dataesr/scientific-paragraphs-categorization huggingface dataset:
A script running on the mail server strips the attachment and embeds it in another e-mail, which is then delivered to the shipboard modem, again using Iridium SBD. The transmission to reception delay is about 30 s. The Iridium satellite network allows for twoway communication, and so it is possible to change the mission parameters for ASIP by interacting remotely with the mission controller. Messages may be sent to ASIP via the shipboard modem, in the reverse of the pathway by which messages are sent from ASIP to the ship. Table  C4  presents the commands that are available to change ASIP's mission parameters during deployment. ASIP will echo back the command via an Iridium message to acknowledge that it has received the command successfully. There are two types of Iridium messages: profile and help, depending on the current status of ASIP.
Acknowledgments. The development of ASIP was supported by  NSF  Grant  OCE-0241834. The Cirene cruise was supported by  NSF  Grant  OCE-0629707. The Labrador Sea cruise was supported by  NSF  Grant  OCE-1036097  and  NUIG Millennium  fund  RM0908. The Strasse campaign was supported by the  Office of Naval Research  through Grant  N62909-12-1-7064  and by the  European Space Agency  under  ESRIN/Contract 4000102925/11/I-AM. B. Ward acknowledges the fellowship support of the  Marie Curie International Reintegration Grant   IRG-224776. G. Sutherland thanks the  National Sciences and Engineering Research Council of Canada  for funding support. A. H. Callaghan  acknowledges financial support from the  Irish Research FIG. A1. Schematic of ASIP showing the major components of the instrument.
Peptide spectrum matching is the current gold standard for protein identification via mass-spectrometry-based proteomics. Peptide spectrum matching compares experimental mass spectra against theoretical spectra generated from a protein sequence database to perform identification, but protein sequences not present in a database cannot be identified unless their sequences are in part conserved. The alternative approach, de novo sequencing, can make it possible to infer a peptide sequence directly from a mass spectrum, but interpreting long lists of peptide sequences resulting from large-scale experiments is not trivial. With this as motivation, PepExplorer was developed to use rigorous pattern recognition to assemble a list of homologue proteins using de novo sequencing data coupled to sequence alignment to allow biological interpretation of the data.
PepExplorer can read the output of various widely adopted de novo sequencing tools and converge to a list of proteins with a global false-discovery rate. To this end, it employs a radial basis function neural network that considers precursor charge states, de novo sequencing scores, peptide lengths, and alignment scores to select similar protein candidates, from a target-decoy database, usually obtained from phylogenetically related species. Alignments are performed using a modified Smith-Waterman algorithm tailored for the task at hand. We verified the effectiveness of our approach using a reference set of identifications gener-ated by ProLuCID when searching for Pyrococcus furiosus mass spectra on the corresponding NCBI RefSeq database. We then modified the sequence database by swapping amino acids until ProLuCID was no longer capable of identifying any proteins.
By searching the mass spectra using PepExplorer on the modified database, we were able to recover most of the identifications at a 1% false-discovery rate. Finally, we employed PepExplorer to disclose a comprehensive proteomic assessment of the Bothrops jararaca plasma, a known biological source of natural inhibitors of snake toxins. PepExplorer is integrated into the PatternLab for Proteomics environment, which makes available various tools for downstream data analysis, including resources for quantitative and differential proteomics. Molecular & Cellular Proteomics 13: 10. 1074/mcp. M113. 037002, 2480-2489, 2014. Very often, groundbreaking discoveries with a significant impact on the biotechnological and biomedical fields have emerged from studying "non-canonical" organisms. For example, the study of Thermus aquaticus allowed us to ultimately pave the way to modern molecular biology with the characterization of that organism's thermostable DNA polymerase  (1).
The characterization of the green fluorescent protein in Aequoria victoria led to a revolution in cellular biology and to a Nobel Prize being awarded to Osamu Shimomura, Martin Chalfie, and Roger Tsien. In Brazil, Sergio Ferreira's work on the venom of the Brazilian poisonous snake Bothrops jararaca enabled the development of the first angiotensin-converting enzyme inhibitor drug (Captopril) for the treatment of hypertension  (2). In scenarios such as these, proteomics has the potential to allow a better understanding of the complexity of biological systems and the process of evolution than the study of the genetic code alone. It enables the characterization of molecular processes according to their protein content, facilitating new discoveries. In proteomics, the most frequently used strategy for protein identification is so-called peptide spectrum matching (PSM), 1 or the comparison of experimental mass spectra ob-tained by fragmenting peptides in a mass spectrometer to theoretical spectra generated from a sequence database.
