dataesr/scientific-paragraphs-categorization huggingface dataset:
A recent approach to RNA-seq data analysis, DE-kupl, combines k-mer decomposition and differential expression analysis to discover transcript variations yet unreferenced in the human transcriptome  (Audoux et al, 2017). Applied to poly(A)+ RNA-seq datasets of in vitro cell system, DE-kupl unveiled a large number of RNA subsequences embedded into novel long noncoding (lnc)RNAs. These transcripts of more than 200 nucleotides in length transcribed by RNA polymerase II from intergenic, intronic, or antisense noncoding genomic locations constitute a prevalent class of human genes. Some lncRNAs are now recognized as precisely regulated stand-alone molecules participating in the control of fundamental cellular processes  (Quinn & Chang, 2015; Jarroux et al, 2017). They show aberrant and specific expression in various cancers and other diseases promoting them as biomarkers, therapeutic molecules and drug targets  (Van Grembergen et al, 2016; Leucci, 2018).
Additional efforts are required for more accurate and exhaustive RNA identification, as well as more rigorous validations of clinical potency through independent RNA measurement technologies and clinical cohorts. Regardless a large number of transcriptomic studies and variety of clinical samples analyzed, discovery of RNA-based biomarkers from publicly available RNA-seq datasets is still limited at two levels: (i) most experimental setups are based on poly(A) selected, unstranded cDNA sequencing, and (ii) computational analyses are generally focused on annotated genes and full-length RNA assemblies. This impedes the detection of low and poorly polyadenylated RNAs but also partially degraded RNAs from formalin-fixed paraffin-embedded tissues or other clinical samples  (Zhao et al, 2014; Zhao et al, 2018).
Identification of PCa-specific RNA variants in the Discovery Set by DE-kupl The biomarker discovery workflow included three major phases: discovery, selection, and validation (Fig  1 ). First, for discovery, we performed a deep total stranded RNA-seq of ribosomal RNAdepleted RNA samples isolated from prostate tissues after radical prostatectomy (Discovery Set, PAIR cohort, Table  S1 ). This Discovery Set was processed by DE-kupl to identify tumor-specific transcripts. DE-kupl directly queries FASTQ files for subsequences (k-mers) with differential counts/expression (DE) between two conditions (Fig  2A )  (Audoux et al, 2017). Overlapping k-mers are then assembled into contigs and, in a final step, mapped to the human genome for annotation. In the aim to focus exclusively on novel, yet unannotated RNA elements, k-mers exactly matching GENCODE-annotated transcripts were masked.
The whole set is filtered according to desired criteria and the top ranked contigs are selected for an independent experimental validation by NanoString in the extended clinical cohort. Finally, predictive modeling infers the best panel of candidate RNAs for validation of its clinical potency in an independent cohort (in this case TCGA). embedded into already referenced GENCODE lncRNA genes, but represented new sequence variations or RNA processing events, as PCAT7 (ctg_111158, P6) or CTBP1-AS (ctg_25348, P10). The rest mapped to intergenic noncoding locations (370 contigs) or antisense to referenced protein-coding or noncoding genes (221 contigs) (Fig  2C ). Intersection with existing annotations revealed 50% sequence overlap of contigs with 400 (33. 93%) GENCODE and 75 (6. 36%) MiTranscriptome lncRNA genes (Fig  2B ).
Naïve assembly of transcription units identifies novel prostate cancer associated lncRNAs To complement the reference-free protocol, we applied a referencebased protocol to build a catalog of lncRNAs from the same Discovery Set. Total RNA-seq produces much more intronic and exon-exon junction reads than poly(A)-selected RNA-seq. This complexity renders laborious in time and machine memory the data analysis by splice graph-based assemblers such as Cufflinks  (Hayer et al, 2015; Kukurba & Montgomery, 2015). To bypass this difficulty, we developed a more straightforward lncRNA annotation pipeline, HoLdUp, which identifies transcription units (TUs) based on coverage analysis  (Fig 3A). In this workflow, uniquely mapped reads were assembled into TUs and mapped to the GENCODE annotation to extract intergenic and antisense lncRNAs (see the Materials and Methods section for details).
