dataesr/scientific-paragraphs-categorization huggingface dataset:
Creation of the Rat Nonredundant Reference Proteome Database The nucleotide sequences of the 99 438 assembled transcript isoforms were translated into the six possible reading frames using the Transeq program (EMBOSS suite of tools)  [49]. Deduced amino acid sequences of at least 10 residues between two stop codons were defined as potential protein sequences; there were 3 348 184 such predicted protein sequences. We assembled a rat nonredundant reference proteome by merging the UniProt (37 175 canonical and isoform sequences; release 2012_10)  [33]  and Ensembl (32 971 known and 44 993 predicted protein sequences; release 3. 4. 68) [50]  proteome databases with the set of predicted protein sequences.
For an optimal duty cycle, the fragment ion spectra were recorded in the LTQ mass spectrometer in parallel with the OrbiTrap full scan detection. For OrbiTrap measurements, an external calibration was used before each injection series, ensuring an overall mass accuracy error below 5 ppm for the detected peptides. MS data were saved in RAW file format (Thermo Fisher Scientific) using XCalibur 2. 0. 7 with Tune 2. 4. Data processing. Three successive LC-MS/MS runs and dynamic exclusion were employed to prevent repetitive selection of the same peptide. Proteome Discoverer software (version 1. 2; Thermo Fisher Scientific) supported by the Mascot (Matrix Science) search engine was used for peptide and protein identification. MS/MS spectra were used to search our rat nonredundant reference proteome database (number of residues: 161 273 757; number of sequences: 3 428 361) and also the randomized version of this database (decoy) to determine the false-positive rate, defined as the number of validated decoy hits/(number of validated target hits þ number of decoy hits) * 100, using the Mascot algorithm (Mascot server v2.
A mass tolerance of 610 ppm was used to reject previously identified peptides within the specified retention time window  [51]. Protein identification. All MS/MS spectra were used to search our rat nonredundant reference proteome database and the decoy database in a single Mascot query to generate one compiled search file (. msf file) per run and per sample. Identified peptides were filtered according to the Mascot score to obtain a FDR of 1%. Peptide identifications were accepted if the individual ion Mascot scores were above the identity threshold (the ion score is À10 * log(P), where P is the probability that the observed match is a random event, P value , 0. 05). In the case of peptides shared by different proteins, proteins were automatically grouped. Only the best matches of the peptides (rank 1) were considered.
Refinement of Transfrag Selection To select high-confidence novel protein-coding loci and thus to eliminate artifacts due to errors in read mapping, transcript assembly, and protein identification, we applied an additional filtering step. Briefly, we defined a background expression cutoff (BEC ¼ 3. 72 FPKM), calculated as the overall median of FPKM values for the assembled transcripts that completely match (Cuffcompare class ''¼'') RefSeq curated mRNAs (RefSeq category ''NM'')  [43]. This allowed the selection of ''expressed'' or ''detectable'' transfrags for which FPKM values in both replicates of a given cell type were !BEC.
Multiple Alignments of Protein Sequences Protein sequences predicted from selected candidates were used as probes to search UniProt, Ensembl and RefSeq protein databases using the BlastP program  [54]. Additional orthologous sequences were retrieved and predicted by querying the Ensembl genome, the EMBL nucleic sequence databases  [55] , and EST databases using the TBlastN program. Multiple sequence alignments were then generated using the MAFFT module  [56]  implemented in the JalView editor  [57]  with default parameters. The CD-search program was used to predict protein domains for the two protein sequence candidates studied in greater detail  [58, 59] , and the JNet algorithm implemented in JalView was used to predict secondary structures  [60].
Experimental Validation RT-PCR and real-time quantitative RT-PCR. Complementary DNA was obtained from aliquots of 4 lg of DNase-treated RNA (DNase I; Promega) using random hexamers and Moloney murine leukemia virus reverse transcriptase (Invitrogen). Two sets of primer pairs were synthesized for PCR or real-time PCR. All primers were purchased from Sigma-Aldrich (Supplemental Data S3 lists the primers and the expected sizes of PCR products and efficiency of the primer pairs for qPCR). Conventional PCR was performed using Taq polymerase (Qiagen), or high-fidelity HotStar HiFidelity DNA Polymerase (Qiagen) for cloning, in a Peltier thermocycler (Labgene). PCR products were then resolved on 1. 5% agarose gels. Real-time PCR was performed using the ABI 7500 Fast Real-Time PCR System (Applied Biosystems) in the presence of SYBR green.
