dataesr/scientific-paragraphs-categorization huggingface dataset:
Fig). Fig). Using a restricted Claudin-low classifier [29], R2 shp53-RAS were classified as Claudin-low, R2 shp53-CCNE1 and R2 shp53-WNT1 as epithelial, while R2 and R2 shp53 formed an intermediate group, combining characteristics of both the Claudin-low and the epithelial group (Fig 7A). Next, we sought to compare the characteristics of our HMEC models with that of the Claudin-low and the Basal-like breast cancer subgroups in the human primary breast cancer METABRIC dataset. Restricting our analysis to TP53 mutated and Basal-like tumors (defined with the PAM50 classifier), we selected 259 tumors, which we stratified in Claudin-low/Basal-like (83) and Basal-like/Non-Claudinlow (176) subsets. We determined that tumors in this subgroup presented a significantly lower incidence of CNAs than the Basal-like/Non-Claudin-low (Fig 7B-C) and that the KRAS pathway was among the top activated pathways in the Claudinlow/Basal-like tumors (S9A-B Fig).
On the same line, a report commanded by the French government on taxation of the digital economy points out that "all digital economy firms use data to improve their supply, obtain productivity gains, diversify their activities or reinforce their position on the different faces of the business model" 4  (Collin & Collin, 2013). Taking into account the data snowball effect, we can expect the existence of a tipping point in which a ride-hailing platform would have a dataset on user activity that would be substantially larger than its competitors'. When that tipping point is met, competitors would be unable to compete on quality. This is all the more important that the literature stresses that offering a high quality service is more relevant than being the first mover for a firm to enjoy the benefits of network effects in multisided platforms  (Rangan & Adner, 2001; Liebowitz, 2002; Iansiti & Zhu, 2007).
In the particular case of ride-hailing platforms, the lawsuit filed against Uber for using a software called Hell 5  , which allowed it to track drivers that were also working for its main American competitor Lyft in order to target economic incentives to them so that they would work less for Lyft, is a telling example of how important observed data can be to secure a competitive advantage. Indeed, the data obtained through Hell allowed Uber to practice price discrimination on the drivers' side of the market in order to reduce multi-homing and, therefore, to conquer that side of the market. It is important to point out that firms can lawfully collect and treat personal data because users give their consent to it when agreeing to the general terms and conditions of use of the app. Moreover, in countries covered by the European General Data Protection Regulation ridehailing firms can also argue that the treatment of this personal data is necessary in terms of their "legitimate interest" (e.
Then, the more riders/drivers there are on the platform in a given territory, the more likely it will be for a nearby driver/rider to demand/offer a ride. Moreover, the probability of a good match happening depends also on the capacity of the matching algorithm to predict which areas will see a rise in demand and, accordingly, nudge drivers to go there by increasing the price of rides in the area and/or providing benefits (e. g. bonuses or granting more rides), as the above-mentioned case about Uber's use of the software Hell shows. In order to do a proper prediction, ridehailing apps recur to past data on how drivers and riders have interacted in the platform  (McLean, 2016). Therefore, the accumulation of large amounts of data by one firm can lead it to secure a competitive advantage that can only increase over time as the data snowball effect keeps operating.
Second, following  Tucker and Wellford (2014)  and  Lerner (2014) ,  Sokol and  Comerford argue that "online providers can gain scale in users in ways that do not involve user data, and that access to data alone is not enough to improve quality and gain scale in users. Additionally, firms can gather data from other sources than users (e. g. data brokers), and can gain scale in data in alternative ways, such as entering into strategic distribution arrangements"  (Sokol & Comerford, 2016). The problem with the first part of the argument is that in ride-hailing platforms quality, as explained above, depends much more on access to large datasets on user activity than in other digital platforms. Contrary to what happens in other digital platforms, differentiation plays a smaller role and takes place largely within platforms, while the service they offer does not have any original content aspect, nor does it rely on the platform's identity 7.
Moreover, the 7 We will develop this argument in subsection 1. 3. quality of the match, which essentially comes down to matching nearby drivers with nearby riders, also depends on the availability of past data. Then, the quality of the service depends highly on the performance of algorithms, which in turn relies on the availability of large datasets on past user activity that allow their perfection. The second part of the argument is difficult to hold in ride-hailing platforms. To our knowledge, there are no data brokers that could sell relevant data to entrant ride-hailing firms. Moreover, we have to be careful on what data we are talking about. The most relevant data for a new ride-hailing entrant firm is data on how users interact within competing incumbent platforms. Contrary to other data-driven businesses such as search engines, the data collected by ridehailing platforms about their users' activity cannot be obtained without platforms' consent.
