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 strongly challenged, 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; (7) several thousand co-conserved paired alternative-reference proteins encoded in the same gene; and (8) presence of clear, striking examples in altMiD51, altDDIT3 and 5 alternative proteins interacting with their reference zinc finger proteins.
Only a relatively small percentage of alternative proteins (22. 6%) are functionally annotated with Interpro signatures, compared to reference proteins (96. 9%). An obvious explanation is the small size of alternative proteins with a median size of 45 amino acids, which may not be able to accommodate large domains. It has been proposed that small proteins may be precursors of new proteins but require an elongation of their coding sequence before they display a useful cellular activity  19, 51. According to this hypothesis, it is possible that protein domains appear only after elongation of the coding sequence. 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.
Generation of alternative open reading frames (altORFs) and alternative protein databases. Throughout this manuscript, annotated protein coding sequences and proteins in current databases are labelled annotated coding sequences or CDSs and reference proteins, respectively. For simplicity reasons, predicted alternative protein coding sequences are labelled 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  37. 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 66. 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.
Identification of TISs. The global aggregates of initiating ribosome profiles data were obtained from the initiating ribosome tracks in the GWIPS-viz genome browser  28  with ribosome profiling data collected from five large scale studies  2, 9, [68] [69] [70]. Sites were mapped to hg38 using a chain file from the UCSC genome browser (http://hgdownload. soe. ucsc. edu/goldenPath/hg19/liftOver/hg19ToHg38. over. chain. gz) and CrossMap v0. 1. 6 (RRID:SCR_001173). Similar to the methods used in these studies, an altORF is considered as having an active TIS if it is associated with at least 10 reads at one of the 7 nucleotide positions of the sequence NNNAUGN (AUG is the predicted altORF TIS). An additional recent study was also included in our analysis  29. In this study, a threshold of 5 reads was used.
Raw sequencing data for ribosome protected fragments in harringtonine treated cells was aligned to the human genome (GRCh38) using bowtie2 (2. 2. 8)  71. Similar to the method used in this work, altORFs with at least 5 reads overlapping one position in the kozak region were considered as having an experimentally validated TIS. 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  72  , 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  73. 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  72. Co-conservation analyses. For each orthologous alternative protein pair A-B between two species, we evaluated the presence and the orthology of their corresponding reference proteins A'-B' in the same species. In addition, the corresponding altORFs and CDSs had to be present in the same gene. In order to develop a null model to assess co-conservation of alternative proteins and their reference pairs, we needed to establish a probability that any given orthologous alternative protein would by chance occur encoded on the same transcript as its paired, orthologous reference protein.
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 PhyloP 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  75. 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 -3 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.
The random distribution of the number of alternative/reference protein pairs that share at least one InterPro entry was computed by shuffling the alternative/reference protein pairs and calculating how many share at least one InterPro entry. This procedure was repeated 1,000 times. Finally, we compared the number and identity of shared InterPro entries in a two dimensional matrix to illustrate which Interpro entries are shared. In many instances, including for zinc-finger coding genes, InterPro entries in alternative/reference protein pairs tend to be related when they are not identical. Mass Spectrometry identification. Wrapper Perl scripts were developed for the use of SearchGUI v2. 0. 11  77 (RRID:SCR_012054) and PeptideShaker v1. 1. 0  59 ID:SCR_002520) on the Université de Sherbrooke's 39,168 core high-performance Mammouth Parallèle 2 computing cluster (http://www.
calculquebec. ca/en/resources/compute-servers/mammouth-parallele-ii). SearchGUI was configured to run the following proteomics identification search engines: X!Tandem  78  , MS-GF+  79  , MyriMatch  80  , Comet  81  , and OMSSA  82. 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 PeptideShaker is available in Supplementary file 2, sheet 1. For PXD000953 dataset  35  , 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  33  and PXD000612  34  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  32  , 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  83, 84. 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  85  , 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  59.
Any peptide matching both a canonical (annotated in Uniprot) and an alternative protein was attributed to the canonical protein. For non-unique peptides, i. e. 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 86. For spectral validation (Figure  6 -figure supplement1, 2, 3, and 4), synthetic peptides were purchased from the peptide synthesis service at the Université de 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).
