dataesr/scientific-paragraphs-categorization huggingface dataset:
5 Da for MS and MS/ MS, respectively. Enzyme selectivity was set to full trypsin, with one missed cleavage allowed. The allowed protein modifications were set to carbamidomethylation of cysteines and variable oxidation of methionine. Proteome Discoverer™ identification results allowed us to manually create reference MGF files composed of MS/MS spectra, selected according to the False Discovery Rate (FDR) calculated in Proteome Discoverer™ and the reliability of the protein identification. MGF files were separated into three reference datasets according to the confidence level of the identifications: the "green" reference dataset (high quality spectra) containing all MS/MS spectra corresponding to identified proteins with at least three peptides and a FDR (computed as described above by the Mascot search engine) < 1%; the "orange" reference dataset (medium quality spectra) containing all MS/MS spectra with proteins identified by one peptide or more, and a FDR > 1% and < 5% and; the "red" reference dataset (low quality spectra) containing all MS/MS spectra without any protein identification and a FDR > 5%.
Workflow construction: step-by-step Step 1: From MGF files to PSTs The first step (Fig. 2 ) consisted of PST generation. We considered three bioinformatics tools for this step: 1) PepNovo + 3. 1  [26] , 2) Peaks  [31] , and 3) Taggor, which is a module of PepLine  [18]. Taggor, initially developed for QTOF mass spectrometer data treatment, was adapted to account for mass tolerance parameters when using an ion trap spectrometer. We tested tool performance using a subset of the green dataset: 10 high quality MS/MS spectra provided by proteome Discov-erer™, exporting the spectra of the two best peptides from each of the five top scoring proteins (see Additional file 2) into a MGF file. This file was used as input for each of the three bioinformatics tools. We then selected the one that generated the most PSTs identical to peptide sequences identified by Proteome Discoverer™ for the same spectra.
Step 2: PSTs mapping and clustering The second step consisted of successive genome translation, PST mapping, and hits clustering. We separately tested PMMatch and PMClust scripts, which were then grouped together into LXPepMatch (Fig. 2 ). We first generated PSTs from the three previously defined reference datasets (see Additional file 2). For PST mapping, we adapted PMMatch, which is a module of PepLine  [18]  designed to locate PSTs on complete genome sequences, by adding an option to specify an absolute mass tolerance. It was set to 0. 5 Da for our test case. We defined the PST length (i. e. , optimal number of amino acids) and compared the results with those for which one or no amino acid modifications were allowed, by mapping PSTs of each reference spectra dataset against the Ectocarpus sp.
gene model proteins (downloaded from ORCAE, Ectsi_prot, 2010) (Fig. 3a ). Proteins matched by PSTs were compared to the expected proteins identified by conventional database spectral identification, using the same spectra and same protein sequence database (Ectsi_prot, 2010) with Proteome Dis-coverer™ 1. 2 software. A hit (the location of a PST on the genome sequence) was considered to be valid if it matched the expected protein. We considered proteins matched by at least two valid hits and corresponding to expected proteins to be true-positives ("true_pos"). We defined "nref" as the number of expected proteins and "nfound" as the number of proteins matched by PMMatch. Sensitivity was defined as the percent of expected proteins matched (true_pos/nref ) and selectivity the percent of expected proteins among all proteins matched using our workflow (true_pos/nfound).
We computed these metrics for each reference dataset by varying tag lengths from 3 to 5. For hit clustering, we only used the green reference dataset to test the program PMClust, another module of PepLine. We optimized this step by selecting the maximal distance between two consecutive hits to be grouped in a cluster, taking into account the mean length of CDSs, exons, introns, genes, and intergenic regions of the Ectocarpus sp. genome. We defined the minimum number of hits (MINHIT) and minimum number of peptides (MINPEP) a cluster could contain to improve the results. Clusters were mapped to the 1591 annotated contigs (GFF3 files downloaded from ORCAE, Ectsi_gff3, 2011) and classified into three categories, according to their mRNA locations: IN when clusters were located inside an mRNA feature, OUT when clusters were located outside of an mRNA feature, and CROSS when clusters were located across an mRNA feature (Fig.
