dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatial proteomic methods identify new candidate conoid proteins To expand our knowledge of the proteins that contribute to conoid structure and function, we applied multiple spatial proteomics discovery approaches. The primary method used was hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) that we have recently applied to extracellular T. gondii tachyzoites  [36, 37]. This approach entailed generating hyperLOPIT datasets from 3 independent samples. In each sample, mechanically disrupted cells were dispersed on density gradients, and the distinct abundance distribution profiles formed by different subcellular structures and compartments were used to identify proteins that belong to common cellular niches. Combining the data from the 3 experiments provided enhanced discriminating power of protein location assignments, and from the 3,832 proteins that were measured in all 3 samples we reported 63 proteins assigned to one of the 2 apical protein clusters, apical 1 and apical 2, above a 99% probability threshold.
Another 13 proteins were assigned to these clusters but below this high-confidence cut-off. The 2 highconfidence clusters were verified as comprising proteins specific to the structures associated with the conoid, APRs, and "apical cap" of the IMC  [36]. In addition to the 3,832 proteins that we reported in this high-resolution spatial proteome  [36] , a further 1,013 proteins were quantified in either only two or one of the hyperLOPIT datasets due to the stochasticity of mass spectrometry sampling. While assignment of these proteins consequently had less data support, a further 32 proteins were assigned to the apical clusters (not reported in the Barylyuk's and colleagues' study) from analysis of either the pairs of LOPIT experiments or the single experiments. From these analyses, 95 proteins were assigned as putative apical proteins across these hyperLOPIT samples (S1 Table ).
(3) Proteins detected by all 3 baits were at the APR2 where the 3 proteins converge. Thus, these data suggest that the combination of spatial proteomics methods used provided an effective enrichment of conoid-proximal proteins. During the hyperLOPIT validation of proteins assigned as Plasma Membrane-peripheral 2 (on the cytosolic leaflet), one protein, MORN2, was found to be enriched as an apical punctum  [36]. Given the close proximity of the conoid apex to the plasma membrane, and unknown molecular interactions between these cell structures that might occur there, we examined the location of MORN2 by 3D-SIM. MORN2 was seen as a small ring anterior to the conoid with a discernible gap between it and the SAS6L-labelled conoid body (Fig  4B ). This location is consistent with MORN2 being associated with the plasma membrane and potentially forming a continuum of the annular structures through from the APRs, conoid base, body, and canopy, to the apical plasma membrane.
Proteins have been previously located to all of these "substructures" including some linking one substructure to the next  [5]. Indeed, the apparent spatial separation of compartments by ultrastructure is smaller than the size of many of the individual molecules that build it  [24]. Thus, at a molecular level, it is unclear what the limits of any one substructure are, if this is even a biologically relevant notion. In this study, we have provided a substantial expansion of knowledge of the molecular composition and architecture of the conoid complex in T. gondii. Previous identification of conoid complex proteins used methods including subcellular enrichment, correlation of mRNA expression, and proximity tagging (BioID)  [30, 31, 44]. Among these datasets, many components have been identified, although often with a high false-positive rate.
For example, the seminal conoid proteomic work of Hu and colleagues (2006)  [44]  detected approximately half of the proteins that we report (49 of 95, see S1 Table ). However, in their study, a further 329 proteins that fractionated with the conoid (�2-fold enriched; ToxoDB Release 49) included many identifiable contaminants including known cytosolic, ribosomal, mitochondrial, apicoplast, and microneme proteins. We have found the hyperLOPIT strategy to be a powerful approach for enriching for proteins specific to the apex of the cell, and BioID has further refined identification of proteins specific to the conoid complex region. Collectively, we now know of 54 proteins that locate at the conoid complex (Table  1 ), with a dataset of many further proteins that await closer inspection (S1 Table ).
