weighted Fisher’s method, the half-binomial weighted Fisher’s method and the rOP method. Full size imageTo look at the pathways associated with the significant lists of probesets, we performed functional annotation clustering analysis using DAVID (Huang et al, 2009), which is available at Some of the top functions that show up include metal-binding, nucleoplasm, ubl conjugation, vasculature development and head/face development. We also looked at the pathways for the probesets that were exclusively detected by one of the methods. Functions such as neuron projection, neuron differentiation and development, which are meaningful in the stem cell study setting, were found to be associated with the probesets exclusively identified by the half-binomial weighted Fisher’s method. These functions did not show up in pathway analyses of the other lists. Figure 5P-value patterns for different methods. Pattern of the original ordered p-values from the 9 studies for probesets detected by one of the three methods only. The x-axis is the order of the p-values from the 9 studies. The y-axis is the p-values. The plot includes a random subset of 20 probesets that are detected exclusively by each of the three methods.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: In this chart, the tf and BM25-tf values for different values of k are shown when the number of gene mutations changes in the range from 1 to 10. The figure demonstrates that, the tf values, which are represented by empty circles, keep increasing as the number of mutations increases. Even a point mutation may be significant for the occurrence of a certain disease. Therefore, a gene with n mutations is not necessarily n times more important than a gene with 1 mutation for disease detection. As shown in Fig. 2 the smoothing parameter k in BM25-tf dampens the effect of high tf values. Fig. 2The effect of the smoothing parameter k in the BM25 calculations for term frequencyFull size image Implementation and experiment designMachine learning modelsA wide range of machine learning algorithms are applied to investigate the effects of the proposed mutation based DNA representation models in the task of disease classification. (NB), K-(KNN), Machines (SVM), (LR), One-Layer-Perceptron (Perceptron) and Feed-(NN) are run on the prepared datasets. For the NN, the model is composed of two or more fully connected layers. Except the last layer, the number of nodes is halved at each layer. If the first layer has N units, then the second layer has N/2 units. With this strategy, each layer represents the information from the previous layer with less units. After each fully connected layer, a dropout is applied. As there are 10 classes, the last layer has 10 nodes with softmax activation function. Categorical cross-entropy is employed as the loss function. The number of epochs is 50 and the batch size is 50. All experiments are implemented with Python. For the traditional machine learning algorithms, the scikit-learn library [34] is used. The feed forward network model is implemented with Keras [35] on Tensorflow [36] backend. Evaluation strategyThe input datasets are first divided into 80% training and 20% test sets. Parameter tuning is accomplished using 10-fold cross-validation on the training set. Testing is also accomplished in 10-folds. In each fold, the model with the best parameters is trained with one of the training sets from the initial cross-validation experiment, which was performed over the 80% of the data, and testing is performed over the test set (the initially separated 20% of the data).

