dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Until 2004, the Web of Science (WOS) was the only international and multidisciplinary database available to obtain the literature of technology, science, medicine and other fields. However, Elsevier introduced Scopus which is become a good replacement  (Vieira & Gomes, 2009). The information provide by these databases specify the active journals in covering current and relevant research as well as prominent in shaping potential research fields. The intense competition between these databases motivated researchers to compare them to identify their similarities and differences. A numerous researches compare these databases from different aspects. In this article, WOS and Scopus databases are compared based on qualitative and quantitative characteristics such as provenance, citations, searching and special features by reviewing prior studies.
The comparison of WOS and Scopus discovers that WOS has strong coverage which goes back to 1990 and most of its journals written in English. However, Scopus covers a superior number of journals but with lower impact and limited to recent articles. Both databases allow searching and sorting the results by expected parameters such as first author, citation, institution and etc. regarding impact factor and h-index, different results obtained from prior studies. Although there is a high association between both databases, researchers interested to know why authors prefer one database over the other one. For further studies, it is suggested to investigate the perceptions of authors and researchers on both databases to find the reasons which make them to use one database more than the other one.
Recent sequencing technologies that allow massive parallel production of short reads are the method of choice for transcriptome analysis. Particularly, digital gene expression (DGE) technologies produce a large dynamic range of expression data by generating short tag signatures for each cell transcript. These tags can be mapped back to a reference genome to identify new transcribed regions that can be further covered by RNA-sequencing (RNA-Seq) reads. Here, we applied an integrated bioinformatics approach that combines DGE tags, RNA-Seq, tiling array expression data and speciescomparison to explore new transcriptional regions and their specific biological features, particularly tissue expression or conservation. We analysed tags from a large DGE data set (designated as 'TranscriRef'). We then annotated 750 000 tags that were uniquely mapped to the human genome according to Ensembl.
The procedure includes the computation of false-positive tag locations (2% in the human genome) and the analysis of a large number of oriented orphan tags (i. e. without genomic annotation)  (19). The transcriptional information given by the annotated DGE tags is then completed by integrating expression data obtained by using other techniques (RNA-Seq and tiling arrays). Currently, one of the major difficulties in characterizing new transcripts is the absence of information on their expression levels, which may help assessing their biological relevance. From a computational point of view, tags are instrumental for measuring and comparing the expression level of transcripts in different tissues. To validate our approach, DGE data from 54 publicly available libraries from normal (including human pluripotent stem cells [hpSCs]) and cancer tissues were used for transcript detection and tissue expression comparison.
Selection of Potential candidates Without filtering Figure  1. The digitagCT pipeline. Schematic representation of the integrative digitagCT pipeline to analyse high-throughput sequencing data (DGE and RNA-Seq). First, DGE tags are mapped to the reference genome by using the crac software with the -emt argument tool (described in 'Materials and Methods' section). The pipeline uses existent annotation sources (Ensembl annotations for human and mouse) and interrogates the subset of uniquely mapped tags using the Ensembl API. In output, a selection of transcripts can be retained with or without filtering. In the filtering process, retained DGE transcripts are compared with RNA-Seq data, which are integrated in the pipeline by using the CRAC (described in 'Materials and Methods' section), tiling arrays and other DGE data.
Users may configure several parameters according to their needs, such as by defining an occurrence threshold for DGE tags or a specific class of transcript (intergenic, non-coding, etc. ). tools, it is advisable to provide a SAM file from CRAC  (20). In fact, one of the CRAC specificities is to deliver computational predictions for point mutations, indels, sequence errors, normal and chimeric splice junctions in a single run and to list this information, which can be used to characterize the nature of new transcripts, in extra columns of the SAM file. For example, the combination of a splice junction and an intergenic tag could indicate a new long intergenic non-coding RNA (lincRNA). Our pipeline is called digitagCT and is part of the CracTools suite (not published). DigitagCT is available at http:// cractools.
gforge. inria. fr/softwares/digitagct/. Here, we used the digitagCT pipeline to combine the strengths of the DGE and RNA-Seq methods. First, sequences were mapped back to the reference genome using the CRAC software (with the '-emt' argument for an exact matching tool that is particularly suitable for DGE data), available at http://crac. gforge. inria. fr (20). As the DGE method often implies that each transcript is represented by one tag, DGE data were annotated according to a GFF file from Ensembl Genome Browser by giving priority first to location in exons and then in intronic or intergenic regions. The GFF file was built from Ensembl API (version 66). First the protein-coding gene and pseudogene categories were considered to determine whether the tags corresponded to intragenic (cds, UTR and introns) or intergenic sequences (process A) (Figure  2 ).
