dataesr/scientific-paragraphs-categorization huggingface dataset:
This differentiation trajectory is characterized by a visible level of uncertainty associated with the branching point. The emerging biological picture is compatible with DC emerging at across a specific, but relatively broad, range of commitment of GMPs. However, it is worth stressing that the number of DCs present in the dataset under analysis is relatively small (30 cells), and hence that the higher uncertainty level associated with the branching from GMPs to DC may be simply due to the relatively small number of DC sequenced at an early committed state. Notably, when we used ElPiGraph on the expression matrix restricted to the most variant genes and used PCA to retain the leading 250 components, we could still discover the branching point associated with the differentiation of CMPs into GMPs and MEPs.
Approximating the Complex Structure of Development or Differentiation from Single Cell RNASeq Data Recently, several large-scale experiments designed to derive single-cell snapshots of a developing embryo  [37, 38]  or differentiating cells in an adult organism  [39]  have been produced. Standard algorithms connecting single cells in a reduced-dimension transcriptomic space are capable of producing informative representations of these large datasets  [62]. However, such representations can be characterised by complex distributions, with areas of varying density, varying local dimensionality and excluded regions. Hence, it may be helpful to derive data cloud skeletons, which would simplify the comprehension and study of these distributions. To this end, we used scRNA-Seq obtained from stage 22 Xenopus embryos  [37]  to derive a 3D force directed layout projection (Figure  5A ).
This graph was then filtered and extended to better capture the data distribution (Figure  5E ). From this analysis, clear non-trivial structures emergence: linear paths, interconnected closed loops, and branching points can be observed. The gene expression profiles have been fitted with a LOESS smoother which include a 95% confidence interval. In the top panel the smoother has been colored to highlight the different paths, with the color indicated in the text of panel F. (H-J) The same approach described by panels A-G has been used to study the single-cell transcriptome of planarians. In panel J, the color of the smoother indicates the predominant cell type on the path. Interactive versions of key panels are available at https://sysbio-curie. github. io/elpigraph/. The different branches (defined as linear path between nodes of degree different from two), display a statistically significant (Chi-Squared test p-value < 5 × 10 -4 ) associations with previously defined populations  [37]  (Figure  5F ).
This feature and also the possibility to use GPUs allows ElPiGraph to construct complex and large graphs containing up to a few thousand nodes in many dimensions. In most practical applications, the number of nodes in a principal graph is advised to be the square root of the number of data points. Therefore, ElPiGraph can be potentially applied to unravel the complexity of large and highly structured data point clouds containing millions of points. The main challenge of approximating a dataset characterized by complex non-linear patterns with a principal graph is finding the optimal graph topology matching the underlying data structure. In order to do that, ElPiGraph starts with an initial guess of the graph topology and graph injection map, and apply a set of pre-defined graph rewriting rules, called graph grammar, in order to explore the space of admissible graph structures via a gradient descent-like algorithm by selecting the locally optimal one (Figure  1D ).
Also, the final structure of the principal graphs can be sensitive to the non-essential particularities of local configurations of data points, especially in those areas where the local intrinsic data dimensionality becomes greater than one. In order to limit the effect of a finite data sample and estimate the confidence of inferred graph features (e. g. , branching points, loops), ElPiGraph applies a principal graph ensemble approach by constructing multiple principal graphs after subsampling the dataset. Posterior analysis of the principal graph ensemble allows assigning confidence scores for the topological features of the graph (i. e. , branching points) and confidence intervals on their locations. The properties of principal graph ensemble can be further recapitulated into consensus principal graph possessing much more robust properties than any of the individual graphs.
Overall, ElPiGraph enables construction of flexible and robust approximators of large datasets characterized by topological and structural complexity. Furthermore, ElPiGraph does not rely on a specific feature selection or dimensionality reduction techniques, making it easily integrable into different pipelines, as it was recently the case for the single cell genomic data. All in all, this indicates that ElPiGraph can serve a valuable method in the information geometry toolbox for the exploration of increasingly complex and large datasets.
