dataesr/scientific-paragraphs-categorization huggingface dataset:
This procedure allows the practical limitations associated with past similar empirical methods (sensitivity to water turbidity and manual image per image correction) to be overcome. While a satisfactory preservation of the information over sun glint free pixels was observed, comparison exercises based on in situ R rs data gathered in sun glint affected areas emphasize the relevance of the proposed methodology (correction by a factor of 14 of the averaged bias in the R rs values after removing sun glint effects). Current limitations in the applicability of this SWIR-based empirical automated method are mainly associated with the presence of high cloud coverage, thin clouds in the OLI scene or highly spatially variable marine or atmospheric signal (around 47%, 42% and 11%, respectively, of the total of 12% of failure over French Guiana OLI archive).
[11]  who proposed a semi-analytical procedure for correcting Sentinel2-MSI data for sun glint effects taking advantage of the short-wavelength infrared (SWIR) information now available for the most recent high spatial resolution sensors. The main limitation of this method however stands in the need of a priori information on the atmospheric properties. This study aims first at evaluating the performance of simple SWIR-based empirical approaches transposing the historical methods developed based on the NIR information  [19, 20, 23]  for correcting large data set of Operational Land Imager (OLI) scenes in coastal waters. An automated empirical SWIR-based sun glint correction procedure is further proposed and applied to the OLI 4-year archive gathered over the turbid coastal waters of French Guiana while its potential is illustrated considering other contrasted coastal regions (Vietnam and Mexico).
In situ measurements Marine reflectance measurements have been performed over estuarine and coastal waters of French Guiana quasi permanently affected by sun glint contamination during concomitant OLI acquisition in order to assess the performance of the sun glint correction procedure developed in the frame of this study. In practice, two in situ cruises (19/10/2016 and 04/11/2016) were performed for this matchup exercise. Above-water hyperspectral radiometric measurements (3 nm resolution) were performed in the 350-900 nm spectral domain using TRiOS radiometers. R rs (λ) spectra were computed using the standard protocol fully described in Ruddick et al. [22]. It is worth mentioning that the R rs in situ data are here assumed to be free of sun glint effects thanks to the specific geometry considered when performing above water radiometric measurements for minimizing these effects (135° azimuth angle to the sun and 45° from nadir  [27] ).
Landsat 8-OLI data set OLI level 1 data (L1T) were downloaded from the EarthExplorer platform (http://earthexplorer. usgs. gov/) for the four scenes covering the study area between April 2013 and April 2017 leading to a total of 265 L8/OLI images. A selection of the OLI scenes presenting at least 5% of cloud-free pixels led to the consideration of a regional data set gathering 227 OLI images. Top-of-atmosphere (TOA) radiances, L TOA , were computed from digital numbers, DN, as follows: TOA L ML DN AL = ⋅ + (1) with ML (multiplicative factor, gain) and AL (additive factor, offset) values provided in the OLI metadata file. TOA reflectance (R TOA ) was computed by normalizing L TOA to the band averaged solar irradiance: 2 0 0 cos TOA TOA L d R F π θ = ( 2 ) where d is the earth-sun distance in Astronomical Units, 0 θ the sun zenith angle and F0 the band averaged extraterrestrial solar irradiance  [2].
OLI R rs (λ) processing Landsat 8 L1T data have been processed using the ACOLITE software (acolite_win 20170718. 0) developed at RBINS (https://odnature. naturalsciences. be/remsem/software-anddata/acolite). The ACOLITE atmospheric correction chain allows simple and fast processing (no external measurements required) of OLI images for marine and inland water applications. Considering the high turbidity level of French Guiana coastal waters, marine reflectance (ρ w (λ)) were computed using the SWIR bands approach  [22]  which rely on OLI bands 6 (1609 nm) and 7 (2201 nm) for estimating the aerosol reflectance  [2]. Note that ACOLITE allows users to define the aerosol properties (ε parameter) on a pixel per pixel basis or using a single value for the whole scene. The current processing of OLI database by ACOLITE was performed regionally setting the threshold value on band 6 (1609 nm) to 0.