Output: corroborate


Input: Claim: All bacteria are harmful to human health. [SEP] Text: Such studies could then be extended to include analyses performed under controlled laboratory conditions that closely align with conditions encountered in the field, using innovative methods such as microfluidics to mimic conditions of the soil and other complex environments (Stanley et al. , 2016). Add a comment + Open annotations. The current annotation count on this page is being calculated. 1756. PubMed 223. 401. PubMed 680. PubMed 543. 1538. PubMed 585. PubMed 1137. PubMed 334. 429. PubMed 247. PubMed 1136. 886. PubMed 150. PubMed 452. PubMed 574. 666. PubMed 392. PubMed 624. PubMed 549. 1575. PubMed 337. PubMed 18. PubMed 127. PubMed 3572. 2369. PubMed 162. PubMed 2352. PubMed 565. PubMed 2018. PubMed 538. PubMed 361. PubMed 1669. 112. PubMed 406. PubMed 176. PubMed 281. PubMed 3211. PubMed 8313. 98. PubMed 6274. PubMed 185. PubMed 87. 332. PubMed 448. 105. 1232. PubMed 1430. PubMed 5523. PubMed 184. 2306. PubMed 1980. PubMed 840. PubMed 540. 552. PubMed 387. PubMed 5835. 198. 817. 2743. PubMed 72. 5963. PubMed 2679. PubMed 251. PubMed 207. PubMed 312. 428. PubMed 273. 201. PubMed 948. PubMed 83. 4167. 718. PubMed 19. 292. PubMed 2931. PubMed 1610. PubMed 38. 69. 1192. PubMed 4776. PubMed 268. 230. PubMed 263. 773. 138. 240. PubMed 262. PubMed 0646. PubMed 76. 0744. PubMed 1507. 448. 115. 241. 80. 1752. 220. PubMed 377. 286. 888. PubMed 246. 577. 15. 8-kb genome suggests mechanisms for DNA replication 24:241–247. PubMed 364. 115. 1555. PubMed 503. PubMed 1347. and and that it is possibly not even a single species.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Full size imageFurther applications and comparisons with the rOP methodTo compare the performances of the WOP methods and the rOP method Song and Tseng [1] in real data application, we applied our WOP methods to the three microarray meta-analysis applications in Song and Tseng [1]. The first application consists of comparisons of two subtypes of brain tumors - anaplastic astrocytoma (AA) and glioblastoma multiforme (GBM), from 7 studies. The second application combines 9 studies comparing post-mortem brain tissues between MDD patients and control samples. In the third application, 16 diabetes microarray studies consisting of different organisms and tissues were combined. and Tseng [1] for more details on the contexts of these three meta-analysis applications. To ensure that the results are directly comparable, for each meta-analysis we directly used the two-sided p-values for each gene and each individual study calculated in Song and Tseng [1]. and Tseng [1], permutation analysis is used for the brain cancer studies and the MDD studies, while theoretical distributions are used to obtain results for the diabetes studies. We follow Song and Tseng [1] and also directly use the two-sided p-values for the permuted datasets provided by Song and Tseng [1] for the brain cancer studies and the MDD studies. and Tseng [1] for more details on the preprocessing of the data and the calculation of the original p-values. We applied our WOP methods, namely the binomial and half-binomial weighted Fisher’s and Stouffer’s statistic, to the three sets of studies. For the brain cancer studies, we test for H S4 out of 7 studies. For the MDD studies we test for H S5 out of 9 studies. For the diabetes studies, we test for H S9 out of 16 studies. We also applied the corresponding rOP methods for testing the same hypotheses, using r=m=4, 5 and 9 respectively for the three meta-analyses. In addition, we applied the rOP methods using the selected r values in Song and Tseng [1] for comparison. To be specific, Song and Tseng [1] used r=5 for the brain cancer studies, r=7 for the MDD studies, and r=12 for the diabetes studies.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: This result can be interpreted as being an indication of the existence of new (currently unknown) causal genes. As the accuracy of the classification models are enhanced with additional genes, the rest of the experiments are applied on the full gene datasets. Table 5 Machine learning experiment test results on the gene sets with the binary representation modelFull size table Comparison of the data representation models with machine learning experimentsMachine learning algorithms are applied in order to explore the effects of the proposed data representations. Table 6 lists the results of the machine learning experiments. The table is designed to compare the data representation models for each algorithm. The row with the best result is shown in italic for each algorithm and the overall best performance is made bold. Table 6 Machine learning experiment test results on the data representation models of the full gene BOUN10datasetFull size table The accuracy scores of NB, KNN and SVM with polynomial kernel are below 57% and the f-score results are below 59%. The remaining 5 algorithms obtain accuracy and f-score levels above 60% (except SVM-rbf with tf-idf). We will focus on these better performing algorithms. The BM25-tf-rf representation scheme leads to the best accuracy and f-score results for all of the 5 algorithms. In addition, the BM25-tf-rf data representation results in nearly 2 to 4 percent accuracy and f-score improvement and 0. 01 to 0. 05 roc-auc improvement compared to the second best representation for all of the 5 successful algorithms. When we consider the FPR results, BM25-tf-rf leads to lowest values in all of the 5 successful algorithms. For the Feed-Forward NN f-score results with binary and BM25-tf-rf representations, the paired t-test produces a p-value < 2. 5e−10, and with the tf-rf and BM25-tf-rf representations, the paired t-test produces a p-value < 1. 1e−05. These results show that the additional statistical information hidden in the BM25-tf-rf representation provides significant gain compared to the other representation models. When we compare the Feed-Forward NN f-score values for the C-score and BM25-tf-rf models, the paired t-test produces a p-value < 3. 2e−06.

