dataesr/scientific-paragraphs-categorization huggingface dataset:
The CT-X antigen gene class was stochastically younger than the housekeeping gene class (two sided test P-value 0. 027) and tumor suppressor gene class (two sided test P-value was 0. 049), but after correction for multiple testing, simultaneously these results are not significant (see Supplementary Dataset 3 for complete pairwise relative evolutionary novelty analysis for different gene classes). Moreover, we discovered that the class of the BMC GSTSE non-coding sequences was stochastically younger than the class of housekeeping genes (two sided test P-value 2. 6 * 10 -4 ) and that the differentiation gene class was stochastically younger than the housekeeping gene class (two sided test P-value 5 * 10 -6 ). The bootstrap rates of the stochastically younger cases agree with these hypotheses (see Supplementary Dataset 4).
We also found that cluster III was stochastically younger than cluster I (two sided test P-value is 1. 7 * 10 -5 ) and the combination of clusters I and II (two sided test P-value is 1. 9 * 10 -5 ). Moreover, cluster III was stochastically younger than all protein-coding genes (P-value 0. 0015) (Supplementary Dataset 5, see also Supplementary Dataset 6 for the bootstrap agreement). Many genes that we studied are in two or more classes (Supplementary Dataset 7). As far as we are interested in co-evolution of differentiation genes, oncogenes and tumor-suppressor genes, we examined the gene age distributions of pairwise intersections of these gene classes (Supplementary Fig. 1 ) and of their pairwise subtractions (Supplementary Fig. 2 ). We found that curves of overlapping gene subclasses (diff x onco, diff x TSG, and onco x TSG) and subtracted gene subclasses (diff-onco, diff-TSG, onco-diff, onco-TSG, TSG-diff and TSG-onco) have similar shapes, and the ages of gene subclasses are similar to the ages of original gene classes (i.
Discussion To study different functional classes of genes we used publicly available gene databases describing different gene classes -The Human Protein Atlas (housekeeping genes); Tumor-Associated Gene database (TAG database) (oncogenes); TSGene (tumor suppressor genes); CTDatabase (cancer/testis (CT) antigen genes); HomeoDB (HomeoBox genes); DeathBase (apoptosis genes); GeneOntology (differentiation genes); Biomedical Center Database (BMC GSTSE protein-coding genes and BMC GSTSE non-coding sequences). All annotated human protein coding genes (Genome assembly GRCh38) and housekeeping genes were used as controls. Although we understand the limitations of such an approach connected with differing philosophies of the authors of databases and continuing upgrading of databases, we were able to obtain meaningful results.
The results were also reproducible for different versions of databases with curves corresponding to different versions almost overlapping (see Supplementary Fig. 5 ). We decided to study the ages of different gene classes in order to verify the predictions which stem from the hypothesis of the possible evolutionary role of heritable tumors formulated by one of us  5. According to this hypothesis, hereditary tumors were the source of extra cell masses, which might be used in the evolution of multicellular organisms for the expression of evolutionarily novel genes and for the origin of new differentiated cell types with novel functions. The evolutionary role of cellular oncogenes might consist in sustaining certain level of autonomous proliferative processes in the evolving populations of organisms and in promoting the expression of evolutionarily new genes.
e. with a significant BLAST score (or HMMER E-value). The age of a functional gene class (or the age of the cluster) was described by distribution of ages of genes belonging to this gene class (i. e. particular gene database). For convenience, the age of the gene class can be measured numerically in million years at the median of distribution, i. e. at the time point on the human evolutionary timeline that corresponds to the origin of 50% of genes in this class. We found that different functional classes of human genes have different evolutionary ages ranging from 894 millions years for housekeeping genes to 50 million years for BMC GSTSE-X non-coding sequences. This reflects the different evolutionary history of different functional gene classes. The curves of the older gene classes occupy the higher-left position and those of younger gene classes occupy the lower-right position on distribution curves (Figs  1 2 3 4 5 6 7 ).
Moreover, we found that differentiation, onco-, and tumor suppressor gene classes partially overlap (Supplementary Dataset 7), and pairwise intersection and subtraction gene subclasses co-evolve with the main gene classes (Supplementary Figs  1 2 3 ). Overlapping of gene classes means that some genes have two (or more) functions, and may belong to two (or more) functional gene classes. It is known that a gene may function in several processes and contain exons that determine diverse molecular functions and biological processes  35. The existence of diff x onco and diff x TSG subclasses confirmes our prediction on co-evolution of differentiation, onco-, and tumor suppressor functions even on a single gene level. As example of gene with dual function could be TGFb. It is known that TGFb may function as tumor promoter or tumor suppressor.