Once the BRDF value is retrieved in the SWIR, the sun glint radiance in the visible-NIR bands is computed considering for a theoretical representation of the BRDF spectral dependence  [11]. The main limitation of the latter automated method however stands in the need of a priori knowledge on the aerosol optical properties, which implicitly requires the use of ground-based (e. g. AERONET) or external data (e. g. CAMS global reanalysis and forecasts data sets) or relies on a coupling with an atmospheric correction procedure able to spectrally describe the aerosol optical thickness. Besides a correction of the visible reflectance, the presence of multiple information in the SWIR (e. g. 2 SWIR bands for OLI) also potentially allows the correction of NIR and SWIR signals for the sun glint effect being of interest for further applying atmospheric correction  [1, 2]  or some bio-optical algorithms  [30].
The possible definition of an automated procedure was, therefore here assessed from an archive gathering 227 OLI images over French Guiana coastal waters. The application of SWIR 2 based automatic sun glint correction requires three key steps which have been specifically investigated on the basis of the latter regional data set: 1) identification of homogeneous water pixels impacted by different levels of sun glint, 2) determination of the slope of the SWIR2 based spectral relationships and 3) SWIR2 offset selection. In practice, the automated approach proposed in the frame of this work was developed following a two-steps procedure. In a first development step, the empirical SWIR based sun glint correction was performed without any a priori knowledge of the data to be corrected in order to assess the practical issues related to the spectral relationships definition and the sensitivity of the TOA signal correction to regional variation in atmospheric and oceanic conditions.
Offset definition The offset value corresponding to the background SWIR 2 signal was assessed over areas not affected by sun glint [Eq. (10) ]. The consideration of the actual SWIR2 minimum for a defined image is sensitive to noise in the SWIR2 signal potentially generating important errors in the corrected R TOA [Fig. 3 (c), 3(d) and 3(e)]. The minimum offset value was therefore estimated as the average value of the pixels showing a SWIR2 signal lower than the 10% percentile of a defined image in agreement with Harmel et al. [11]. Using the latter ROI, the offset definition still has to consider the actual distribution of the SWIR data that might be significantly impacted by the presence of thin clouds, not detectable using current existing cloud masks over sun glinted areas. Thin cloud free scenes globally show a normal distribution of the lowest SWIR2 values [Fig.
If the average offset value is used, results will be biased due to the partial representation of the SWIR2 background signal when selecting the pixels corresponding to the first SWIR2 peak [Fig. 4(f) ]. The processing of the whole OLI data set over French Guiana coastal waters indicates that the offset SWIR2 R TOA values vary strongly at regional scale from 0. 00094 to 0. 0516 with an average of 0. 0118. This indicates the need to consider image specific offset definition in contrast with other studies which were relying on the use of a unique regional offset parameter  [36]. For the vast majority of the images (87%) the definition of the offset as the average of the glint free SWIR2 pixels was providing the best preservation of the R TOA signal over sun glint free pixels. The mode value was instead found to be more appropriated for the remaining maps which showed a bimodal distribution of SWIR signal.
The application of image-based sun glint correction is expected to impact the original signal even in areas not affected by sun glint. Considering the potential spectral variation in the precision of the sun glint correction process and in the context of further biogeochemical applications based on sun glint corrected OLI data, an examination of the R rs preservation over glint free areas should be performed on a band specific basis considering the user input bands of interest associated with a defined bio-optical inversion model. Note that the OLI data set considered in the frame of this study over French Guiana was voluntarily not restrictive regarding the level of cloud coverage (selection of the OLI scenes showing at least 5% of cloud-free pixels). The average performances here reported therefore include extreme cases, presumably not considered by the user in the context of an exploitation of OLI data for biogeochemical applications.
