dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition of Medication Adherence and Persistence Measures These 2 outcomes were defined from the pharmacy refill database. Adherence was measured by the Medication Possession Ratio (MPR)  [22, 23] , which is defined as the number of days of treatment divided by the number of days between cohort entry and end of follow-up. For each woman, the number of days of treatment was calculated from the number of tablets dispensed combined with dosage instructions. Overlapping prescriptions were included in the calculation. Patients with an MPR below 80% were considered non-adherent  [14, 24, 25, 26]. We defined non-persistence to treatment as the first treatment discontinuation lasting more than 3 consecutive months.
The proportional hazards assumption was checked by examining the log-minus-log survival plot drawn up for each cofactor. We included in the initial multivariate model variables with a p-value,0. 20 in the univariate analysis. We kept in the final model only variables that were still significantly associated with AI intake discontinuation with a pvalue,0. 10. Statistical analyses were performed using the STATA version 9. 0 software program (STATA Corp, College Station, TX).
Results From October 2006 to December 2008, 678 women over 65 with BC were included in the ELIPPSE 65 cohort. A prescription of AIs was filled at least once by 447 of these women (65. 9%). Among the latter, 65 (14. 5%) were found to be non-eligible (Figure  1 ). In short, 382 women were included in the cohort used to describe rates of non-persistence to AI therapy. The 382 women included in the cohort were followed for a median period of 3. 2 years (IQR = [2. 6-3. 9]). Among these women, 3 had a BC recurrence, 4 developed a new cancer and 8 died during follow-up. Non -persistence to AI defined from the pharmacy refill database In addition to the 382 women who filled at least one prescription of AI medication, 57 women (13%) included in the ELIPPSE 65 cohort had positive hormonal receptors but did not fill any prescription.
Determinants of non-persistence to AI To study determinants of non-persistence to AI therapy, we excluded another 33 women who had no data collected in first face-to-face interview and 18 who discontinued their treatment or were censored before first face-to-face interview (Figure  1 ). We conducted the analysis after restricting our sample to women who reported they were taking their medication without outside help. Determinants of treatment discontinuation were thus studied for 292 women. Furthermore, CAM was found to be used only by women between 65 to 75 years old. Thus, the multivariate analysis was conducted among the 233 women whose age was lower than 75. Patients' characteristics are described in Table  2  for this sample of 233 women. At the time of diagnosis, women's mean age was 71.
Our study has several strengths. First, since the French system provides universal health coverage, our study is population-basedi. e. it does not focus on any specific socio-economic group. Second, we had access to several data sources, namely NHIS databases, medical records, and data obtained through face-to-face interviews with patients. This ought to be emphasized because even though most database studies in pharmacoepidemiology use high quality databases, they are rarely able to link these data with medical records or with questionnaires measuring patient-reported outcomes or psychosocial variables. By combining data from different sources, we were able to identify women who were taking their medication without outside help, as well as study the association between CAM use (which is not recorded in pharmacy databases) and a reliable measure of adherence to AI.
Acknowledgments We would like to thank both the women who agreed to participate in this study and their physicians for the time they devoted to medical data collection. We would also like to thank the physicians and technicians in the different insurance schemes who contributed to patient selection and data extraction ( Direction Regionale du Service Medical PACA, Caisse Maladie Regionale Cote-d'Azur, Caisse Maladie Regionale Provence, Caisse Maladie Regionale Corse, Caisse des professions liberales Provence, Caisse Nationale Militaire de SecuriteSociale , l'ensemble des  Caisses locales d'Assurance Maladie de la MutualiteSociale Agricole des Regions Provence-Alpes-Cote d'Azur et Corse. ) We are indebted to  Arianne Dorval  for proofreading the English manuscript.