They were further ranked according to their expression level, presence of splice junctions, and existence of matched ESTs. In total, we retained 168,163 TUs with above-threshold expression of 0. 2 quartile of mRNA expression (Class 2) and, within this group, the most robust 2,972 TUs with at least one splice junction and one EST (Class 1) (Fig  3B ). Globally, newly detected transcripts were as much expressed as GENCODE-annotated lncRNAs but lower than mRNAs (Fig  S2A ). Only 0. 33% of Class 1 lncRNAs were present with at least 50% nucleotide sequence overlap in the recent GENCODE v27 catalog and 43. 37% of TUs in the MiTranscriptome lncRNA repertoire; the rest represented putative novel lncRNA genes  (Figs 3B and S2B). Of 2,972 TUs, DE analysis retrieved 127 of Class 1 TUs significantly up-regulated in tumor specimens (adjusted P-value below 0.
01, DESeq), including multiple intergenic transcripts and transcripts antisense to protein-coding genes, such as HDAC9, TPO, and FBXL7 (Table  S3  and Fig S2B). Intersection of DE-kupl contigs with PCa up-regulated HoLdUp TUs (N = 127) and the recent GENCODE lncRNA annotation (N = 206) showed that 687 DE-kupl contigs of 1,179 make part of the standalone transcripts. Moreover, up to 85. 5% and 96. 8% DE-kupl contigs embedded into GENCODE and HoLdUp Class 1 lncRNA genes, respectively, were also detected by DESeq as significantly upregulated transcripts in the same dataset, when the RNA-seq reads were counted within the entire TU  (Figs 3C and S2C). One such example is the contig ctg_23999 (P22) embedded into a novel HoLdUp assembled Class 1 TU antisense to the protein-coding FBXL7 gene  (Fig 3D).
We retained several contigs embedded into already annotated PCa associated lncRNA genes, such as CTBP1-AS (ctg_25348, P10), PCAT7 (ctg_111158, P6), and PCAT1 (ctg_105149, P18), or lncRNAs referenced elsewhere as ctg_104447 (P11) mapped into LOC283177, ctg_123090 (P5) into AC004066. 3, and ctg_73782 (P8) into LINC01006. It should be noted that the GENCODE referenced genes enclosing these new subsequences also showed differential expression when counting on the whole gene annotation (Fig  S3B ). However, in contrast to DEkupl ranking, they were not among the strongest hits in the DESeq analysis with exception of PCAT7 (Table  S4 ). This observation points to the fact that through expression counting within the small subsequences, DE-kupl is more resolutive and hence sensitive in the discovery of DE sequences.
Validation of contig The occurrence of sequences representing 23 DE-kupl contigs was measured and compared with PCA3. In total, 16 of 23 DE-kupl contigs had significant support for overexpression in tumor specimens in the TCGA-PRAD cohort (Wilcoxon P-value < 0. 01, Fold Change [FC] > 2)  (Fig 4B and Table   S11 ). Among the best scored candidates, the two novel DE-kupl contigs, P16 (ctg_111348) antisense to DLX1 and intergenic P1 (ctg_17297), surpassed PCA3 that ranked third. However, important discrepancies were observed between expression counts in poly(A)+ RNA-seq TCGA datasets and NanoString or total RNA-seq PAIR datasets. First, P22 (ctg_119680) was detected as DE in TCGA-PRAD but failed the DE test when measured by NanoString (Figs  4  and S5 ). Second, the expression of nine DE-kupl contigs were near the base line in the TCGA dataset, including those showing relatively high expression and low P-values in the PAIR cohort, such as P14 (ctg_61528) antisense to TPO or the intergenic P9 (ctg_9446).
Logistic regression did not retain PCA3 within the mixed signature set, instead contigs embedded into the well characterized PCAT1 lncRNA and into two already annotated but yet functionally uncharacterized lncRNAs LOC283177 and LINC01006 were present. We also compared predictive performances of signatures retrieved by the k-mer-based classifier to the one inferred using conventional gene expression counting. Differential expression analysis for GENCODE-annotated genes of the Discovery Set retrieved 520 up-regulated genes, protein-coding and noncoding, with adjusted P-values lower than 0. 05 and a logFC higher than 2 (Table  S4 ). These genes were then selected on the Discovery Set using LASSO penalized logistic regression to extract a GENCODE whole gene counting based (WGC) signature for further validation (Fig  S7B ).
