dataesr/scientific-paragraphs-categorization huggingface dataset:
However, HTS requires manipulating very high amount of data up to several gigabytes, which is not supported by classical analysis software with low input capacity. Furthermore, there are several ways to analyze this data studying the enrichment of primary sequences and/or predicted secondary structure motifs. Therefore, the development of new bioinformatics approaches has been increasing in the past few years. This review presents these methods and how they improve the identification and characterization of aptamers. Pharmaceuticals 2016, 9, 76 2 of 14 sequencing. Recently, High-Throughput Sequencing (HTS) has been used to replace this approach. This technique, which already enables remarkable progress for transcriptomic and genomic analyses  [6] [7] [8]  and it is expected to dramatically change the way of performing molecular evolution processes.
However, HTS requires manipulating very high amount of data up to several gigabytes, which is not supported by classical analysis software with low input capacity. Furthermore, there are several ways to analyze this data studying the enrichment of primary sequences and/or predicted secondary structure motifs. Therefore, the development of new bioinformatics approaches has been increasing in the past few years. This review presents these methods and how they improve the identification and characterization of aptamers. Improvements of aptamers identification and characterization with the use of High-Throughput Sequencing. HTS analysis can investigate faster the enrichment of (sub-)sequences or predicted (sub-)structures. Variants of a same family sequence can be compared in order to extract mutants with higher affinity than the most abundant sequence.
coli genome. Then, sequences that overlapped were grouped into clusters. 1522 individual clusters were obtained and 94 of them were known as Hfq-regulated genes from previous micro array analysis. Unexpectedly, it was demonstrated that these clusters are four-fold more frequent on the antisense strand of protein coding genes than on the sense strand. Furthermore, a consensus motif could be predicted using MEME software and this motif was validated with DMS footprint experiments. Another demonstration of the HTS sequencing usefulness was also presented in 2010 using SELEX to identify the DNA binding motifs that are recognized by transcription factors (TFs). Transcription factors (TFs) control when and where proteins are expressed inside cells. Therefore, it is important to know their DNA-binding specificity for a comprehensive understanding of gene regulation as well as for promoter engineering in biotechnological applications.
An average of 37,590 sequences was analyzed from each round of SELEX using a computational pipeline called 'IniMotif'. The frequency of all subsequences of 5-11 nucleotides from the 14-mer dsDNA library was counted. Then, the most enriched subsequences at each length were compared to all others with a hamming distance (number of substitution between two subsequences) to identify clusters and obtain a consensus motif of each round. The validation of the method was achieved by comparison with already identified motifs. Interestingly, the large amount of data allows the analysis of composite/dimeric sites and reveals unexpected dimeric modes of binding for several TFs. The same group further used this method to analyze the sequence-specific binding of hundreds TFs  [17]. A similar approach to identify binding sites of TFs has been developed by Reiss et al.
using genomic SELEX  [18]. For that purpose, another software was developed and called Transcription Factor Analysis using SELEX with High-Throughput sequencing ("TFAST")  [19]. TFAST counts and aligns millions of sequences to a genome in order to identify peaks that correspond to genomic sequences predominantly amplified during the SELEX. Evolution of peaks is analyzed at each round of selection and at the end a score is calculated to discriminate peaks with a higher probability to contain a binding motif from dummy sequences. The score is based on the rate of enrichment, with different weight applied in each round to take into account the progression in the SELEX. This method was applied to analyze a genomic SELEX of four rounds against a TF of E. coli named PapX. Around 24 million sequences were analyzed per round.
This huge amount of data covered 400 times the CFT073 genome of E. coli. A total of 457 peaks provided a better enrichment throughout the different cycles and were used to identify a consensus motif using MEME software. This study revealed that PapX interacts with the flhDC promoter to repress the expression of flagellar genes. The strategies described previously are useful to identify natural interactions of nucleic acids with proteins that are known to bind DNA or RNA. However, these methods are difficult to apply for other SELEX that are realized to identify artificial binders commonly named aptamers. In such cases, there is no a priori knowledge of the lengths and the structures of nucleic acids that could be selected during SELEX. Therefore, the analysis of sequencing should be more global and it should take into account that several sequences with different foldings could be selected to bind the same target.