Funding: This work has been partially supported by the  Ministry of Science and Higher Education of the Russian Federation  (project No. 14. Y26. 31. 0022 ), by  Agence Nationale de la Recherche  in the program Investissements d' Avenir  (project No. ANR-19-P3IA-0001 ;  PRAIRIE 3IA Institute ), by  European Union 's  Horizon 2020 program  (grant No. 826121 , iPC project), by grant number  2018-182734  from the  Chan Zuckerberg Initiative DAF , an advised fund of  Silicon Valley Community Foundation , by  ITMO Cancer SysBio program (MOSAIC)  and  INCa   PLBIO program  (CALYS,  INCA_11692 ), by the  Association Science et Technologie , the  Institut de Recherches Internationales Servier and the doctoral school Frontières de l'Innovation en Recherche et Education Programme Bettencourt.
The semantic similarity between words in W D nm and words in W D ds is calculated pair-wisely through the calculation function SimCal based on WordNet  [16]  (line 4). Generally, WordNet is a lexical database of the English language, and it has been widely used for supporting the semantic computation of similarity for paragraphs. If the similarity between wd nm and wd ds is no smaller than the pre-specified threshold thd sim , wd ds is assumed semantically similar to wd nm , and thus, wd ds in W D ds can be selected and inserted into the keyword set KW (line 5), which is considered semantically similar to words in the name. The co-occurrence situation in the Algorithm 1 Representative Words Selection Require: -ACT : a set of activities and sub-workflows thd sim : the threshold for semantic relevance thd rel : the threshold for co-occurrence relevance -wdSz: the size of co-occurring text window Ensure: -W D: a set of representative words for ACT end for 20: end for text description is explored as follows.
When the value is larger than thd tp , this topic is assumed as significant. Consequently, the probability of this topic is reserved when the value is no smaller than thd tp , and this topic is assumed as representative for a certain activity or sub-workflow. Since the probability for non-representative topics is set to zero, a probability normalization procedure is conducted for the topics of activities and sub-workflows afterwards. For instance, given an activity with the name "Kegg gene ids" in the workflow entitled "Pathways and Gene annotations for QTL region", the short document is generated as "kyoto genome encyclopedia gene". Sample topics are generated as (topic 332 , 0. 6117) and (topic 73 , 1. 3951E-9), where the topic topic 332 contains the top 10 keywords including "{gene, encyclopedia, kyoto, genome, provide, database, description, get, rest, national}".
Experimental Setting A prototype has been implemented by Java programs, and scientific workflows in the category of Taverna 2 of the myExperiment repository are collected and cleansed, where there are 1573 scientific workflows in this category till to June 12, 2018. Experiments are conducted upon a desktop with an Intel(R) Core(TM) i5-6500 processor, a 6. 00GB memory, and a 64-bit Windows 7 system, and topic models are generated by a tool executed on Linux.
Data Cleansing Before conducting the experimental evaluation, scientific workflows are cleansed as follows: • The titles or text descriptions of scientific workflows lacks specification. In this case, similarity computation for activities cannot be conducted, and the topic discovery is inaccurate. Therefore, these workflows are removed from the dataset. • Activities corresponding to slim services are mostly glue codes, and they do not represent invocation relations between functionalities. Therefore, activities with the slim service type are filtered out. As aforementioned, the semantic similarity between words is computed by WordNet. However, improper words that are not recognized by WordNet are contained in names or text descriptions of activities and sub-workflows. We have to manually handle them case-by-case as follows: • 334 abbreviations are used in the bioinformatics domain, but they cannot be recognized by WordNet.
An example is "documentfind", which is transferred into two words "document" and "find". After handling the names and descriptions of activities and sub-workflows, stopwords words such as can, of, and and, are removed. A table including 883 stopwords is constructed according to the dataset, and it helps the dataset cleansing procedure. Besides, words like accessed, accesses, and accessing have a common word root access. Affixes are removed to keep the root only. This is important for topic model algorithms. Otherwise, they are treated as different word entities, and thus, their topic relevance is inaccurate. To remedy this issue, the lemmatization technology is applied by establishing common reduction rules, which are divided into general deformation rules for nouns and verbs, deformation rules for irregular verbs or nouns, and so on.