In general, the identification process follows from the sequence whose theoretical spectrum yields the highest matching score according to some empirical or probabilistic function. Examples of search engines adopting this strategy are SEQUEST (3), X!Tandem (4), and Mascot  (5). Back in the 1990s, establishment of a cutoff score for confident identification relied mostly on user experience; for example, given a specific charge state, Washburn et al. established cross-correlation and deltaCn cutoff values for SEQUEST in order to allow the selection of a subset of confident identifications from LCQ data. This has since been termed "the Washburn criterion. " In what followed, targetdecoy databases were implemented to allow for more sophisticated refinements in filtering the data  (6). In 2007, Elias and Gygi published a seminal paper on the target-decoy approach to shotgun proteomics (7) that ultimately firmed this approach as a standard and motivated the development of several statistical filters capable of converging to a list of confident identifications satisfying a user-specified false-discovery rate (FDR) with significantly more sensitivity than the conservative Washburn criterion.
Such statistical filters include mixtures of probabilities  (8) , quadratic discriminant analysis  (9) , semisupervised learning with support vector machines  (10) , and Bayesian logic (11) using a semi-labeled decoy analysis to account for overfitting  (12). With so many advances, the PSM workflow has become the gold standard, as it is very sensitive and the least error-prone method when a database is available with the corresponding proteins. The latter factor limits the application of PSM to those organisms for which accurate sequence databases have been established. If a peptide's sequence is not contained within the sequence database, it cannot be identified via the PSM method. However, efforts in developing error-tolerant PSM approaches such as implemented in Mascot have made it possible to handle minor sequence modifications constrained by a simple set of rules.
Nevertheless, increasing the search space in the PSM approach leads to decreased sensitivity  (13). Even though the concept of computer-aided de novo sequencing predates that of PSM  (14) , advances in the quality of mass spectrometry data and the power of computer hardware have allowed it to reemerge at the heart of a highly active field. De novo sequencing is unbiased insofar as it is not constrained by a sequence database, and it is therefore complementary to PSM. However, it has remained the most error prone of the two methods  (15). The challenges of de novo sequencing notwithstanding, a few recent and notable improvements in computer-aided de novo analysis are PepNovo  (16) , which combines graph theory with machine learning; pNovoϩ  (17) , which is optimized for high-resolution HCD data; NovoHMM  (18) , relying on hidden Markov models for increased sensitivity; and PEAKS  (19) , which creates a spectrum graph model by performing dynamic programming on the mass values regardless of the presence of an observed fragment ion.
These algorithm developments have improved de novo sequencing, shifting the bottleneck to post-sequence processing of data. This is because the output of de novo software is a long list of highly similar full and partial peptide sequence and scores. An initial attempt to overcome these limitations consisted of a tag approach that was a hybrid of de novo sequencing and database searching: short sequence tags were derived from tandem mass spectra and used to search a sequence database  (26). In what followed, a modified version based on the FASTA homology search tool was proposed for homology-driven proteomics  (27). This strategy was implemented as part of the CIDentify tool, whose novelty was to account, in the alignment score, for limitations of mass spectrometry sequencing such as switching between leucine and isoleucine or other combinations of amino acids having the same mass.
Assembling protein information from a list of peptides is not a simple task, and usually it is not performed using state-of-the-art de novo tools. Although there are great tools for doing this at the PSM level, there is still a lack of similar tools for de novo sequencing. To tackle the aforementioned shortcomings, and in line with our strong interest in diversity-driven proteomics  (29) , we present a methodology for post-processing de novo sequenc-ing data that allows inference of protein identification through statistical mapping of de novo sequencing results to a protein sequence database. Our approach begins with the use of Gotoh's version of the Smith-Waterman algorithm, based on affine gap scoring  (31)  for increased scalability, to align de novo sequences against those in a target-decoy database.
Then a radial basis function neural network (RBF-NN) is used to rank results according to alignment score, de novo score, precursor charge state, and peptide length. Finally, a heuristic method is used to present protein identification results in a user-friendly, interactive report. The resulting algorithm was implemented as the software PepExplorer. In essence, its goal is somewhat similar to that of post-processing tools such as DTASelect  (9) , Percolator  (10) , and SEPro  (11) , but with an extra layer of complexity inherent from de novo sequencing. PepExplorer can handle the output of several widely adopted de novo tools, such as PepNovo, pNovoϩ, and PEAKS, and accepts a generic format to enable result analysis from a broader range of tools once results are run through simple parsers.
Similarly, the software accepts a series of database formats for input analysis. These features are not found in other tools. PepExplorer is freely available to the scientific community and is provided with the necessary documentation. The effectiveness of our methodology has been verified in two distinct scenarios, the first a real but controlled experiment and the other pertaining to comprehensive profiling of the plasma components of Bothrops jararaca, a venomous viper endemic to Brazil, southern Paraguay, and northern Argentina. The first scenario's purpose was to validate the effectiveness of the tool in analyzing a published Pyrococcus furiosus dataset  (11). We note that this organism is recognized by the proteomics community as well suited for benchmarking, because it allows for the rigorous testing of identification algorithms at the peptide and protein levels  (32, 33).