To solve this drawback, a threshold is often used to analyze only sequences that are higher than a defined frequency. This parameter decreases the computation time and avoids analyzing sequences that are not significantly enriched during SELEX. Additionally, an option exists with FASTAptamer to search for a known primary motif in the pool. Another way of clustering has been used in the software AptaCluster based on a Local Sensitive Hashing (LSH) method that allow comparing sequences with a reduced number of dimensions. The hashing function randomly chooses a defined number of dimensions (for instance, it could be a string of nucleotides) to compare and group sequences that have a similar LSH values based on hamming distance. Then, a k-mer distance is applied in each group, on the entire length of sequences, to define more precisely clusters.
k-mers refer to all the possible subsequences (of length k) from a sequence. Such a method is fast since it does not require building a matrix distance in contrast to Levenshtein distance. However, several pitfalls exist: (1) hamming distance does not take into account insertion and deletion; (2) The method cannot be used with sequences of different size. Therefore, it is necessary to filter data based on the length of sequences before using these methods; (3) Manual parameters of LSH method such as number of used dimensions and number of hashing function iterations have to be correctly adjusted for each different SELEX to reduce the probability of wrong pre-grouping as much as possible. Finally, another approach of clustering has been implemented by the company AptaIT GmbH using the software "COMPAS" (COMmon PAtternS)  [26].
Identification of Secondary Structure Motifs Clustering based on the primary sequence is important for the analysis of SELEX, but it is well known that aptamers recognize their target by adopting a precise 3D conformation (e. g. , kissing complex, A-minor motif. ). Therefore, it is important to study the enrichment of sequences in the context of a structure. While 3D structures of nucleic acids are still difficult to predict, several software can be used to predict their secondary structures (e. g. , stem loop, bulge, internal loop) such as RNAalifold  [27]  or Mfold  [28]. However, such programs are not designed to manage high throughput data. To solve this drawback, Thiel et al use a series of software to analyze height rounds of a SELEX that were performed to identify aptamers able to internalize inside vascular smooth muscle (VSM) cells  [29].
After removing all sequences that did not have more than three copies and present at least in two rounds, the frequency of 2312 unique sequences was measured at each round. These sequences were clustered in families using two methods. The first approach used a Levenshtein distance as previously described. For the second method, a program (named "process. seqs") was developed using RNAfold and RNAdistance from the Vienna RNA Package  [30]. It first predicts the most likely structure of all sequences and then determines the edit/tree distance of all sequences/structures. Based on these two clustering methods, 32 sequences were selected for internalization studies. Several aptamers showed a better internalization in VSM cells compared to a control sequence. Noticeably, the best-internalized sequence was only identified by secondary structure clustering.
Another bioinformatics pipeline was used by Ditzler et al. to find a better RNA inhibitor of HIV reverse transcriptase reanalyzing a SELEX experiment previously performed in 1996  [31, 32]. Around 1,000,000 sequences from round 14 were analyzed. First, the copy number of each unique sequence was counted before clustering them using Levenshtein distance from seed sequences. Then, the 5000 most abundant clusters were extracted and sequentially used by different programs: (1) Mafft  [33]  aligned sequences in each cluster;  (2)  this alignment is used by RNAalifold from the Vienna RNA package  [30]  to build a consensus predicted secondary structure for each cluster, based on conservation and co-variation; (3) this information is then further used by the software Infernal  [34]  to construct covariance models (CMs) among the 5000 clusters.