Our prediction that there should be a general correspondence between the number of oncogenes and the number of cell types is also supported by the other existing data. Thus, the TAG database, which we used in this study, currently contains 245 human oncogenes, of which 224 are found by ProteinHistorian. Domaset-Loso and Tautz used other data sets (Sanger Cosmic, NCBI Entrez section in CancerGenes, the CancerGenes and the Network of Cancer Genes (NCG)). They found 380 oncogenes in these databases  26. On the other hand, the current estimate of the number of the cell types in humans produced the numbers from 240  39, 40  to 411 cell types  41. Supplementary Dataset 8 contains a table of correspondence of the number of oncogenes and cell types in different multicellular organisms (Supplementary Dataset 8).
That is, the general correspondence between the number of cell types and the number of oncogenes does exist, as was predicted in  2,3. It is noteworthy that when such correspondence was first predicted in 1987, only 20 oncogenes have been described  42  , and by 1996 -only 70 oncogenes  43. We further hypothesized that at least three different classes of genes are necessary for the origin of a new cell type in evolution: oncogenes, tumor suppressor genes, and evolutionarily novel genes, which determine a new function  5. The existence of cluster I supports our hypothesis of co-evolution of differentiation, onco-, and tumor suppressor genes  5. The bootstrap values are always the highest for differentiation, onco-, and tumor suppressor genes. This strongly supports the existence of cluster I and co-evolution of differentiation, onco-, and tumor suppressor gene classes, although the number of protein coding tumor suppressor genes (TSGene database, 1018 genes) and differentiation genes (Gene Ontology, 3697 genes) is higher than the number of oncogenes (TAG database, 245 genes).
We already described the evolutionary novelty of CT-X antigen gene class earlier  19. Later other authors reproduced our results with appropriate reference to our original paper  49. Here we confirmed the evolutionary novelty of CT-X gene class using the current upgraded database of CT genes -CTDatabase, and with another method -ProteinHistorian. In this paper, we also described the new class of TSEEN genes -BMC GSTSE ncRNA genes. In our other work we discovered a new long non-coding RNA (lncRNA) -OTP-AS1 (OTP-antisense RNA 1)  50  , which belongs to cancer/testis sequences. Statistical analysis supported the existence of two classes of TSEEN genes -CT-X gene class and BMC GSTSE ncRNA gene class (Supplementary Dataset 1), which constitute cluster III. Cluster III was stochastically younger than the combination of two clusters I and II (Supplementary Datasets 5 and 6).
Methods The following public databases were used as a source of human gene classes in this study: housekeeping genes -The Human Protein Atlas; oncogenes -TAG database; tumor suppressor genes -TSGene; differentiation genes -GeneOntology; HomeoBox genes -HomeoDB; apoptosis genes -DeathBase; cancer-testis (CT) antigen genes -CTDatabase; BMC GSTSE protein-coding genes and non-coding sequences -Biomedical Center Database; and all annotated human protein coding genes -Genome assembly GRCh38 (21694 genes). CT antigen genes were divided into two groups: autosomal genes and genes located on X chromosome. BMC GSTSE non-coding sequences located on X chromosome were also separately studied. This was done because X chromosome contains relatively more evolutionarily novel genes than autosomes  5. Housekeeping genes are 7367 genes expressed in all analyzed tissues in the Human Protein Atlas  25.
This database contains information for a large majority of all human protein-coding genes regarding the expression and localization of the corresponding proteins based on both RNA and protein data. The Atlas contains information about 44 different human tissues and organs  25. The TAG database (Tumor Associated Genes Database) (245 oncogenes) was designed to utilize information from well-characterized oncogenes and tumor suppressor genes to facilitate cancer research. All target genes were identified through text-mining approach from the PubMed database. A semi-automatic information retrieving engine collects specific information of these target genes from various resources and store in the TAG database. At the current stage, TAG database includes 245 oncogenes  54  , which were used in ProteinHistorian analysis (see below).
The database we used was modified for the last time on 2014. 10. 03. TSGene 2. 0 database contains 1217 human tumor suppressor genes (1018 coding and 199 non-coding genes) curated from a total of over 5700 PubMed abstracts  55. In ProteinHistorian analysis we used only 1018 protein-coding tumor suppressor genes. Differentiation genes (3697 genes) were obtained by manual search for "differentiation" in the Gene Ontology database  35. Homeobox gene database (HomeoDB2) (333 genes) is a manually curated database of homeobox genes and their classification. HomeoDB2 includes all homeobox loci from 10 animal genomes (human, mouse, chicken, frog, zebrafish, amphioxus, nematode, fruitfly, beetle and honeybee) plus tools for downloading sequences, comparison between different species and BLAST search  56, 57.