We modified the P. furiosus sequence database in such a way that no more peptides were identified via the PSM approach or another widely adopted error-tolerant search tool, Mod-A  (34). We then found that we could recover protein identifications using our tool. The B. jararaca scenario has allowed us to explore uncharted territory, as this organism has an incomplete sequence database and we were therefore required to rely on those of orthologous organisms. In particular, B. jararaca plasma was chosen because it is a main research model studied at the Laboratory of Toxinology (FIOCRUZ, Brazil), and several natural inhibitors of snake toxins have already been identified/characterized from this biological matrix  (35) (36) (37).
1200). We acquired tandem mass spectra from the 10 most intense ions by means of HCD fragmentation (minimum signal of 10,000 required; isolation width of 2. 0; normalized collision energy of 45. 0; and activation time of 30 s) followed by MS2 acquisition on the Orbitrap analyzer at 15,000 resolution. The dynamic exclusion option was enabled and set with the following values for each parameter: repeat count ϭ 1; repeat duration ϭ 30 s; exclusion list size ϭ 500; exclusion duration ϭ 45 s; and exclusion mass width ϭ 10 ppm. Charge state rejection was enabled for unassigned charges and for those equal to 1. Preparation of Sequence Databases Used for Similarity-driven Identification and PSM-Reference sequences for P. furiosus were obtained from UniProt, and those for Reptilia together with Amphibia are from the NCBI RefSeq; all were downloaded in June 2013.
The sequences obtained from the Reptilia and Amphibia databases were merged into a single structure and then joined by 127 sequences of common mass spectrometry contaminants, as well as, for each database entry, a reversed version of the corresponding sequence (a decoy sequence). The final P. furiosus and Reptilia plus Amphibia databases had 4347 and 302,287 sequences, respectively. Three P. furiosus proof-of-concept databases were generated by repeatedly adding "mutations" and insertions to the sequence database. These databases are referenced as PFU_Gap25_ Substitution15, PFU_Gap20_Substitution10, and PFU_Gap15_ Substitution8. In the PFU_Gap25_Substitution15 database, for example, an amino acid was inserted at every 25th position, and every 15th amino acid was replaced by some other, randomly chosen amino acid.
These databases provide increasing distance from the original database and thereby presented the algorithms with different levels of difficulty. As no new proteins were added to obtain any of them, each of these databases has the same number of sequences as the initial P. furiosus database. Our goal has been to modify the native sequences from an organism's database to simulate the appearance of different, but phylogenetically close, organisms that would render PSM useless. Peptide Spectrum Matches and Quality Assessment-The mass spectra were exported to the MS2 format (39) from the RAW files using PatternLab's RawReader module. The ProLuCID  (40)  search engine was used to compare experimentally obtained spectra against theoretical spectra generated from a sequence database and select the most similar.
This procedure was independently performed on each data subset, resulting in a falsepositive rate that was independent of charge state. Additionally, an amino acid sequence at least six residues long was required. Results were post-processed to only accept matches with less than 5 ppm and proteins supported by at least two spectral counts. This last filter led to a 0% FDR in the search results. De Novo Sequencing-De novo sequencing was performed using PEAKS Studio 6. 0 (Bioinformatics Solutions Inc. , ON, Canada). The parent mass error tolerance was 7 ppm, and the fragment mass error tolerance was 0. 05 Da. Carbamidomethylation of cysteine was considered as a fixed modification. Acceptable results required an average local confidence score of at least 70% and a total local confidence score of at least 5 and were exported to a CSV file using the export option built into the software.
Blind Post-translational Modification Search with Mod-A-Mod-A was used to search the original and modified versions of the PFU dataset using its automatic precursor mass detection mode and allowing for arbitrary modifications in the peptides. The parameter files used by Mod-A are included in the supplemental material. PepExplorer Algorithm-The PepExplorer algorithm was coded in C# 4. 5. It has a graphical user interface but can also be executed from the command prompt, which enables it to work in cluster environments. The algorithm's workflow can be summarized in four steps: de novo result parsing, sequence alignment, result filtering, and result presentation (Fig. 1 ). Below we detail each of these steps. All parameters can be adjusted using the graphical user interface (Fig. 2 ). De Novo Result Parsing-PepExplorer currently contains parsers for three widely adopted de novo sequencing algorithms: PepNovo, pNovoϩ, and Peaks.
