dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We have provided the first functional annotation of altORFs with a minimum size of 30 codons in different genomes. The comprehensive annotation of H. sapiens altORFs is freely available to download at https://www. roucoulab. com/p/downloads (Homo sapiens functional annotation of alternative proteins based on RefSeq GRCh38 (hg38) predictions). In light of the increasing evidence from approaches such as ribosome profiling and MS-based proteomics that the one mRNA-one canonical CDS assumption is untenable, our findings provide the first clear functional insight into a new layer of regulation in genome function. While many observed altORFs may be evolutionary accidents with no functional role, several independent lines of evidence support translation and a functional role for thousands of alternative proteins: (1) overrepresentation of altORFs relative to shuffled sequences; (2) overrepresentation of altORF Kozak sequences; (3) active altORF translation detected via ribosomal profiling; (4) detection of thousands of alternative proteins in multiple existing proteomic datasets; (5) correlated altORF-CDS conservation, but with overrepresentation of highly conserved and fast-evolving altORFs; (6) overrepresentation of identical InterPro signatures between alternative and reference proteins encoded in the same mRNAs; and (7) presence of clear, striking examples in altMiD51, altDDIT3 and 5 alternative proteins interacting with their reference zinc finger proteins.
While far from proven in our study, three of these lines of evidence (5, 6, and 7) would support the intriguing hypothesis that many altORFs code for proteins that cooperate functionally with the proteins coded by their CDSs. This hypothesis would also agree with recently increasing evidence that small proteins often regulate the function of larger proteins  (Couso and Patraquim, 2017). Further experimental examples and more detailed co-conservation studies will be needed to address this hypothesis. The presence of two or more coding sequences in the same gene provides a mechanism for coordinated transcriptional regulation. Consequently, the transcription of these coding sequences can be turned on or off together, similar to prokaryotic operons. We speculate that having genes composed of a CDS and one or more altORFs gives rise to fewer, denser transcription units and allows cells to adapt more quickly with optimized energy expenditure to environmental changes.
Alternatively, InterPro domains were identified by investigating the reference proteome, and alternative proteins may have new domains and motifs that remain to be characterized. Finally, an unknown fraction of predicted altORFs may not be translated or may code for non-functional peptides. In conclusion, our deep annotation of the transcriptome reveals that a large number of small eukaryotic proteins, possibly even the majority, are still not officially annotated. Our results with alt-MiD51, altDDIT3, and some zinc-finger proteins also suggest that some small and large proteins coded by the same mRNA may cooperate by regulating each other's function or by functioning in the same pathway, confirming the few examples in the literature of unrelated proteins encoded in the same genes and functionally cooperating (Table  3 )  (Quelle et al.
Generation of alternative open-reading frames and alternative protein databases Throughout this manuscript, annotated protein coding sequences and proteins in current databases are labeled annotated coding sequences or CDSs and reference proteins, respectively. For simplicity reasons, predicted alternative protein coding sequences are labeled alternative open-reading frames or altORFs. To generate MySQL databases containing the sequences of all predicted alternative proteins translated from reference annotation of different organisms, a computational pipeline of Perl scripts was developed as previously described with some modifications  (Vanderperre et al. , 2013). Genome annotations for H. sapiens (release hg38, Assembly: GCF_000001405. 26), P. troglodytes (Pan_troglodytes-2. 1. 4, Assembly: GCF_000001515.
6), M. musculus (GRCm38. p2, Assembly: GCF_000001635. 22), D. melanogaster (release 6, Assembly: GCA_000705575. 1), C. elegans (WBcel235, Assembly: GCF_000002985. 6) and S. cerevisiae (Sc_YJM993_v1, Assembly: GCA_000662435. 1) were downloaded from the NCBI website (http://www. ncbi. nlm. nih. gov/ genome). For B. taurus (release UMD 3. 1. 86), X. tropicalis (release JGI_4. 2) and D. rerio (GRCz10. 84), genome annotations were downloaded from Ensembl (http://www. ensembl. org/info/ data/ftp/). Each annotated transcript was translated in silico with Transeq  (Rice et al. , 2000). All ORFs starting with an AUG and ending with a stop codon different from the CDS, with a minimum length of 30 codons (including the stop codon) and identified in a distinct reading frame compared to the annotated CDS when overlapping the CDS, were defined as altORFs.