All samples were studied in triplicate. Specificity of the product amplification was confirmed by melting curve analyses and agarose gel electrophoresis. A stable gene in testicular cells (Snx17) was selected for normalization based on its low coefficient of variance in microarray data of germ and somatic cells. Relative expressions were calculated according to the delta Ct method. Gene cloning and recombinant protein production. The XLOC_001949 PCR product was resolved by electrophoresis on a 1. 2% agarose gel, excised, and purified using the QIAquick Gel Extraction Kit (Qiagen). The purified DNA was digested with BamH1-HF in the appropriate CutSmart Buffer (New England Biolabs) to obtain the sequences of interest as BamH1 fragments; these fragments were inserted into a pQE-30 vector (Qiagen) using the T4 DNA ligase in the Quick ligation buffer (New England Biolabs), according to the manufacturer's instructions.
Data Access The RNA-seq data files were submitted to NCBI's Sequence Read Archive and to the NCBI Gene Expression Omnibus under accession numbers SRP026340 and GSE48321, respectively. The mass spectrometry proteomics data were deposited with the ProteomeXchange Consortium with the data set identifier PXD000872. Sequences of the identified peptides were mapped on the rat genome using BLAT  [62]  and subsequently deposited in the ReproGenomics Viewer (http://rgv. genouest. org). Selected unannotated transfrags were deposited with the GenBank Transcriptome Shotgun Assembly Sequence Database as bioproject no. PRJNA209702.
Experimental Design and PIT Workflow A large set of lncRNAs and testicular unannotated transcripts (TUTs) has previously been characterized by highresolution expression profiling of male germ cells in the rat using next-generation sequencing  [13]. We used a PIT strategy to identify those transcripts that code for proteins, as summarized in Figure  1. A RNA-seq analysis was conducted previously to characterize the transcriptome of four testicular cell types: SC, SPG, pSPC, and rSPT  [13]. Briefly, the paired-end reads resulting from the sequencing of RNAs were aligned on the rat genome sequence and assembled into nonredundant transfrags. Reconstructed transcripts were then translated into protein sequences and a database of predicted open reading frames (ORFs) was generated. This data set was merged with a canonical list of rat proteins from UniProt (37 175 canonical and isoform sequences; release 2012_10) and Ensembl (32 971 known and 44 993 predicted protein sequences; release 3.
4. 68), to generate a rat nonredundant reference proteome. In parallel, comprehensive rat proteomes from isolated pSPC and rSPT were generated by shotgun proteomic analyses. Briefly, protein extracts were sequentially prefractionated, digested with trypsin, and analyzed by LC-MS/MS. The spectral data were then used to search the rat nonredundant reference proteome with the Proteome Discoverer software using the Mascot algorithm. A highly stringent refinement strategy was developed to select those protein identifications corresponding to novel unanno-tated or noncoding transcriptional events (TUTs and lncRNAs) that were unambiguously detected in meiotic and/or postmeiotic germ cells. The Rat Nonredundant Reference Transcriptome Contained About 100 000 Transcripts and the Proteome 3 000 000 Predicted ORFs Of the 140 million paired-end reads resulting from the Illumina sequencing experiment  [13]  about 80% were properly aligned on the rat genome (Fig.
2 ). These reads were subsequently assembled into a unique set of 99 438 transfrags, termed the rat nonredundant reference transcriptome (Fig. 2 ): 32 024 of these (29 668 loci) were classified as novel intronic (cuffcompare class code ''i'') or intergenic (class code ''u'') long (cumulative exon length ! 200 nt) TUTs, and 1274 (795 loci) appeared to correspond to known (class code ''¼'') or novel (class code ''j'') isoforms of annotated, long, noncoding genes (lncRNAs) (Fig. 2 ). We then predicted about 3. 3 million ORFs (!10 amino acids [aa]) from all six frames of each reconstructed transcript, and the deduced aa sequences were combined with UniProt (release 2012_10) and Ensembl (release 3. 4. 68) public proteome databases, leading to a unique set of 3 428 361 proteins that we used as our customized rat nonredundant reference proteome.