Validation A comparison of the in situ and satellite R rs values gathered simultaneously during OLI overpass was performed. Results of these R rs matchup exercises performed in coastal waters of French Guiana affected by sun glint are presented in the Fig. 10  and Table 4. The different in situ stations presented moderated levels of sun glint contamination as estimated from the SWIR 2 signal using Eq. (  9 ) (10 to 25% of increase in the R TOA (2201) signal [Fig. 10(b) ]. Fig. 10. Validation of the sun glint correction method through match-up data gathered in sun glint affected coastal areas of French Guiana (a). The panel b) illustrates the percentage of sun glint present in the sampled stations, estimated using Eq. (  9 ), while the comparison between in situ and OLI R rs data (before and after correction) is provided for the OLI visible bands in panels c to f, making a distinction between the points with sun glint percentage in the SWIR2 lower or greater than 10%.
First, the results have illustrated the advantage of the availability for the most recent sensors, such as OLI, of information in the SWIR spectral domain, where the marine information is negligible, for now correcting the marine signal over coastal waters including the most turbid ones. Further, the developments performed based on the considered regional OLI data set have demonstrated the possibility to routinely apply SWIR based empirical sun glint correction approaches. The proposed automated sun glint correction procedure, which aims at correcting the water signal before atmospheric correction, mainly relies on the definition of adapted masks (clearest waters, glinted and non-glinted pixels) based on scene specific relative statistical descriptors. The overall performance of the proposed methodology is satisfactory as illustrated by the global good recovery of the signal over the whole sun glint contaminated area, as also confirmed from the match ups exercises performed in French Guiana coastal waters.
Motivations Pacific Biosciences (PB) and Oxford Nanopore Technologies (ONT) long reads, despite their high error rates and complex error profiles, were rapidly adopted for various ap-plications. An increasing number of projects, especially for assembly  (1) , long-distance haplotyping or structural variant calling  (2) , indeed benefit from the long-range information these reads provide. These reads display high error rates (from 5% to 12%, according to technologies and libraries, to as much as 30% for the oldest datasets), that largely surpass those of Illumina reads. Given these high error rates, the first step of many applications is error correction. However, this stage can be a time bottleneck  (2). Moreover, contrary to Illumina, where the majority of errors are substitutions, long reads mainly contain insertions and deletions (indels) (ONT reads are more deletion-prone, whereas PB reads contain more insertions).
Besides, the quality of the error correction has considerable impacts on downstream processes. Hence, it is interesting to know beforehand which corrector is best suited for a particular dataset (according to its coverage, its error rate, the sequenced genome or the sequencing technology, for instance). Developing methods allowing to evaluate error correction tools with precise and reliable statistics is, therefore, a crucial need. Methods for evaluating correctors should allow tracking the novelties of the methods. Indeed, since long read technologies still evolve, current correctors implementations are 2 NAR Genomics and Bioinformatics, 2020, Vol. 2, No. 1 prone to many changes. Methods for evaluating correctors must be usable on datasets of various complexity (from bacteria to eukaryotes) to reproduce a wide variety of possible scenarios.
Indeed, algorithms to process short reads are different from those at stake in our case, due to the length, the high error rates and the complex error profiles of the long reads. Moreover, LRCstats relies on a multiple alignment scheme that suffers from high resource needs when processing large numbers of reads, i. e. when coverage or genome sizes are large. For the same reason, LRCstats alignment scheme becomes limited when sequences to process grow. However, the sequencing depth and the length of the long reads keep on increasing, especially with so-called ONT ultra-long reads (up to 1 M bases) starting to appear in recent works for larger genomes  (8). Moreover, deep coverages are expected to help the correction of very long sequences (2). Thus, novel methods must be proposed in order to evaluate the correction of such datasets in a reasonable amount of time.