Generation of shuffled transcriptomes Each annotated transcript was shuffled using the Fisher-Yates shuffle algorithm. In CDS regions, all codons were shuffled except the initiation and stop codons. For mRNAs, we shuffled the 5'UTRs, CDSs and 3'UTRs independently to control for base composition. Non-coding regions were shuffled at the nucleotide level. The resulting shuffled transcriptome has the following features compared to hg38: same number of transcripts, same transcripts lengths, same nucleotide composition, and same amino-acid composition for the proteins translated from the CDSs. Shuffling was repeated 100 times and the results are presented with average values and standard deviations. The total number of altORFs is 539,134 for hg38, and an average of 489,073 for shuffled hg38. AltORFs and kozak motifs in the 100 shuffled transcriptomes were detected as described above for hg38.
Identification of paralogs/orthologs in alternative proteomes Both alternative and reference proteomes were investigated. Pairwise ortholog and paralog relationships between the human proteomes and the proteomes from other species, were calculated using an InParanoid-like approach  (Sonnhammer and O ¨stlund, 2015) , as described below (RRID:SCR_ 006801). The following BLAST (RRID:SCR_001010) procedure was used. Comparisons using our datasets of altORFs/CDS protein sequences in multiple FASTA formats from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Xenopus tropicalis Bos taurus, Mus musculus, Pan troglodytes, Homo sapiens were performed between each pair of species (Homo sapiens against the other species), involving four whole proteome runs per species pair: pairwise comparisons (organism A vs organism B, organism B vs organism A), plus two self-self runs (organism A vs organism A, organism B vs organism B).
BLAST homology inference was accepted when the length of the aligned region between the query and the match sequence equalled or exceeded 50% of the length of the sequence, and when the bitscore reached a minimum of 40  (Remm et al. , 2001). Orthologs were detected by finding the mutually best scoring pairwise hits (reciprocal best hits) between datasets A-B and B-A. The self-self runs were used to identify paralogy relationships as described  (Sonnhammer and O ¨stlund, 2015).
Analysis of third codon position (wobble) conservation Basewise conservation scores for the alignment of 100 vertebrate genomes including H. sapiens were obtained from UCSC genome browser (http://hgdownload. soe. ucsc. edu/goldenPath/hg38/ phyloP100way/) (RRID:SCR_012479). Conservation PhyloP scores relative to each nucleotide position within codons were extracted using a custom Perl script and the Bio-BigFile module version 1. 07 (see code file). The PhyloP conservation score for the wobble nucleotide of each codon within the CDS was extracted. For the 53,862 altORFs completely nested inside 20,814 CDSs, the average Phy-loP score for wobble nucleotides within the altORF region was compared to the average score for the complete CDS. To generate controls, random regions in CDSs with a similar length distribution as altORFs were selected and PhyloP scores for wobble nucleotides were extracted.
Alternative protein analysis using InterProScan (RRID:SCR_005829) InterProScan combines 15 different databases, most of which use Hidden Markov models for signature identification  (Jones et al. , 2014). Interpro merges the redundant predictions into a single entry and provides a common annotation. A recent local version of InterProScan 5. 14-53. 0 was run using default parameters to scan for known protein domains in alternative proteins. Gene ontology (GO) and pathway annotations were also reported if available with -goterm and -pa options. Only protein signatures with an E-value <10 À3 were considered. We classified the reported InterPro hits as belonging to one or several of three clusters; (1) alternative proteins with InterPro entries; (2) alternative proteins with signal peptides (SP) and/or transmembrane domains (TM) predicted by at least two of the three SignalP, PHOBIUS, TMHMM tools and (3) alternative proteins with other signatures.