The same approach was used with genes on the opposite strand (if any): anti-sense tags could thus be exonic (tag6, tag7, tag8), inxonic (tag9) or intronic (tag10). If a tag was not annotated, we assessed its possible intergenic localization and then classified the tag as proximal (tag11) or distal (tag12) relative to a 3 0 gene. The second step (process B) of our pipeline allowed compiling all the previously Ensembl-annotated, noncoding and unclassified transcripts to specify their. Classification of 'TranscriRef' sequences using process A and process B of the digitagCT pipeline. We adopted a two-step strategy to annotate the subset of 'TranscriRef' tags (from Ensembl annotation) that were uniquely mapped to the human genome using CRAC: a general annotation process that considers protein-coding genes and pseudogenes (process A) and a non-coding annotation process that considers only noncoding genes (process B) (see schematic representation in Figure  1  and 'Materials and Methods' section for details).
tag 1 corresponds to the 3' UTR region of a coding transcript and also to a non-coding transcript). In this case, we consider that the non-coding transcript overlaps with a protein-coding gene. The internal pie chart shows the global genomic distribution of DGE sequences assigned to non-coding transcripts. genomic proportion and distribution (Figure  2 ). Most ncRNAs were annotated by aligning their genomic sequence against RFAM using BLASTN. MicroRNAs (miRNA) were predicted by BLASTN of genomic sequence slices against miRBase sequences. LincRNAs were predicted using the lincRNA Ensembl gene annotation, cDNA alignments and chromatin-state maps  (21). For any tag, the digitagCT transcriptome pipeline could also analyse RNA-Seq data to complement the DGE information and help constructing the transcript sequence by generating supplementary features, such as splice variants or polymorphisms.
nci. nih. gov/pub/SAGE/) for human and mouse data and the kidney DGE library described in Philippe et al. (19). One hundred base pairs of RNA-Seq paired-end reads of the HD291 human embryonic stem cell (hESC) line were obtained using an Illumina HiSeq2000 device at DNAVision (Gosselies, Belgium). The DGE libraries for the induced pluripotent stem (iPS) cell line M4C2 and human foreskin fibroblasts were generated at the MGX platform (Montpellier, France) using the NlaIII restriction enzyme (CATG sites) (unpublished data, Bai et al.
Annotations and ncRNA categories For clarity, 'tag' defines a representative sequence and 'occurrence' indicates all sequences that are identical to that tag. Thus, the number of occurrences (occnb) indicates the number of times a tag is found in the library collection and it is considered a measure of its biological validity. Specifically, a tag observed only once may be the result of a sequencing error, whereas a tag observed at least 10 times is more likely to be a valid biological observation. Non-coding transcripts were classified in four groups according to the Ensembl annotation and the annotation proposed by ENCODE: (i) small ncRNAs (mainly miRNAs, snoRNAs and snRNAs); (ii) lincRNAs defined by Ensembl; (iii) other lncRNAs: this is an ENCODE category that includes processed, antisense and sense intronic transcripts and is described in  (9) ; and (iv) other ncRNAs: this category is defined by Ensembl.
Manual validation and curation To select specific candidate tissue-specific transcripts, the corresponding DGE and RNA-Seq libraries were analysed with the pipeline using the filtering process (Figure  1 ). We retained all tags seen at least 10 times (i. e. occnb !10) in at least one DGE sample and covered by at least 3 RNA-Seq reads. Each tag was annotated to give its chromosome position, orientation, expression level in 'TranscriRef', tiling array information, proximity to coding and noncoding genes (at both 3 0 -and 5 0 -ends) and coverage by RNA-Seq reads. These features were then used for biological selection and validation by real-time quantitative PCR (qPCR). The selection criteria can be adjusted according to the user's requirements. Selected candidates were verified in silico by integrating our personal DGE and RNA-Seq data in the Ensembl Genome Browser using a DAS server.