Full-length representation of most cellular RNAs from sequencing data requires computational reconstruction of transcript structures. The majority of such programs infer transcript models from the accumulation of read alignments to the genome 1-4 ; some take the alternative approach of de novo reconstruction, in which contiguous transcript sequences are assembled without the use of a reference genome  [5] [6] [7]. Here we present a detailed evaluation of computational methods for transcript reconstruction and quantification from RNA-seq data, in a framework based on the Encyclopedia of DNA Elements (ENCODE) Genome Annotation Assessment Project (EGASP) 8. Developers of leading software programs were invited to participate in a consortium effort, the RNA-seq Genome Annotation Assessment Project (RGASP), to benchmark methods to predict and quantify expressed transcripts from RNA-seq data.
results We evaluated a total of 25 transcript reconstruction protocols, basing our analysis on alternate parameter usage of 14 software packages on RNA-seq data sets for three species (Supplementary Fig. 1 , Supplementary Table  1  and Supplementary Note). Programs were run by the original developers, with the exception of Cufflinks, iReckon and SLIDE. So that we could assess the ability of each method to interpret transcript expression from RNA-seq data without prior knowledge of gene content, programs were run without genome annotation, aside from iReckon and SLIDE, which require such information. Performance was benchmarked relative to the subset of annotated exons to which RNA-seq reads mapped (coverage of ≥1 read pair per 100 bp) and their corresponding transcripts (Online Methods).
Even so, iReckon attained low precision at the nucleotide level, primarily owing to the prediction of transcript isoforms with retained introns. Augustus, Exonerate, GSTRUCT, NextGeneid, Trembly and Velvet attained both precision and recall above Assessment of transcript reconstruction methods for rnA-seq 60% on the human data. The highest recall for methods without annotation was observed for Tromer and Cufflinks, albeit at the cost of low precision. These programs consistently displayed high sensitivity across the three species, but the precision rates for Tromer in particular indicate a tendency for overprediction.
3  and Supplementary Table  3 ). Allowing for variable transcript boundaries led to substantial improvements (Supplementary Tables  4  and 5 ). Although most protocols exhibited the lowest precision for the human RNA-seq data, for all three species, performance approached that of iReckon and SLIDE, despite the latter two benefiting from the use of high-quality gene annotation. Coding exons can be identified directly from genomic sequence by the presence of translation start and stop sites and of splice acceptors and donors. Programs such as Augustus, Exonerate, mGene, NextGeneid, Tromer and Transomics use these features to improve exon discovery. Of these programs, Augustus, mGene and Transomics identified a greater proportion of annotated coding exons than did Exonerate, mTim, NextGeneid and Tromer (Supplementary Fig.
4 ). These methods augment data-driven transcript reconstruction with ab initio gene prediction, leading us to conclude that higher sensitivity measures are due to more extensive utilization of the underlying genomic sequence, which reduces the need for support from RNA-seq data. We investigated the impact of sequencing depth on exon detection rates (Fig. 3a ). Through the use of ab initio prediction, Augustus, mGene and Transomics were able to detect exons from protein-coding transcripts present at very low abundance. All other methods required a minimum average read depth to identify exons. Exon detection increased with sampling coverage at a roughly linear rate until reaching a plateau. One exception was Tromer, which often reported short exon fragments of 50-75 bp flanking introns without extending them to full exons (Supplementary Fig.
5 ). With increasing coverage, Tromer showed a tendency to predict very long exons spanning multiple annotated features. To a lesser extent, Oases and Velvet also showed reduced performance for high-coverage exons (Supplementary Fig. 6 ). The ab initio prediction advantage of Augustus, mGene and Transomics was lost for noncoding transcripts, which lack the sequence features exploited by these methods (Supplementary Fig. 7 ). Nevertheless, detection rates were similar to those of other protocols (Supplementary Fig. 8 ). Noncoding RNAs tend to be expressed at lower levels than protein-coding genes (Supplementary Fig. 9 ) and were detected with lower sensitivity even when we controlled for differences in sequencing coverage (Fig. 3a  and Supplementary Fig. 7 ). Exons of long intergenic noncoding RNAs were usually identified with lower frequency than those from pseudogenes and unclassified processed transcripts (Supplementary Fig.