Crossing-workflow Fragments Discovery When No Changes Are Applied to Sample Fragments Experiments are conducted when sample fragments remain as they are retrieved from the dataset. An expected result should be exactly matched fragments compared with the fragment of requirement. Experiments for 20 sample crossing-workflow fragments return the right recommendations, which contain original sample fragments, and these fragments are ranked as the first in terms of the fragment similarity value. For instance, the sample requirement as shown in Fig. 4  is represented as follows: This experiment demonstrates that most of the same or similar relations are discovered, and most suitable candidate fragments can be recommended. • E = {swf sub3 , act 4 , act 5 , act 6 , act 7 , act 8 , swf sub9 } • R = {Invok} • S = {(act To compare the effectiveness of our approach against the strategy that recommends single workflows, we conduct experiments where the requirement as shown in Fig.
4  is chosen. The similarity for this requirement and workflows are calculated as the semantic transition-labeled graph edit distance  [20]. Top three legacy workflows are recommended as follows: • Candidate 1 = "Phenotype to pubmed", sim = 0. 598 • Candidate 2 = "Pathway and Gene to Pubmed", sim = 0. 592 • Candidate 3 = "Gene to Pubmed", sim = 0. 560 For example, candidate 1 represents the first workflow to be recommended, whose title is Phenotype to pubmed. Workflows of the sample requirement and candidate 1 are partially relevant with pubmed, which represents a free database of the biomedical paper search. This result demonstrates that this requirement can hardly be achieved by any single legacy workflow, and the discovery of crossingworkflow fragments is more appropriate.
Performance Metrics The metrics precision, recall, and F1 are adopted for our evaluation purpose. Given a testing fragment swf tst , a reusable fragment swf ept contained in the repository is assumed to be included in the expected list of recommendations (denoted SW F ept ) when the similarity between swf tst and swf ept is no smaller than a pre-specified threshold thrd ept. In our experiments, an activity stub in swf tst is replaced by relevant activities and sub-workflows in the dataset, to obtain a series of crossing-workflow fragments, where links on activities and sub-workflows are checked whether they are retained based on the relations specified upon scientific workflows in the repository. Importantly, similarities of fragments are calculated by line 8 of Algorithm 5 through balancing their structural and semantic similarities with respect to swf tst.
Moreover, the high coverage provided by Illumina sequencing led to the identification of consensus sequences for each TE family. Several computational methods were developed, such as RepeatExplorer  [10]  and dnaPipeTE  [11] , to analyze Illumina data from different Drosophila species, and to study TE biology at the populational level. In TE biology, it is also important to estimate the TE insertion (TEI) rate to determine the degree of polymorphism within and between populations. This is an indicator of the activity level of each TE family and can help to date transposition events  [12, 13]. Illumina sequencing of pools of individuals allowed determining TEI frequency in natural samples (from large number of individuals to populations)  [14]. To study individual TEI, dedicated software tools were developed (e.
Genome size estimations Flow cytometry: genome size was estimated according to  [29]  using fresh samples of 4-day-old females heads, with 10 replicates (five heads per replicate) for each Drosophila wild-type strain. findGSE: k-mer distribution was established from the Illumina reads using findGSE  [30]. Briefly, adaptors were first removed from the reads with Skewer version 0. 2. 2 (paired-ends) or NxTrim version 0. 4. 3-6eb8d5e (mate pairs), when necessary. Reads were then treated essentially as previously described  [31]  to remove duplicates, filter out reads mapping to reference mitochondrial genomes (GenBank AF200854. 1 and AF200828. 1  [32] ) or microbial contaminants. This allowed establishing the 21-mer distributions from which genome sizes were estimated using findGSE  [30]  with default parameters, except for dmsj23 in which the k-mer distribution clearly displayed a peak corresponding to heterozygous regions and was thus treated accordingly.
4. 1 flow cells (FLO-MIN106, ONT) and a Nanopore MinIon Mk1b sequencer (ONT) controlled by the ONT MinKNOW software (version 18. 3. 1 for G0, version 19. 05. 0. 0 for isogenic wildtype strains, and version 19. 10. 1 for the G73 and G0-F100 samples). Base calling was performed after sequencing using Albacore (version 2. 3. 3) for G0, and the GPUenabled guppy basecaller in high accuracy mode for isogenic wild-type strains (version 3. 1. 5), G73 (version 3. 3. 3) and G0-F100 samples (version 3. 4. 4).