Given the high dimensional setting (more variables than observations available), we performed the stability selection  (Meinshausen & Bühlmann, 2010)  and kept the five genes that had a probability of being selected higher than 0. 5 on 2,000 samplings of the original dataset. Remarkably, the retrieved subset was composed majorly of noncoding transcripts (four of five), although PCA3 did not pass the selection. Of them, the protein-coding HPN mRNA, the PCAT7 lncRNA, and the GLYATL1P4 pseudogene have been already associated with PCa in other studies  (Willard & Koochekpour, 2012; Du et al, 2013; Kim et al, 2019). Notably, the GLYATL1P4 transcript makes part, together with 21 other RNAs, of the Decipher test proposed in clinics to guide timing of radiation therapy after radical prostatectomy in men with high-risk cancer  (Alford et al, 2017).
The predictive performance in discrimination between normal and tumor specimens of the WGC signature was tested by the ROC analysis on the Validation Set and resulted in the mean AUC of 0. 91 (Fig  S7B ). Hence, k-mer based signature discovery method retrieving yet unreferenced RNA subsequence was as powerful as the signature derived from GENCODE-annotated genes. Although the predictive modeling enabled to reach the same performance only from 23 contig probes instead of 520 DE-genes and, remarkably, this was achieved in TCGA-PRAD datasets where contigs expression counting is most likely disfavored considering all aforementioned drawbacks of poly(A)-selected datasets of low coverage. Discovery of novel RNA signatures with high tumor predictive potential also highlights both the incompleteness of current cancer transcriptome datasets and the biological value of transcript information that can be extracted through different experimental (total stranded RNA-seq and NanoString quantification) and computational (DE-kupl) tools.
Here, we presented an innovative experimental and computational platform that permits discovery of RNA biomarkers of high clinical potency from total stranded RNA-seq datasets of clinical origin. As a proof-of-concept, we focused on PCa as the only type of cancer using, so far, a lncRNA-based diagnostic test (Progensa). The Discovery Set based on comparison of 8 normal with 16 tumor specimens from total RNA-seq datasets was processed by DE-kupl to extract the most significant differentially expressed subsequences in the form of k-mer contigs. Further filtering based on contig length, genomic position, and expression levels powered the pipeline towards the discovery of putative lncRNAs, for the majority, yet unreferenced in the human transcriptome. Then, the catalog of contigs was manually refined and tested for expression using the NanoString single-molecule RNA counting technology in the extended cohort of 144 specimens.
Contig expression was next assessed in the independent, publicly available TCGA-PRAD dataset generated by the poly(A)+ unstranded RNA-seq technology. The expression of contigs was systematically compared with that of the benchmark biomarker lncRNA, PCA3. In total, 16 of 23 contigs were validated in both setups but with important differences. Primarily, RNA measurements were consistent between two different technologies: NanoString and total stranded RNA-seq. In contrast, the TCGA poly(A)+ unstranded datasets revealed weakness and high heterogeneity of contig counts over the selected regions, resulting in unexpectedly low signals even for PCA3, considered as a highly expressed lncRNA. Hence, our results promote the total stranded RNA-seq as a first-choice for discovery of RNA biomarkers from clinical samples and when searching transcripts others than highly abundant mRNAs.
An additional cohort using the same NanoString technology as the Selection Set should now be tested to explore the clinical potential of the obtained signature. In addition to the clinical value, functions of the newly discovered lncRNA variants embedding DE-kupl contigs will be important to explore. Foremost, proper assignment of contigs to stand-alone transcripts is required, and this task can be accomplished computationally through ab initio discovery and assembly of novel transcripts as demonstrated here by HoLdUp or other assemblers, and then through experimental validation at the transcript-specific or transcriptomic level. In the latter case, highthroughput RACE (rapid amplification of cDNA ends) or long-read RNA-seq approaches can be useful. Among others, detailed examination of newly discovered contigs revealed a genomic locus on chromosome 19 transcribed in PCa specimens in both directions into the GENCODE-annotated AC011523.
The workflow can be applied to any RNA-seq datasets of any clinical origin (tissue, blood, and urine) to generate a probe panel that may be implemented as a multiplex platform for simultaneous detection of RNAs in clinical samples. Moreover, different experimental contrasts (normal versus pathology, low-versus high-risk grade, chemoresistant versus sensitive, etc. ) will define the biomarker usage in diagnosis, prognosis, or other clinical applications, hence providing clinicians and researchers with a simple and highly sensitive tool for genomic and personalized medicine.