Code availability. Computer codes are available upon request with no restrictions. Data availability. Alternative protein sequence databases for different species can be accessed at https://www. roucoulab. com/p/downloads with no restrictions. Cloning and antibodies. Human Flag-tagged altMiD51(WT) and altMiD51(LYR→AAA), and HA-tagged DrP1(K38A) were cloned into pcDNA3. 1 (Invitrogen) using a Gibson assembly kit (New England Biolabs, E26115). The cDNA corresponding to human MiD51/MIEF1/SMCR7L transcript variant 1 (NM_019008) was also cloned into pcDNA3. 1 by Gibson assembly. In this construct, altMiD51 and MiD51 were tagged with Flag and HA tags, respectively. MiD51 GFP and altMiD51 GFP were also cloned into pcDNA3. 1 by Gibson assembly. For MiD51 GFP , a LAP tag  32  was inserted between MiD51 and GFP.
HeLa cells (ATCC CRM-CCL-2, authenticated by STR profiling, RRID:CVCL_0030) cultures tested negative for mycoplasma contamination (ATCC 30-1012K), transfections, immunofluorescence, confocal analyses and western blots were carried out as previously described  87. Mitochondrial morphology was analyzed as previously described  88. A minimum of 100 cells were counted (n=3 or 300 cells for each experimental condition). Three independent experiments were performed. Immunoprecipitations. Immunoprecipitations experiments were conducted using GFP-Trap (ChromoTek) protocol with minor modifications. Briefly, cells were lysed with coip lysis buffer (0. 5 % NP40, Tris-HCl 50 mM pH 7. 5, NaCl 150 mM and two EDTA-free Roche protease inhibitors per 50 mL of buffer). After 5 mins of lysis on ice, lysate was sonicated twice at 11 % amplitude for 5 s with 3 minutes of cooling between sonication cycles.
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 μM), FCCP (0. 5 μM), and antimycin A/rotenone (1 μM) 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 μl/well of RIPA lysis buffer (1% Triton X-100, 1% NaDeoxycholate, 0. 1% SDS, 1 mM EDTA, 50 mM Tris-HCl pH 7. 5). Protein concentration was measured using the BCA protein assay reagent (Pierce, Waltham, MA, USA). Reactive oxygen species (ROS) levels were measured using Cellular ROS/Superoxide Detection Assay Kit (Abcam #139476). HeLa cells were seeded onto 96-well black/clear bottom plates at a density of 6,000 cells per well with 4 replicates for each condition.
95, and blue signalling accelerated evolution DQ0. 05) with 6,428 of 19,705 altORFs (36. 2%). A random distribution would have implied a total of 10% (or 1,970) of altORFs in the extreme values. This suggests that 26. 2% (36. 2%-10%) of altORFs (or 4,458) undergo specific selection different from random regions in their CDSs with a similar length distribution. (a) Percentage of CDSs and altORFs with detected TISs by ribosomal profiling and footprinting of human cells  23. The total number of CDSs and altORFs with a detected TIS is indicated at the top. (b) Alternative and reference proteins detected in three large proteomic datasets: human interactome 32 , 10,000 human proteins  35  , human phosphoproteome  34  , EGFR interactome  33                Pixels show the number of times entries co-occur in reference and alternative proteins.
Both of these methods are themselves conservative, as they do not account for the conservation of the pairing. The larger of these values for each species was then used to generate 1 million random binomial distributions with n=# of orthologous altProts; the maximum of these percentages is reported in column I. 1 The UniProtKB accession is indicated when available. Percentage of altORFs with a TIS within an optimal Kozak sequence in hg38 (dark blue) compared to 100 shuffled hg38 (light blue). Mean and standard deviations for sequence shuffling are displayed, and significant difference was defined by using one sample t test. **** P<0. 0001. Note that shuffling all transcripts in the hg38 transcriptome generates a total of 489,073 altORFs on average, compared to 539,134 altORFs in hg38. Most transcripts result from alternative splicing and there are 183,191 unique altORFs in the hg38 transcriptome, while the 489,073 altORFs in shuffled transcriptomes are all unique.
Flag. using R and Bioconductor. Proteomics 15, 1375-1389 (2015). 86. Perkins, D. N. , Pappin, D. J. C. , Creasy, D. M. & Cottrell, J. S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551-3567 (1999). 87. Vanderperre, B. et al. An overlapping reading frame in the PRNP gene encodes a novel polypeptide distinct from the prion protein. FASEB J. 25, 2373-86 (2011). 88. Palmer, C. S. et al. MiD49 and MiD51, new components of the mitochondrial fission machinery. EMBO Rep. 12, 565-573 (2011). 89. Vanderperre, B. et al. MPC1-like: a Placental Mammal-Specific Mitochondrial Pyruvate Carrier Subunit Expressed in Post-Meiotic Male Germ Cells. J. Biol. Chem. (2016). doi:10. 1074/jbc. M116. 733840 90. Vives-Bauza, C. , Yang, L.