Contribution To cope with the identified limits of LRCstats, we propose ELECTOR, a new evaluation tool for long read error correction methods. ELECTOR can evaluate the correction of simulated as well as real long read datasets, provided a reference genome that is available for the sequenced species. It takes as input a reference genome in FASTA format, a set of corrected reads in FASTA format, and the corresponding uncorrected reads, either via a FASTA format file in the case of real data or via the suite of files provided by the simulator in case of simulated data. In its output, ELECTOR provides a broader range of metrics than LRCstats, which evaluates the actual quality of the correction. In particular, it measures recall, precision and error rate for each read. ELECTOR also informs about typical difficulties long read correctors can encounter, such as homopolymers, and reads that have been trimmed, split or extended during the correction.
Finally, it also provides reads remapping and assembly metrics. In order for ELECTOR to provide these additional metrics, we propose a novel multiple sequence alignment (MSA) strategy. This new algorithmic approach is designed to allow the MSA computation to scale to ultra-long reads and to large datasets of several billions of base-pairs. It compares in a fast way three different versions of each read: the 'corrected' version, the 'uncorrected' version and the 'reference' version, which is a substring of the reference genome. For each read, we perform a MSA of its triplet. A key idea of this strategy is a divide-and-conquer approach that divides the reads into smaller sequences with an anchoring process, and thus allows to compute several smaller MSAs. These multiple, smaller MSAs, are then combined to obtain the final MSA, of the whole length of the sequences.
The anchoring process is designed to work with erroneous sequences and takes into account gapped alignment due to truncated corrected reads. We believe that the interests of this novelty are not limited to the ELECTOR framework. Indeed, it may be a useful strategy for any domain requiring MSAs of long and highly erroneous sequences. For simulated reads, ELECTOR is compatible with stateof-the-art long reads simulation tools, such as NanoSim  (9)  or SimLoRD  (10) , on which introduced errors are precisely known. Moreover, it is meant to be a user-friendly tool, which delivers its results through different output formats, such as graphics that can be directly integrated into the users' projects. This tool was designed to be directly compatible with a wide range of state-of-the-art error correction tools without requiring any pre-processing by the user.
Inference of quality evaluation metrics from MSA Classification of corrected reads. ELECTOR reports different categories of 'corrected' reads: regular reads, trimmed/split reads, extended reads, soft-clipped reads, bad quality reads and short reads. Figure  4  shows how we deduce the trimmed, split and extended categories from the MSA result. Regular reads are neither trimmed, split, extended nor soft-clipped. Trimmed reads are reads that lack a part of their prefix, suffix or both (first scenario in Figure  4 ). Split reads are reads composed of several fragments that come from a single original read, which could only be corrected on several distinct parts (second scenario in Figure  4 ). Split reads are aligned as trimmed reads are. However, in the case of split reads, we gather all fragments that come from a single initial read, in order to build a single MSA from the several, distinct MSAs induced by the different fragments.
Post-correction assembly metrics. Again, in addition to metrics obtained thanks to our MSA strategy, we assess the correction quality through its consequences on the assembly quality of the corrected reads. We perform the assembly of the corrected reads using Miniasm (  5 ), as we mainly seek to develop a pipeline providing fast results. We acknowledge that assemblers such as Smartdenovo (https: //github. com/ruanjue/smartdenovo) or Canu (  16 ) are more sensitive, but as they display much larger runtimes, Miniasm provides a satisfying compromise. As for the metrics of the assembly, we output the overall number of contigs, the number of contigs that could be aligned, the number of breakpoints of the aligned contigs, the NGA50 and NGA75 sizes of the aligned contigs, as well as the genome coverage.
Validation of the segmentation strategy for MSA To validate our segmentation strategy for MSA, we show to which extent its results differ from the classical MSA approach. In particular, we expect that recall, precision and error rate hardly differ, thus showing that both behaviors produce very similar results. Conversely, we expect a decisive gain in time with our segmentation strategy compared to the original algorithm. We thus compared multiple alignment results obtained with our strategy to results obtained with the regular implementation of partial order alignment graphs on multiple datasets of different read lengths, which affects the run-time of the alignments. Results are presented in Table  2. We observe that while the two strategies provide very similar metrics, the segmentation strategy can reduce the runtime by orders of magnitude compared to the regular approach, especially when the reads grow longer.