RNA extraction, quantification, and cDNA library production Total RNA was extracted using the TRizol reagent (Thermo Fisher Scientific), according to the manufacturer's procedure, quantified, and quality-controlled using a 2100 Bioanalyzer (Agilent). RNA samples with RNA Integrity Number (RIN) above six were depleted for ribosomal RNA and converted into cDNA library using a TruSeq Stranded Total Library Preparation kit (Illumina). cDNA libraries were normalized using an Illumina duplex-specific Nuclease protocol before a paired-end sequencing on HiSeq 2500 (Illumina). At least 20× coverage per sample was considered as minimum of unique sequences for further data analysis. "tumor" RNA-seq subsets build by random sampling of 10% of reads from each raw data sample  (Descrimes et al, 2015). The normal samples were assigned to the group "controls" and the tumor specimens-to the group "cases," with the assumption that the "cases" should have higher values than "controls.
Unsupervised clustering of prostate specimens Specimens were ranked based on the Log10(norm. counts) levels contigs assessed by the NanoString nCounter assay using a Com-plexHeatmap R-package  (Gu et al, 2016). Scripts are available from GitHub: https://github. com/MorillonLab/Prostate_additional_scripts. Variable selection using the LASSO penalized logistic regression and external validation of signatures Signature inference was performed in R using the normalized Selection Set (23 probes in 144 observations) as a variable selection dataset and contigs counts table of the Validation Set (23 probes in 557 observations) as an external validation dataset (R Core Team). First, we performed penalized logistic regression using the glmnet R package to select probes predicting the tumor status on the Selection Set upsampled to correct the imbalance class distribution (9 normal versus 135 tumor specimens)  (Friedman et al, 2010).
Selection was performed using all probes (signature_mixed including PCA3) or using only new-lncRNA contigs only (signature_new-lnc)  (Fig S7). Second, we built predictors using the boosted logistic regression from the caTools and caret packages  (Kuhn, 2008; Tuszynski, 2008). Note that the final gene subsets (signatures) do not have coefficients computed on the Selection Set over the Validation Set because in contrast to NanoString, the TCGA-PRAD RNA-seq datasets are poly(A)-selected and unstranded. To build the ROC curves, we sampled 100 datasets in two, for training (70%) and testing (30%) preserving the relative ratio of labels in each. We used boosted logistic regression with upsampling, setting the number of boosting iterations to 100 and using leave-one-out cross validation scheme on the training set.
After training, we evaluated the predictor on the testing set and repeated the procedure for each one of the 100 training and testing sets described above to obtain an average ROC curve, mean and SD for AUC scores. Contig expression counts in the Validation Set (TCGA-PRAD) were obtained as described above using the DE-kupl derived representative k-mer for each contig. Quantifications based on 10 randomly sampled k-mers per contig did not alter predictive performance  (Fig S8C). To build a classifier based on the conventional WGC procedure, we used DESeq2 across the GENCODE annotation on the Discovery Set and kept only up-regulated genes with adjusted P-value lower than 0. 05 and Log2FC higher than 2. To perform gene selection on the Discovery Set, we used LASSO penalized logistic regression combined with stability selection.
Only genes with probability above 0. 5 on 2,000 up-regulated samples from the initial dataset were retained. The remaining genes were then used to build ROC curves and compute the mean and SD of the AUC on the Validation Set as described above for the DE-kupl-derived representative k-mers. The results file, R codes, and data tables are provided through the GitHub repository: https://github. com/MorillonLab/Prostate-kmer-signatures.
LASSO) logistic regression on the Selection Set of 144 PAIR specimens (Ghosh & Chinnaiyan, 2005). First, the initial 21 DE-kupl contigs and PCA3 validated for expression by NanoString were submitted to LASSO to define the best mixed signature comprised of already known and yet unannotated lncRNA probes for discrimination of tumor from normal tissues (Fig S7A). Then, LASSO was performed with the probe subset composed uniquely of contigs assigned to putative novel lncRNAs (N = 15) to infer the best new-lncRNA signature. It resulted in two panels of nine mixed and nine new-lncRNA candidates (Figs 6A and S7B). Retrieved signatures were then used to predict a tumor status in the Validation Set of the TCGA-PRAD cohort using a leave-one-out cross-validated boosted logistic regression. To assess the sensitivity of DE-kupl contigs in PCa diagnosis, a predictive accuracy index, area under curve (AUC) of the receiver-operating characteristic (ROC), was calculated for each signature and PCA3 alone in the PAIR (Selection Set) and TCGA-PRAD (Validation Set) datasets (Figs 6B and.