Acknowledgements This research was supported by  CIHR  grants  MOP-137056  and  MOP-136962  to X. R;  MOP-299432  and  MOP-324265  to C. L; a  Université de Sherbrooke  institutional research grant made possible through a generous donation by  Merck Sharp & Dohme  to X. R; a FRQNT team grant  2015-PR-181807  to C. L. and X. R;  Canada Research Chairs in Functional Proteomics and Discovery of New Proteins  to X. R, in  Evolutionary Cell  and  Systems Biology  to C. L and in Computational and  Biological Complexity  to A. O; A. A. C is supported by a  CIHR New Investigator Salary Award ; M. S. S is a recipient of a  Fonds de Recherche du Québec -Santé   Research Scholar Junior 2 Career Award ; V. D is supported in part by fellowships from  Région Nord-Pas de Calais  and PROTEO; A. A. C,  D. J.
Moreover, the sensitivity analysis showed that results were robust to wide variations in the underlying clinical parameters. Secondly, the estimates for sensitivity (90%) and specificity (90%) of liver biopsy were obtained from a study whose estimates are themselves based on clinician opinion [25]. Nevertheless, the sensitivity analysis showed that variations in the accuracy of biopsy did not affect the main results. Finally, cost estimates were obtained from our hospital or from Spanish sources. Differences in economic data across countries may affect the final results. However, the wide variation in the unit costs that we have used in the sensitivity analysis systematically corroborated that the sequential ELF test/LSM strategy was costeffective. In conclusion, testing for liver fibrosis annually with sequential ELF test/LSM or ELF test alone resulted in a substantial reduction in the number of events (cirrhosis, hepatocellular carcinoma, and fibrosis-related death) and a significant increase in QALYs for HCV and ALD patients, compared with a single liver biopsy.
Sequence and structure similarity Because the structure of Tll0287 is unique in that it does not resemble those of the typical c-type cytochromes, we searched for possible similar structures with the DALI server  (47). The results (supplemental Table  S1 ) showed that the structure of Tll0287 is similar to that of a sensor domain contained in some kinases or sensor proteins possibly involved in cellular signal transduction, such as the sporulation kinase d domain from Bacillus subtilis (KinD, PDB code 4JGO) (48) (Fig. 6b ) and a methyl-accepting chemotaxis protein (Mcp, PDB code 3C8C) (Fig. 6c ), even though they do not contain a heme. These proteins have a PAS-like domain, especially a PDC domain as extracellular sensors  (49) , although the RMSD values between the overall structures of Tll0287 and these proteins were ϳ2.
2 and 3. 2 Å, respectively. Fig. 7  shows the results of multiple structural alignments by the DALI server  (47). It is clear that although the primary sequences of Tll0287 and other proteins are not similar, the secondary structures appear to exhibit significant similarities. On the basis of the comparisons of the secondary (Fig. 7 ) and three-dimensional structures (Fig. 6 ), the structure of Tll0287 can be divided into three domains: a PAS-like domain (Ala 26 -Ala 49 , Val 67 -Gln 138 , and Val 169 -Ala 181 ), a heme binding site (Ala 139 -Arg 168 ), and a loop domain (Ser 50 -Ala 66 ) that separates the PAS-like and hemebinding domains. PAS domains were discovered as the large regions of per, arnt, and sim genes in Drosophila  (50, 51)  that are related to circadian rhythmicity  (52)  and are signaling modules that monitor changes in the redox potential, light, oxygen level, small ligands, or overall energy level of the cell  (53).
So far, proteins with the PAS domain are found as transcription factors in clock genes  (54) , ion channels such as HERG  (55) , and sensor proteins such as kinases  (53). The PAS domains are composed of a single antiparallel, five-stranded ␤-sheet with the strand order of 2-(1)-5-4-3 (some proteins have only four-stranded ␤-sheet without the first strand, such as the sensor domain of histidine kinase DcuS (PDB code 3BY8)) (  48 ) and several ␣-helices  (56). Although the PAS domains have a conserved three-dimensional fold, these domains share little primary sequence identity  (57). The homologous structure of Tll0287 revealed in this study is consistent with these features and suggests that Tll0287 also contains the PAS-like domain. A subclass of the PAS domain is the PDC domain, which is a general feature of periplasmic PAS domains (58) and may bind multiple ligands  (59).