Output: corroborate


Input: Claim: All bacteria are harmful to human health. [SEP] Text: Overall, the reviewers recommend that the authors acknowledge the confusion here and minimize the discussion, perhaps just to refer that some members of the genus are psychrophilic/cryotolerant and recognized occasionally by the patches of watermelon color on snow. [Notably, the related Chloromonas nivalis is more likely a true psychrophile but, because it belongs to a different genus, it is beyond the scope of this essay]. 10) Some of the section on deflagellation is counter to the conclusion of the cited Quarmby, 2009 10. 1038/s41467-017-01547-8 or similar). These show the wolf pack feeding behavior and the deflagellation response very nicely. 12) There was much debate in the reviewer consultation about whether the word "pelagic" can refer to organisms living in freshwater lakes (in part because of its root from the Greek "pelagikos", which relates to the sea). Please revise the text to make it clearer whether sections are referring to oceanic/marine algae or lake-dwelling algae. If "pelagic" is still used, it should be clearly defined for the benefit of unfamiliar readers. Please consider something like "organisms that live in the water column of lakes and oceans, but not on or near the bottom of the lake or ocean". 13) In the text in Box 1. Taxonomy and laboratory history of C. reinhardtii, please add "from two parents or haplotypes" to the end of the sentence that reads "The sequencing of 39 common laboratory strains show that they fall into five genetically distinct lineages". This should be mentioned; otherwise it may sound like there are 5 haplotypes. 14) The concluding paragraph could be strengthened. For example, the sentence: "It would be interesting to examine the fitness and adaptation of a domesticated laboratory strain to its native environment. " Why would this be interesting? Which strain? What questions would be addressed? Author response Essential revisions: 1) The current title ("Hatched to be wild: Chlamydomonas reinhardtii in nature") does not represent the précis of the 2018. 6c) There was a concern that the literature supporting the current section on chemotaxis is limited (subsection “Physiological and metabolic capabilities”).

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Permutation analysis is used for the brain cancer studies and the MDD studies for all methods. For the diabetes studies, we used our numerically simulated distribution to obtain the p-values for the WOP statistics. Table 2 shows the numbers of significant genes found using the different methods for the three meta-analyses with the FDR controlled at the 0. 05 level. We can observe that the WOP methods using the binomial weighting scheme generally detects more significant genes than the half-binomial weighting scheme. In most cases, the rOP methods (using either r=m or selected r) detect less genes than the WOP methods, although in general closer in number to the WOP methods using the half-binomial weighting scheme. Table 2 Number of significant genes for the three meta-analyses by different methods Full size tableOne interesting observation is that for the MDD studies, the rOP method based on r=m=5 detects less genes than based on the selected r=7. This result is counterintuitive, since one would expect that genes that are differentially expressed in at least 7 studies would be a subset of the genes that are differentially expressed in at least 5 studies. The result could be due to the fact that r is selected in Song and Tseng [1] to optimize the number of significant genes and therefore outperforms a general choice of r=m. Nonetheless, this reflects the fact that the rOP method is sensitive to the choice of r. To further investigate this problem, we looked at the overlap of the detected genes by the rOP methods using either r=m or selected r, as well as with the detected genes by the half-binomial weighted Fisher’s method. 6 for a Venn diagram of the genes detected by the aforementioned three methods for the MDD studies. As shown in Figure 6, only 269 genes overlap between the rOP methods with two different choices of r, which is less than half of the genes detected by either method. However, all 269 genes are detected by the half-binomial weighted Fisher’s method. In addition, the WOP method also picked up 251 of the genes only detected by rOP based on selected r and 239 of the genes only detected by rOP based on r=m, which accounts for most of the genes detected by either method.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: 3, PRSS3, AQP3, HAPLN3, RASSF8, BRF1 and GSE1 are shown to be effective on classification of breast cancer and there is supporting evidence in the literature for their relatedness to the disease. PRSS3 was found to promote the growth of breast cancer cells [39]. AQP3 is studied in [40]. In [41], HAPLN3 was shown to be among the overexpressed genes for breast cancer. In [42], HAPLN3 was suggested to be involved in the development of breast cancer and to be a biomarker for the treatment of breast cancer. In [43], RASSF8 was proposed to be used in discrimination of benign and malignant breast tumors. In [44], they investigated whether BRF1 expression is increased in the samples of human breast cancer and their results indicated that it is overexpressed in most cases. In [45], it is reported that GSE1 is overexpressed in breast cancer and silencing of GSE1 significantly suppressed breast cancer cells. The effect of BM25-tf-rf weighting is illustrated through an example for the HAPLN3 and BRF1 genes of two sample patients. Sample-79 is a breast cancer patient and Sample-164 is a brain cancer patient. HAPLN3 and BRF1 have been found relevant to breast cancer in the literature and they are both found to be among the most effective genes for our classification system. But there is no scientific evidence about the relationship of these genes and brain cancer. The BM25-tf-rf value for HAPLN3 in Sample-79 is 1. 016021 whereas it is 0. 794813 in Sample-164. There is a nearly 28 percent increase in the weight of this gene for the breast cancer sample. For BRF1, the BM25-tf-rf value in Sample-79 is 1. 323967 whereas it is 1. 085498 in Sample-164. There is a nearly 22 percent increase in the weight of this gene for the breast cancer sample. When we consider the NN classification model, the weights for these gene features is the same for both samples. Therefore, the distinction of predicted cancer type results arise from the difference in BM25-tf-rf weights. This difference effects the decision and helps to distinguish between cancer types. In Fig.