elegans mean 50%, maximum 73%), and for those only one isoform was typically identified (Supplementary Fig. 12 ). A substantial reduction in sensitivity was also observed from the gene to transcript level, even when the flexible evaluation mode was used for first and terminal exons (Fig. 5a  and Supplementary Table  5 ). The best-performing methods identified at most 56-59% of spliced protein-coding transcripts from C. elegans (Augustus, mGene and Transomics), 43% from D. melanogaster (Augustus) and merely 21% from H. sapiens (Trembly). Sensitivity increased by roughly 10% when partial isoform matches were considered, as did precision when partial predictions consistent with annotated isoforms were included (Fig. 5a ). Greater sequencing depth improved transcript assembly for D. melanogaster and C.
elegans (Supplementary Fig. 13a ), whereas in H. sapiens transcript detection remained low despite sequencing coverage in excess of 4,000 read pairs per kilobase in exonic regions. Generally, at least one consistent isoform was identified for highly expressed genes: >50% in D. melanogaster and C. elegans and >35% in H. sapiens (Supplementary Fig. 13b ). Detection rates were even lower for noncoding RNAs (Supplementary Fig. 14 ). Pseudogenes were reported with similar frequency to that of protein-coding genes by Augustus, mGene, NextGeneid and Transomics, as pseudogenes retain partially intact coding sequences that can be identified by these methods (Supplementary Fig. 15 ). The dramatic differences between species is further due to the tendency of methods to assign one splice product per gene (Supplementary Table  1 ).
elegans to greater than 60% in H. sapiens. Interestingly, although Trembly did not perform as well as Augustus, mGene and Transomics at the exon level, this method reported the highest number of transcripts for which all exons were represented from H. sapiens data. In contrast, Augustus, mGene and Transomics detected at least one exon for most transcripts. The remaining methods failed to identify any exons for nearly 20% of all transcripts expressed in the RNA-seq data. SLIDE exhibited the same trend despite the provision of annotated exon coordinates. We then examined the topology of transcript structures to determine how well each method was able to link exons into complete isoforms. Even in cases in which all exons of an annotated transcript had been identified, full isoforms were often not assembled (Supplementary Fig.
16 ). For C. elegans and D. melanogaster, most methods were able to reconstruct 60% of transcripts from the RNA-seq data. However, from the H. sapiens data, less than 40% of known transcripts were assembled. Tromer stands out as an exception: the program identified all exons for relatively few genes; but once accounted for, these were frequently linked into annotated transcript structures. Further inspection showed that these tended.
AnAlysis to be short isoforms comprising 2-3 exons on average and thus represent a more tractable subset of the transcriptome. Provision of transcript start and end sites gave iReckon an advantage for the more complex human transcriptome, as evidenced by increased accuracy in assembling partial transcripts. In contrast, SLIDE consults exon coordinates but ignores their connectivity, performing at a level similar to methods without any prior transcript-level information. Reported.
Quantification of expression levels from rnA-seq data A common feature of transcript reconstruction software is the estimation of expression levels from transcribed genes. These are given as digital read counts normalized by transcript length and sequencing depth (reads per kilobase of exon model per million mapped reads, RPKM)  26. RPKM values were reported at the transcript level from a subset of methods. A range of expression-level distributions was evident (Supplementary Fig. 18 ), but generally there was strong agreement among Augustus, iReckon, mGene and Trembly for all three RNA-seq data sets (Supplementary Figs. 19 20 21 22 ). One source of variation arises from gene loci where divergent or incomplete transcript models have been computed (Fig. 5a  and Supplementary Figs. 23  and 24 ).
However, expression-level estimates can vary considerably even where concordant transcript structures are reported (Supplementary Fig. 17 ). Such differences were also apparent after we scaled the RPKM distributions to equalize median expression values. To establish independent expression-level quantification, we assayed a set of human genes using the NanoString nCounter amplification-free detection system  27  (Supplementary Tables  7 8 9 ). Correlation between NanoString counts and RNA-seq RPKMs ranged from 0. 34 for Transomics to 0. 68 for Cufflinks (Fig. 6c  and Supplementary Fig. 25 ). Many methods failed to report numerous targeted exons or junctions that were expressed according to NanoString counts. Read support at those loci was typically sparse, with 19 probes having no corresponding alignments from the RNA-seq data.
