dataesr/scientific-paragraphs-categorization huggingface dataset:
A database fshape mean is hence estimated, to be used as a template, by an adaptive gradient descent algorithm that jointly optimizes a total fshape dissimilarity measure between each target fshape X i , f i and the transformed template Xi , f i for all i taken together. The process is summarized in Figure  2. A similar mean template generation procedure was repeated for the choroid. This maps all the target observations' function values (layer thickness) into a common coordinate system of the mean template where statistical analysis can be applied on the residuals ζ i to compute the point-wise variability of the function values at each point on the template geometry across the database.
Group Analysis Two analyses were conducted (1) the fshape analysis of computing the mean fshape geometry and function and the residual function for each subject indexed on the common mean template, enabling point-wise comparison across the subjects in the database and (2) sectoral averages within each subject that enable comparison across subjects by the sectors defined over the subject's layer geometry. The database consisted of members from three groups: young normal (Group A), older normal (Group B), and older glaucomatous (Group C) individuals. These groups enable analyses of two main questions (1) the effect of age on RNFL and choroid layer thicknesses in healthy young and older individuals (Group A and B comparison) and, (2) the effect of glaucoma between age-matched individuals (Group B and Group C comparison).
Point-Wise Visualization of RNFL and Choroid Thickness Pattern To visualize the measurements across the database highlighting the variability and trends, the fshape measures for each subject on the common template were normalized by using a z-score computed by subtracting the mean of a reference group and dividing by the standard deviation of the measures in the reference group. The z-score was calculated point-wise as z k = (x k -xh k )/σ h i , where x k is the thickness at k th point for, xh k is the mean thickness of the reference group at k th point, and σ h k is the standard deviation of the reference group at k th point. The reference groups were chosen to be Group A for age comparison and Group B for glaucoma comparison such that the average measures of the young normal (Group A) or older healthy (Group B) subjects are removed to visualize the residual effects of age and glaucoma, respectively.
The fshape registration estimates a residual function that is added to the template thickness such that the template geometry after transformation matches the subject geometry, and the template thickness plus the residual function after transformation matches the subject thickness. This maps an individual's layer thicknesses onto the common coordinate system of the template geometry via the specific residual function estimated for that individual. At each location on the template surface, subsequent statistical analysis across the database can reveal trends and features that are common across individuals as a function of age and disease. A more conventional approach utilizes sectorization of the layer surface by calculating the average of all thickness measurements within each sector for each individual eye.
We determined, for the first time in a ciliate, candidate P. tetraurelia transcription start sites using an adapted Cap-Seq protocol. We developed TrUC, multi-threaded Perl software that in conjunction with TopHat mapping of RNA-Seq data to a reference genome, predicts transcription units for the annotation pipeline. We used EuGene software to combine annotation evidence. The high quality gene structural annotations obtained for P. tetraurelia were used as evidence to improve published annotations for 3 other Paramecium species. The RNA-Seq data were also used for differential gene expression analysis, providing a gene expression atlas that is more sensitive than the previously established microarray resource.
tetraurelia protein-coding genes  [9]. The data were used to show that gene expression level is a major determinant of gene dosage and protein evolution  [10]. The Paramecium aurelia species complex is now recognized as an outstanding system to study the consequences of WGD in a unicellular eukaryote  [11]  and should also prove powerful for investigation of genetic and epigenetic mechanisms that drive speciation. In this context, the MAC genomes of many species are being sequenced. It thus became necessary to develop a pipeline optimized for the Paramecium lineage, able to make accurate gene predictions for AT-rich, compact (>80% coding) eukaryotic genomes with unusually small introns  (20-30 nt). To this purpose, we generated oriented P. tetraurelia RNA-Seq and Cap-Seq data, as input for software we developed to predict transcription units  (TrUC).
Transcription units Genome-guided transcription unit construction was pioneered by Denoeud et al. [13]  (G-Mo. R-Se software) using short-read mapping. The widely used Cufflinks software  [14]  then adopted fragment alignment as part of its assembly strategy. To take into account alternative splicing, Cufflinks finds the minimal number of paths through the mapped fragments. We decided to develop our own transcription unit prediction software rather than use Cufflinks, because in our hands, Cufflinks did not always predict transcription units despite good fragment coverage in regions presenting strong evidence of protein-coding genes, probably because of overlapping UTRs in the very compact Paramecium genome. Our approach is conceptually similar to G-Mo. R-Se but takes into account improvements in library construction and sequencing, especially orientation information.