Output: corroborate


Input: Claim: All bacteria are harmful to human health. [SEP] Text: The general feeling was that findings based on, at best, a single published paper were not substantive contributions and did not warrant inclusion. There are a number of papers on chemotaxis in C. reinhardtii. Chemotaxis to ammonium was described by Byrne et al. , (1992); to nitrite by Ermilova and Zalutskaya, (2014); and to nitrate by Ermilova et al. , (2009). To cover these three 10. 2478/botcro-2013-0012 and and that it is possibly not even a single species. Overall, the reviewers recommend that the authors acknowledge the confusion here and minimize the discussion, perhaps just to refer that some members of the genus are psychrophilic/cryotolerant and recognized occasionally by the patches of watermelon color on snow. [Notably, the related Chloromonas nivalis is more likely a true psychrophile but, because it belongs to a different genus, it is beyond the scope of this essay]. This statement has been rephrased and is more general. It now reads: "[…] and some members of the genus Chlamydomonas are carotenoid-rich organisms present on the surface of snow, giving it a red appearance (Remias et al. , 2005). " 10) Some of the section on deflagellation is counter to the conclusion of the cited Quarmby 2009 10. 1038/s41467-017-01547-8 or similar). These show the wolf pack feeding behavior and the deflagellation response very nicely. We have now reused the video previously published by Aiyar et al. , (2017). This is now Video 1. 12) There was much debate in the reviewer consultation about whether the word "pelagic" can refer to organisms living in freshwater lakes (in part because of its root from the Greek "pelagikos", which relates to the sea). Please revise the text to make it clearer whether sections are referring to oceanic/marine algae or lake-dwelling algae. If "pelagic" is still used, it should be clearly defined for the benefit of unfamiliar readers. Please consider something like "organisms that live in the water column of lakes and oceans, but not on or near the bottom of the lake or ocean".

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Further, we noticed that even for the brain cancer studies and the diabetes studies, where rOP based on r=m did detect more genes than rOP based on selected r, there are still a large number of genes detected by rOP based on selected r that are not detected by rOP based on r=m. On the other hand, most of these genes that are detected by only one of the rOP methods are detected by the half-binomial weighted Fisher’s method. Figure 6Venn diagram for MDD studies. Venn diagram for the genes found significant in the meta-analysis of the MDD studies by the rOP method based on r=m, the rOP method based on selected r, and the half binomial weighted Fisher’s method. In this case, m=5 and the selected r=7. Full size imageFor comparison, to see how the results would differ for different choices of r for the WOP methods, we applied the WOP method to the MDD studies again - this time testing H S7. We used the half-binomial versions of the three weighting schemes for m<r≤K that were discussed earlier: whb1, whb2 and whb3. We used Fisher’s summary statistic in the WOP method. The numbers of genes found by using the three weighting schemes are 760, 770 and 774 respectively. The three weighting schemes are fairly consistent with each other, since for each weighting scheme more than 90% of the genes found were also found by the other two weighting schemes. Comparing to the number of genes found by the half-binomial weighted Fisher’s method for testing H S5, which is 930, notice that the corresponding WOP methods for testing H S7 yield smaller numbers, conforming to our expectations. As discussed earlier, the ideal result would be that the genes detected for H S7 be a subset of the genes detected for H S5. In reality, over 70% of the genes detected by WOP methods for H S7 were also detected for H S5 (the percentage being 72. 6%, 71. 4% and 74. 2% for the three weighting schemes respectively). Recall that for the rOP method, only 43. 6% of genes detected for r=7 were also detected for r=5.