The PIT and Refinement Strategies Revealed 44 Novel Potentially Protein-Coding Loci Mascot and a target decoy strategy were used for searches with the MS/MS spectra data from pSPC and rSPT protein extracts against the rat nonredundant reference proteome. This led to the identification of 19 966 nonredundant peptides corresponding to 4999 nonredundant proteins in pSPC (16 611 peptides; 4056 proteins) and/or rSPT (11 755 peptides; 3061 proteins) (Supplemental Data S1 and S2). To identify the most likely protein-coding candidates associated with a germ cell line expression pattern, an additional refinement step based on transcript abundance was applied. This resulted in a final set of 5379 long, nonredundant transcripts (4065 loci) significantly expressed in pSPC and/or rSPT: they included 4458 TUTs (3559 loci) (Fig.
The Features of MS-Identified Transcripts Diverge Significantly from Those Typical of Known lncRNAs TUTs share many genomic characteristics with known lncRNAs in vertebrates  [13] , including relatively short length, low exon number, low GC content, low sequence conservation (comparable to that of introns), low abundance, and highly temporally and spatially restricted expression patterns  [25] [26] [27] [28] [29] [63] [64] [65]. To determine whether or not the 69 MS-identified TUTs and lncRNAs share features with nonidentified transcripts expressed in meiotic and postmeiotic germ cells, we compared a list of genomic traits between transcript populations (Fig. 3 ). This analysis included the 5355 MS-identified mRNAs, those annotated protein-coding transcripts assembled in our RNA-seq data set and for which at least one highconfidence peptide was identified by mass spectrometry in pSPC and/or rSPT extracts.
10 À13 ) but without being as long as those in MS-identified mRNAs (q1 ¼ 229 aa, med ¼ 374 aa, q3 ¼ 653 aa; P , 2. 10 À12 ) (Fig. 3C ). The numbers of spliced isoforms for MS-identified TUTs and lncRNAs (q1 ¼ 1. 0, med ¼ 2. 0, q3 ¼ 4. 0) were about double those for non-MS-identified transcripts (q1 ¼ 1, med ¼ 1, q3 ¼ 2; P , 2. 10 À3 ) but only two thirds those for MS-identified mRNAs (q1 ¼ 2. 0, med ¼ 3. 0, q3 ¼ 5. 0; P , 3. 10 À2 ) (Fig. 3D ). Sequence conservation. To assess the conservation of exons and introns in MS-identified transcripts and that in non-MS-identified transcripts, we compared the averaged base-bybase phastCons conservation score calculated for nine vertebrates, as provided by the UCSC genome browser  [45].
CHOCU ET AL. nt and a maximum ORF size of 78 aa (Figs. 4A  and 5A ). This TUT was among the most conserved among vertebrates (phastCons score ¼ 0. 735; Figs. 4A  and 5A ). It shows a peak expression in pSPC based on the RNA-seq data set (Fig. 4A ) that was confirmed by qPCR and RT-PCR using the four testicular cell populations (Fig. 4, B  and C ). The RT-PCR experiment also included seven healthy tissues; small amounts of the RNA were detected in somatic tissues (brain, liver and lung; Fig. 4C ). Two distinct bands corresponding to two alternative isoforms were observed. Pachytene SPC up to rSPT and elongated spermatids in adult testis sections showed a cytoplasmic staining in in situ hybridization analysis (Fig. 4, D  and E ). Protein, genome, and EST database searches using Blast programs  [54]  unambiguously identified or predicted corresponding protein sequences in 13 vertebrates (Fig.
4F ), a finding confirmed by qPCR and   (A-E ) and XLOC_001949 (F-J) were investigated. A and F) The gene structure for both (yellow boxes correspond to exons), the sequence conservation between nine vertebrates as provided by the UCSC genome browser (phastCons scores, red histograms), and the transcript abundance determined by RNA-seq in four isolated testicular cell types as a color-coded blue heat map. B and G) The amounts of each transcript in each cell type according to the RNA-seq (left y-axis) and the quantitative RT-PCR (right y-axis) experiments. C and H) XLOC_013843 and XLOC_001949 transcript detection by RT-PCR in the total testis (TT), SC, SPG, pSPC and rSPT, other tissues including bone marrow (BM), brain (BR), kidney (KI), liver (LI), lung (LU), and muscle (MU). RTÀ, reverse transcription negative control.