TE content and TEI site estimates from Illumina sequencing TE abundance was estimated using forward reads and two methods: the TEcount module of TEtools  [33]  and dnaPipeTE (v1. 0. 0 and v. 1. 3. 1) [11]. TEcount estimates TE abundance by quantifying reads that map to a set of known TE sequences, here the rosetta fasta file  [34]. This tool was run using default parameters and Bowtie2 (v2. 2. 4)  [35, 36]. dnaPipeTE assembles repeated sequences from a subsample of reads (<1x) and quantifies reads mapping to these sequences to estimate TE abundance. dnaPipeTE was used with the following parameters: -sample_number 2, -genome_coverage 0. 25). Concerning the genome size option, 175 Mb and 147 Mb were used for D. melanogaster and D. simulans samples, respectively. The rosetta fasta file was used as library  [34].
Genome assembly Raw nanopore reads were QC checked using Nanoplot v1. 10. 2 for sequencing run statistics. Reads with QC <7 were removed by the sequence provider (Montpellier Genomix) before QC. For each dataset, mean length, N50 reads, total reads and bases are listed in Table  S1  and Table  1. Reads were submitted to Flye v2. 6  [40]  with standard options, except --plasmids and -threads 16. Raw contigs were polished using four rounds of RACON v1. 3. 2  [41]  with standard options and 20 threads (-t option; the required mapping was performed using minimap2  [42]  v2. 16 and -x map-ont -t 20 options). At each step, basic assembly metrics (N50, length, L50) were recorded using Assembly-Stats v1. 0. 1 (https://github. com/sangerpathogens/assembly-stats). Once polished, assemblies were visually inspected using D-genies v1.
Global structural variant detection Global variant detection (i. e. variants common to most genomes of a considered sample compared with the reference genome, see below) was performed using the svTEidentification. py tool (available at https://github. com/DrosophilaGenomeEvolution/TrEMOLO). Briefly, this tool recovers the insertion and deletion positions and creates the associated fasta sequence, based on the Assemblytics report from the RaGOO scaffolding (the deletions are extracted from the reference and the insertions from the new assembly). Once the fasta file corresponding to the SVs was recovered, these sequences were matched with BLASTN+ v2. 4. 0 to a specified TE database. Hits larger than 80% of the TE sequence and identical to more than 80% at the nucleotide level were considered as candidate for new TE insertions/deletions (TEI/TED) in the G0, G0-F100 and G73 samples.
LTR minor insertional variant (LTR MIV) detection Each raw long read was mapped using minimap2 v2. 16 (-ax map-ont -t 16 as options) to the assembly corresponding to that set of long reads. After recovering the sam file, samtools v1. 10. 0 was used to compress and sort the sam file in BAM with samtools view and samtools sort (basic options, but with 16 threads), and the MD tag was added using samtools calmd. Then, SV were detected in the resulting sorted BAM file using Sniffles v1. 0. 10 with at least 1 read and --report_seq -s 1 -n -1 as parameters  [51]. These sequences longer than 1,000 bp were aligned with BLASTN v2. 4. 0+ (-outfmt 6) to the LTR subset (60 families) of the database used before. A nucleotide alignment of more than 94% identity and a minimum of 90% of the total length of the TE consensus sequence were then considered as criteria to validate a putative LTR minor insertion variant (LTR MIV), if the length of the variant did not exceed the total size of the TE by more than 18 nt.
Automatic identification of the Target Site Duplication for LTR MIV The putative LTR MIVs matching to six LTR families (blood, gtwin, mdg3, ZAM, roo, and copia) were studied. One read supporting each MIV, previously extracted in a fasta file, was compared by BLASTN v2. 4. 0+ with the corresponding consensus sequence. To automatically check for the presence of a TSD, the positions of the 5' and 3' end of the TE alignment were determined within the read. 30nt-long sequences upstream and downstream the putative insertion site were extracted and were aligned to detect the presence, on both sides of the insertion, of a short duplication, the size of which was previously reported by  [55]  for ZAM and by  [52]  for the other TEs. The resulting TSD sequences were then extracted and used to create sequence logos with WebLogo (https://github.