The raw data were managed in XCalibur v2. 1 (Thermo Fisher Scientific). LC-MS/MS data processing and protein identification. The raw LC-MS/MS data were interpreted, and peak lists were generated using Proteome Discoverer 1. 4 (Thermo Fisher Scientific). The peak lists were exported as Mascot generic files and searched against the annotated protein sequences of Toxoplasma gondii strain ME49 (ToxoDB. org release 29, retrieved on 20 December 2016; 8,322 sequences) and common contaminant proteins (cRAP, 115 sequences) with Mascot v2. 6. 0 (Matrix Science, Boston, Massachusetts, USA) assuming digestion by trypsin with up to 2 missed cleavage sites. The precursor and fragment ion tolerances were set to 5 ppm and 0. 1 Da, respectively. Carbamidomethylation of cysteine was set as a static modification.
hyperLOPIT spatial proteomics The hyperLOPIT spatial proteomics data were acquired and analysed as described in  [36]. Three independent hyperLOPIT datasets contained 4,189, 4,547, and 4,292 proteins identified at FDR < 5% based on at least 1 unique high-confidence peptide (FDR < 1%) and quantified across all 10 TMT quantification channels. These three 10plex datasets were considered individually. In addition, the data on proteins shared across pairs or all 3 individual datasets were concatenated affording three 20plex (4,021, 3,916, and 4,078 proteins for 1\2, 1\3, and 2\3, respectively) and one 30plex (3,832 proteins), thus yielding 7 datasets in total. For each dataset, the normalised protein abundance distribution profiles were derived from the TMT reporter ion intensity values. A Bayesian machine-learning classification method based on t-augmented Gaussian mixture models with maximum a posteriori prediction (TAGM-MAP)  [86]  was applied to probabilistically attribute proteins to 26 subcellular niches defined by 718 marker proteins.
5H with a thickness of 170 μm ± 5 μm) were used in cell culture, and Vectashield (Vector Laboratories, Burlingame, California, USA) was used as mounting reagent. For protruded conoid imaging of extracellular parasites, cells were treated for 45 s with 5 μm 5-benzyl-3-iso-propyl-1H-pyrazolo  [4. 3-d] pyrimindin-7(6H)-one (BIPPO) prior to fixation. Samples were excited using 405, 488, and 594 nm lasers and imaged with a 60x oil immersion lens (1. 42 NA). The structured illumination images were reconstructed in softWoRx software version 6. 1. 3 (Applied Precision). All fluorescence images were processed using Image J software (http:// rsbweb. nih. gov. /ij/). Electron microscopy. Samples of extracellular T. gondii tachyzoites and P. berghei ookinetes, oocysts, sporozoites, and merozoites were fixed in 4% glutaraldehyde in 0.
3 (http://hmmer. org). This custom profile HMM was used to search for divergent homologues in the SAR proteome database using hmmsearch (E-value cutoff for the best scoring domain: 0. 01); these were added to the OrthoGroup. The expanded OrthoGroup was then submitted to additional rounds of PREQUAL-MAFFT-hmmbuild-hmmsearch, until either (1) no new homologue was detected, or (2) the total number of accumulated OrthoGroup sequences exceeded 10,000, or (3) 10 rounds had been performed. For search strategy (2), a single round of PREQUAL-MAFFT-hmmbuild-hmmsearch steps was applied with a more stringent E-value cut-off for the selection of new homologues, which was customised for each OrthoGroup. For this, the E-values of the sequences already in the OrthoGroup were retrieved. The highest of these E-values was used as the inclusion cutoff for new hits, unless the latter was higher than 0.
It is to be noted that, in some cases, putative orthologues (sequences that are clearly homologous to orthologues in other species) are too divergent to be retrieved as the best hit in a reverse BLASTp search against the Toxoplasma proteome. Such cases may well present themselves in our dataset, and caution is particularly warranted when only a few species of a given clade have a detected putative orthologue and others of the same clade seemingly do not. The numbers of putative orthologues in the Alveolata subset of our SAR proteome dataset were inferred from these OrthoGroup sets (S3 Table ). For the OrthoGroups that contained 2 query proteins (see "Exhaustive expansion of identified homologues"), the collected sequences were split and selected based on which of the 2 T. gondii proteins represented their best BLASTp hit.
The "none" homologous sequences were retained for both protein queries. The resulting putative orthologous sequences for each protein query can be found in FASTA files (S1 Data). The putative orthologues for P. berghei ANKA and P. falciparum 3D7 are listed by their GenBank and UniProt identifiers and original assembly gene names (S4 Table ). For each alveolate species in our dataset, the completeness of its predicted proteome was inferred using BUSCO 4. 0. 5 with automatic selection of the reference lineage using theauto-lineage-euk option  [91]. These proteome completeness scores facilitate the interpretation of putative absences in the examined Alveolata species, for low completeness may indicate that a putative orthologue is not necessarily absent but may also simply result from an incomplete dataset (S3 Table  , S5    ) to calculated Jaccard distances for all protein pairs across the Alveolata, and proteins were clustered according to these distances using UPGMA.