We added detection of transcription termination sites (TTS) using the polyA signal and the optional use of Cap-Seq data to predict transcription start sites (TSS). The TrUC (TRanscription Units by Coverage) pipeline is schematized in Fig. 1. TrUC predicts TSS and TTS using the consensus position of Cap-Seq and polyA coverage, respectively. TrUC uses oriented data to predict oriented transcription units, however the software can predict introns from un-oriented RNA-Seq data, using the GT. AG splice site consensus to determine orientation. TrUC does not consider alternative splicing since exon skipping is not found in Paramecium  [15]. Like Cufflinks, TrUC can identify non-coding transcripts as it does not look at translation. TrUC multi-threaded Perl software is available from https://github.
com/oarnaiz/TrUC. Since not all genes are expressed at all stages of the life cycle, use of different life-cycle time points can help annotation by increasing the number of genes that are covered by RNA-Seq data. For P. tetraurelia transcription unit prediction, we sequenced polyA+ RNA from a time-course through the sexual process of autogamy as well as from vegetative cells. Combining all the RNA-Seq and Cap-Seq data (Additional file 1: Table  S1 ), TrUC predicts 37, 847 transcription units greater than 300 nt in size, 12,389 TSS and 5967 transcription termination sites (TTS). We found 85% of the previously annotated P. tetraurelia genes  [6]  (hereafter called "v1" annotation) covered by a predicted transcription unit. The average size of the predicted transcription units, 1229 nt, is close to the average size of the v1 genes (Table  1 ; Additional file 2: Figure  S1 ) indicating that most of the predicted transcription units correspond to one gene.
However, some transcription unit predictions may be split, owing to reduced RNA-Seq coverage within the unit. Alternatively, since genes in the compact P. tetraurelia genome a b Fig. 1  Gene annotation strategy. a Overview of the workflow. EuGene software, using a Paramecium-trained matrix, combines (i) transcription unit predictions, (ii) TSS predicted positions, (iii) TTS predicted positions, (iv) Paramecium predicted proteins mapped on the reference genome using BLASTX then Exonerate, and (v) non-coding gene predictions obtained using the Rfam database. b Schema of the TrUC pipeline. TrUC is able to predict transcription units, TSS and TTS positions. To achieve this, the software uses oriented polyA + mRNA-Seq and Cap-Seq data. The upper part of the schema represents RNA-Seq insert coverage of the genome.
Gene annotation Accurate, user-friendly gene annotation tools for eukaryotes, such as AUGUSTUS  [16] , would require code modifications to correctly identify tiny introns  [17]. Indeed, ~98% of P. tetraurelia introns are 20-30 nt in size, with a mean of 25 nt. A handful of significantly larger introns, ~80 nt in size, contain snoRNAs  [18]. We therefore decided to train the highly configurable Eu-Gene annotation software  [12]  for Paramecium, using gene models completely confirmed by RNA-Seq coverage (see Methods). Table  1  compares gene annotations predicted by Eu-Gene for P. tetraurelia, using the transcription units assembled with TrUC and other lines of evidence (labeled 'v2'), with the v1 gene annotation for this species  [6] , long considered a gold standard for ciliate gene annotation.
Since the RNA-Seq (TrUC predictions) are expected to show the true distribution of intron sizes, we are more confident in the v2 annotations site signals are weak, presumably because of the mutational burden, and splicing is not very accurate. The nonsense-mediated decay (NMD) pathway  [23]  cleans up the mess on the pioneer round of translation, provided that there is a Premature Termination Codon (PTC) in the poorly spliced transcript. That this is the case is easily visualized in Paramecium, thanks to the unique TGA stop codon, by comparing the frequency of introns that do or do not contain a STOP codon in phase with the upstream exon, as a function of their size modulus 3. As shown in (Additional file 2: Figure  S2 ), stopless introns that are 3n in size are counter-selected in all 4 species: these introns cannot give rise to a PTC if retained in the transcript so are potentially deleterious.