Clustering Hierarchical clustering was performed with the Cluster and Treeview software packages  (24). To obtain a homogeneous proportion of data from different tissues, we removed the over-represented libraries. Thus, only 33 of the 54 available libraries (highlighted in green, Supplementary Table  S1 ) were used, and each individual tissue was represented at most by three different libraries. Tissue-specific tags were selected with the percentile method. As each tissue was represented in $10% of all libraries (3/33), a 90% percentile was used for clustering. A compressed archive (clustering-digitagCT_ transcriRef_34000intergenic_tags. tar. gz) is available at http://www. get. univ-montp2. fr/en/ncRNA with the 'Clustering-digitagCT_transcriRef_34000intergenic_tags. txt' file containing the 34 000 tags used for clustering and all the files generated by the clustering program.
Global transcription distribution in the human genome (process A) We developed a computational bioinformatics tool called digitagCT pipeline (Figure  1 ) that combines DGE data with the available Ensembl annotations to obtain an overall view of transcription distribution across the human reference genome. For this purpose, DGE data from 54 human tissue libraries (25 normal and 29 cancer tissues) were compiled in the 'TranscriRef' data set. These DGE libraries represent $268 million 21-bp-long sequences and correspond to $5 million distinct tags (Supplementary Table  S1 ). Tags were then mapped to the human genome using the crac software in perfect match mode. Of all sequences, $147 million (750 110 distinct tags) matched to unique genomic locations, $69 million (142 786 distinct tags) showed multiple matches and $50 million (4 247 867 distinct tags) had no match in the genome.
96% of all TranscriRef occurrences with a unique match in the human genome). In parallel, we also analysed separately two DGE libraries included in the 'TranscriRef' data set to study transcriptome variability in specific tissues. The first library was generated using normal hESCs (hESC-hs0238: 3 636 083 total sequences and 293 179 unique tags) and the second one using a peripheral blood sample from a patient with AML (AML-hs0430: 6 399 705 total sequences and 204 169 unique tags). The distribution of tags in exonic, intronic and intergenic regions (90%, 4-5% and 5-6%, respectively) in these two libraries was comparable with the overall profile obtained for the whole 'TranscriRef' data set (Supplementary Figures  S2  and S3 ).
Distribution of non-coding annotated transcripts in the human genome (process B) We then evaluated the proportion of Ensembl-annotated non-coding transcripts by considering the 'TranscriRef' tags that matched non-coding sequences (process B of Figures  1  and 2 ). Non-coding transcripts amounted to 41 252 distinct tags, corresponding to 7194 non-coding genes among the 20 365 (35. 33%) listed in Ensembl (Table  2 ). Non-coding transcripts were classified in four groups (as defined in 'Materials and Methods' section): (i) small ncRNAs (mainly miRNAs, snoRNAs and snRNAs), (ii) lincRNAs, (iii) other lncRNAs (processed, antisense and sense intronic transcripts) and (iv) other ncRNAs (Figure  2  and Table  2 ). As expected for DGE data, the proportion of small ncRNAs was negligible (0. 96%). Within the 'TranscriRef' data set, lincRNAs represented $34% of all non-coding genes (2450 of 7194) and were thus over-represented compared with Ensembl data set (24%: 4883 of 20 365 non-coding genes) (Table  2 ).