This pipeline allows to group different sequences that share enriched sub-sequence-structures. Five distinct converged structural motifs were identified. Among them, three classes of pseudoknots represent 93. 8% of the pool. These structures have been already identified from the previous analysis using classical cloning and sequencing methods. However, HTS analysis provided two new stem loop structure motifs including an asymmetric internal loop structure ((6/5)AL) that represents only 2. 9% of the pool. This new structure was impossible to detect with low throughput sequencing but, interestingly, it demonstrated a strongest inhibition of the reverse transcriptase. "AptaTrace" is another software that has been recently developed to identify structure motifs that are enriched during the SELEX  [35].
A CUGUG motif highly represented in an apical loop structure was found and two mutants of this family (C1-A and C2-A) demonstrated binding by flow cytometry assays. However, these two sequences were already identified by a classical approach since they were the most abundant sequences in the cell-SELEX. Therefore, it is difficult to know whether such a method can be used if aptamer sequences are not fully enriched. Another software, called "APTANI", proposes to use secondary structure information  [38]. First, secondary structures are predicted using RNAsubopt from the Vienna RNA package  [30]. Then all suboptimal secondary structures within a user defined energy range above a minimum free energy (MFE) threshold are recovered. Based on these predicted structures, four kinds of sub-structures are considered (apical loops, bulge loops (either right or left) and intra-strand loops).
The effect of PCR on the SELEX process has also been study using HTS by Takahashi et al. [37]. Two cell-SELEX were performed against CCR7. The first SELEX used classical PCR and leaded rapidly to the amplification of a sequence (C-1A) that represented 85. 97% of the pool after five rounds. In contrast, droplet digital PCR (ddPCR) was used in the second SELEX and seemed to provide a slower evolution of the pool. Indeed, the same sequence (C-1A) was the most enriched sequence after five rounds but it represented only 18. 81% of the pool. In order to discriminate the enrichment that originates from the binding to a target from other factors (binding to matrix support, PCR bias. ), Jiang et al. developed another software called "MPBind"  [45]. MPBind calculates a meta-Z score for each sequence taking into account several parameters.
The comparison of the evolution of these variants can provide important information on the binding site of aptamers. Therefore, a mutant of the (6/5) AL motif was highlighted due to its higher enrichment during the increase of stringency compared to its enrichment in absence of target. This new mutant was at a very low frequency in the pool but it strongly inhibited the RT, even more than the dominant mutant of (6/5) AL motif. The study of mutation incidence using HTS was even more explored by Pitt et al. who analyzed a doped-SELEX with a dedicated software "SEWAL" (Sequence Evolution With Adaptive Landscape)  [49]. A doped-SELEX uses as a starting pool a well-characterized in vitro selected nucleic acid structure that is mutagenized and subjected to a reselection. SEWAL builds an empirical fitness landscape that links genotypes and the enrichment during a selection process.
Such analysis is only possible with HTS, because it needs to access a very large experimental sequence space. Sequences are plotted in a 3D graph where their distance to a sequence seed (the parent of the doped library) is in the x-axis, their distance to a control sequence is in the z-axis, and their enrichment during the selection is in y-axis. Fourth information could be added on the 3D scatter plot by changing the color of spots. For example, it is possible to change the color to highlight sequences that contain a particular nucleotide at one position. This software was used to study an RNA class II ligase ribozyme  [50]. A doped pool from this ribozyme was subjected to one round of in vitro selection with increasing stringency. The 3D graph showed an artificial fitness landscape that was relatively steep and narrow.
It demonstrates that most of the individual mutations greatly decreased the functionality of the ribozyme. Furthermore, it identified some positions that appeared to be selectively neutral. Another tool, named "AptaMut", was developed by Hoinka et al. to study the evolution of mutants during a SELEX using HTS  [51]. This software first needs to cluster sequences. Then, for each family, the enrichment between two rounds of the most abundant sequence is compared to the other ones. If a variant has the same enrichment of the most abundant sequence, it should contain mutations that have no impact on the affinity. In contrast, if a variant has a higher or lower enrichment, it should contain mutations that provide a higher or lower affinity, respectively. A score is calculated based on this comparison to highlight the variants that could provide a better affinity than the most abundant sequences.
