dataesr/scientific-paragraphs-categorization huggingface dataset:
However, given the incompleteness of allele state at informative positions, it is not possible to determine whether the majority of ancient samples indeed belong to Q1a2(xQ1a2a, Q1a2b), as the Tajik sample, or a further downstream marker defining Q1a2a or Q1a2b, and therefore they were placed at the root of all Q1a2 branches: DA355 Q1a2(xQ1a2b2,Q1a2a1b,Q1a2a1c); DA361 Q1a2 (xQ1a2b,Q1a2a1b,Q1a2a1a1,Q1a2a1a2); RISE672 Q1a2(xQ1a2a,Q1a2b1,Q1a2b2); and RISE674 Q1a2(xQ1a2a,Q1a2c,Q1a2b1,Q1a2b2). In ancient groups, lineage Q1a2a-L53 was identified solely in the Baikal Early Bronze Age samples from Shamanka and Ust'Ida, which closely match one individual from Turkmenistan. Only individual DA336, which presents Q1a2a(xQ1a2a1), could be excluded from the downstream Q1a2a1 branch, with the others not having enough data to clarify their membership status.
Despite this, the data obtained for a subset of Shamanka_EBA samples provided substantial evidence that these did not belong to either a Clovis-related branch Q1a2a1b defined by M971 or to Kennewick's M930-Q1a2a1a branch, specifically DA335 Q1a2a (xQ1a2a1a,Q1a2a1b2), DA337 Q1a2a (xQ1a2a1a,Q1a2a1c); DA338 Q1a2a (xQ1a2a1a,Q1a2a1b2); DA353 Q1a2a (xQ1a2a1a,Q1a2a1b,Q1a2a1c1); and DA356 Q1a2a (xQ1a2a1b,Q1a2a1a1d,Q1a2a1a1e). 1 Okunevo sample and 1 UstIda_EBA belong to Q1a2a1, and where data is available, these samples carry ancestral alleles at markers defining American lineages: DA343 Q1a2a1 (xQ1a2a1a,Q1a2a1b); RISE662 Q1a2a1 (xQ1a2a1b,Q1a2a1a1,Q1a2a1a2). 1 ShamankaBA (DA339) and 3 Okunevo (RISE664, RISE718, RISE719) belong to Q1a2a1c-L330 (xQ1a2a1c1), lineage also present in the Yeniseian-speaking Kets in our dataset.
S2. 14. 1. 6. 4 Turkmenistan and Anatolia The Namazga samples from Turkmenistan belong to J-M304 (DA379) and to J2a1-L26 (DA381). The later Iron Age sample Turkmenistan_IA from the same region belongs to the F992/Z93-R1a1a1b2 lineage, which has also been identified in Srubnaya Late Bronze Age Steppe (LBA) populations (47). In our dataset, this lineage and their subclades have been identified in 4 Altaians, 2 Kyrgyz, 2 Bashkirs, 2 Tajiks, 1 Teleut, and 1 Uyghur individual. In a larger survey of R1a derived males, it was determined that the vast majority of Z93 lineages occur in Central and South Asian groups, while the sister branch Z282 is mostly restricted to Central and South Asians  (148). The fact that the Turkmenistan_IA sample shares the Z93 lineage with Srubnaya is in agreement with the increased affinity of the Turkmenistan sample to LBA steppe populations.
All Anatolian Early and Middle Bronze Age individuals belong to J2a derived lineages with the exception of the Anatolian MLBA sample MA2208, which instead carries the G2a2b1 lineages, closely related to those present in Anatolian and European Neolithic samples  (47, 149). Regarding the J2 lineages identified, transmission through contact with populations related to Caucasus hunter-gatherers or Iranian Neolithic groups is a possible explanation, given they have been shown to carry J/J2 clades  (7, 42)   To investigate mitochondrial DNA lineages in our ancient and present-day dataset, we selected reads aligned to the mtDNA with samtools and uploaded the resulting individual bam files to the mtDNA server  (151). We submitted the resulting hsd output file to haplogrep V2, which we used for haplogroup identification, and downloaded the resulting aligned mtDNA sequences in fasta format.
In the present-day dataset we analyze here, it has been found in individuals of the Yukpa, Tsimshian, Athabaskan, and Mayan populations. The C5c lineage was identified in 4 Okunevo individuals. Interestingly, this lineage has been suggested to be restricted to Altai populations, which would suggest some extent of temporal mtDNA continuity in the region where Okunevo samples were excavated  (143). We identified the C4a2a1 in 3 ShamankaBA, 1 Kazakh individual as well as closely matching 1 Yakut (C4a2a1a) and 1 Evenk individual (C4a2a1b). 2 UstIdaBA and 1 ShamankaBA carry the mitochondrial lineage C4a1a3, which was also identified in 1 ancient individual from Ust'-Belaya, dated between 4410-4100 BCE  (154). CentralSteppe_EMBA samples both present subclades of the C4 lineage, with one of the samples carrying C4 and the other C4a1a4a.
Regarding modern samples, our results are concordant with other observations that have shown that while C4a1 lineages are more widespread across Siberia, C4a2 are more restricted to Evenks and Yakuts  (155). The Copper Age Botai sample BOT2016 is placed as the root of the Z clade, and it presents haplogroup Z1a. In our modern dataset, haplogroup Z1a was found in an Altay-speaking Teleut individual and it has been reported to be broadly distributed across East/Central Siberia  (156). Notably, the presence of the Z1a lineage in Saami, Finns, and Volga peoples has been linked to movements from Siberia into Northern Europe occurring between 3,000-2,000 years ago  (157). Clade D appears to have persisted in the Baikal region from the Neolithic to the Early Bronze Age, with occurrences of lineage D4 lineages across this period of time.