3b ). We performed the first test by analyzing their distribution into each category for various values of MINHIT and MINPEP, with the aim of obtaining the maximum number of IN clusters. In the second test, we compared proteins that matched IN and CROSS clusters to the expected proteins identified by conventional database spectral identification, using the same spectra and protein sequence database (Ectsi_prot, 2010) with Proteome Discoverer™ 1. 2 software (Fig. 3c ). We considered proteins matched by an IN or CROSS cluster and corresponding to an expected protein to be true-positives ("true_pos"). All IN and CROSS clusters corresponded to found proteins ("nfound"), whereas all expected proteins corresponded to proteins identified by the databasedriven approach ("nref"). We calculated the sensitivity ("true_pos"/"nref"), which is the percent of expected proteins matched by an IN or CROSS cluster among all expected proteins, and the selectivity, which is the percent of expected proteins among all proteins found by IN or CROSS clusters (Fig.
3c ). We computed these metrics using the green reference dataset for various values of MINHIT and MINPEP. Step 3: Annotation and visualization of cluster results In the third step (Fig. 2 ), we developed a specific script, LXQualify, allowing the annotation of clusters with three labels. The first was "UNANNOTATED" or "ANNO-TATED", if no hits or at least one hit was included in a CDS of a protein-coding gene, respectively. The second was "DUBIOUS", "POSSIBLE", or "SURE", to assign a confidence degree to each cluster according to the number of typical spectra (i. e. , specific to the cluster) and the number of hits. A cluster was "DUBIOUS" if it contained zero or one typical spectrum; "POSSIBLE" if it contained two or more typical spectra, but less than three different peptides, and "SURE" if it contained two or more typical spectra and three or more different peptides.
The third was "OK" or "CHECK", if all the hits were included in an annotated CDS or at least one hit did not match with the annotated CDS, respectively. Labeled clusters were listed in a tabular output file. We developed a specific script, LXClust2Gff, to convert PMClust output to GFF files for visualization. Artemis (Sanger Institute, England)  [27]  was used as the genome sequence viewer to conveniently assess our results. Some clusters of interest were validated by comparison with transcriptomics data and RT-PCR experiments. Transcriptomic data (ESTs and RNA-Seq coverage) were downloaded from the ORCAE public database of the Ectocarpus sp. genome. These data were previously published with research articles  [32, 33]. For RT-PCR experiments, approximately 100 mg wet weight of frozen samples of Ectocarpus sp.
Results We designed and built our proteogenomics tool using the brown alga E. sp. as our test case. The Ectocarpus sp. (formerly included in E. sp. ,  [35] ) has become a model organism for brown algal biology because of its amenable features for morpho-genetic, life-cycle, and genetic studies. Publication of the Ectocarpus sp. genome in 2010 propelled brown algal research into the genomic era and several post-genomic tools have been subsequently developed using this species to explore diverse aspects of brown algal biology, including its life cycle, development, metabolic processes, and interactions with the environment  [32, 36]. Resources for Ectocarpus sp. now include two genetic maps  [37, 38] , gene mapping techniques, microarrays  [39, 40] , transcriptomic data  [41, 42] , proteomic techniques  [43, 44] , and bioinformatics tools for the prediction of peptide addressing  [45]  and metabolic reconstruction  [46].
Workflow: settings and test results We optimized our proteogenomics approach (Fig. 1a ) using the three reference datasets. It required fine-tuning for the type and quality of the MS data and adaptation to the characteristics of the Ectocarpus sp. genome. The initial genome V1 annotation retrieved 16,256 protein-coding sequences, among which 6655 had no EST support and 5819 concerned specific brown algal genes encoding proteins with no known function  [32]. Moreover, the high number of introns per gene (an average of seven), the extended 3'UTR regions, with an average length of 845 bp, and short intergenic regions often hampered accurate gene prediction. Step 1: From MS/MS spectra to PSTs We selected the best from among three programs (i. e. , Taggor, Peaks, PepNovo+) to generate PSTs from MS/MS spectra.
The results of three PST files generated by Pep-Novo + from the green, orange, and red reference spectra datasets (see Additional file 2) were used as input. We then compared protein encoding genes matched by PSTs to the expected proteins identified by Proteome Discov-erer™ from the same spectra dataset, using the same Ectocarpus sp. protein database (ORCAE, Ectsi_prot, 2010) as that used by PMMatch (Fig. 3a ). Sensitivity and selectivity were calculated for each dataset. Sensitivity measures the proportion of positive IDs that were  correctly identified among all expected proteins and selectivity the proportion of positive IDs that were correctly identified among all matched proteins. The results of each reference dataset are reported in Fig. 4  for three different sequence tag lengths (i.