Output: pass


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

Output: corroborate


Input: Claim: All bacteria are harmful to human health. [SEP] Text: Parent – Bacteriophage Hunting: Searching for the 1, 2022 | Medical & Bacteriophages are tiny viruses that infect and kill bacteria but are harmless to humans. With the world facing devastating antibiotic resistance in the coming years, they may be our best hope for treating bacterial disease. There are believed to be 10^31 bacteriophages in the world, and most of them have not yet been identified. Parent from University is working on exciting, collaborative projects hunting for bacteriophages in their natural environment. She has made huge strides in elucidating the mysterious and important world of bacteriophages. Shigellosis is a nasty infection of the Shigella bacteria with over 164 million cases each year leading to 1. 1 million deaths. Sadly, many of these deaths are children, as those under the age of 5 are most likely to catch a Shigella infection. This is partly due to the method of transmission – accidentally ingesting the bacteria residing in the stool of an infected person. Often this occurs in childcare settings if carers don’t thoroughly wash their hands after toilet training, for example. Swimming and drinking contaminated water or eating food by an infected person can also pass along Shigella. The symptoms of shigellosis include fever, nausea, stomach pain and diarrhoea which often contains blood. These issues usually last for around a week, but they can result in secondary complications such as dehydration, seizures and blood infections. Whilst antibiotics are sometimes given for shigellosis, many strains are now resistant so treating the diarrhoea and consequent dehydration is often the priority until the infection runs its course. Shigellosis is the most common form of dysentery; other types are caused by Salmonella and E. coli, among other bacteria. There are four different species of Shigella called S. boydii, S. dysenteriae, S. flexneri, and S. sonnei. S. flexneri is the most common type and is often associated with low-income countries, however, cases of S. sonnei, which tends to be found in high-income countries, are increasing. This is due to the low dose of bacteria needed to cause illness and the ever-increasing antibiotic resistance of the bacteria. Antibiotic resistance is a huge and growing issue globally, for many different bacteria.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Even though the WOP methods are not perfect, we can still see the great improvement in robustness compared to the rOP method. In summary, our observations confirm that the results of the rOP method are indeed heavily dependent on the choice of r. Whereas our WOP methods show much higher robustness. In particular, the WOP methods for testing H S m has shown superior robustness by being able to cover most of the genes detected by the rOP methods using different r. Since in practice it is not often clear which particular H S r should be tested, we believe the WOP methods for testing H S m is a better choice when the goal is to detect signal in the majority of studies. ConclusionsMeta-analysis is a useful tool in integrating data from different sources to test a particular hypothesis. While this paper mainly discussed the application of meta-analysis on microarray differential expression studies, other areas of genomic studies have increasingly relied on the use of meta-analysis, such as genome-wide association studies (GWAS). Some seminal studies in this area include Scott et al. [25] and Willer et al. [26]. Meta-analysis is also frequently used in clinical studies, psychological studies and statistical applications in other social sciences. More and more meta-analyses nowadays aim at detecting consistent findings across a number of studies. While most of the traditional meta-analysis methods test for significance in at least one of the studies, it is important to develop new meta-analysis methods that focus on testing for significance in the majority of studies. The weighted ordered p-value (WOP) method provides such a framework. It is unique in its use of weights that are based on the order of the p-values. The rOP method Song and Tseng [1], which is also based on ordered p-values, can be considered a very special case under the WOP framework, where all the weight is placed on one single ordered p-value. The WOP methods do not require pre-specification of r and is less sensitive to the choice of its value. The half-binomial weighted WOP methods have been shown to be more robust and have better receiver operating characteristics compared to the corresponding rOP method. As pointed out by a reviewer, one of our previously published meta-analysis methods also considers the issue of heterogeneity and utilizes weights. However, these two methods are quite different in concept.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The normalization effect of BM25-tf further improves the classification performance of tf-rf. We investigated the most effective mutated genes in our proposed system for breast and lung cancers. A subset of the resulting genes have also been suggested as causal or target genes in previously published studies, which demonstrates that the proposed approach can also be used to recommend candidate genes. The introduced data representation models are evaluated for the task of cancer type classification, which is an important problem in bioinformatics, since the appropriate treatment is determined according to the primary site. However, they can also be utilized for other genetic diseases, which we plan to investigate in our future studies. AbbreviationsBM25-tf: BM25 definition of term frequency CADD: Combined annotation dependent depletion idf: inverse document frequency KNN: K-nearest neighbor LR: Logistic regression NB: Naive bayes NN: feed-forward multilayer neural network Perceptron: one-layer-perceptron tf: term frequency tf-idf: term frequency-inverse document frequency tf-rf: term frequency-relevance frequency rf: relevance frequency SVM: Support vector machine TCGA: The cancer genome atlas VCF: Variant call format 2017. 2018. Cancer Institute. (NCI). 2018. . Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme. Med. 2009; 62:1609–18. 6. Zulpe N, Pawar V. Glcm textural features for brain tumor classification. Comput Sci. 2012; 9:354. 42. 9. 971. . Novel image markers for non-small cell lung cancer classification and survival prediction. BMC Bioinformatics. 2014; 15:310. 26. 83. 319. 60. PubMed Pub9. 9. 7. 95. 2018. VCF Specification. 2017. Access date: May 2018. . Utilizing somatic mutation data from numerous studies for cancer research: proof of concept and applications. Oncogene. 2017; 36:3375–83. 7662. 5. 894. 62. Society. (ACS). 2018. Genome Atlas. (TCGA). 2018. . Annovar: functional annotation of genetic variants from high-throughput sequencing data. Res. 2010; 38:e164. 2018. Symbol Checker.