To generate the heatmaps in  Fig 6 and S5 Fig, " heat-map2" of the R package gplots (https://cran. r-project. org/web/packages/gplots/index. html) was applied onto a "ternary" matrix, which distinguished between a protein being absent, it being present as a reciprocal best hit ("rbh"), or it being present as a none hit ("none") (see "Filtering homologues for putative orthologues"). To indicate the phylogenetic relationships of the species, a tree dendrogram without meaningful branch lengths was assembled by reviewing recent literature for Apicomplexa  [92] [93] [94] , Dinoflagellata  [95] [96] [97] [98] [99] , Ciliophora  [100] [101] [102] [103] [104] [105] [106] , and Colponemidia  [107].
On day 21 post feeding, 20 mosquitoes were dissected, and their guts and salivary glands crushed separately in a loosely fitting homogenizer to release sporozoites, which were then used for imaging. sequences from this particular strain. However, because strain "3D7" is the one more commonly studied, we mapped the P. falciparum IT sequences to P. falciparum 3D7 by searching for reciprocal best blast hits among their proteomes. Therefore, P. falciparum 3D7 identifiers in the FASTA files (S1 Data) also provide information on which P. falciparum  IT   Methods ). Files are named according to the T. gondii apical protein query. The sequence headers comprise the protein identifiers in our in-house proteome database for the SAR clade (Methods), starting with an abbreviation for the species strain (see S3 Table ).
Acknowledgments We are grateful to Mike Deery who performed the LC-MS/MS analysis of peptide samples,  Julie Howard Murkin  for data processing of the  BioID LC-MS/MS  (both at the  Cambridge Centre for Proteomics) ,  Emilie Daniel  for technical assistance with P. berghei cell line generation,  Yi-Wei Chang  and  Maryse Lebrun  for useful discussions, and  Sarah Marsden  for comments on the manuscript. We are grateful for support and assistance with super-resolution imaging at the  Cambridge Advanced Imaging Centre  and the  Gurdon Institute , and thank  Nicola Lawrence  for imaging support.
Motivation: The annotation of small open reading frames (smORFs) of <100 codons (<300 nucleotides) is challenging due to the large number of such sequences in the genome. Results: In this study, we developed a computational pipeline, which we have named ORFLine, that stringently identifies smORFs and classifies them according to their position within transcripts. We identified a total of 5744 unique smORFs in datasets from mouse B and T lymphocytes and systematically characterized them using ORFLine. We further searched smORFs for the presence of a signal peptide, which predicted known secreted chemokines as well as novel micropeptides. Four novel micropeptides show evidence of secretion and are therefore candidate mediators of immunoregulatory functions.
The periodicity pattern has been used by several algorithms and pipelines including ORF-RATER  (Fields et al. , 2015) , RibORF  (Ji et al. , 2015) , RiboTaper  (Calviello et al. , 2016) , RP-BP  (Malone et al. , 2017)  and RiboCode  (Xiao et al. , 2018). Other metrics can be used in conjunction with ORFScore to improve actively translated ORF identification. For example, the ribosome release score (RRS) detects the termination of translation at the stop codon and can robustly distinguish protein-coding transcripts from ncRNAs.
3152 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. (Guttman et al. , 2013). Moreover, combined Ribo-seq and RNAseq analysis not only provides evidence for host transcripts of smORFs but also enables context-based identification of smORFs, such as during cell stress  (Choudhary et al. , 2020; Li et al. , 2020; Martinez et al. , 2020). Here, we describe a new analytical pipeline that we call 'ORFLine' that performs a comprehensive and systematic analysis of RNA-Seq and ribosome profiling to identify actively translated smORFs. Predicted smORFs are classified according to their host transcript type and the position of the smORF relative to other ORFs within the same transcript.
smORFs identified by ORFLine We analysed several datasets to identify smORFs in mouse lymphocytes, including a published dataset from our lab of lipopolysaccharide (LPS)-activated B cells  (Diaz-Munoz et al. , 2015) ; a new dataset of B cells activated with LPS plus interleukins IL-4 and IL-5 for 48 h; na ı ¨ve CD4þ T cells stimulated with antibodies to CD3 and CD28, which mimics activation by antigen; and a published timecourse of Th1 T cells re-stimulated with anti-CD3þanti-CD28  (Davari et al. , 2017)  (see Supplementary Table  S3 ). ORFLine predicted a total of 5744 unique smORFs in all samples analysed (union of 2607 smORFs predicted in B cells and 4935 smORFs predicted in T cells) (Supplementary Table  S4 ). ORFLine classified smORFs according to their relative position with the annotation, if any, of the host transcript (Fig.