Acknowledgements We deeply thank  Dominika Foretek ,  Maxime Wery , and  Alexandre Serero  for editorial suggestions;  Camille Gautier ,  Claire Bertrand , and  Anna Almeida  ( Morillon lab, Institut Curie ) and  Sylvain Baulande  for  RNA-seq (Next Generation Sequencing platform, Institut Curie ); and  Cedric Saule  and  Jeremy Le Coz  for the DE-kupl run (Gautheret lab, I2BC). The  Cancer Genome Atlas RNAseq data  for prostate adenocarcinoma (PRAD) were downloaded from the dbGaP Web site under authorization granted to  D Gautheret  (project # 13359 ). Funding: Constitution of the prostate cancer cohort was performed with the financial support from the  INCa-Ligue-ARC   PAIR program  to  Y Allory  and  A Londoño-Vallejo. The Genomics platform and NanoString technology of Institut Curie were set up with the support of  Agence Nationale de la Recherche (LabEx and EquipEx :  ANR-10-IDEX-0001-02   PSL ,  ANR-11-LBX-0044 ),  Insitut National du Cancer (INCa-DGOS-4654 ,  SIRIC11-002 ).
RNA-seq efforts were supported by a grant from the  ICGex program at Institut Curie  to  A Londoño-Vallejo  and  A Morillon  and benefited from the facilities and expertise of the  Next Generation Sequencing platform of Institut Curie , supported by  Agence Nationale de la Recherche  ( ANR-10-EQPX-03 ,  ANR10-INBS-09-08 ) and  Cancerop ôle Ile-de-France. M Descrimes ,  M Gabriel ,  A Morillon ,  M Pinskaya , and  Z Saci  were supported by  Agence Nationale de la Recherche  ( DNA-Life ) and the  European Research Council  ( ERC-consolidator DARK-616180-ERC-2014 ) attributed to  A Morillon ;  D Gautheret  and HTN Nguyen were supported by  ITMO Cancer-Systems Biology  ( bio2014-04 ) and  Agence Nationale de la Recherche  " France G énomique"  ( ANR-10-INBS-0009 ) attributed to  D Gautheret.
( LASSO) logistic regression on the Selection Set of 144 PAIR specimens (Ghosh & Chinnaiyan, 2005). First, the initial 21 DE-kupl contigs and PCA3 validated for expression by NanoString were submitted to LASSO to define the best mixed signature comprised of already known and yet unannotated lncRNA probes for discrimination of tumor from normal tissues (Fig S7A). Then, LASSO was performed with the probe subset composed uniquely of contigs assigned to putative novel lncRNAs (N = 15) to infer the best new-lncRNA signature. It resulted in two panels of nine mixed and nine new-lncRNA candidates (Figs 6A and S7B). Retrieved signatures were then used to predict a tumor status in the Validation Set of the TCGA-PRAD cohort using a leave-one-out cross-validated boosted logistic regression. To assess the sensitivity of DE-kupl contigs in PCa diagnosis, a predictive accuracy index, area under curve (AUC) of the receiver-operating characteristic (ROC), was calculated for each signature and PCA3 alone in the PAIR (Selection Set) and TCGA-PRAD (Validation Set) datasets (Figs 6B and.
The virtual species approach: simulation stages and key choices Here we will define a VS as a simulated entity that has a known occurrence-environment relationship and for which we can control the sampling strategy, as well as any other desirable property that might be useful in understanding model behavior or in proposing sampling, calibration or validation strategies. Some authors have developed virtual distributions based on simulations of abundances or population dynamics  (Austin et al. 2006 , Soberón 2010 ). However, the vast majority of the SDM literature today is based on occurrence data (i. e. presence-absence or presence-only datasets). Therefore, for the purposes of this review, we will limit ourselves to VS studies based on presence-absence simulations. Notice that the SDM literature is vast, and a review of SDMs per se is outside the scope of this article.
• Species prevalence seems influential mainly when it is extreme (> 90% or < 10%), especially when sample size is small. • Some have proposed an 'environmental favorability function' that makes model output independent from sample prevalence and could be useful when different species need to be compared in the same scale (e. g. prioritization of conservation sites). Lauzeral et al. 2012 , Sheth et al. 2012 , Kramer-Schadt et al. 2013 , Owens et al. 2013 , Dorazio 2014 , Guillera-Arroita et al. 2014a , Lahoz-Monfort et al. 2014 , Thibaud et al. 2014 , Varela et al. 2014 , Stolar and Nielsen 2015 , Fei and Yu 2016 , Ranc et al. 2017 , Liu et al. 2018  • Unbiased presence-absence data is always better than biased presence-only data, but several strategies to correct bias and use presence-only datasets have been proposed.