We used the database, which was updated for the last time on 2011. 08. 08. Deathbase (53 genes) is a database of proteins involved in different cell death processes. It is aimed to compile relevant data on the function, structure and evolution of this important cellular proccess in several organisms (human, mouse, zebrafish, fruitfly and worm). Information contained in the database is subject to manual curation  58. The database was updated for the last time in 2011. CTdatabase (286 genes) provides basic information including gene names and aliases, RefSeq accession numbers, genomic location, known splicing variants, gene duplications and additional family members. Gene expression at the mRNA level in normal and tumor tissues has been collated from publicly available data obtained by several different technologies.
Manually curated data related to mRNA and protein expression, and antigen-specific immune responses in cancer patients are also available, together with links to PubMed for relevant CT antigen articles  59. We used the update of 2017. To construct the BMC database of sequences that are expressed in tumors but not in normal tissues, the normal EST set was subtracted in silico from the tumorous EST set. This approach is known as computer-assisted differential display (CDD). In total, 4564 cDNA libraries categorized as "tumorous" and 2304 "normal" libraries were used in CDD experiments. 251 EST clusters with tumor predominant expression were described in  10  , and 196 clusters -in  12. From these clusters 60 protein-coding genes and 121 non-coding sequences were selected for analysis. All annotated human protein coding genes (21694 genes) were obtained from Genome assembly GRCh38 60 with Ensembl tool  61.
The genome assembly was submitted on 2013. 12. 17. The ProteinHistorian tool was used to perform homology search in genomes of different taxa. The ProteinHistorian tool is an integrated web server, database and a set of command line tools which estimates the phylogenetic age of proteins based on a species tree, several external datasets of protein family predictions from the Princeton Protein Orthology Database (PPOD)  62  and two algorithms for ancestral family reconstruction (Dollo and Wagner parsimony)  63. The ProteinHistorian tool searches the orthologs in 34 completely sequenced eukaryotic and prokariotic genomes from 16 taxa in the human lineage (Cellular Organisms, Eukaryota, Opisthokonta, Bilateria, Deuterostomia, Chordata, Euteleostomi, Tetrapoda, Amniota, Mammalia, Theria, Eutheria, Euarchontoglires, Catarrhini, Homininae, and H.
sapiens). The species tree used in analysis is presented in Supplementary Fig. 6. Divergence time is estimated in millions of years ago (Ma) for each internal node in the species tree. It is important to note that a protein could have appeared at any time along the branch to which it is assigned, so the divergence time estimate reported is a lower bound. The ages are taken from the TimeTree database  20. Time tree database collects estimation of time of divergence among species data from publications in molecular evolution and phylogenetics. These included phylogenetic trees scaled to time (timetrees) and occasionally tables of time estimates and regular text. The data was collected from more than 2300 studies that have been published since 1987  20. The ProteinHistorian tool detected the following gene numbers in databases mentioned above: The Human Protein Atlas (housekeeping genes) -6789 genes; The TAG database (oncogenes) -224 genes; TSGene (tumor suppressor genes) -984 genes; GeneOntology (differentiation genes) -3697 genes; HomeoDB (homeobox genes) -231 genes; DeathBase (apoptosis genes) -53 genes; CTDatabase (CT-antigen genes) -187 genes, including 109 autosomal and 78 X-chromosome located genes; Biomedical Center Database -60 protein-coding genes; Genome assembly GRCh38 (all protein-coding genes) -19911 genes.
The nucleotide BLAST algorithm, HMMER tool and the original Python script were used to analyze the ages of non-coding sequences. The orthologs were searched in 25 completely sequenced eukaryotic and prokaryotic genomes (Supplementary list 1). The processing of datasets obtained with ProteinHistorian tool was carried out with Python script and Grep tool. The age of the gene is defined by the most recent common ancestor on human evolutionary timeline containing genes with similar sequences, i. e. with a significant BLAST score (or HMMER E-value)  21. The age of the functional gene class (or cluster) is described by distribution of ages of genes belonging to this gene class. For convenience, the age of the gene class can be measured numerically in million years at the median of distribution, i.