Output: corroborate


Input: Claim: All bacteria are harmful to human health. [SEP] Text: The misuse of antibiotics in humans and animals has resulted in bacterial strains that are not killed by conventional antibiotics. When these new strains are passed on, so is the antibiotic resistance and the infections they cause are much harder to treat and mortality rates become higher. A well-known example of this is the superbug methicillin-resistant Staphylococcus aureus, better known as MRSA, a type of bacteria that is now resistant to most common antibiotics and is becoming resistant to even the last-resort antibiotics. Bacteriophages and are working to combat this global health threat by innovating alternatives, or supplements, to antibiotics. One of these approaches utilises bacteriophages, the tiny viruses that are harmless to humans but instead, infect and kill bacteria. They do this by attaching to a bacterium, injecting them with their genome, replicating and multiplying inside and then bursting out to destroy the bacteria and repeat the process. Excitingly, bacteriophages have already successfully been used to treat some antibiotic-resistant strains of bacteria. However, different bacteriophages infect only specific bacteria, so specific bacteriophages need to be found to treat different bacterial infections. Even though one of the first bacteriophages was identified in 1917 and was found to cure S. dysenteriae, very few studies have subsequently been conducted on Shigella bacteriophages. Previously, there were only around 35 of them logged on public databases, while over 400 Escherichia and Salmonella bacteriophages are known. This is one reason that Parent from University in the USA decided to study this area. In addition, in 2016, her university’s state of Michigan experienced the worst outbreak of shigellosis in 30 years, bringing to light the importance and relevance of anti-microbial work. In a collaborative effort, set out to discover and characterise Shigella bacteriophages in her area so that they might be used in novel therapeutics. ‘Our constantly expanding library of phage types will allow us to gather an unprecedented wealth of knowledge of phage interactions with enteric bacteria. ’ of 16 an estimated 10^31 bacteriophages on the planet – more than every other organism on earth combined – there were many to be found.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: In the meantime the protein structure classification database SCOP [13] (release 1. 69) lists 534 different structure data sets classified in 32 sequence families. The family includes carboxylic acid ester hydrolase, lipid hydrolase, thioester hydrolase, peptide hydrolase, haloperoxidase, dehalogenase, epoxide hydrolase and C-C bond breaking enzymes, and even members that have lost the catalytic triad like the cell adhesion proteins gliotactin, glutactin or neuroligin [1, 16]. Thus, the α/β-hydrolase fold is a typical example for a divergent evolution where the development of different functions evolved from a common ancestor. We describe here the data warehouse system DWARF, which is dedicated to the analysis of protein families. So far there is no integrative approach described, that focuses to the analysis of the sequence-structure-function relationships within a single protein fold family. In an iterative process sequence clusters are generated and functional annotation is enriched by an automated annotation transfer. The validated data content of DWARF facilitates the analysis of sequence-structure-function analysis and guides protein engineering within protein families. We applied the DWARF data warehouse to the family of α/β-hydrolases, to hostthe Database, which has proven to be a powerful tool in analyzing the protein fold family of α/β-hydrolases. Construction and contentSource dataDWARF was designed to store data on protein sequences, structures, functional annotations, and a hierarchy of families. Protein sequence related data are extracted from GenBank [17], though the data model is designed to hold also sequence data from other data sources. For each GenBank entry the sequence, position-specific features, the source organism, the source database, available accession codes, and the protein name are extracted. The main data source for protein structure coordinates is ExPDB [18], since DWARF is designed to store single structure chains. ExPDB is derived from the Bank (PDB) and provides an individual file for each single protein chain. Beside the coordinates, additional data on experimental resolution, authors, and publication year are extracted. The monomer sequences were derived from the ATOM section of the coordinate files.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Mapping transcription mechanisms from multimodal genomic data 9 of the 2010 AMIA Summit on access Published: 28 October 2010 Mapping transcription mechanisms from multimodal genomic data Hsun-Hsien Chang1, Michael McGeachie1,2, Gil Alterovitz1 & …Ramoni1,2 Show authors BMC Bioinformatics 2. 0 platforms. TIM of pediatric ALLThe transcriptional information of SNP-gene pairs was quantified by mutual information. To account for noise in the data, we used a permutation test to determine the noise level, and found that a mutual information score of 0. 4 or below in the ALL data could be attributed to noise. Therefore, we consider a transcriptional channel to exist between a SNP and a gene when their mutual information is above 0. 4. Figure 1 shows a portion of the TIM between SNPs on chromosome 21q11 and genes on chromosome 21q11-q22; in the figure, the red squares denote SNPs, and blue circles denote genes. The map displays existing transcriptional channels, represented by the straight lines, where the color of each line represents the signal strength of each channel as mutual information. Figure 1The TIM mapping SNPs on chromosome 21q11 and genes on chromosome 21q11-q22. The red squares denote SNPs, and blue circles denote genes. This map displays existing transcriptional channels, represented by the straight lines. The color of each line represents the signal strength of each channel as measured by mutual information. Full size imageCis/trans regulatory analysisThe TIM in Figure 1 is a tool to identify cis- and trans-eQTLs. From the map, we can trace which SNPs are linked to the genes of our interests, resulting in the recognition of candidate genomic locations whose genotypes significantly affect expression of the genes. For example, the TIM shows multiple eQTLs of JAM2 (junctional adhesion molecule 2) which was reported to be associated with leukemia in copy number variation studies [18]. Our analysis indicates that the structural genetic variations can induce changes in expression of JAM2, which in turn may be a source of leukemia pathogenesis. Another gene indicated by our TIM is 1, which encodes a member of the (a disintegrin and metalloproteinase with thrombospondin motif) protein family. It is located on chromosome 21q21.