Validation on synthetic datasets In this section, we present the results of ELECTOR and LRCstats on several simulated datasets from different species. Further details about these datasets are given in Table  1. The choice of synthetic data was motivated by the need to know the 'reference' sequences (which are portions of the reference genome, representing perfect versions of the original reads, on which no error would have been introduced) to precisely control the results brought by the assessed correction method. ELECTOR sample output. As previously mentioned, ELECTOR computes general metrics: recall, precision, error rate, among other metrics, and provides a graphic representation of their distributions. A subset of the metrics produced by ELECTOR using reads corrected by the following tools: HALC  (18) , HG-CoLoR  (19) , LoRDEC  (20) , Canu  (16) , Daccord (Tischler, G.
, & Myers, E. W. (2017). Non-hybrid long read consensus using local de Bruijn graph assembly. bioRxiv, 106252. ) and MECAT (  21 ) is presented in Table  3. These metrics are Downloaded from https://academic. oup. com/nargab/article-abstract/2/1/lqz015/5625503 by INRIA Rennes, pierre. peterlongo@inria. fr on 19 November 2019 Three datasets were simulated, with a 10% error rate, a coverage of 100× and a fixed read length of 1 k bases, 10 k bases and 100 k bases, respectively. The reads were corrected using Canu with default parameters. consistent with the results presented in the respective tools' publications. The whole set of metrics, including remapping and assembly assessment, are presented in Supplementary Tables  S3  and S4. Comparison to state-of-the-art. Recently, several benchmark analysis were proposed for long reads (comparison of hybrid correction methods  (22) , comparison of hybrid and self-correction methods (Zhang, H.
et al. (2019). A comprehensive evaluation of long read error correction methods. (BioRxiv, 519330. ), analysis of long read correction on transcriptomic reads (  23 )). In this work, we focus on the methodological basis allowing to efficiently perform and reproduce such benchmarks, rather than highlighting the pros and cons of available correction methods. The presented correction performances are thus showed for validation purposes and are not intended to be a benchmark of existing correction methods. In the rest of the result section, we report comparisons to the only other automated evaluation tool for long reads correction: LRCstats. In Table  4 , we compare the metrics displayed by ELEC-TOR and LRCstats. Corrections of the S. cerevisiae dataset by HALC (a hybrid correction method) and Canu (selfcorrection method) are evaluated and reported as an example output.
The complete results provided by LCRstats and ELECTOR, for each correction tool, and on each dataset, are presented in Supplementary Tables  S2  and S3. Both LRCstats and ELECTOR compute metrics on 'corrected' reads and the corresponding 'uncorrected' sequences of those reads (reported respectively as 'corrected' and 'uncorrected'). The first result to notice in Table  4  is that the error rates and the amount of processed bases announced in the 'uncorrected' reads can differ from one correction method to the other, both for ELECTOR and LRCstats. Such differences can be explained by the fact that HALC and Canu do not correct the same set of reads, which leads to different set of 'uncorrected' reads to evaluate. As ELECTOR and LRCstats rely on different rules to exclude reads from the analysis, and do not align split reads in the same way, we observe that they do not process the same quantity of reads.
LRCstats concatenates the different parts of a split read before aligning the concatenation, even if a missing region can exist between two consecutive fragments. This behavior can complicate the alignment task and introduce a bias in the output metrics. On the contrary, ELECTOR processes the different fragments separately before reconstituting the whole alignment and thus takes into account missing regions. These differences thus have an impact on the metrics displayed for corrected reads. ELECTOR processes slightly A dash in the uncorrected columns indicates that the metric is not computed for the 'uncorrected' reads. Daccord could not be run on the C. elegans dataset, and reported an error. more bases than LRCstats on the two studied datasets. However, reads falling into particular categories (very short reads and low-quality reads) are not taken into account in ELECTOR's redcounts, and are reported apart, while they are absent from LRCstats's output.