Mass spectrometry identification Wrapper Perl scripts were developed for the use of SearchGUI v2. 0. 11 (Vaudel et al. , 2011)  (RRID: SCR_012054) and PeptideShaker v1. 1. 0 (Vaudel et al. , 2015)  (RRID:SCR_002520) on the Universite de Sherbrooke's 39,168 core high-performance Mammouth Parallele two computing cluster (https:// www. computecanada. ca/research-portal/accessing-resources/available-resources/). SearchGUI was configured to run the following proteomics identification search engines: X!Tandem  (Craig and Beavis, 2004) , MS-GF+  (Kim and Pevzner, 2014) , MyriMatch  (Tabb et al. , 2007) , Comet  (Eng et al. , 2013) , and OMSSA  (Geer et al. , 2004). SearchGUI parameters were set as follow: maximum precursor charge, 5; maximum number of PTM per peptide, 5; X!Tandem minimal fragment m/z, 140; removal of initiator methionine for Comet, 1.
A full list of parameters used for SearchGUI and Pepti-deShaker is available in Supplementary file 2, sheet 1. For PXD000953 dataset  (Rosenberger et al. , 2014) , precursor and fragment tolerance were set 0. 006 Da and 0. 1 Da respectively, with carbamidomethylation of C as a fixed modification and Nter-Acetylation and methionine oxidation as variable modifications. For PXD000788  (Tong et al. , 2014)  and PXD000612  (Sharma et al. , 2014)  datasets, precursor and fragment tolerance were set to 4. 5 ppm and 0. 1 Da, respectively, with carbamidomethylation of cysteine as a fixed modification and Nter-Acetylation, methionine oxidation and phosphorylation of serine, threonine and tyrosine as variable modifications. For PXD002815 dataset  (Hein et al. , 2015) , precursor and fragment tolerance were set to 4.
5 ppm and 0. 1 Da, respectively, with carbamidomethylation of cysteine as a fixed modification and Nter-Acetylation and methionine oxidation as variable modifications. Datasets were searched using a target-decoy approach against a composite database composed of a target database [Uniprot canonical and isoform reference proteome (16 January 2015) for a total of 89,861 sequences + custom alternative proteome resulting from the in silico translation of all human altORFs (available to download at https://www. roucoulab. com/p/downloads)], and their reverse protein sequences from the target database used as decoys. In order to separate alternative and reference proteins for FDR analyses, PeptideShaker output files were extracted with target and decoy hits. PSMs matching reference target or decoy proteins were separated from those matching alternative targets or decoys as previously described  (Menschaert and Fenyo ¨, 2017; Woo et al.
, 2014). PSMs that matched both reference and alternative proteins were automatically moved to the reference database group. PSMs were then ranked according to their PeptideShaker score and filtered at 1% FDR separately. Validated PSMs were selected to group proteins using proteoQC R tool  (Gatto et al. , 2015) , and proteins were separately filtered again using a 1% FDR cut-off. Only alternative proteins identified with at least one unique and specific peptide were considered valid  (Vaudel et al. , 2015). Any peptide matching both a canonical (annotated in Uniprot) and an alternative protein was attributed to the canonical protein. For non-unique peptides, that is peptides matching more than one alternative protein, the different accession IDs are indicated in the MS files. For subsequent analyses (e.
g. conservation, protein signature. ), only one protein is numbered in the total count of alternative proteins; we arbitrarily selected the alternative protein with the lowest accession ID. Peptides matching proteins in a protein sequence database for common contaminants were rejected  (Perkins et al. , 1999). For spectral validation (Figure  5 -figure supplements 1, 2, 3 and 4), synthetic peptides were purchased from the peptide synthesis service at the Universitede Sherbrooke. Peptides were solubilized in 10% acetonitrile, 1% formic acid and directly injected into a Q-Exactive mass spectrometer (Thermo Scientific) via an electro spray ionization source (Thermo Scientific, Canada). Spectra were acquired using Xcalibur 2. 2 (RRID:SCR_014593) at 70,000 resolution with an AGC target of 3e6 and HCD collision energy of 25.