Comparison of TE abundance in the isogenic wild-type strains measured by Illumina and ONT sequencing. To validate the ONT approach, the TE abundance in the isogenic wild-type D. melanogaster and D. simulans strains was evaluated using dnaPipeTE  [11]  and TEcount  [33]  for Illumina sequencing data, and RepeatMasker for ONT assembled chromosomes (Figure  2 ). Overall, TE content (expressed as genome percentage) was often higher when estimated using dnaPipeTE (Illumina data) (Wilcoxon matched-pairs signed rank test; p=0. 0234) than with RepeatMasker (ONT assemblies) (Wilcoxon matched-pairs signed rank test; p=0. 0156). This might be explained by the fact that unlike the RepeatMasker TE database, dnaPipeTE is based on the de novo detection of TEs and the local assembly of TE families, independently of a previously annotated reference genome, thus recovering the maximum number of reads that correspond to known and unknown TEs.
In agreement, the correlation was higher between the results obtained with RepeatMasker (ONT data) and the results obtained with TEcount, which is based on the read similarity against a curated database of known TEs  [34]  (r= 0. 8921, p<0. 0001), than with dnaPipeTE (r= 0. 8504, p<0. 0001) (Figure  2 , right panel). As previously reported, the LTR group was more abundant than the LINE and DNA transposon groups in all Drosophila genomes (see  [60]  for a review).
Comparison of the TEI sites identified in the isogenic wild-type strains using the Illumina and ONT data. Before focusing on the results provided by the ONT approach, we first compared these data to the classically used Illumina results based on discordant pairs of reads (method developed in the laboratory, see Material and Methods). The number of TEI sites tended to be higher when using the Illumina data than ONT data (Wilcoxon paired test, p-value = 0. 023). This could be due to the presence of false positives caused by PCR artefacts during the Illumina library preparation  [18] , and/or to the fact that some TEIs might have been too short (fragmented or partially deleted) to be identified using the assembled ONT data. Using the Illumina approach, TEI numbers were significantly lower in the D.
simulans than in the D. melanogaster strains (Wilcoxon test, p-value = 0. 029), but not when using the ONT data (Wilcoxon test, p-value = 0. 343) (Figure  3 ). This may reflect a bias towards D. melanogaster sequences in our TE reference file, and/or a long-term difference in TE dynamics between these species  [25, 61]. Comparisons (chi-square tests) of TEI distributions across TE groups (DNA, LINE, LTR) (see Table  S3 ) showed that in D. simulans, the distributions obtained using both approaches were similar. Conversely, in D. melanogaster, the TEI number for retrotransposons was significantly higher relative to the other groups, when using the Illumina approach. This may be due to the higher propensity of D. melanogaster retrotransposons to be involved in Illumina PCR chimeras  [18]  because of their higher genome occupancy (Figure  2 ), and this difference may be amplified by the exponential behavior of the PCR reaction.
Indeed, for a subset of 15 families, Lerat et al. found that TE copies were more internally deleted (i. e. shorter) in D. simulans than in D. melanogaster  [24]. However, analysis of these 15 families using our ONT data indicated that they displayed, on average, longer fragment sizes compared with the other TE families in D. melanogaster (Wilcoxon test, p-value = 8e-19), but not in D. simulans (Wilcoxon test, p-value = 0. 34)  [24]. This suggests that  Lerat et al. 2011  focused on TE families that have particularly large copies in D. melanogaster  [24] , probably because they have been more studied in the past due to their easier analysis by in situ hybridization on polytene chromosomes  [7, 25, 64]. Then, the Refiner module of RepeatModeler (http://www. repeatmasker. org/RepeatModeler) was used to compute the intra-family sequence divergence (average Kimura distance) (Figure  5b ).
Recent TEIs may not be frequent enough to be incorporated in the assembled genomes To challenge the ONT assembly approach, a bioinformatic analysis was performed to identify recent LTR TEIs that occurred during the last 73 generations (G73) in the unstable piwi KD line (Materials and Methods and  [28] ). As a control, to estimate the basal transposition rate when TEs are normally repressed by the functional piRNA pathway, the genome of the hundredth generation (called G0-F100) after establishment of the stable G0 isofemale line was also sequenced. Using the pipeline for detection of global variants (Figure  1 ), no new ZAM insertion could detected in the G73 assembled genome compared with the G0 reference genome. This is not consistent with previous data obtained by PCR quantification of the ZAM copy number  [28].