Sensitivity and selectivity were higher when MIN-HIT = 2 and one amino acid modification was allowed, regardless of the quality of the spectra (Fig. 4c ). We thus set allowed amino-acid modifications to one for PST mapping. We performed further tests on the cluster results to set the optimal minimum hits per protein parameter (MINHIT). We compared proteins matched by clusters to the expected proteins identified by conventional database spectral identification using Proteome Discoverer™ 1. 2 software and the same spectra and protein database as above (ORCAE, Ectsi_prot, 2010) (Fig. 3c ). We generated PSTs from only the green reference dataset, running PMMatch on the Ectocarpus sp. genome sequence (ORCAE, Ectsi_genome_V2_cleaned. tfa) translated by PMTrans. We clustered hits with the aim of uncovering regions potentially associated with genes or, at least, exons.
Final workflow: validation on all samples of Ectocarpus sp. We produced a PST file from each biological sample using LX_RunPepNovo. PSTs were mapped on the six-frame translations of the Ectocarpus sp. genome (ORCAE, Ectsi_genome_V2_cleaned. tfa) using the LXPepMatch program with the optimized parameters described above, thus generating 20-hit lists that were pooled to obtain only one file per sample. We used various strains to isolate biological samples of Ectocarpus sp. We thus needed to avoid mistakes linked to small genetic differences due to polymorphisms. Before clustering, we merged hits files of each strain: Ec32 (soluble, membrane, and cell wall fractions), Ec594 (gametophyte and nuclei fractions), and Ec410 (gamete fraction). We generated clusters from each hits file, i. e. , Ec32, Ec594, and Ec410, with the optimized parameters described above, using GFF3 files (ORCAE, Ectsi_gff3_Jun2013).
7b ). Our results showed that the prediction of the last exon of the Esi0326_0032 gene model appeared to be false. Consequently, we selected this cluster as a candidate for correction of a mispredicted gene. We designed primer pairs to amplify portions based on the sequences of the PSTs for validation (Fig. 7c ), as public databases suggested that the "A" cluster was expressed in vivo by E. sp. PCR products of the expected size of 244 bp were obtained (Fig. 7d ) and sequencing confirmed the presence of the expected nucleotide sequences (data not shown). The second cluster, named "cluster B" was present on sctg_39 between 759,682 and 762,422 (Fig. 8a ). It was "ANNOTATED_SURE_CHECK" and only identified in Ec32 samples. Most (191 "hit in") of the total hits (257  "tot hit") were present in the predicted sequence of an Ectocarpus sp.
New CDS discovery "Cluster C", was mapped against sctg_8 from position 419,782 to position 422,806 (Fig. 9a ) with four peptides (Fig. 9b ). It was "UNANNOTATED_SURE_CHECK" and observed in the three datasets. An MS-Blast search  [47]  (http://genetics. bwh. harvard. edu/msblast/), using PST sequences included in this cluster, revealed similarity with ribosomal protein L22 (RPL22) from other species. In addition, cross-analysis with Ectocarpus sp. transcriptomic data available from the ORCAE website (http:// bioinformatics. psb. ugent. be/blast/moderated/?projec-t=orcae_Ectsi) showed that a complete cDNA sequence (ACA17YE21) was present in cluster "C". This sequence appears to be a good candidate for a new protein coding gene as no coding gene has yet been predicted in this region (Fig. 9a ).
We designed primer pairs to amplify portions based on the PST sequences (Fig. 9c ) for validation, as transcriptomic data in public databases suggested that the "C" cluster is likely to be expressed in vivo in E. sp. PCR products of the expected size of 248 bp were obtained (Fig. 9d ) and sequencing confirmed the presence of the expected nucleotide sequences (data not shown). The last cluster, named "cluster D", was present on sctg_203 between 20,569 and 25,604 (Fig. 10a ). It was observed in two datasets (Ec32 and Ec594) and is present on LG02 of the genetic map  [38]  (Fig. 10b ). We also selected it for validation by RT-PCR analysis (Fig. 10d ). We designed PCR primers based on the coding sequences of the most distant PSTs identified for this cluster (VVLPTWELR and IADFVGIETTPEIIEK). In addition, four ESTs were found to cover the entire length of the cluster.