These were, however, represented by low NanoString counts, which indicated that the nCounter assay exhibits higher sensitivity for low-abundance transcripts than RNA-seq (Supplementary Fig. 26 ). For ten of the unsupported NanoString probes, consistent isoforms were still reported by either Augustus, iReckon, mGene, SLIDE or Transomics. Thus, although the expression levels of these genes reflect the lower limits of detection for both technologies, sequencing reads dispersed over the gene body can allow for adequate transcript identification where ab initio methods or gene annotation were applied. In general, all methods displayed higher identification rates for exons and junctions with higher NanoString counts, and reliable detection from RNA-seq data was dependent on read depth (Supplementary Fig.
No single protocol excelled at all metrics. Comparing the performance of Augustus with and without RNA-seq data as input revealed that using experimental evidence only slightly improved exon-level detection but increased transcript-level precision. Transomics featured enhanced precision for high-abundance transcripts, but expression-level differences had little impact on detection sensitivity. Precision was a consistent strength of GSTRUCT, whereas mGene exhibited diminished performance on human RNA-seq data, a result underscoring that choice of method can depend on the organism under study. Considerable variation was observed in the range of expression-level estimates reported for transcripts arising from the same gene loci. This was exacerbated by nonuniform exon detection and linkage between methods but was also apparent when similar or identical transcript structures were reported.
To improve the accuracy of existing annotation using RNA-seq data, both Cufflinks and iReckon consult known gene structures during the transcript assembly process and may be useful in refining the coordinates of exon and transcript boundaries. Where a finished genome and high-quality annotation are available, Cufflinks and rQuant (part of the mGene protocol) can be applied solely for transcript quantification, which can be further improved by correcting for fragment bias. Gene prediction algorithms such.
Note: Any Supplementary Information and Source Data files are available in the online version of the paper. online methods RNA-seq data. RNA-seq data were generated as part of the ENCODE 31 and modENCODE projects  32  , along with a third data set of compatible sequencing format and read depth, and represent three widely studied species: H. sapiens (liver hepatocellular carcinoma cell line HepG2)  33  , D. melanogaster (L3 stage larvae)  34  , and C. elegans (L3 stage larvae)  35. These were chosen to reflect realistic examples of varying transcriptome complexity and where high-quality annotated reference genomes are available. Libraries were prepared for the Illumina platform and sequenced in 76-nt paired-end format to obtain approximately 100 million read pairs per sample. H. sapiens RNA-seq data correspond to ENCODE 33 HepG2 whole-cell long poly(A) + RNA CALTECH replicate 2, available from http://www.
encodeproject. org/. The D. melanogaster data set comprised a total of five sequencing runs from the modENCODE project  34  for three L3 stage larval samples and can be obtained from the Sequence Read Archive (http://www. ncbi. nlm. nih. gov/sra) under accession numbers SRR023546, SRR023608, SRR023505, SRR027108 and SRR026433. The C. elegans RNA-seq data have previously been described  35  and are available under accession SRR065719. All of the data used in this study have been consolidated as a single experimental record in the ArrayExpress repository (http://www. ebi. ac. uk/arrayexpress/) under accession E-MTAB-1730.
Reference gene annotation. As not all genes are expressed in the samples used in the study, benchmarking methods against the entire set of annotated genes would underestimate transcript detection sensitivity. Therefore, we processed the genome annotations (H. sapiens: GENCODE 31 v. 15 (Ensembl release 70), D. melanogaster: FB2013_01, C. elegans: WS200) to include only exons and transcripts with sufficient support in the RNA-seq data. Reads were mapped to the reference genomes using STAR version 2. 2. 0c, an independent RNA-seq aligner that is not a component in any of the evaluated transcript assembly methods  36. To improve spliced alignment, STAR was provided with exon junction coordinates from the reference annotations. Default alignment parameters were used for the human data. For D. melanogaster and C.