Conclusions Plummeting genome sequencing costs and rising interest in Paramecium species for studies of genome evolution in unicellular eukaryotes, prompted us to build a new pipeline for gene annotation that takes into account specificities of the genus, in particular high gene density and stereotyped small intron size. This has been achieved with new software to predict transcription units (TrUC) and specific training of the highly configurable EuGene gene annotation software. High quality gene annotations will be important for future comparative and functional genomics analyses of Paramecium species. The mRNA-Seq data used to predict P. tetraurelia transcription units for the gene annotation enabled us to generate an improved gene expression atlas and carry out differential gene expression analysis of the sexual cycle of autogamy that is more complete than previously possible with microarrays.
Transcription unit determination We developed the multi-threaded Perl software TrUC (Transcription Units by Coverage), dependent on the Bio::DB::Sam module. The software is organized in 3 independent modules (Fig  1 ). The TSS module uses Cap-Seq data, which need not be paired-end, to predict transcription start sites. The predicted TSS is the position with the highest Cap-Seq coverage in the interval defined by the size of the fragments. The TTS module uses oriented paired-end mRNA-seq reads. If one of the reads in the pair maps partially on the reference genome and ends in polyA, then the insert is used to specify a transcription termination site. The predicted TTS is the position with the highest polyA coverage in the interval defined by the size of the fragments. The transcript module takes paired-end TopHat2 mapping (BAM files;  [14] ) and optionally, the output of the TSS and TTS modules, to predict transcription units including intron positions, based on fragment coverage.
TrUC was run with the following parameters for P. tetraurelia annotation: truc TSS -min_coverage 15 -nb_replicates 2 -min_score 500; truc TTS -min_coverage 5 -nb_replicates 2 -min_score 10 -nb_min_A 5; truc transcript -min_splicing_rate 0. 7 -no_overlap -min_coverage 15 \ -intron_consensus -min_intron_length 15 -max_intron_length 100 \ -min_intron_coverage 15 -min_length 300 -min_score 45 -tss [truc TSS GFF3 output file] -tts [truc TTS GFF3 output file]. For P. biaurelia, P. sexaurelia and P. caudatum, TrUC was used with unoriented RNA-Seq data reported in  [7, 8]  (Accessions PRJNA268243, PRJNA268244 and PRJNA268245, respectively), to predict introns, with the following parameters: truc transcript -not_stranded -min_splicing_rate 0. 7 -min_coverage 10 -intron_consensus -min_intron_length 10 \ -max_intron_length 100 -min_intron_coverage 3 -min_length 300 -min_score 10.
Gene annotation The workflow for gene annotation is schematized in Fig. 1. EuGene software  [12]  was used to predict gene structure. EuGene was trained with 1597 curated Paramecium tetraurelia genes to generate a prediction matrix that takes into account the unusually small size of Paramecium introns  [15]. The prediction matrix is available from http://paramecium. i2bc. paris-saclay. fr/download. The evidence sets used for annotation of the 4 Paramecium genomes are available on request.
Differential gene expression Paired-end RNA-Seq reads were mapped to the reference P. tetraurelia strain 51 genome  [37]  using TopHat2 (v2. 0. 12, --mate-inner-dist 50 -mate-std. -dev 100 -minintron-length 15 -max-intron-length 100 -coveragesearch -keep-fasta-order -library-type fr-secondstrand -min-coverage-intron 15 -max-coverage-intron 100 -min-segment-intron 15 -max-segment-intron 100 -max-multihits 1 -read-mismatches 1 -max-deletionlength 1 -max-insertion-length 1). Raw fragment counts for the genes in each sample, determined using htseqcount (v0. 6. 0 -stranded = yes -mode = intersection-nonempty) on filtered BAM files (samtools v0. 1. 18 samtools view -q 30), were used as input for DESeq2 (v1. 4. 1)  [38] , an R Bioconductor package, which normalizes the fragment counts, calculates the dispersion in the data using the biological replicates, and then determines differential gene expression using negative binomial linear models.
Acknowledgements We thank  Thomas Schiex  for advice about training EuGene. This work has benefited from the facilities and expertise of the High-throughput Sequencing Platform of the I2BC. We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing help and support. This work was carried out in the context of the CNRS-supported European Research Group " Paramecium Genome Dynamics and Evolution ".