In order to check bootstrap reliability of the obtained results we bootstrapped independently from the original classes the same size classes 10000 times and performed exploratory analysis for each of the genes age curves, including the bootstrapped mean value, mean square error, median and quartiles for all the taxon break points (Supplementary Dataset 9). At each of the replications we obtain the hierarchical classification based on the Kolmogorov-Smirnov distance, and report the bootstrapped rates for all classes and for all nodes of the initial trees. Moreover, at each of the replications for each pair of the bootstrapped genes age curves we checked for intersections, and finally report the bootstrapped rates of stochastically larger and stochastically smaller cases. Some genes are included in several databases.
This was taken into account in statistical analysis. To investigate intersections of gene classes, for each pair of gene classes we report the observed number of genes belonging to both classes and the corresponding expected counts, which were calculated under assumption of independent attendance of genes to classes (Supplementary Dataset 7. 1). More precisely, with each of gene classes we associate a binary variable taking value 1 if the corresponding gene belongs to the gene class and 0 otherwise. The independent attendance of genes to a pair of gene classes means that the corresponding variables are independent. Moreover, for each pair of gene classes we create the 2x2 contingency table and report P-values of Chi-square and Fisher's exact tests (Supplementary Dataset 7. 2). Several genes belong to three or even four gene classes.
For triple and quadruple intersections of gene classes we report their counts and share of the intersection in each of the classes (Supplementary Dataset 7. 3). In order to check reliability of the classification by age of gene classes with respect to the dual functionality we use additional six subclasses to classification: the subclass of genes belonging to both differentiation and tumor suppressor gene classes (diff x TSG); the subclass of genes belonging to both differentiation and oncogene classes (diff x onco); the subclass of genes belonging to differentiation but not to tumor suppressor gene classes (diff-TSG); the subclass of genes belonging to differentiation but not to oncogene classes (diff-onco); the subclass of genes belonging to oncogenes but not to differentiation gene classes (onco-diff); the subclass of genes belonging to tumor suppressor gene but not to differentiation gene classes (TSG-diff).
Acknowledgements This study was supported by 5- 100-2020 program  grant at Peter the  Great St. Petersburg Polytechnic University  and a grant  No833  from  Ministry of Education and Science of the Russian Federation  to A. P. K. We thank staff the  Department of Information and Control Systems of Peter the Great St. Petersburg Polytechnic University  for providing the access to processing power required to perform the analysis of the non-coding sequences. We thank  D. Frishman ,  P. Drobintsev , and  I. Selin  for their help during this work.
The broad use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens is restricted by the limited power of conventional reference-based protocols relying on uniquely mapped reads and transcript annotations. Here, we implemented a blind reference-free computational protocol, DE-kupl, to directly infer RNA variations of any origin, including yet unreferenced RNAs, from high coverage total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into unannotated putative long noncoding (lnc)RNAs expressed in prostate cancer tissues. Through filtering and visual inspection of 1,179 candidates, we defined 21 lncRNA probes that were further validated for robust tumorspecific expression by NanoString single molecule-based RNA measurements in 144 tissue specimens.
Predictive modeling yielded a restricted probe panel enabling over 90% of true positive detection of cancer in an independent dataset from The Cancer Genome Atlas. Remarkably, this clinical signature made of only 9 unannotated lncRNAs largely outperformed PCA3, the only RNA biomarker approved by the Food and Drug Administration agency, specifically, in detection of high-risk prostate tumors. The proposed reference-free computational workflow is modular, highly sensitive and robust and can be applied to any pathology and any clinical application.
A recent approach to RNA-seq data analysis, DE-kupl, combines k-mer (subsequences of fixed size) 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 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  (Jarroux et al. 2017) ,  (Quinn and Chang 2015). They show aberrant and specific expression in various cancers and other diseases promoting them as biomarkers, therapeutic molecules and drug targets  (Leucci 2018) ,  (Van Grembergen et al.
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 molecular 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 (FFPE) tissues or other clinical samples  (Zhao et al.
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 RNA-depleted RNA samples isolated from prostate tissues after radical prostatectomy (Discovery Set, PAIR cohort, Supplemental 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.
Naïve assembly of Transcription Units identifies novel prostate cancer associated lncRNAs To complement the reference-free protocol, we applied a reference-based 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, which is deleterious for splice graph-based assemblers such as Cufflinks  (Kukurba and Montgomery 2015) ,  (Hayer et al. 2015a ). To bypass this difficulty, we developed a more straightforward lncRNA annotation pipeline, HoLdUp, which identifies transcription units (TU) 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 Methods for details).