Peptimapper workflow distribution Our proteogenomics workflow, Peptimapper, is composed of four scripts from the Ectoline github project: LXRunPepNovo, LXPepMatch, LXQualify, and LXClust 2Gff (Fig. 2 ; see command line arguments and output file descriptions into Additional file 4). PMMatch, used by LXPepMatch, was adapted from the Pepline suite (version 2. 0. 1) to fit our workflow. LXRunPepNovo is a new version of PepNovo + (version 3. 1 beta), adapted for our data. This version contains sources and pre-compiled binaries for Linux and MacOS platforms. Ectoline project is distributed under the GPL or CECILL license. The text of both licenses is attached (and should remain attached) to this distribution and is available at https:// github. com/laeticlo/Ectoline. We built a Docker image, called peptimapper (see dockerfile into Additional file 5), to allow easier distribution and interoperability of Ectoline scripts.
Anyone can retrieve this image from a public repository and run it as a package without any specific configuration or installation requirements. This package contains all workflow components (Fig. 2 ), along with dependencies and running environment. We use two different cloud-based public registry services for storing and distributing our Docker image: the Docker Hub (https:// hub. docker. com/r/dockerprotim/peptimapper/) and Bio ShaDock  [48] , a public curated and bioinformaticsfocused repository (https://docker-ui. genouest. org/app/ #/container/dockerprotim/peptimapper). In the context of this project, every computational tool for each step of the overall workflow was integrated and deployed on our own Galaxy server https://galaxy. protim. eu, using this Docker image (see Additional file 5).
Discussion Most sequence-centric proteogenomics available pipelines are based on the generation of customized protein databases from genome, exome, or RNA sequencing to, e. g. reannotate genes, predict splice isoforms or discover novel proteins, using classical database-driven methods  [7] [8] [9]. These methods are based on a direct comparison between experimental MS/MS spectra and theoretical MS/MS spectra generated from in silico digestion of these customized protein databases. A major advantage of such approaches is the specificity of the databases, including variations such as single amino acid variants (SAAVs) and alternative splice junctions. However, one of their weaknesses is the size of these databases, larger than those used in conventional proteomic searches and containing only known proteins.
Consequently, it requires iterative search strategies and a specific FDR calculation to be sensitive enough to avoid false positive identifications  [7]. Peptimapper overcomes this statistical drawback by first partially interpreting MS experimental spectra before mapping them onto the translated genome. Other similar pipelines currently exist that map MS-based proteomics data onto genomic coordinates as the Proteogenomic Mapping Tool  [51] , proteoAnnotator  [52] , PGMiner  [53] , Protk (https://github. com/iracooke/ protk), IggyPep  [54] , or PepLine  [18]  (Table  2 ). However, for most of these pipelines, peptides are derived from database-driven methods, except for IggyPep and PepLine that also use de novo Peptide Sequence Tags (PSTs) obtained from partial interpretation of mass spectrometry data.
According to the important criteria we mentioned above a comparison of available pipelines is presented Table  2  based on these functionalities, i. e. , database-driven for peptide identification or de novo peptide interpretation, then mapping onto the translated genomic sequence; pipeline interface; user-friendly for biologists; results curation; results visualization. By re-using and improving PepLine former modules, our pipeline extends the process by providing the users with new functionalities, thus meeting the important criteria and being as complete as possible: i) It is compatible with ion trap mass spectrometry data; ii) it allows quality annotation of results and their visualization through a genome browser; and iii) it makes the workflow easily accessible through the Galaxy framework  [49, 50].
Annotation of the Ectocarpus sp. genome has become remarkably more accurate through the application of extensive RNA sequencing approaches and new informatics tools  [60]. Similarly, the EctoGEM metabolic network has been considered to complement annotations within the Ectocarpus sp. genome database to support the understanding of metabolic networks in this organism  [46]. The problems caused by many features of the Ectocarpus sp. genome (high number of introns per gene, extended 3'UTR, short intergenic regions) can be alleviated by accurate annotation through the use of the proteogenomics approach developed in this study. RT-PCR experiments combined with transcriptomic data (available on ORCAE website) allowed us to confirm the predictions, validate two new genes (RPL22, AST), and correct one gene model (Dihydrolipoamide acetyltransferase), all corresponding to clusters obtained by our combined approach of proteomics and bioinformatics.