Funding This work was supported by  Centre National de la Recherche Scientifique  intramural funding, the  Agence Nationale de la Recherche Scientifique   ANR-14-CE10-0005-04 'PIGGYPACK'  to M. B. , S. D. and L. S. and  ANR-12-BSV6-0017-03  ' INFERNO ' to M. B. , S. D. and L. S. , the  'Comité d'Ile de France de la Ligue Nationale Contre le Cancer ' to S. D. and an 'Equipe FRM DEQ20160334868' grant to S. D. The funding bodies had no role in the design of the study, analysis, or interpretation of data or in writing the manuscript.
, 2001). In addition, there is still a discrepancy in numbering the exons of the HLA-G gene. According to the Ensembl database, the HLA-G gene might possess a supplementary exon at the 5 0 -end that is absent from the IMGT/HLA database (Fig. 1A ). The transcription of this supplementary exon would modify the size of the 5 0 -UTRs and the location of the promoter. This may alter the regulation of the gene, by modifying the binding of regulatory proteins and/or miRNA. Moreover, the alternative exon selection might produce new protein variants that might potentially redefine cell fate and cancer progression. For HLA-G, as for most genes in the human genome, the complete annotation of all associated transcripts is still far from being exhaustive and identifying the complete set of isoforms that are generated remains a great challenge.
Immunohistochemistry An immunohistochemical study was performed for each tumor on 4-lm-thick, formalin-fixed, and paraffinembedded tumor tissue sections. The following murine antibodies were used: 4H84, an IgG1 recognizing an epitope located into the alpha 1 domain common to all HLA-G isoforms (dilution 1/200; Santa Cruz Biotechnology, Santa Cruz, CA, USA), and two antibodies 5A6G7 and 2A12 recognizing the epitope encoded by the retained intron 5 (Ensembl database) present in soluble HLA-G5 and -G6 isoforms (dilution 1/100, Exbio antibodies; Exbio, Praha, Czech Republic). The staining was performed on automated slide stainers from Roche (BenchMark ULTRA system, Tucson, AZ, USA) using the OptiView DAB IHC Detection Kit (Roche, Basel, Switzerland), Cell Conditioning 1 short or standard antigen retrieval, an antibody incubation time of 32 min at 37 °C, ultraWash procedure, counterstaining with hematoxylin II for 4 min, and bluing reagent for 8 min.
The lack of labeling of tumor sections with this antibody normally accounts for the absence of HLA-G expression. However, a diffuse and strong granular intracytoplasmic 5A6G7 immunostaining and a diffuse, thin, and granular intracytoplasmic immunostaining were observed in tumor cells of patients 3 and 8, respectively. This was unpredictable considering our current knowledge on the structure of the seven reported HLA-G isoforms as they all contain the alpha 1 domain recognized by the 4H84 antibody. To try to better understand these differences, we have performed a similar analysis using an antibody that also recognizes the epitope encoded by the retained intron 5 (Ensembl database) present in soluble HLA-G5 and -G6 isoforms named 2A12 (Fig. 2 , right column). The results revealed different and unanticipated immunostaining patterns, notably the labeling of hyaline globules in patients 1 and 2.
Survey of HLA-G1 transcripts expressed in ccRCC To gain a better insight into the HLA-G isoforms that are expressed in ccRCC and clarify the results of the immunohistochemical analysis, a survey of HLA-G isoform diversity was further assessed by RT-PCR. The tumor sections of the eight patients studied above were amplified with the well-known G257F and G526R primers  (Paul et al. , 2000b)  schematically represented in Fig. 1B  (to avoid misunderstandings, the Ensembl nomenclature will be used throughout the text). These primers amplify a region that contains the epitope recognized by the 4H84 antibody. Amplification of actin mRNA was performed for each sample as control. A predicted band of 290 bp, specific for the amplification of HLA-G1 transcripts, was found in all tumor sections for patients 1, 2, and 6, whereas this band was only detected in one or two regions of tumors of other patients (Fig.
RNAseq reveals unannotated HLA-G transcripts RNAseq technology provides the most powerful method to analyze expressed isoforms, offering the opportunity to detect alternative splicing events and unannotated transcripts which are essential for understanding development and disease mechanisms in a species  (Wang et al. , 2009). As a first look, we have undertaken the sequencing of four representative samples at a very high depth of coverage (depth > 3009). Reads were aligned and quantified according to the Ensembl 70 (GRCh37. p8) reference annotation as described in Materials and methods. Alternative spliced isoforms were mainly categorized into two major groups: exon skipping and intron retention, in which a single exon or intron is alternatively spliced or included out of the mature message, respectively.