Thus, it will be mandatory to define a good balance between the cost and the benefit of using HTS. Currently, several programs with different strategies have been developed to analyze HTS data from SELEX (summarized in Table  1 ). Some can be freely downloaded and used, but most of the time they require expertise in bioinformatics. Notably, it is mandatory to understand the different parameters that are used to limit false positive results. In contrast, some other tools are available through paid services that guarantee technical support provided by companies or academic platforms. One drawback in the field is a lack of comparison between the different programs on the same batch of data. Moreover, most of HTS data from SELEX experiments are not currently deposited in public databases such as Sequence Read Archive (SRA) and, as a consequence, they are not available to the research community.
, 2011). Modelling has long been a primary tool for quantification of the ocean carbon sink (e. g. Le Quéré et al. , 2014)  and ocean acidification  (Orr et al. , 2005). The availability of large surface ocean CO 2 data synthesis products, such as the Lamont Doherty Earth Observatory (LDEO) surface ocean pCO 2 (partial pressure of CO 2 ) database  (Takahashi et al. , 2009 (Takahashi et al. , , 2014) )  and the Surface Ocean CO 2 Atlas (SOCAT)  (Pfeil et al. , 2013; Sabine et al. , 2013; Bakker et al. , 2014;  this study), now enables data-based estimates of the ocean carbon sink, as well as direct model-to-data comparison for surface ocean f CO 2 and ocean carbon sink estimates (Le  Quéré et al. , 2014 Quéré et al. , , 2015a, b;, b; Séférian et al. , 2014; Turi et al. , 2014). A challenge for data-based estimates of the ocean carbon sink is the gap-filling required for times and locations without surface ocean f CO 2 data.
Data retrieval and data upload on the SOCAT Upload Dashboard In version 3, new and updated data sets were obtained from the Carbon Dioxide Information Analysis Centre (CDIAC), PANGAEA ® and public websites. In addition, many data sets were directly submitted to SOCAT. As well as 887 new data sets, version 3 also contains 1258 updated version of data sets previously submitted to versions 1 and 2, with revised metadata or data. Some of these were updates of data sets previously suspended from SOCAT (e. g. Table  10  in  Bakker et al. , 2014). As in previous versions, all new and updated data sets were put in a uniform format  (Pfeil et al. , 2013). Similar to version 2, an expocode was assigned to all data sets, including moorings and drifters  (Bakker et al. , 2014). In general, an expocode consists of 12 characters, describing the country, the vessel or platform, and the data set start day  (Swift, 2008).
Individual data set files Individual data set files are available for all data sets with flags of A, B, C, D and E. Each individual data set has a DOI. The files contain all original CO 2 measurements and recommended f CO 2 values with a WOCE flag of 2, 3 and 4 (Table 8), as set by the data originator, by the automated range checker or during the secondary quality control. The files also contain other parameters, such as atmospheric pressure from reanalysis, climatological salinity and the atmospheric CO 2 mole fraction. Metadata reported by the data provider accompany the files and links to the original data sets are provided. The files are available in text format at PANGAEA ® (https://doi. org/10. 1594/PANGAEA. 849770).
Global synthesis product The global and regional synthesis files contain recommended f CO 2 values with a WOCE flag of 2 (good) for data sets with flags of A, B, C and D (Table  8 ). A separate synthesis file is available for data sets with a flag of E. Each line of the global and regional synthesis files contains the DOI for the corresponding individual data set, as archived at PANGAEA ® , thus enabling retrieval of metadata, name of the data provider and the original CO 2 values reported by the data provider (Table  9 ) (Sect. 5. 2). Global and regional files are available as compressed zip text files via CDIAC (http://cdiac. ornl. gov/ftp/oceans/SOCATv3/). Matlab code is available for reading these text files. Regional files for the SOCAT regions (Table  4 ) only contain data for a specific region with no overlap, so that many data sets on moving ships are split between several regional files.