After 24 hr, cells were transfected for 24 hr with an empty vector (pcDNA3. 1) or with the same vector expressing alt-MiD51 Flag with GeneCellin tranfection reagent according to the manufacturer's instructions. Cells were equilibrated in XF assay media supplemented with 25 mM glucose and 1 mM pyruvate and were incubated at 37 ˚C in a CO 2 -free incubator for 1 hr. Baseline oxygen consumption rates (OCRs) of the cells were recorded with a mix/wait/measure times of 3/0/3 min respectively. Following these measurements, oligomycin (1 mM), FCCP (0. 5 mM), and antimycin A/rotenone (1 mM) were sequentially injected, with oxygen consumption rate measurements recorded after each injection. Data were normalized to total protein in each well. For normalization, cells were lysed in the 96-well XF plates using 15 ml/well of RIPA lysis buffer (1% Triton X-100, 1% NaDeoxycholate, 0.
Fluorescence was monitored in real time. ROS accumulation rate was measured between 1 and 3 hr following induction. After the assay, total cellular protein content was measured using BCA protein assay reagent (Pierce, Waltham, MA) after lysis with RIPA buffer. Data were normalized for initial fluorescence and protein concentration. ATP synthesis was measured as previously described  (Vives-Bauza et al. , 2007)  in cells transfected for 24 hr with an empty vector (pcDNA3. 1) or with the same vector expressing altMiD51 Flag.
Acknowledgements This research was supported by  CIHR  grants  MOP-137056  and  MOP-136962  to XR;  MOP-299432  and  MOP-324265  to CL; a  Universitede Sherbrooke institutional research  grant made possible through a generous donation by  Merck Sharp and Dohme  to XR; a FRQNT team grant  2015-PR-181807  to CL and XR;  Canada Research Chairs in Functional Proteomics and Discovery of New Proteins  to XR, in  Evolutionary Cell  and  Systems Biology  to CL and in Computational and Biological Complexity to AO; AAC is supported by a  CIHR New Investigator Salary Award ; MSS is a recipient of a  Fonds de Recherche du Quebec -SanteResearch Scholar Junior 2 Career Award ; VD is supported in part by fellowships from  Region Nord-Pas de Calais  and PROTEO; AAC, DJH, MSS and XR are members of the  Fonds de Recherche du Quebec Sante-supported Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke.
In order to improve the understanding of the mechanisms involved in the generation of early DNA damage, a new calculation chain based on the Geant4-DNA toolkit was developed. This work presents for the first time the simulation of the physical, physicochemical and chemical stages of early radiation damage at the scale of an entire human genome (fibroblast, male) and using Geant4-DNA models. The DnaFabric software was extended to generate and export this nucleus model to a text file with a specific format that can be read by Geant4 user applications. This calculation chain was used to simulate the irradiation of the nucleus by primary protons of different energies (0,5; 0,7; 0,8; 1; 1,5; 2; 3; 4; 5; 10; 20 MeV) and the results, in terms of DNA double strand breaks, agree with experimental data found in the literature (pulsed field electrophoresis technique).
Their complexity generally makes it hard to adapt them to the different biological conditions that may influence DNA topology, although the lack of complete knowledge of the organisation of the DNA within a cell nucleus may require this adaptation. That is, although the double helix structure of the DNA has been well described, not yet the case for the higher levels of DNA organisation such as chromatin distribution within the chromosome territories. Furthermore, the organisation of the DNA within a nucleus is also dynamic and changes with the cell cycle and the cell type. DnaFabric software  17  was therefore developed to facilitate the generation of complex DNA models that can go from a few pairs of nucleotides to whole-genome representations. This software makes it possible to generate, modify, and visualise complex DNA geometries which can also be exported for use in Geant4-DNA calculations.
Modelling the DNA within a cell nucleus DnaFabric software. DnaFabric is a C++ program to generate, edit, display and export complex DNA geometrical models from the nucleotide scale to the entire DNA content of a cell-nucleus. A previous paper  17  described an early version of the software and presented a first set of DNA geometrical models. That first version, however, was unable to deal with geometries composed of more than 10 5 elements; in practice, it could only generate and manipulate a DNA fibre of roughly 18 kbp. Recent improvements enable it to work with a cell nucleus filled with an entire human genome of 6 Gbp (36 ⋅ 10 9 distinct volumes). The geometry generated can then be exported to a text file (extension ". fab2g4dna") with a specific format that can be read by Geant4 user applications.