1B ). About 80% (4615) of the 5744 predicted smORFs are uORFs, whereas downstream ORFs (dORFs) is the rarest class (260; Fig. 1C ). A total of 501 smORFs in putative non-coding RNAs (long non-coding RNAs and pseudogenes) were predicted, which are termed ncORF. We also detected 338 canonical smORFs, which matched exactly to annotate CDSs, and an additional 30 extended canonical smORFs, which start upstream of an annotated CDS but have the same stop codon. Of the canonical smORFs, we found direct biochemical and functional evidence for their protein products for only ~40% (135) in the UniProt protein database  (Bateman, 2021)  ; these smORFs include diverse entities, such as chemokines and subunits of mitochondrial complexes. The other smORF classes and the remainder of canonical smORFs (5609) have either not been functionally characterized or have not been annotated at all.
Comparison between ORFLine and RiboCode We also analysed the same datasets with the recently published ORF-detection pipeline RiboCode  (Xiao et al. , 2018) , which assesses the triplet periodicity of RPFs in an ORF with modified Wilcoxon signed-rank test and has been incorporated into a recently published integrated tool (RiboToolkit) to analyse ribosome-profiling data  (Liu et al. , 2020). RiboCode is claimed to outperform RiboTaper, Rp-Bp and ORF-RATER  (Calviello et al. , 2016; Fields et al. , 2015; Malone et al. , 2017). Using its default settings RiboCode predicted a total of 15 920 unique smORFs, we removed 3667 smORFs nested in longer smORFs, 48 from non-expressed transcripts and 3337 internal or frameshift smORFs. We then compared the remaining 8868 non-internal smORFs predicted by RiboCode with the 5744 predicted from ORFLine (Supplementary Fig.
S2 ). Of these, 1957 (22. 1% in RiboCode and 34. 1% in ORFLine) are found as exact genomic coordinate matches by both pipelines (Table  1 ). We sampled the annotated smORFs, which are differentially identified by the two pipelines and noticed that smORFs predicted by RiboCode typically have low RPF coverage or are assigned a low or negative ORFScore, or low RRS, and are filtered out by ORFLine (Supplementary Fig. S3 ). Our criteria for metrics have shown to be robust in smORF prediction in previous studies  (Bazzini et al. , 2014; Guttman et al. , 2013). ORFLine also predicted 356 smORFs encoded by low abundance transcripts (25% percentile) that are not predicted by RiboCode. Overall, RiboCode predicted more putative smORFs, but some of which appeared to share the stop codon but different start codons.
smORFs in non-coding RNAs Non-coding ORFs (ncORFs) are smORFs that are found in annotated long non-coding RNAs (lncRNAs) and pseudogenes. They are typically short with a median length of 33 codons. By definition, non-coding RNAs are not translated into protein. However, annotated lncRNAs have been predicted from their sequences to contain six smORFs on average  (Couso and Patraquim, 2017). We have predicted 501 translated ncORFs and 14. 4% of these are considered conserved or weakly conserved. We noticed very different distributions of size and PhyloCSF score between ncORFs and canonical smORFs (Fig. 5A ). The distribution of TE for ncORFs is also different from that for long CDS, the median TE of ncORFs is greater than long CDS (Fig. 5B ). Three ncORFs identified in LPSactivated B cells (Cct6a, Gm16675 and 6330418K02Rik) were found to have a high PhyloCSF score and TE, so we infer them to encode functional micropeptides (Fig.
5C ). We searched the micropeptides they encode in NCBI BLASTp database  (Altschul et al. , 1990) , but did not find any match for Gm16675 and a partial match for 6330418K02Rik to three uncharacterized proteins with 35. 59-78. 18% identity in Habropoda laboriosa and Gulo gulo. The examples likely reflect that these genes are misclassified as non-coding, although it is possible that they could also have functions as a noncoding RNA in addition to their peptide coding capacity.