Crossing the data generated by our bioinformatics workflow for another cluster (cluster B) with transcriptomic data allowed us to identify an alternative ATG start codon of a gene encoding a nucleoside diphosphate kinase. This finding suggests that there may be two alternative ATGs for this gene. Such a result shows that direct mapping of MS/MS data to genomic information provides a valuable approach to complement automatic annotation. The methodological development focused on: i) workflow development and the optimization of parameters to apply it to all our 'sub-proteome' MS/MS datasets and ii) the search for the best qualifying criteria to sort clusters according to specific aims (e. g. , re-annotation, identification of small ORFs in the 3'UTR, etc. ). Parameter adjustment is based both on MS/MS spectra and genomic features.
Future studies Recently, extensive RNA-seq data were used to improve 11,108 existing gene models and identify 2030 new Ectocarpus sp. protein-coding genes  [60]. New data available in public databases has advanced functional annotation associated with protein-coding genes. To date, 61% of genes now have a functional assignment, compared to 34% in the V1 annotation  [60]  we used in our workflow. We are now applying our workflow, tailored for this organism, using the most recent Ectocarpus sp. genome annotation (http://bioinformatics. psb. ugent. be/orcae/overview/EctsiV2). In the future, we will analyze short ORFs, focusing on small clusters corresponding to gene models ≤ 150 nucleotides. In such a study, the proteogenomics approach is a clear asset to confirm whether some small transcripts are translated.
We also successfully tested Peptimapper using MS data produced by a mass spectrometer of the latest generation (i. e. , Q Exactive™ HF, ThermoFisher Scientific), currently used to analyze another organism (i. e. , Homo sapiens) and other MS data analysis software (i. e. , Mascot Distiller V2. 6; software supported by a more recent version of the Mascot server v2. 5. 1; http:// www. matrixscience. com) to create MGF. This shows that Peptimapper is fully adaptable to the most recent MS instruments and MS analysis software and is relevant to study other eukaryotic organisms.
Acknowledgements We are grateful to  Laurence Dartevelle  and  Susana Coelho  for the production of unialgal Ec 32, Ec 410 and mutant oro of Ectocarpus. Rachel Lefevre  who participates to the elaboration of the protocols to obtain soluble, membrane and cell wall proteins. We thank the  GenOuest bioinformatics facility , in particular  Anthony Bretaudeau  and  Cyril Monjeaud  for helping with Peptimapper Galaxy integration.
Funding This work was supported by the  French National Research Agency  via the investment expenditure programme  IDEALG  ( ANR-10-BTBR-04-02 ). This work was conducted at the  PROTIM core facility  (https://www. protim. eu)and supported by grants from  Biogenouest ,  Infrastructures en Biologie Santé et Agronomie (IBiSA)  and  Conseil Régional de Bretagne  awarded to C. P.
Availability and requirements Project name: Ectoline Project home page: https://github. com/laeticlo/Ectoline Operating system(s): this distribution contains sources and pre-compiled binaries for Linux, and MacOSX platform Licence: GPL license or under the CECILL licence Ectoline Docker image name: peptimapper Docker hub repository: http://hub. docker. com/r/dockerprotim/peptimapper/ Docker bioshadock repository: https://docker-ui. genouest. org/app/#/container/dockerprotim/peptimapper Galaxy platform: https://galaxy. protim. eu/ Availability of data and materials The datasets used and/or analysed during the current study are from the corresponding author on reasonable request.
gen- ouest. org/app/#/container/dockerprotim/pepti- mapper https://github. com/laeticlo/Ectoline IPAW (2018) [61] Command √ - - √ - This is an Integrated Proteomics Analysis Identification of line Workflow: i) Peptide spectra are searched in two Pseudogenes, different databases in parallel: VarDB filtered by lncRNAs, nsSNPs class-specific FDR for SAAV peptides and 6FT of and somatic the human genome filtered by peptides pI. ii) mutations SAAV candidates are curated by SpectrumAI and potential novel proteins are blasted onto public databases. ii) Curated results are validated by dif- ferent controls. https://github. com/yafeng/ proteogenomics_python JUMPg (2016) [62] Command √ - - √ √ This pipeline includes multiple customized Improves genome line databases construction, tag-based database annotation search, peptide-spectrum match filtering, ans data visualization.