S1 ). In addition, we assess whether other HLA loci might bias the results. To this end, we aligned the whole HLA-G sequence extracted from human genome hg19 against hg19 using BLASTN 2. 2. 26 (BLAST+ 2. 6. 0, National Center for Biotechnology Information, U. S. National Library of Medicine, Rockville Pike, Bethesda, MD, USA) with default options. We found that using only high-quality mapped reads (MAPQ ≥ 30), the best match is always HLA-G. Secondary hits have at most 90 bp (in the 150-bp reads) that match exactly (no gap, no mismatch) with other HLA sequences. For the whole locus of HLA-G and exons 1, 6, 7, 8 as well as introns 3 and 5, no significant match was found with other HLA loci. For the other exons/introns, the longest alignment (HSP) does not exceed 70 bp (Data S2, Tables  S2  and S3 ).
Undescribed intron retention events in expressed HLA-G transcripts Intron retention is the rarest type of alternative splicing in mammals and accounts for only approximately 3% of alternate transcripts  (Wong et al. , 2016). So far, only the retention of intron 3 or intron 5 (previously known as intron 2 and intron 4, according to IMGT/ HLA nomenclature) was reported in the literature for HLA-G transcripts. Transcripts that retain intron 3 encode HLA-G7  (Paul et al. , 2000a) , and those retaining intron 5 encode HLA-G5 and HLA-G6  (Fujii et al. , 1994). In our RNAseq analysis, introns subsumed by an exon were labeled as retained. The results, represented graphically on Fig. 4  and summarized in Table  2 , showed that reads representing the retention of introns 3 and 5 were the most abundant.
B00E4I3 and B00E4IS are the two samples with the highest HLA-G expression within the CAGEKID (CAncer GEnome of the KIDney)  (Scelo et al. , 2014). using primers Int1F and G257R. An amplified band of the expected size (521 bp) was obtained, consistent with the presence of intron 1 in HLA-G transcripts (Fig. 5B , right panel). This event was not reported before in the literature as the initiation of transcription of HLA-G was solely assigned to exon 2 (Geraghty et al. , 1987). We did not detect a PCR amplification band of 649 bp that would correspond to the concomitant retention of intron 2. This is consistent with the results of the RNAseq analysis showing that intron 2 is infrequently retained. Further analysis was conducted to validate the retention of intron 4 (Fig. 5C ). To this end, RT-PCR was performed using primer G257F in combination with a primer that specifically targets intron 4 (named Int4R).
Novel HLA-G transcripts with 5 0 -extended end The RNAseq data further revealed that some of the reads aligned on either side of exon 1 (Fig. 4 ). Transcripts that originate from this area were not previously reported. In fact, the structure of this region is still a matter of debate as information contained in the Ensembl database suggests that HLA-G transcripts  may be initiated at this exon, which is located 5 0 of the exon 1 defined by IMGT/HLA nomenclature (Fig. 1A ). The presence or absence of this exon may result in major modifications which include the promoter localization, the length of the 5 0 -UTR, and the transcription/translation initiation site. We assess whether HLA-G transcripts may be initiated in this exon or even upstream by RT-PCR amplification. Two specific primers were designed: primer Ex1F, whose sequence is complementary to a region located in exon 1 (Ensembl database), and primer PrPr, whose sequence is complementary to a region located further upstream currently considered as the promoter region (schematically represented in Fig.
1B ). RT-PCR using these two upstream primers in combination with G526R produced two bands of expected sizes: 690 bp (for Ex1F-G526R) and 725 bp (for PrPr-G526R), respectively (data not shown). To verify the specificity of these fragments, amplified DNA samples were sequenced and nucleotide similarities were searched in public databases using BLAST. The results demonstrated a high degree of similarity with HLA-G except for a deletion of 106-bp fragment. Resulting from this deletion, the distance between the ATG located at the end of exon 1 and the one located in exon 2 was reduced from 118 to 12 bp (Fig. 6A ). As a consequence, the 106-bp deletion brings both ATG in frame. This may now allow the initiation of translation at the ATG located in the first exon and generate a protein that would have a 5 0 -extended end of five additional amino acids (MKTPR).