CHOCU ET AL. RT-PCR (Fig. 4, G  and H ). The RT-PCR experiment also demonstrated that its expression was restricted to the testis (Fig. 4H ). In situ hybridization analysis of adult testis sections showed that cytoplasmic staining increased from round SPT at stage IV to step 18-elongated spermatids (Fig. 4, I  and J ). Protein sequence analysis confirmed the good conservation of the predicted ORF and its predicted secondary structures (two beta sheets and two helices) in 26 other mammalian species, including 13 for which there is EST evidence of the corresponding transcript (Fig. 5B  and Supplemental Data FIG. 5. conservation of the two proteins selected for detailed analysis, XLOC_013843 (VAMP9), and XLOC_001949 (T-ENOL). Predicted orthologous proteins of XLOC_013843 (A) and XLOC_001949 (B) in several mammalian species were retrieved using NCBI Blast programs with RefSeq, UniProt, and EST databases.
The annotation bars are as follows: JNetPRED, the consensus prediction; JNetHMM, HMM profile based prediction; JNETSOL25 and JNETSOL5, solvent accessibility predictions (binary predictions of 25% or 5% solvent accessibility). The JNetCONF profile shows the confidence estimate for the prediction. High values mean high confidence prediction. Helices are marked as red tubes, and sheets as bright green arrows. A consensus prediction and a conservation profile with conservation scores from 0 to 10 (high values mean high conservation) are given below the JNetCONF profile. The presence of ESTs in each species is represented by a color code on the protein information: Black, no EST; green, presence of ESTs; orange, presence of ESTs sequenced in testis; and, red, a majority of ESTs sequenced in the testis.
PIT REVEALS NOVEL GENES EXPRESSED IN GERM CELLS S5). In seven mammals, a majority of EST has been sequenced in the testis. The expression of the TCONS_0012079 homolog transcript was verified by RT-PCR in the mouse and human testis (Supplemental Data S6). Domain prediction analysis indicated the presence of a highly significant enolase domain (pfam domain PF00113, E value ¼ 8. 27 3 10 À3 ) in the protein sequence. We further tested for the distribution of the protein during spermatogenesis by immunohistochemistry with a polyclonal antibody raised against the recombinant XLOC_001949 (T-ENOL) ORF. XLOC_001949 immunoreactivity in the adult rat testis was weak in rSPT (Fig. 6A ) and stronger in elongating spermatids at all stages; localized immunoreactivity was also observed in the residual bodies of spermiating spermatozoa (Fig.
The primary aim of our study was therefore to characterize and validate at the protein level novel protein-coding genes expressed in meiotic and postmeiotic germ cells. We exploited our RNA-seq data set by combining it with a Shotgun proteome analysis of rat pSPC and rSPT in a PIT approach  [30]. Until recently, proteomic studies could detect only proteins encoded by known genes because they relied on the information present in sequence databases. However, the possibility of using sample-specific databases derived from RNA-seq data is revolutionizing large-scale proteomics  [34]. PIT approaches will allow better characterization of the protein pool present in a sample, increase protein identification rates  [34] , and improve the genome annotations (by identifying novel genes, exons, splicing events, translated UTRs, frame shifts, and reverse strands)  [37].
These observations are consistent with meiotic and postmeiotic TUTs and lncRNAs, for which there was mass spectrometry evidence of protein corresponding to novel protein-coding loci. We generated an inferred protein sequence database of about 3 million sequences derived from the reconstructed transcripts translated in the six reading frames. These sequences were merged with canonical databases. Searching a higher-eukaryote, six-frame translation database is problematic because most of the search space will consist of translated noncoding sequences; indeed, for example, only 1%-2% of the human genome encodes proteins  [68, 69]. The problem of such custom databases is the balance between increasing the exhaustivity of the available peptide sequences and decreasing the FDR. Both sequence redundancy due to size of the database and the presence of sequence errors increase the FDR  [70] [71] [72].
Some peptides might not be identified because their sequence is not in the database; others might be missed because of the lower sensitivity of proteomics than transcriptomics and because all the peptides in an experiment are not equally detectable  [73]. In their impressive work to improve mouse genome annotation using proteomics, Brosch and coworkers  [74]  found that up to 91% of all protein-coding exons and 86% of all introns could theoretically be confirmed from peptides identified by proteomics experiments. The number of potentially predicted peptides does not correlate with the number of identified peptides because the latter is directly related to the expression dynamics of the proteins in a given sample  [74]. Gene expression is dependent on tissue, cell type, and environmental stage, and is often transient.