In the hESC-hs0238 and AML-hs0430 DGE libraries, 4% of all tags corresponded to annotated ncRNAs, independently of the cell type (normal or cancer), and were similarly distributed as those obtained for 'TranscriRef' (Supplementary Figures  S2  and S3 ). We then repeated the analysis by allowing a tag to overlap both coding and non-coding annotations. This procedure enabled to extract not only overlapping annotations but also transcripts overlapping with non-annotated genomic regions (Figure  3A  and Table  1 ). A negligible fraction of the non-coding sequences (5%) was in cds regions, irrespective of the tag orientation. Conversely, non-coding sequences were abundantly represented in the antisense categories. For example, 20% of the 5 0 UTR antisense occurrences (i. e. 294 070/1 429 397) overlapped with non-coding transcripts.
As expected, most non-coding transcripts (33. 5%) overlapped with intergenic distal regions (1 723 166 of the 5 138 478 occurrences): $50% were lincRNAs and the rest belonged mostly to the 'other lncRNAs' category. Interestingly, in this category, 3 415 312 sequences remained without annotation and could thus The output of the 'TranscriRef' library (after process A or B) was compared with the Ensembl database version 66. The first two columns describe the distribution of 'TranscriRef' tags and genes, respectively. The third column describes the distribution of Ensembl genes and the fourth column represents the 'TranscriRef' genes/Ensembl genes ratio. correspond to either potential new transcripts or methodological artefacts (Figure  3B ). These transcripts corresponded to 144 981 ('TranscriRef' dataset), 8131 (AML-hs0430 library) and 6273 (hpESC-hs0238 library) intergenic tags (Supplementary Figures  S2  and S3 ).
The filtering step of the digitagCT pipeline: selection with high accuracy of new tissue-specific intergenic transcripts To address the question of the biological significance of intergenic transcripts and to avoid the problem of false locations that could generate random annotations (methodological artefacts), we optimized our data mining procedure by integrating other available transcriptomic data, as described in 'Materials and Methods' section (filtering and species-comparison processes). We first tested the species-comparison process (an optional filtering procedure) to validate the hypothesis that sequence conservation could provide interesting biological information. For this purpose, we investigated tags conserved between the human and the mouse genomes. We intersected all the human 'TranscriRef' tags with a unique location with a collection of murine DGE/SAGE libraries to select tags that are expressed in both species.
In all, 8705 distinct tags corresponding to 6 363 596 input sequences were expressed and located in The first inset pie chart represents all intergenic 'TranscriRef' sequences. The second inset corresponds to the four identified categories of non-coding transcripts in intergenic regions (small ncRNAs, lincRNAs, other lncRNAs and ncRNAs) with their relative proportions (%). The difference between the two pie charts represents orphan sequences without annotation that could correspond to potential new transcripts or methodological artefacts. New non-annotated sequences are more abundant than the identified non-coding sequences. both genomes. The distribution of annotation categories for this set of tags was comparable with that of the whole 'TranscriRef' data set (Supplementary Figure  S5 ). We then considered the subset of common exonic tags (in the sense orientation for orthology analysis).
Among the 3268 protein-coding gene tags (4 005 620 sequences), 96. 1% corresponded to orthologous genes. We then thoroughly analysed their functional relevance using the DAVID database. The same analysis was performed with common intronic or antisense tags, and the enrichment in GO molecular functions is reported in Supplementary Table  S2. Not surprisingly, this analysis revealed enrichment in GO annotations that correspond to highly conserved molecular functions, thus validating our selection and annotation processes. The three categories of conserved tags (exon, intron and antisense) showed common GO function enrichment profiles, such as nucleotide binding, transcription factor activity or RNA polymerase activity. Interestingly, the antisense group presented specific annotations, such as pyrophosphatase activity, hydrolase activity or nuclear hormone receptor binding.
Finally, species-comparison selection also revealed 293 conserved non-coding transcripts, including 40 lincRNAs and 222 other lncRNAs. Moreover, we identified 486 potential new intergenic transcripts that were conserved and expressed in both species. Other parameters could be used and collected for each intergenic tag, based on its chromosome position and strand orientation. Complementary data could be associated, including expression level (i. e. occurrence in SAGE Genie and 'TranscriRef' data sets), tiling array information, proximity to coding and non-coding genes on both 3 0 -and 5 0 -ends and coverage by RNA-Seq reads. To validate these parameters in the filtering process for identifying potential tissue-specific transcripts, first the hESC-hs0238 DGE and hpSC RNA-Seq libraries were analysed and 36 potentially relevant intergenic tags were randomly chosen (see 'Manual validation and curation' in the Materials and Methods section for the selected parameters).