For data brokers to have access to it (which, to our knowledge, is not the case for ride-hailing user data), they would need to buy it from ride-hailing platforms. It is difficult to imagine why an incumbent ride-hailing platform would sell that valuable data to a data broker that could in turn make a profit by selling it to its competitors. A third similar argument offered by Sokol and Comerford is that "data is ubiquitous, inexpensive, and easy to collect  (Tucker 2013 )" because "customers leave multiple digital footprints on the internet  (Lambrecht & Tucker 2015 )"  (Sokol & Comerford, 2016). While this is true for data in general, it is not the case when it comes to the specific data a new firm would need to challenge incumbent ride-hailing platforms. The digital footprints that are useful to new entrants are mostly located in proprietary databases owned by incumbents.
However, it is not true for most of the relevant data new entrants would need to compete in a ride-hailing market. Following our previous example, if Uber has information about a certain amount of trips users did using its application, Juno will never be able to collect that same data to improve its algorithms because Uber's exclusive intellectual property rights over the database that contains that data and/or its right to use trade secret to keep the data away from competitors gives it a legal right to exclude Juno from its use. In other words, intellectual property and trade secrecy can create rivalrousness where technical conditions do not. The fifth argument is that "data has a limited lifespan -old data is not nearly as valuable as new data -and the value of data lessens considerably over time"  (Sokol & Comerford, 2016).
This option is particularly likely if Uber is expected to adopt a top dog strategy in which overinvesting entails pricing below cost instead of reducing costs. Indeed, drivers' incentives do not increase productivity and they increase Uber's marginal and average cost. One way of profiting from its dominance in ride-hailing markets to create revenue in other markets is to use the data generated there to gain a competitive advantage in a market in which competition takes place through innovation. This is what Uber is currently doing by using ride-hailing data to develop artificial intelligence software that will allow it to enter the autonomous vehicle (software) market. Given that the amount of data is a crucial factor affecting the speed of innovation in artificial intelligence, Uber's driving and traffic data, which amounts one billion kilometers driving per month worldwide  (Durand, 2017) , is a precious asset.
To our knowledge, no method exists that allows for a straightforward data processing of CpG o/e for non-specialists that is usable for all types of CpG o/e data. Here, we describe such a tool that we called Notos. We tested Notos on all data available in dbEST  [39]  since this database is one of the most widely used and covers a wide range of species. Notos integrates into Galaxy but is also available as suite of stand-alone scripts, it requires little computational resources, and the analysis is done within minutes. It is thus suitable for the routine first-pass prediction of DNA methylation in many biological settings.
Methods Notos is a kernel density estimation (KDE) based tool. Its implementation is computationally efficient and allows for processing even large data sets on an ordinary personal computer. The analysis carried out by the Notos suite is composed of two steps and corresponds to two separate programs (see Fig. 1  for the work flow): First, the preparatory procedure CpGoe. pl calculates the CpG o/e ratios of the sequences provided by a FASTA file. Any CpN o/e can be calculated if supplied as parameter. Secondly, the core procedure KDEanalysis. r, which consists of an R script  [40]  carrying out two principal parts: data preparation and analysis of the distribution of the CpG o/e ratios using KDE. It is also possible to skip the preparatory procedure and directly provide KDEanalysis. r with CpG o/e ratiosor other data of comparable structure.
Notos The script CpGoe. pl allows the calculation of CpG o/e ratios from a multi-FASTA sequence and uses the formulation of  [41]  (i. e. the first formula above) by default, the others are optional. Moreover, sequences having less than 200 unambiguous nucleotides are eliminated from the calculation in the default setting, since our test runs indicated that too short sequences led to large amount of zeros or other extreme values.
Core procedure: data cleaning and analysis via KDE The core procedure KDEanalysis. r carries out two steps: first, data preparation, which is mainly necessary to remove data artifacts, and secondly mode detection via KDE. Both steps return the user results in form of CSV files and figures. In addition, they allow overriding the default settings, if this is required by the user. Note, however, that such changes should be carried out with care, since all settings have been calibrated through intensive testing procedures on several hundred species from the dbEST database. In the following paragraphs, we describe these two steps in detail.
The output of this step consists of a table with various summary statistics in CSV format, and a figure displaying the data before and after this step. Figure  2  corresponds to the output resulting from an arbitrarily selected species, the locust Locusta migratoria. The content of the resulting table is described in detail in the documentation of Notos, which can be found in the readme file or the help section of the galaxy interface. Additional files 1 and 2 contain results from this step for 603 species from dbEST.
Finally, we selected the probably most common Gaussian kernel function with K(y) = 1 √ 2π e -1 2 y 2 due to the satisfactory results obtained in practice. In order to determine the value for the smoothing parameter, which is commonly termed bandwidth as well, we investigated different possible approaches, such as cross-validation, Silverman's rule  [45] , and Scott's variation of Silverman's rule  [46]. Extensive testing on a large variety of species from different data sources suggested that the well-established bandwidth proposed by Scott provides the best results in terms of interpretability. In particular, it showed a satisfactory stability for species with either a very high or a very low number of observations. Number of modes Subsequently, the number of modes is then determined by counting the number of local maxima of the estimated density, and a probability mass is assigned to each mode.