To rule out potential bias imparted by the choice of alignment program, we calculated sensitivity and precision metrics for expressed genes using several different spliced aligners (GSNAP, STAR and TopHat2), with no substantial change to the results (Supplementary Figs. 29 30 31 ). Transcript prediction and assembly. Developer teams were provided with RNA-seq data and reference genome sequences for each species. So that we avoided potential biases, teams were not informed of the final evaluation criteria and were not provided with gene annotation unless otherwise noted (for example, iReckon and SLIDE). Developers providing transcript models for evaluation could not access submissions from other teams and were prohibited from participating in the analysis phase as part of the study design. Details of transcript reconstruction protocols are provided in the Supplementary Note.
Data processing for Cufflinks, iReckon and SLIDE. RNA-seq reads were aligned with TopHat version 2. 0. 3 using parameters suited to each species. The genomes of D. melanogaster and C. elegans contain a high percentage of small introns (Supplementary Table  2 ); examining their size distributions led us to set the parameters -i, -min-coverage-intron and -minsegment-intron to 30 for C. elegans, 40 for D. melanogaster and 50 for H. sapiens. Cufflinks was run with default settings except for the parameter -min-intron-length, which was set to 30 for C. elegans, 40 for D. melanogaster and 50 for H. sapiens, consistent with the TopHat alignments. So that we maintained the greatest compatibility with submitted results that were computed without annotation. The protocol iReckon ends was run with the minimum annotation requirements, i.
e. , start and end sites of all annotated transcripts (not filtered by read coverage), whereas iReckon full was provided with the complete reference annotation. SLIDE was run in discovery mode and provided with the full unfiltered annotation for each genome. Evaluation of prediction sets. Feature predictions were evaluated against the filtered reference annotation sets at four structural levels: nucleotide, exon, transcript and gene. The nucleotide-level metrics measure the ability of methods to identify exonic regions, ignoring the strand and exact boundaries of features. Nucleotidelevel precision was computed as the number of genomic base pairs within both annotated and predicted exons, divided by the number of genomic base pairs within predicted exons. Similarly, nucleotide recall was computed as the number of genomic base pairs shared between annotated and predicted exons, divided by the number of genomic base pairs within annotated exons.
Microarray procedures A double-stranded complementary DNA (cDNA) synthesis kit (InvitroGen) was used to produce cDNA from 2-mg aliquots of total RNA. Aliquots of 1 mg of cDNA were labeled using the one color DNA labeling kit and 2 mg of labeled cDNA were hybridized onto E. coli K-12 gene expression arrays (Nimblegen, Roche) for 17 h at 42 C according to the manufacturers' instructions. Arrays were washed and then scanned with a MS200 Microarray Scanner (Nimblegen, Roche). The images were analyzed with DEVA 1. 2. 1 software. Only raw data were used for further analyses. All array procedures were performed by the GeT-Biopuces platform (http://get. genotoul. fr).
mRNA concentration and mRNA half-life determination by microarray experiments For transcriptomic analysis, raw probe intensities (three replicates at four growth rates) were processed and analyzed with the R computing environment (26) using the affy  (27)  and limma (28) packages of Bioconductor. Raw data were submitted to an Robust Multi-array Average (RMA)-based background correction  (29) ; after background correction, intra-replicate quantile normalization was performed at each growth rate. A set of probes in the background for which the ranks were roughly invariant across all 12 arrays was selected. The median value of the invariant probeset intensities in each condition was used as a scaling factor for normalization between growth rate conditions. After normalization, the intensity of a transcript was calculated by an RMA-summarization procedure (29) within each condition.
In addition, the experiments of Bernstein et al. were performed using different growth media (rich and minimal) whereas we used the same medium for all cultures. Nevertheless, we generally confirmed the relative ranking of half-lives within functional categories at similar growth rates (Supplementary Figure  S2 ). We believe that our exhaustive and reliable datasets represent a significant advance compared to previous estimates of mRNA lifetimes in E. coli. Growth rate-dependent mRNA stability has also been observed in lactic acid bacteria and mycobacteria, with transcripts having shorter average lifetimes at higher doubling times  (24, 25). Our results show that the general trend for mRNA destabilization as the growth rate increases is associated with higher concentrations of the transcripts for enzymes of the mRNA degradation process (canonical degradosome components, RppH and poly(A) polymerase I).