Then, analysis of their distribution in the 54 available libraries allowed to classify them as ubiquitous or ESC-specific (Figure  5A ). The 36 selected intergenic candidates were not annotated by Ensembl version 62 at the time of the screening. Following the release of the current version 66, 16 of these tags have been annotated (6 are associated with antisense transcripts, 3 with intronic transcripts and 7 with newly annotated genes or pseudogenes), whereas the other 20 tags are still non-annotated. We measured their expression level by qPCR in normal and cancer cell lines and validated 78% of all candidates (90% when considering only the 20 non-annotated intergenic tags) (Supplementary Table  S3 ). We further categorized the selected candidates by manual curation using the Ensembl display window with the DGE and RNA-Seq data.
Among the annotated tags, some corresponded to new non-coding variants (Figure  4A ), and among the non-annotated ones, a large fraction of candidates were lncRNAs (longer than 500 bp) (Figure  4B  and Supplementary Figure  S4 ) and a few could correspond to short exons or small RNAs (Figure  4B  and Supplementary Figure  S4 ). As the coverage by RNA-Seq data could be used for tissue-specific selection, we tested specifically the hpSC data. Clustering analysis was performed on a selected data set of intergenic distal tags (see 'Materials and Methods' section for details). This set included $34 000 potential new transcribed regions (Figure  5B ). Clustering allowed 'checking' tissue specificity. As highlighted by the focus in Figure  5B , all embryonic cell libraries clustered together and showed a tissue-specific pattern with 1121 highly expressed tags.
When applying the digitagCT pipeline filtering process (by comparing tags with the HD291 human embryonic stem cell RNA-Seq reads) to extract the more reliable transcripts, we identified 524 intergenic tags that were covered by both DGE and RNA-Seq ($50% of the 1121 highly expressed tags). Of note, the majority of the 1121 intergenic tags represented novel ncRNAs (931 candidates including 423 tags detected by RNA-Seq), whereas only 190 tags corresponded to already annotated non-coding transcripts (Supplementary Figure  S6 ). All these data are available at http://www. get. univ-montp2. fr/en/ncRNA. Looking at the nearest 5 0 and 3 0 genes, we observed that intergenic tags were often neighbours of lincRNAs, indicating transcriptional clusters. The search of neighbouring genes could be an interesting mean to provide potential functional information, as illustrated by the case of the PIWIL4 neighbours that are specifically expressed in hESCs (Figure  4A ).
1%  (31). This effect can be minimized by taking into account the tag occurrence. For instance, for a highly expressed transcript (10 000 occurrences of the canonical tag), the non-canonical enzymatic cutting generated only 10 occurrences, thus strongly reducing the weight of second rank tags in the global sequence distribution. Moreover, the total number of tags from 'TranscriRef' with a unique location ($750 000) included in the analysis represents an interesting biological data set of human transcription with strand orientation information. Why combining DGE and RNA-Seq data?  the total RNA starting material and can be used to detect stranded transcripts and to provide an interesting approach for overall gene expression data mining. A DGE tag is a transcript signature. Hence, the tag allows determining the tissue expression profile of a transcript by examining its number of occurrence in each available DGE library and comparing it in all available libraries.
In the present work, we investigated transcript conservation in the human and mouse genomes because their evolutionary distance is considered sufficiently high to allow separating conserved functional sequences that are under purifying selection from background neutral DNA  (33). Using the digitagCT pipeline to investigate the conservation of protein-coding genes in the human and mouse genomes, we found significant enrichment of GO molecular functions. Moreover, by comparing conserved exonic, intronic and antisense transcripts, we show the presence of specific and common molecular functions, suggesting that conserved non-coding transcripts have essential functions. Specifically, conservation analysis of intergenic noncoding transcripts revealed 303 expressed and conserved novel ncRNAs, the function and tissue-specific expression of which could be further investigated.