PepExplorer treats the de novo algorithm with an abstraction layer that allows for the inclusion of new parsers upon request. The software also allows one to analyze a list of peptides by copying and pasting them in the corresponding text box found in the de novo output box (Fig. 2 ). However, in this scenario its neural network runs in a simplified mode and does not consider precursor charge states, scan numbers, or de novo score quality. Sequence Alignment-PepExplorer relies on Gotoh's version of the Smith-Waterman algorithm  (31) , built into its core for aligning peptide sequences against a target-decoy sequence database specified by the user. The user can specify several alignment parameters, such as the open gap and extend gap penalties, the number of de novo sequence results to be considered per spectrum, and a substitution matrix of choice.
For this study these values were 13, 5, 1, and the PAM30MS matrix, respectively. These default open gap and extend gap parameters resulted from a grid search also made available in PepExplorer through the "Advanced Analysis" menu. This enables the algorithm to explore the landscape of combinations of these two parameters within userpredefined bounds and report the combination yielding the greatest number of alignments under a user-defined FDR. For this work, we performed the grid search in the PFU dataset allowing both the open gap and the extend gap penalties to vary from 2 to 30. The grid search results are available as part of the online supplementary files in the software's website. Result Filtering with the RBF-NN-Each obtained sequence alignment is internally treated by PepExplorer as an alignment object containing the following properties: peptide length, de novo score, precursor charge, number of gaps, identifier, similarity, and alignment scores.
All these parameters are used for result quality assessment, together with complementary information relevant to report assembly, such as scan number, raw file name, and details on the sequence alignment. As a first step, these alignment objects are separated into two lists: those originating from peptide ions with charge state less than or equal to 2, and those from peptide ions with charge states greater FIG. 1. A de novo tool is used to generate candidate sequences from mass spectra. The output from the de novo tool, together with a target-decoy database, serves as input to PepExplorer. PepExplorer uses the Smith-Waterman algorithm to align the de novo sequences against the target-decoy database (  1 ). An RBF neural network is employed to rank the de novo alignments according to a confidence score that takes into account the de novo sequencing score, the alignment score, and the number of amino acids contained in the peptide (2).
The report is provided as two interactive panels, the upper one being related to protein information and the lower to identification data. The upper panel provides information such as protein identifier, protein length, coverage percentage, sequence count, alignment count, and description. When a protein is selected, detailed information is made available in the lower panel of all alignments that mapped to it such as the scan number, file name, de novo score, precursor charge state, identifier, similarity, number of gaps, alignment score, sequence found in the database, and sequence provided by the de novo sequencing tool (Fig. 3 ). When a row of interest is selected in this lower panel, a new window displaying the sequence alignment is made available. In this window, when a row is selected in the upper panel with the protein information, a graphical coverage report is displayed (Fig.
PFU Proof of Concept-A Venn diagram showing the overlap of the protein identifications from ProLuCID/SEPro, Mod-A, and PepExplorer on the unmodified PFU database is found in Fig. 5. We recall that only proteins having two or more spectral counts were considered. We further manually examined the non-decoy proteins uniquely identified by PepExplorer; because we individually analyzed each case, based on spectral quality, alignment scores, and coverage, we feel comfortable in considering them as correctly identified, even though they were not found in our gold-standard search, ProLuCID. The results of the application of these tools to the modified versions of the PFU dataset are discriminated in Table  I. All results are made available as part of the supplemental material or at the PepExplorer website.
However, selecting an adequate substitution matrix remains an issue for the user's careful consideration. The results provided herein can be used to compare three paradigms for performing spectral identification: PSM, an error-tolerant/blind post-translational modification approach, and a similarity-driven approach. The strategies are shown to be complementary, each having advantages and disadvantages. For example, the PSM approach was found to be the most sensitive one on the PFU dataset. This happened because we were dealing with a model organism, and thus fully (and tightly) relying on the restrictions provided in the sequence database would yield the best sensitivity. However, its performance rapidly degraded as more distractions and modifications were inserted into the database. Although Mod-A did not outperform PSM on the original PFU database, it was able to retain significantly more identifications as more distractions were inserted in the database.
Mod-A most likely did not outperform the PSM approach on the original PFU dataset because the latter takes into account many more possibilities, resulting in a larger search space and sacrificing sensitivity  (13). However, it would not be surprising if Mod-A outperformed PSM with higher organisms, as it will tolerate amino acid substitutions and unanticipated post-translational modifications. Indeed, taking into account multiple post-translational modifications can also quickly degrade the performance of de novo tools, and for this reason Mod-A will always provide results that are complementary to those of PepExplorer. Finally, PepExplorer presented the least sensitive results on the original PFU dataset, as de novo approaches are know to be error prone. However, the alignment paradigm is able to effectively retain the results as distractions are included in the database.
Finally, we would like to point out some potential applications of PepExplorer. Our algorithm is used to pinpoint a subset of de novo results that are similar to the database at hand. Yet there can be several de novo results, having a very high de novo sequencing score, that are not included in the PepExplorer output. These results should be given special attention: what PepExplorer discards could actually turn out to be truly novel molecules, given the high confidence of the de novo results.