Examples are figures of f CO 2 or sea surface temperature as a function of time, salinity or latitude. The data shown on the Data Set Viewer have been subsampled for system efficiency, such that only part of the data are shown. Visual display of these data sets on maps in the Data Set Viewer is subject to further improvement, as the interpolation of sparse data ignores topographic features. As a result cruise tracks occasionally appear to cross land. This issue does not affect the data sets themselves. The Table of Datasets (previously known as the Table of Cruises) can be accessed from the Data Set Viewer. It provides access to the original CO 2 measurements; f CO 2 values with a WOCE flag of 2, 3 and 4; metadata; comments entered during quality control; and thumbnail plots (Table  8 ) (Sect.
4. 4). Thumbnail plots consist of a series of scatter plots for key parameters in an individual data set and are useful for obtaining a quick overview of a data set. Both the Data Set Viewer and the Table of Datasets allow download of data sets in NetCDF and text format (Tables  8  and 9 ). All downloadable files now contain the same parameters (Table  9 ). The performance speed of the Data Set Viewer may be slower if the full SOCAT data collection is accessed. Subsetting the data collection by decade or region considerably improves the system speed of the Data Set Viewer. Updates of web browsers occasionally result in less than perfect web access to the Data Set Viewer. In such cases, another web browser may provide better access. The web manager (socat. support@noaa. gov) may have useful advice.
Direct data upload and annual SOCAT releases The SOCAT automation system was formally launched on 7 September 2015  (SOCAT and SOCOM, 2015). Data providers can now directly upload, check and submit their data on the SOCAT quality control system for future SOCAT versions. The SOCAT automation was first discussed at the 2011 Data2Flux Workshop in Paris (SOCAT, 2011). The automation system was designed at the 2012 Automation Planning Meeting (SOCAT, 2012a) and approved shortly afterwards by global and regional group leads (SOCAT, 2012b) (Table  5 ). The automation system has been implemented in the background, with all the work for the biannual SOCAT releases of versions 2 and 3 taking place in the foreground  (Bakker et al. , 2014, this study). This considerable achievement has been made possible by the hard work and planning of the NOAA-PMEL and University of Washington Live Access Server team and other members of the SOCAT automation team (Table  3 ).
(Tans and Keeling, 2016). Note the changing scale on the y axis. Similar figures have been made for versions 1 and 2  (Olsen et al. , 2013; Sabine et al. , 2013). The new automation system allows data providers to upload their data, to check their data with the automated data checker and to visualise their data. Finally, if the data provider deems the data of good quality, he or she can submit them to the SOCAT quality control system. As part of the data submission to SOCAT, the data provider is encouraged to make the original data set public, for example at CDIAC  (SOCAT and SOCOM, 2015) , either immediately or upon the release of the SOCAT version of which the data set is part. The automation system will enable annual SOCAT releases. The timetable for future SOCAT versions envisages that data upload will end in late January of each year and quality control in late March for a release in summer later   (Sabine et al.
, 2013; Bakker et al. , 2014). The higher resolution of 0. 25 • × 0. 25 • , available for coastal seas (Sect. 5. 5), is not shown. that year. With the new system it is now possible to upload and submit data to SOCAT, while quality control of previously submitted data sets is in progress. Thus, both data upload and quality control can now be carried out in parallel. Data upload and quality control for the next SOCAT version will start as soon as they have finished for the preceding version. Thus, the automation system will enable rolling, continuous data upload and quality control, as well as annual SOCAT releases. The system for automated data upload is under continuous improvement. Metadata templates and upload will be integrated into the SOCAT data upload system. Other planned improvements include searchable information for funding agency and entry of preliminary data set flags by the data provider.