Except for Zdhhc5, all are in different reading frames from the main CDS. To examine the expression of signal sequencecontaining micropeptide host transcripts, we compared mouse RNA-Seq datasets for lymphocytes spanning B cell terminal differentiation  (Shi et al. , 2015) , Th1 cell activation  (Davari et al. , 2017)  and regulatory T cells  (Luo et al. , 2016)  as well as epidermal cells  (Sendoel et al. , 2017). These data revealed dynamic expression patterns for several of the host transcripts. For example, BC031181 was down-regulated during B cell differentiation but up-regulated during Th1 cell activation, it was also highly expressed in epidermal cells (Fig. 6B ). To determine if the selected putative micropeptides are likely functional, we compared the conservation of amino acid sequence between different mammalian species (Supplementary Fig.
Acknowledgements We thank  Sebastian Lukasiak  for sharing datasets for analysis. We thank  Joanna Krupka  and  Daniel Hodson  for critical reading of the manuscript. We thank  Simon Andrews  and  Joanna Krupka  for testing ORFLine and sharing their valuable feedback to improve the code. Public RNA-Seq datasets (Supplementary Table S6) used in this study were processed by  Babraham Bioinformatics group.
Pipeline availability Pipeline code is publicly available on the source code hosting platform GitHub. The URL is https://github. com/boboppie/ORFLine. We also created a singularity image (https://singularity. lbl. gov/), which enables the users to execute and test the pipeline easily in a virtual environment. All dependencies including bioinformatics tools are pre-installed in the image, the URL is https://github. com/boboppie/ORFLine-singularity.
Accessing, visualizing and analyzing heterogeneous plasma datasets has always been a tedious task that hindered students and senior researchers as well. Offering user friendly and versatile tools to perform basic research tasks is therefore pivotal for data centres including the Centre de Donn ees de la Physique des Plasmas (CDPP http://www. cdpp. eu/) which holds a large variety of plasma data from various Earth, planetary and heliophysics missions and observatories in plasma physics. This clearly helps gaining increased attention, relevant feedback, and enhanced science return on data. These are the key ideas that crystallized at CDPP more than 15 years ago and resulted in the lay-out of the concepts, and then development, of AMDA, the Automated Multi-Dataset Analysis software (http://amda.
Introduction The genesis of AMDA 1 dates back to 2005 when the CDPP 2 team realized that no tool existed in the space physics domain to search for and visualize data from various origins and often with heterogeneous formats. This variety of formats (because of varied instruments, IT systems, habits, persons, …) was indeed a key obstacle to efficient instrumental inter-calibration campaigns, cross-mission and cross-instrument analysis and probably of enhanced scientific results. In brief, it appeared that a certain level of uniformity in data products, physically (format) and conceptually (structure), is needed for a database to be 1/more attractive and 2/able to build value-added services on its data. If those ideas prevailed at CDPP, they were in fact shared by others. In magnetospheric physics, this is the general vision that pre-dates to the births of the Cluster Active Archive (now Cluster Science Archive,  Laakso et al.
(2010) ) and the CDAWeb  (NASA CDAWeb Development Team, 2019) , and in planetary sciences in a lesser measure (because services are more difficult to build from the diversity of data), the NASA/Planetary Data System  (McMahon, 1996; Walker et al. , 1996)  and the ESA/Planetary Science Archive  (Besse et al. , 2018). In Japan the Data ARchives and Transmission System (DARTS) followed a similar path, but at yet another level as it also aggregates astrophysical data in the same system  (Miura et al. , 2000; Tamura et al. , 2004). On the software side, the concepts behind SPEDAS  (Angelopoulos et al. , 2019)  share many similarities with those that led to AMDA. The ability to integrate space physics scientific functions and models, a key element in AMDA, took its inspiration from the development of the QSAS software.
3 Finally the versatility for displaying different kinds of data permitted by Autoplot 4  is also an example  (Faden et al. , 2010). At CDPP, a prototype was therefore rapidly put in shape from existing bricks: a data server, a rudimentary web interface, and some IDL codes for plasma data analysis. The initial guidelines that are still in use today are: 1/using a limited set of objects that are represented and manipulated by the user in the interface (parameters, TimeTables/catalogues, requests), 2/using standard formats for import/export (CDF, netCDF, VOTable, json, tabular ASCII), and 3/favoring interoperability with other similar systems (web-services, protocols). Being accessible via a browser was also a novelty at the time, and it proved to be quite efficient in term of maintenance over the years as javascript evolved but maintained backward compatibility.