Output: corroborate


Input: Claim: All bacteria are harmful to human health. [SEP] Text: As part of a course, enlisted the help of microbiology graduate students at University who were asked to develop hypotheses for promising locations surrounding the university in which to hunt bacteriophages. They took samples from a variety of places including from river sediment downstream from a wastewater treatment plant, from pond and river water and even from water at the bottom of university bathroom hand dryers. These samples were filtered to remove the native bacteria residing in them. The leftover filtrate (the sample without the bacteria but hopefully with bacteriophages) was then used to inoculate a number of different Enterobacteria. This is the family of bacteria that tends to be found in the digestive tract and the one to which Shigella, Salmonella and E. coli belong. In microbiology, inoculation refers to introducing micro-organisms into a medium that facilitates cell growth. For this experiment, this meant adding bacteriophages in the filtrate to the culture of Enterobacteria. After the team had allowed the bacteria to grow and therefore, the bacteriophages that infect them to multiply, they isolated the bacteriophages from the bacteria cells to examine them. Interestingly, most of the bacteriophages that they found were on Shigella bacteria, specifically, S. flexneri, and all of them were extracted from samples that came from rivers. The bacteriophages were then purified and placed onto agar plates with additional types of Enterobacteria and left overnight. This method allowed to observe which bacteriophages infect which bacteria, because clear spots on the plate indicate that the specified bacteria has been killed by the added bacteriophage. Overall, the team discovered 16 new bacteriophages that infect Shigella and that six of these can infect more than one species of the bacteria. Having identified these novel bacteriophages, then named each of them; for example, those that infected S. flexneri were given the prefix Sf and then a number. These were exciting findings for Dr Parent’s initial bacteriophage hunting project and she continued to build on the work. Shigella Bacteriophages: in the study took place in Nebraska, which is located far from Michigan and is typically warmer and drier as well as having a different main water source and a unique salt marsh.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Relational data modelThe data model is implemented in Firebird [19], an open source relation database management system (RDBMS). It is divided in three major sections (Fig. 1): entities describing (1) the protein, (2) the protein sequence, and (3) the protein structure. The data schema was developed for integrating data on protein families sharing a common fold. Figure 1Conceptual data schema for DWARF, using Structure (LDS) notation. Each box represents an entity, which is implemented as a database table. attributes that identify each data instance of the entity are underlined. Entities associated with the protein are shaded in dark grey on the upper left, protein structure in white on the upper right, and entities describing protein sequence properties are shaded in light grey. Lines between the entities describe existing relationships. Full size imageProtein sectionProteins are stored in Proteins and are defined by the source organism (Species and Genus). A protein entry can hold several sequence entries, because often slightly different sequences are available from different authors or the sequences depend on the source strain. Thus, a single protein entry is assumed to hold multiple sequences if their sequence identity is higher than 98%. In the case of multi-domain proteins only the domain sharing the same fold is used for determination of sequence identity. For each protein entry a reference sequence entry is defined in Protein_Ref_Seq. Based on sequence identity, the hierarchy of protein entries is defined by three levels: homologous superfamilies (S_Fam), homologous families (H_Fam), and proteins (Proteins). For each homologous family a reference sequence is defined in H_Fam_Ref_Seq. Sequence sectionSequence entries (Sequences) are checked for duplicates and a set of non-redundant protein sequences is created. The source database entries are stored in DB_Entries for each sequence entry. Each amino acid is stored as a single data set (Positions), thus annotations (Seq_Annotations) can be easily mapped on single amino acids. Three types of annotations are defined in Annotation_type: single position specific, pairs of positions, and regions. The feature properties are defined in Descriptions and can be adjusted to the needs of the investigated protein family. Annotation information for a single sequence is merged from different source databases.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Using our cluster analysis and abstract network analysis [25], we can infer that CYYR1 is modulated by a number of SNPs across the q arm of chromosome 21. Furthermore, the interplay between CYYR1 and DSCAM leads us to the hypothesis that CYYR1 affects leukemia through Down’s syndrome. Figure 2Four cis/trans subnetworks abstracted from the TIM containing 1. These networks explain that there are 4 crucial cis and trans regulatory mechanisms centered at RIPK4, CYYR1, PCP4, and 1. Colors encode the cytobands of the SNPs and genes. Full size imageDiscussionWe have presented a general framework, based on the well-established mathematics of information theory, to create a map of the relationships linking genetic variations to gene expression and regulation. We applied our methods to the analysis of genotype and gene expression for eQTL identification, where we found several established and putative eQTLs in leukemia cells. Our results have been based on a partial Map, while we anticipate the full TIM of leukemia will enable the identification of many new eQTLs, and subsequent experiments to test causal connections between genetic variants and gene expression and regulation. However, TIMs need not be limited to SNP and gene expression data; other genetic data modalities are equally appropriate. For example, other genetic datasets may contain copy number variation, DNA methylations, microRNAs, or exon splicing information. With sufficient computation power, particularly parallel architectures, whole genome transcription maps can be constructed, from datasets such as the Project ( We expect that TIMs of various organisms, cells, or tissues will reveal new gene regulation mechanisms and foster the discovery and understanding of new molecular processes. ConclusionsThis paper presents an information theory approach to infer cis- and trans-eQTLs from SNP and gene expression microarrays. Our method develops a mutual information formula between discrete and continuous variables. The mutual information captures transcriptional information flux between SNPs and genes, resulting in transcriptional information maps (TIMs). Further analyses of TIMs include grouping SNPs and genes into similar clusters, inferring causal regulation within groups, and abstracting meaningful biological networks. The application of our method on a pediatric leukemia study shows how the TIM helps to find cis- and trans-eQTLs and to extract modulation patters between SNPs and genes.