The data contribute to a better understanding of germ cell differentiation events and represent a valuable resource for functional investigations into the role of numerous new genes and proteins in normal and pathological spermatogenesis. Graphical displays of both transcriptomics and proteomics datasets used in this study are available in open access through the ReproGenomics Viewer (http://rgv. genouest. org). Further progress will also be made available on this comprehensive viewer to help scientists in the field to improve their understanding of the molecular events underlying spermatogenesis. Although PIT approaches are increasingly widely used, they require extensive advanced skills in all of transcriptomics, genomics, and proteomics. This currently limits their application to various fields of cell biology.
ACKNOWLEDGMENT We acknowledge  Olivier Sallou  and  Olivier Collin  ( Genouest Bioinformatics Platform, IRISA ) and  Laetitia Cloarec  ( Inserm U1085, IRSET ) for continued development, data upload, and maintenance of the ReproGenomics Viewer database. We thank  Dominique Mahe Poiron ,  Nathalie Dejucq-Rainsford , and  Nathalie Rioux-Leclerq  for providing the human samples. We thank all members of the SEQanswers forums for helpful advice;  Steven Salzberg  and  Cole Trapnell  for continuous support with the Tuxedo suite; and  Emmanuelle Com  and the  PRIDE team  for the submission of the mass spectrometry proteomics data to ProteomeXchange via the PRIDE database. Sequencing was performed by the  IGBMC Microarray and Sequencing platform , member of the  France Genomique program.
Labeling and microarray hybridization Microarray experiments were performed by using an Agilent microarray platform obtained in a previous study  [26] , which included a total of 59,951 probes, 85. 7% out of them were annotated by blastx (cut off e-value of ,1. 0 E-5) searches against high quality proteins of UniProtKB/SwissProt (release 2013_07 -June 26, 2013), Danio rerio, Gasterosteus aculeatus, Drosophila melanogaster, Homo sapiens, Strongylocentrotus purpuratus, Ciona intestinalis, Caenorhabditis elegans and Lottia gigantea available on Ensembl Genome Browser (release 72, June 2013), Crassostrea gigas (http://oysterdb. cn/) [29]. and Daphnia pulex v1 (http://genome. jgi-psf. org/Dappu1/Dappu1. home. html). Probe sequences and other details on the microarray platform can be found in the GEO database (http://www.
ncbi. nlm. nih. gov/geo/) under accession number GPL17766. Microarray experiments were carried out on a total of 15 samples corresponding to unfertile stripped oocytes and spawned oocytes. Sample labeling and hybridization were performed according to the Agilent One-Color Microarray-Based Gene Expression Analysis protocol with the Low Input Quick Amp Labeling kit. Refer to de Sousa et al. [26]  for details about labeling and hybridization procedures.
Data acquisition, correction and normalization Hybridized slides were scanned at 2 mm resolution using an Agilent G2565BA DNA microarray scanner. Each slide was scanned two times at two different sensitivity levels: XDR Hi 100% and XDR Lo 10%. The two generated images were analysed together, data were extracted and background subtracted using the standard procedures provided in the Agilent Feature Extraction (FE) Software version 10. 7. 3. 1. To evaluate goodness and reliability of spot intensity estimates the software returns a series of spot quality measures. All control features (positive, negative, etc. ), except for Spike-in (Spike-in Viral RNAs), were excluded from subsequent analyses. The fluorescence values were normalized by performing a quantile normalization in R statistical software.
Statistical analyses were performed on 31,862 out of 59,951 probes with signal higher than background in at least 5 out of 15 target samples. A log base 2 transformation was applied to all expression values and finally the parametric Combat algorithm  [30]  was implemented in R in order to adjust for the known between-experiments batch effect (i. e. different microarray slides). Normalized data were deposited in GEO archive under accession number GSE58906.
Data analysis A Principal Component Analysis (PCA), using the TMeV 4. 5. 1 (TIGR MULTIEXPERIMENT VIEWER)  [31]  was applied, to assess the distribution of the studied groups. In addition, in order to find out the probes which most affected the Principal Component 1 (PC1) and Principal Component 2 (PC2) variance, the eigenvector values of each component were retrieved. To our knowledge, a generally accepted threshold to retain the most significant eigenvectors does not exist, thus for both PCs the whole set of probes was sorted in descending order by the absolute eigenvector values, and the first 5% of probes was arbitrary selected (a total of 1,593 probes). Statistical tests implemented in the program Significance Analysis of Microarray (SAM) were used to identify differentially expressed probes between the stripped oocytes and the t spawned oocytes.