Comparison We investigated the performance of the Silverman test, the different criteria, and Notos on our data base with 603 species from dbEST. Table  1  shows the results from 17 arbitrarily chosen species, which display patterns that are representative of the full sample. The principal results are the following: (ii) The model selection criteria AIC and BIC generally produce non-interpretable results: both criteria allow for models with too many parameters, which regularly results in the selection of models with a far too high number of modes and no biological interpretability. This effect is illustrated in panel a of Fig. 4  which shows the fitted density and the location of the component-specific means for L. migratoria, determined by the AIC solution. The discrepancy between the relatively clearly visible bimodal shape and the selected model with nine components is rather large.
Interpretation In conclusion, while conventional methods can perform well in many cases, they will also often fail to produce biologically interpretable results. For the 603 species from dbEST, the information criteria mentioned above as well as the test of Silverman fall short for approximately 60% of the data in this regard. In contrast, Notos performed well with all tested data sets. After having firmly established that Notos provides robust descriptions of mode locations and mode numbers, we attempted to establish a link between these parameters. As outlined above, a CpG o/e ratio around 1 is assumed to occur in non-methylated sequences and a ratio below 1 in methylated sequences. Consequently, if both situations are detected, both types of sequences co-exist in the studies sequence population.
Especially researchers with little biomolecular knowledge will hesitate to engage in investigations on DNA methylation even though they possess a perfect expertise about their species of interest and epigenetic insights would present advancements to them. These technical difficulties have led to a distortion in the available methylation information. A review of the available data in databases and in the literature showed that at least 300 methylomes are available for Human, mouse and the model plant Arabidopsis thaliana but only 63 for a total of 16 other species  [68] [69] [70] [71] [72] [73] [74] [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] [85] [86]. normal distribution. This approach is at least questionable for two reasons. Firstly, model selection should be carried out taking a large number of possible models into account, and not just two (conveniently) selected alternatives.
Funding This work has been supported by  Campus France  and the  Norges  forskningsrad (program AURORA, nr. 34040YK ) to  C. Grunau  and  J. Bulla , the grant Felleslegat til fordel for biologisk forskning ved  Universitetet i Bergen  to  J. Bulla , the  ANR  grant  ANR-10-BLAN-1720  (EpiGEvol) to  C. Grunau , a PhD grant for disabled students by the  French Ministry of Education and Research  to  B. Aliaga , and a  DFG return grant  to  I. Bulla  ( BU 2685/4-1 ).
Genome-wide analyses estimate that more than 90% of multi exonic human genes produce at least two transcripts through alternative splicing (AS). Various bioinformatics methods are available to analyze AS from RNAseq data. Most methods start by mapping the reads to an annotated reference genome, but some start by a de novo assembly of the reads. In this paper, we present a systematic comparison of a mapping-first approach (FaRLine) and an assembly-first approach (KisSplice). We applied these methods to two independent RNAseq datasets and found that the predictions of the two pipelines overlapped (70% of exon skipping events were common), but with noticeable differences. The assembly-first approach allowed to find more novel variants, including novel unannotated exons and splice sites. It also predicted AS in recently duplicated genes.
In the last 10 years, the prevalence of alternative splicing has been completely re-evaluated. Recent reports claim that more than 90% of multi-exon genes produce at least two splicing variants  1, 2. The depth at which transcriptomes can be sampled with next generation sequencing techniques opens the possibility not only to annotate splicing variants in various conditions, but also to detect which transcripts are differentially spliced across pathological and physiological conditions. This growing interest in splicing both as a fundamental process and because of its implication in pathologies  [3] [4] [5]  has been accompanied by an increasing number of methods aiming at analyzing RNAseq datasets  [6] [7] [8]. The ultimate goal of these methods is to identify and quantify full-length transcripts from short sequencing reads.
Mapping-first approaches have been the most used so far, essentially because they were the first to be developed and because they initially required less computational resources. De novo assembly methods were also thought to be restricted to non-model species, where no (good) reference genome is available, and they seemed to be inadequate when an annotated reference genome is available. Recent progress in de novo transcriptome assembly is clearly changing this view, and the argument of the heavier computational burden does not hold anymore. The application of de novo assembly to human RNAseq datasets however still remains rare, although some studies have already shown its potential to detect novel biologically relevant splicing variants  15, 16. The generalization of de novo assembly approaches for studying splicing in human seems to be mostly impeded by the lack of a clear evaluation of its potential interest in comparison to more traditional mapping-based approaches.
This is the gap we aim at filling with the work presented here. To achieve this goal, we performed a systematic evaluation of an assembly-first and a mapping-first approach on two RNAseq datasets. As a first step, we compared pipelines that we developed in parallel, namely KisSplice and FaRLine, because we could easily control their parameters. Any difference between the predictions that is solely due to a parameter setting could be fixed easily, which enabled us to obtain a precise understanding of the irreducible differences between the two approaches. In a second step, we confirmed the generality of our findings by benchmarking our methods against Cufflinks  6  , MISO  11  and Trinity  17  , which are widely used pipelines. A significant part of our work has been to manually dissect a number of cases found by only one of the two methods.
This enabled us to go beyond a simple qualitative description and provide the community with a precise understanding of which cases are overlooked by each type of method, and where new methods are needed. All the software and step-by-step protocols presented in this work are freely available at http://kissplice. prabi. fr/pipeline_ks_farline. This should facilitate the reproducibility of our work, and applications to other datasets. From a general point of view, the combination of approaches we propose should enable to improve splicing-related transcriptomic analyses in physiological and pathological situations.
The majority of frequent isoforms are identified by both approaches. Applying KisSplice and FaRLine to the same RNAseq datasets generated by the ENCODE consortium (SK-N-SH cell lines treated or not with retinoic acid), we noticed that 68% of the alternatively skipped exons (ASE) identified by KisSplice were also identified by FaRLine and that 24% of ASEs identified by FaRLine were also identified by KisSplice (Fig. 2A ). This observation highlights that the mapping-first approach predicts a much larger number of events. This difference in sensitivity is due to the fact that while mapping-first approaches require that each exon junction is covered by at least one read, assembly-first approaches require overlapping reads across the entire skipped exon. Therefore, it can be anticipated that low abundant isoforms, that are covered by few reads, will be reported by mapping, but not by the assembly-first approach.
Supporting this prediction, we observed that for ASEs reported only by FaRLine, the number of reads supporting the minor isoform is much lower than in the other categories (Fig. 2 B ). The same results were obtained using another RNAseq dataset representing MCF-7 cells expressing or not the DDX5 and DDX17 splicing factors (Supplementary Figure  S1 ). Having clarified that rare variants are better handled by the mapping-first approach, we decided to filter them out, in order to analyse other differences between the two approaches. Experimental validations by RT-PCR that we performed on rare variants stratified by read support enabled us to clarify that both an absolute and a relative cutoff on the number of reads are required to discriminate variants which can be validated from those which cannot.
From this mapping, annotated and quantified events are extracted. Finally, the differential analysis is done with the same method as in the KisSplice pipeline. There are two reasons to explain why the mapping-first approach does not detect these events. First the mapper may fail to map the reads, or map them to an incorrect location, as junction discovery using short reads is a challenging task. Second, even in the case where the mapper succeeds, FaRLine may fail to report the event because it relies on annotations. Among these 1864 cases, we distinguished 3 sub-categories of errors due to the annotation. Either the exon is unannotated (30%), one of its flanking exon is unannotated (13%) or both exons are annotated but no transcript combining them was annotated (57%). The assembly-first approach, KisSplice, does not consider annotations, and an interesting resulting advantage is that novel junctions have the same chance to be assembled as known junctions.
This is again due to differences in the quantification of the inclusion rate, especially for ASE with multiple 5′ and 3′ splice sites. Second, some of the exons that were missed out by one method at the identification step happened to be condition specific. This is the case of an exon in NINL intron 5 (Fig. 4D ), only identified by KisSplice because it was not annotated. This is also the case of SAR1B exon 3 (Fig. 4E ), only identified by FaRLine because it overlaps with an Alu element. The analysis of the MCF-7 RNAseq dataset gave very similar results (Supplementary Figure  S3 ). The observation that many of the AS events that were annotated only by one method are differentially regulated across conditions confirms that these AS events should not be discarded from the analysis. Focusing only on AS events annotated by one approach may lead to miss splicing events which are central in the biological context.
Overlap with other methods. In a first step, we picked FaRLine and KisSplice as examples of a mapping-first and an assembly-first approach respectively. Clearly, there are other published methods in both categories. MISO is probably the most widely used to annotate AS events. We therefore ran it on the same datasets to check how its predictions overlapped with ours. As shown in Fig. 5A  (SK-N-SH dataset), 77% of predictions made by MISO were common to both FaRLine and KisSplice, 18% were only common with FaRLine, 2% were only common to KisSplice and the remaining 3% were specific to MISO. The overlap between the different methods was very similar when the MCF-7 RNAseq dataset was used (Supplementary Figure  S4A ). Overall, almost all candidates predicted by MISO were also predicted by FaRLine.