Among the various improvements to DnaFabric is a new module ("Engines"), which allows users to implement a simulation to modify a predefined DNA geometrical model. This module includes tools that can work with the hierarchical organisation of the DnaFabric geometrical models and perform multi-threaded simulations while updating the geometry rendered on the visualisation screen. Furthermore, the hierarchical organisation of the DNA models is now based on a graph structure that can define several memory-light placeholder objects used as references to a single memory-heavy object. This refinement of the hierarchical organisation allows DnaFabric to deal with billions of heavy object instances. The visualisation module was also modified to enable it to render such a huge number of objects. In practice, a level of detail (LOD)  18  management system was implemented to define several 3D representations for each geometrical object.
DnaFabric does this by randomly positioning one cylinder per chromosome in the cell nucleus. Each cylinder contains all the spherical domains of the chromosome in a "condensed" form. The next stage of the simulation involves "relaxing" the genome in order to obtain a distribution of the domains consistent with the G0/G1 phase. DnaFabric simulates this process according to the model previously described in the literature  22. Once the relaxed genome is built, the domains are filled with DNA by adding voxels within each domain with a filling algorithm implemented in the "Engines" module. This algorithm generates DNA loops within each domain and ensures that the DNA chromatin fibre is continuous in each chromosome territory. Once the filling process is complete, the nucleus model can be exported to a ".
We therefore modify Geant4 to enable us to parameterise the 5 different types of voxels and resolve this issue; principally; some variables were made thread-local to avoid data race issues when the multithreading mode is activated. This modification finally allowed the simulation of the physical stage in a cell nucleus filled with around 6. 4 ⋅ 10 9 pairs of nucleotides. Two datasets are stored in an ntuple generated by the ROOT-CERN library  23  during this simulation. The first set is used to calculate the DNA damage induced by the physical interactions (direct effects) and the second to generate the input data for the chemical stage (indirect effects). The simulations can thus be separated so that those for the different stages can be run independently when necessary. The first dataset corresponds to the physical interactions located in the DNA volumes: 2-deoxyribose, phosphate, adenine, guanine, thymine, cytosine and their hydration shells.
Specifically, the information recorded there concerns the type of interaction (ionisation, elastic etc. ), the particles (type and energy) and the DNA molecules involved (name and spatial localisation). The second set of data includes the water molecules that have been ionised or excited and the solvated electrons (physical characteristics and position). Water molecules and solvated electrons are saved only if they are located within a voxel to limit the size of the output file. Physicochemical and chemical stages. Like the physical stage, the physicochemical and chemical stages are simulated with a Geant4 user-application ("chem_geo") built especially to take the geometrical description of the DNA exported from DnaFabric into account. This time, however, the general idea is to consider the DNA model not as a group of Geant4 physical volumes but as a set of spatially ordered molecules that should not diffuse over time.
Moreover, reactants can react with one another only if they are produced by the same track (independent track approximation). The separation of the chemical stage simulation within the different voxels is due to the need to minimise memory use and simulation time. Considering the entire nucleus with its human genome simultaneously during the chemical stage would have required loading about 36 ⋅ 10 9 individual molecules, which exceeds the limitations of not only the chemistry module (Geant4-DNA version 10. 1) but also current hardware. Fragmentation of the simulation to isolated voxels allowed us to reduce the amount of memory required drastically. In such a configuration, each voxel contains no more than 10000 individual molecules, which is easily manageable. The drawback of this separation is the need to run numerous different simulations (one simulation per event/voxel pair).
In this work, this value was set to 2% which means that around 5000 primaries were simulated for each energy. Simulations were performed in parallel on a computer cluster which each node was in charge of computing results for one LET value. There were 24 threads per node and the simulations lasted around 3 weeks (depending on the particle energy). = ⋅ ⋅ ⋅ × ∞ N N E l LET E n F ( ) (1 Initially, the default parameters of the calculation chain were used to compute the number of DSBs in the DNA and compare them with both experimental  [43] [44] [45] [46]  and simulated data  31, 40  from the literature. A minimum fragment size of 10000 bp was used to compute the number of DSB/pp for all the results of section title:dna_damages. In a second simulation, the criterion used to determine the SB direct (17.