Output: corroborate


Input: Claim: All bacteria are harmful to human health. [SEP] Text: These factors all mean that Nebraska gives rise to different environments for bacteriophages to live in and so, the possibility of unearthing even more new species. Each group in Dr Parent’s study followed the same processes for bacteriophage hunting as before with the goal of identifying new S. flexneri phages in the environment and comparing them to other Enterobacteria bacteriophages. Through this collaborative research, and her colleagues identified around 100 new bacteriophages each year, including many Shigella bacteriophages, even though there had been no recent shigellosis outbreak in Nebraska as was the case in Michigan. As a result, they concluded that Shigella bacteriophages are unexpectedly abundant in the environment and subsequent molecular studies of the bacteriophages helped them to better understand their inner workings and structure. Students learning how to apply science in the real world. Credit: in their Microbiome Currently, is researching how viruses like bacteriophages infect hosts via host recognition and transfer of their genetic material across their cell membranes. Often, viruses recognise a specific receptor on their host that facilitates infection and so, if this receptor is altered or deleted, they are better protected. However, found that the S. flexneri bacteriophage, Sf6 can recognise multiple receptors and therefore, overcome Shigella defence mechanisms. She is utilising this knowledge to investigate how the structure of Sf6 allows it to do this but also expanding her research into more complex environments. The relationship of bacteriophages with bacteria means that they can drive the evolution of their microbiome, as the bacteria try to adapt to evade the destructive effects of the tiny viruses. is studying the mechanisms of this phage-mediated evolution within complex microbial networks, which has scarcely been attempted before. Summing up her fascinating research, says that, ‘we have made a ground-breaking discovery that Shigella phages are highly abundant in the environment. In addition, we have found some highly novel isolates that have different genome sizes, shapes, and structures compared to other previously identified bacteriophages that infect bacteria such as Salmonella and E. coli. ’ Her work has opened exciting new avenues to explore in the world of bacteriophages and antibiotic resistance and will be incredibly useful for more research to come, as well as capturing the minds of budding scientists at High School.

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast