dataesr/scientific-paragraphs-categorization huggingface dataset:
However, these methods were defined on small series thereby limiting their applicability  (30) (31) (32) (33) (34) (35). A recent work  (36)  on a large series of 272 variants in consensus regions suggested the used of a MES threshold of relative decrease of 10%, however this threshold leads to a specificity of 50% (21/42) on our validation datasets. The UGG network previously published a large series of splicing variants and accompanying guidelines for in silico predictions  (7). Importantly SPiCE outperforms our previous results as demonstrated on the validation sets of variants from BRCA1, BRCA2 and other genes (Tables  3  and 4 ). At this point in time, SPiCE predicts potential splicing alteration of variants at 5 and 3 ss but neither the type of the effect (exon skipping or use of alternative splice site) nor the importance of the effect (partial or total) are predicted, although the tool is able to detect a trend in the prediction severity of splicing defects (Supplementary Figure  S5 ).
FUNDING  NHMRC Senior Research Fellowship  [ ID1061779  to A. B. S. ];The  Cancer Council Queensland  [ ID1086286  to M. P. , in part];  French National Cancer Institute   Translational Research Grant ;  Direction Générale de l'Offre des Soins (INCa/DGOS). Funding for open access charge:  Laboratoire de Biologie et Génétique du Cancer ;  Centre Francois Baclesse, Caen, France.
A 9. 6-m-high EC flux tower has been operating close to the watershed center since 2010. The sensible and latent heat fluxes data collected from 2010 to 2013 were quality controlled, and the REddyProc software was used to fill gaps at the hourly timescale. Adapting REddyProc method consisted of splitting the dataset according to wind direction, which improved the flux data at the hourly timescale, but not at the daily and monthly timescales. Finally, complete time series permitted to analyze seasonal and inter-annual variability of ETa.
This induces the necessity to evaluate the existing gap-filling methods, then to adapt these methods if necessary. The current study aimed at evaluating a robust and widely used gap-filling method, for obtaining complete time series of ETa measurements over hilly cropping systems at the extent of the small watershed. We used a nearly four-years long EC measurements dataset, collected from a flux tower within an agricultural watershed. The latter was typified by hilly topography, and the footprint of the flux tower measurements integrated a patchwork of crop fields. The crops were rainfed, which minimized advection processes induced by changes in soil moisture. We evaluated the gap-filling method proposed by  [24] , which is widely distributed through the REddyProc package version 0. 6-0. We evaluated this method on λE for ETa, but also on H that is used to estimate water status indicators (e.
Experimental Overview The meteorological station was located at 36 • 52 10. 5 N, 10 • 52 6. 3 E, 108 m asl, near the catchment outlet. The measurements of ETa at the watershed scale were collected from an eddy covariance (EC) device set up on a 9. 6 m-height flux tower. The flux tower was located at 36 • 52 39. 0 N, 10 • 52 36. 4 E, 115 m asl. , close to the center of the watershed (see Figure  1 ). In the current study, we focused on the data collected over four years (2010 to 2013). All instruments were manufacturer-calibrated and carefully checked during the experiment.
Quality Control The quality control of the 30-min flux data was performed using two standard tests that are routinely employed over flat and sloping terrains, i. e. , the Steady State test and the Integral Turbulence Characteristics test. These tests permitted to ensure that the theoretical requirements for the EC measurements were fulfilled  [27]. On the same site, references  [22, 23]  applied these tests over EC datasets collected under conditions of hilly topography, and reported good energy balance closure for the selected data. We kept the high and good quality classes as defined by  [28, 29] , since these two classes are considered as suitable for long-term observations.
The Dataset to be Gap-Filled Before quality control, the rate of missing because of experimental failures (deficient energy supply, sensors malfunctioning) ranged between 20% and 57% for H, and between 28% and 81% for λE (see Table  1 ), on a yearly basis. The rate of missing data after quality control filtering ranged between 44% and 69% for H and between 66% and 92% for λE, on a yearly basis. The rates of missing data before and after quality control filtering were similar to those reported in former studies that addressed missing data from flux tower with EC devices  [30]. In terms of gap temporal distribution that might directly impacts the performances of gap filling methods, we identified 4 long periods with continuously missing data for H (from  15.
REddyProc Gap Filling Method In this study, the gap filling method proposed by  [24]  was chosen. This choice was motivated by the following reasons. First, this method was tested and proven for a worldwide variety of land surface conditions, since it was adopted by the global network of micrometeorological measurement sites FLUXNET (http://fluxnet. fluxdata. org/) as a standardized method  [33, 34]. Second, this method does not involve much ancillary information, since it only requires meteorological data. Third, this method is world-widely spread through the R package labelled REddyProc that was developed by [24] at the Max Planck Institute for Biogeochemistry. This package relies on a gap-filling technique similar to that proposed by  [11] , and additionally includes (1) the co-variation of fluxes with meteorological variables, and (2) the temporal auto-correlation of the fluxes.
REddyProc Gap Filling Method In this study, the gap filling method proposed by  [24]  was chosen. This choice was motivated by the following reasons. First, this method was tested and proven for a worldwide variety of land surface conditions, since it was adopted by the global network of micrometeorological measurement sites FLUXNET (http://fluxnet. fluxdata. org/) as a standardized method  [33, 34]. Second, this method does not involve much ancillary information, since it only requires meteorological data. Third, this method is world-widely spread through the R package labelled REddyProc that was developed by  [24]  at the Max Planck Institute for Biogeochemistry. This package relies on a gap-filling technique similar to that proposed by  [11] , and additionally includes (1) the co-variation of fluxes with meteorological variables, and (2) the temporal autocorrelation of the fluxes.
• Second, REddyProc was applied after splitting the data according to the two main wind directions, i. e. , north-west (NW) and south (S). We split the complete time series into two datasets. The NW (respectively S) dataset included the H ORI and λE ORI data collected under NW (respectively S) wind conditions. REddyProc was applied over each of these two datasets. The resulting gap-filled datasets were finally merged. The obtained energy fluxes were labelled H RNS and λE RNS.
Cross-Validation of REddyProc: Artificial Gaps Splitting the initial time series of quality controlled data into two datasets for north-western and southern winds induced the removal of 34% and 66% for the NW and S datasets, respectively (see Section 2. 5). In order to quantify the resulting deterioration on the REddyProc gap-filling performances, we introduced artificial gaps in the initial time series, to which we applied the REddyProc procedure. We then compared the gap-filled data against the initial values  [10, 35]. Conducting this cross-validation analysis also permitted to quantify the REddyProc gap filling performances for the specific conditions of the study area. Creating artificial gaps consisted of randomly removing part of the existing dataset. We removed 10% to 70% data from the original time series, with a 10% step.
Impact of Discriminating Wind Directions with REddyProc Figure  4  presents the comparison of the sensible and latent heat fluxes estimated by REddyProc if splitting (RNS) or not (REP) the H and λE datasets according to wind direction. Figure  4  displays results at the hourly, daily and monthly timescales. We recall wind direction are labelled NW for north-western winds and S for southern winds. At the hourly timescale and for NW winds, the sensible heat flux estimates H REP and H RNS were very similar, with a regression line close to the 1:1 line (slope = 1. 07, intercept = 1. 9), and a small dispersion (RMSE = 11. 0 W•m -2 ). Conversely, under S winds, applying REddyProc without discriminating the wind directions (H REP ) distinctly overestimated the sensible heat flux estimates (H RNS ) obtained when discriminating wind directions (slope = 0.
Discussion The results obtained when cross-validating REddyProc confirmed that the latter was able to provide un-biased and acceptable estimates of missing flux data, even for large rates of missing data (see Table  2 ). The REddyProc appeared to be robust when facing large gap rates, as we observed a slight increase of relative RMSE values as gap rates largely increased. This was an important outcome, since adapting REddyProc to hilly cropping systems at the extent of the small watershed was likely to induce larger gap rates because of splitting the H and λE dataset according to wind direction. Finally, the RMSE values obtained were slightly better than those reported by  [10]  who obtained values between 20% and 150% for an artificial gap rate of 20% with λE daytime conditions and different gap filling methods.
When comparing REddyProc retrievals with and without discrimination of wind directions, the obtaining of very different H and λE flux values for southern winds underlined the need to conduct such discrimination at the hourly timescale. This was ascribed to the prevalent replacement of missing flux data under southern (S) winds by flux data under north-western (NW) winds when applying REddyProc without discrimination of wind directions, since NW wind were more frequent that S winds in the original dataset (66% versus 33%, see Section 2. 5). Further, this is consistent with former studies that reported the need to discriminate wind directions when processing EC measurements in similar conditions  [22, 23]. At the daily timescale, we observed very small differences between REddyProc retrievals with and without discrimination of wind directions.
Conclusions The main outcomes of the current study are the following. First, the REddyProc method is robust when facing large rates of missing data to be filled. This is of importance when the dataset to be filled have to be split in accordance to different land surface conditions, wind direction in our case. This is also of importance since REddyProc relies on meteorological data that are usually collected along with EC measurement. Second, it appears to be necessary to discriminate wind directions when filling gaps within time series of land surface convective fluxes at the hourly timescale, in the context of hilly cropping systems at the extent of small watershed. This is consistent with former studies that reported the need to discriminate wind directions when processing EC measurements in similar conditions.
Acknowledgments: The author's express their thanks to (1) the  Environmental Research Observatory OMERE  (http://www. obs-omere. org)which policy drives the availability of the data used in the current study (https:// zenodo. org/record/821527#. WVZcssbpORs); (2) the MISTRALS  / SICMED Lebna  project and the  French National Research Agency (ANR)   TRANSMED ALMIRA project  (contract  ANR-12-TMED-0003 ) for their financial support; as well (3) the technical staff from  INRGREF  and  UMR LISAH , particularly  Rim Louati  and  François Garnier.
Background: Protein or nucleic acid sequences contain a multitude of associated annotations representing continuous sequence elements (CSEs). Comparing these CSEs is needed, whenever we want to match identical annotations or integrate distinctive ones. Currently, there is no ready-to-use software available that provides comprehensive statistical readout for comparing two annotations of the same type with each other, which can be adapted to the application logic of the scientific question. Results: We have developed a method, SLALOM (for StatisticaL Analysis of Locus Overlap Method), to perform comparative analysis of sequence annotations in a highly flexible way. SLALOM implements six major operation modes and a number of additional options that can answer a variety of statistical questions about a pair of input annotations of a given sequence collection.
We demonstrate the results of SLALOM on three different examples from biology and economics and compare our method to already existing software. We discuss the importance of carefully choosing the application logic to address specific scientific questions. Conclusion: SLALOM is a highly versatile, command-line based method for comparing annotations in a collection of sequences, with a statistical read-out for performance evaluation and benchmarking of predictors and gene annotation pipelines. Abstraction from sequence content even allows SLALOM to compare other kinds of positional data including, for example, data coming from time series.
However, their proposed solution does not consider all the details (e. g. , dealing with overlapping benchmark CSEs). Furthermore, their benchmarking software is not available as a standalone application. Kalkatawi and colleagues  [8]  describe the problem of genome annotation comparison and provide a context-specific solution in the form of the software package BEACON. They suggest applying the length percentage threshold to classify a pair of compared genes as either matching or discrepant. By default, genes must overlap by at least 98% to be considered as a match. Their tool, BEACON, outputs the site-wise similarity score as the result. Other described solutions for comprehensive comparison of gene annotations are: the software package 'GenoMatrix'  [9] , annotation enrichment analysis  [10] , the GeneOverlap R package (part of Bioconductor  [11] ) developed in the lab of Li Shen (e.
This is clearly not the desired behaviour. Optimizing for site-based measures, on the other hand, usually leads to prediction of overly extended motifs, which have an increased probability of covering the benchmark motifs just by chance. Finally, one should consider, whether all sequences under consideration should be treated equally, as simple averaging of results across all sequences may not produce an adequate measure for the overall performance. Group-wise macro-averaging could for instance be desirable, if a dataset contains clusters of highly similar sequences (e. g. clusters of closely related homologs). In other cases, sequences may be grouped by a common feature, such as protein sequences belonging to the same complex, pathway, or the groups can represent regions with different properties in the same sequencessocalled class intervals  [15].
The latter will show underperformance in comparison to the former, if the predictor selectively prefers longer CSEs. The ambiguity source 4sequence length diversity is addressed through the choice between turning the adjustment for the sequence length on (with the option '-A/-adjust_for_seqlen') or off (the default). The former will convert the symbol counts (TP, FP, etc. ) into percentages (or shares) of the sequence length for each sequence individually before averaging them group-wide or dataset-wide. The latter will sum up the counts group-wide before converting them into shares. With adjustment for sequence length turned on, the relative number of symbols is considered, rather than their absolute counts. As a result, for CSEs of equal length, the performance in shorter sequences outweighs the performance in longer sequences, if the adjustment is turned on.
A schematic example of the impact of sequence length adjustment on the resulting metrics is shown on Fig. 3. Note that although the adjustment for sequence length can be viewed as macro averaging when calculating the shares of TP, FP, etc. at the group level, we do not use the term 'macro averaging' in this context in SLALOM, to avoid confusion with averaging of performance measures, which has a different impact on results. For the performance measures, we implement three averaging approaches: sequence-wide (macromacro), group-wide (micro-macro; the default) and dataset-wide (micro-micro), which can be chosen with the option '-a/-averaging'. The detailed description of the options is provided in Additional file 1: Table  S1.
3 Schematic example of evaluating a predictor with and without adjusting for sequence length. Black lines illustrate two query sequences (100 and 25 residues long). Two benchmark CSEs (both 5 residues long) are drawn as short blue lines; two predicted CSEs (also 5 residues long) are shown as short red lines. In the upper panel, the prediction worked correctly in the longer sequence but not in the shorter, and vice versa in the lower panel. With sequence length adjustment turned on, the actual residue counts are divided through the sequence length before proceeding to averaging and calculating performance measures. Otherwise, residue counts are summed up. The precision is computed as TP/(TP + FP) the ELM database to develop, optimize and test the HH-MOTiF algorithm. Our goal was to make our predictions match benchmark motifs annotated in ELM as closely as possible.
The dependence of performance measures of HH-MOTiF on N, P and the chosen application logic is shown in Table  4. Data we received on the dependence of performance on the chosen application logic is itself informative about the properties of the input data. For instance, on the basis of Tables  3  and 4 , we could make some fundamental observations: first, there are not many overlaps and duplications in the benchmark dataset in contrast to Fig. 4  Schematic example illustrating principles of CSE matching criteria. The length of the CSE in the annotation being currently considered (the query) is 10 symbols/residues. It partially overlaps with two CSEs -the match candidates -in the subject annotation: with a 12-symbol long CSE by 5 symbols; and a 4-symbol long CSE by 3 symbols. In all four scenarios, both match candidates are evaluated to determine, if the current CSE has a match or not.
In the first three scenarios (current, longest, shortest), they are tested separately, while they are treated cumulatively, if patched is selected. If at least one test succeeds, the current CSE has a match, otherwise -not. For example, if the user sets a length threshold of 60%, the current CSE has no match when selecting current or longest, but has a match if shortest or patched is chosen the predicted dataset. This is based on the observation that the residue-wise PPV is influenced to a much greater extent than the TPR by switching between gross and symbol-resolved modes. Closer inspection of the input data confirms this hypothesis (see input files in Case Study 1 in Additional file 1). Second, the predictor performs better on shorter proteins. This is based on the observation that the performance is slightly lower with adjustment for sequence length turned on.
1d ). This hypothesis is based on the significant drop of site-wise PPV upon requiring at least 75% of the longest motif in the benchmark-prediction pair to overlap. For details and files, see also Case Study 1 in Additional file 1. Case study 2: Comparison of ORF calling from two independent genome annotations Glossary Sequence: chromosome sequence of the archaeon Natronomonas pharaonis. Groups: reading frame categories (which includes strand selection). First annotation: Genome annotated by manual curation in our previous works  [20, 21]  as submitted to GenBank  [22]. Second annotation: Genome annotated by RefSeq  [23]. Rapid expansion of sequencing capacities allowed for the rise of big data genomics. As of June 2017, around 25,000 organisms were fully sequenced (data from NCBI  [24] ).
The latter can be also used to map the names on the basis of positional similarity. Alternatively, the list can be limited only to unmatched or discrepant genes to focus on the differences. In the provided example, we mapped 2694 proteincoding genes from the GenBank annotations to 2608 genes from the RefSeq annotation of the genome sequence of Natromonas pharaonis. This genome is quite dense, which is typical for microorganisms, as 89. 79% of all base pairs are part of an annotated gene in both annotations. We ran the comparison in the symbolresolved and the gross modes. Genes from compared annotations were matched, if they overlapped by at least 50% of the length of the gene under investigation (current gene). According to the expectations, the genomes are highly similar (F1 and ACC exceeding 98% in all modes).
Case study 3: Analysis of a time series as exemplified by analysis of economical data A potential application of SLALOM is to analyse data from epidemiological studies as consecutive series of events (e. g. , decreases in temperature as putative causes and spikes in disease or mortality rates as putative consequences  [26] ), as well as from appearing and disappearing of symptoms in the course of a disease progression (or psychological condition, as, for example, in  [27] ) in a cohort of patients. The options 'shifting' start and stop time point (see the option '-a1bs/-anno1-file_begin_shift' in Additional file 1: Table  S1 ) allow detecting events (CSEs) related by assumed causality even with significant time lags. However, as we did not have a large enough clinical dataset at our disposal, we show the possibilities of the proposed method on nonbiological time series data, demonstrating the general applicability of our tool.
In brief, we looked for possible causality relations between economical news releases and movements in currency exchange rates. News data were extracted from the event database (FXStreet. com). For exchange rates (EUR to USD) we used open, high, low and close (OHLC) values for 1-min intervals throughout the calendar year (downloaded from HistData. com). From the OHLC data we computed start and finish time points of the trends (time intervals of rapid directional price movements) and inspected, if such trends would correlate with the appearance of economical news. We demonstrate that there is no evidence that news releases precede strong price movements (which can be clearly seen from Additional file 1: Table  S3 ). Full details are provided in Case study 3 in Additional file 1.
Comparison to other CSE analysis methods Software tools, which are similar to SLALOM, assess overlaps between annotation features and are freely available, include BEACON, GeneOverlap (part of R Bio-Conductor), and diffReps. Albeit performing similar calculations to our methods, GeneOverlap and diffReps evaluate the resulting statistics from a different angle Moreover, both methods have a much narrower scope of application. Therefore, we find them not generally comparable with our method. However, BioConductor contains other functions, which can replicate the functionalities of SLALOM to a significant degree. Thus we compared our method with BioConductor. Especially close is the IRanges package. It should be noted that working with BioConductor requires actual writing of R scripts, including, among others, handling of data import and export, and thus also basic knowledge of R as a programming language.