Of these, lineage D4e1 occurs exclusively in 2 ShamankaEN. The mitochondrial lineage D4j, however, was identified in both Baikal Neolithic and Bronze Age individuals and typically presents a South Siberian distribution (158). Additionally, D4j was also found in 1 Ottoman individual, which may be the result of contact with Central Asia during this period, as also supported by autosomal ancestry observations for this sample. In our modern dataset, multiple subclades of D4 were identified in Dai, Buryat, Teleut, and Khanty individuals. We note that the North American Clovis sample carries the D4h3a7 haplotype and that a Devil's Gate Neolithic sample belongs to the D4 haplogroup  (25). In the ancient samples of the present study, clade G is represented by 3 ShamankaEN and 1 ShamankaBA individuals that belonged to G2a1, a subclade of G2a that is mostly restricted to Central Asian populations  (159).
Interestingly, we note one Scythian individual presented a closely related haplogroup, G2a4 (47). G2a is frequent in Turkic-and Mongolic-speaking populations in Asia (158), which is in agreement with the higher amount of Eeast-Asian-related ancestry identified in the Baikal Neolithic group. In the present-day dataset, it is more closely related to 1 Buryat and 1 Dungan, which present subclades of G2a and G2b, respectively. Regarding clade H, 3 Okunevo individuals belong to H6a1b and one to H6a. The closely related H6b was also identified in one Tajik individual. H6 lineages can be commonly found in Central Asian populations (158). Lineage F1b and sublineages were identified in 3 Baikal_EBA and 1 Baikal_EN, and in 1 individual each of the present-day Kalmyk, Turkmenistan, and Kyrgyzstan populations.
Rare variants are typically the result of recent mutations that have taken place since the out-of-Africa dispersal and are geographically distributed in patterns that reflect the dispersal of descendants from the original carrier of the mutation (163). As such, they can provide useful markers of dispersal and recent ancestry  (164). S2. 15. 1 Relative abundance of rare variant sharing with European and East Asian populations at a regional scale We first merged the dataset consisting of ancient whole-genome sequences from the Botai, Yamnaya, and other samples across Eurasia with the individuals from the 1000 Genomes Project. We then removed all variants that were either C-to-T or G-to-A transitions to avoid confounding due to DNA damage  (165). This merged dataset was used to assess rare variants shared between ancient genomes and modern populations.
We determined rare variants to be variants that had a global minor allele frequency < 1% in the 1000 Genomes Project Phase 3 dataset. To explore broad-scale spatial patterns of rare variant sharing between ancient and modern genomes, we determined the number of rare variants that were shared between European populations (EUR) and East Asian populations (EAS) of the 1000 Genomes Project for each of several ancient sequenced genomes (Fig. S33 ). The Yamnaya consistently share a higher proportion of rare variants with European populations, whereas the Botai share a higher proportion of rare variants with East Asian populations (Fig. S33 ).
Complex organisms originate from and are maintained by the information encoded in the genome. A major challenge of systems biology is to develop algorithms that describe the dynamic regulation of genome functions from large omics datasets. Here, we describe TETRAMER, which reconstructs gene-regulatory networks from temporal transcriptome data during cell fate transitions to predict "master" regulators by simulating cascades of temporal transcription-regulatory events. npj Systems Biology and Applications (2018)4:29; doi:10. 1038/s41540-018-0066-z While previous efforts described cell fate transitions by the reconstruction of transcription factor (TFs)-driven gene-regulatory networks (GRNs) from the analysis of publicly available data (e. g. , microarray transcriptomes;  1, 2  Cap Analysis of Gene Expression (CAGE) data;  3  enriched TF-DNA binding motif analysis  4, 5  ), none of them considers the inherent temporal dimension of this process.
Also, neither provided a modular strategy for reconstructing specific GRNs by incorporating novel information as it becomes available in public databases. Here, we present TETRAMER (TEmporal TRAnscription regulation ModellER); a Cytoscape App that (i) reconstructs cell fate transition-specific GRNs by integrating user-provided temporal transcriptomes with a variety of pre-established TF-target gene (TG) relationships issued from different types of public information; (ii) predicts master regulator TFs by modeling temporal transcriptional regulation propagation; and (iii) reveals the temporal transcription-regulatory relationships between the TFs participating in cell fate transition (Fig. 1 ). TETRAMER generates a GRN that includes the temporal evolution of global transcription by using information derived from three sources: GRNs constructed from a plethora of transcriptomes (CellNet 1 ), the genomewide mapping of human promoters and enhancers in multiple cell types/tissues by CAGE of the FANTOM5 consortium (regulatory circuits  3  ), and the systematic analysis of ChIP-seq information in the NGS-QC database  6  (http://ngs-qc.
Among them, several factors implicated in the maintenance of pluripotency and self-renewal of ESCs (SALL4, SOX2, NANOG, NR0B1, or POU5F1) were shown to be activated at late reprogramming stages (Supplementary Fig. 3 ). TETRAMER predicted in addition several other factors that were previously reported to be involved in, or enhance reprogramming, like the KLF4-interacting SWI/SNF catalytic subunit SMARCA2/BRM,  9  the DNA demethylase TET1, which can replace OCT4 in some reprogramming cocktails,  10  or the PRC2 subunit JARID2. 11, 12 e previously reported that tumorigenesis of pre-transformed human fibroblasts is induced by conditioned medium from senescent cells. 13 TETRAMER (Supplementary Fig. 4 ) revealed in this system the induction of an immediate early gene program involving proto-oncogenes (JUN, JUNB, MYC), suggested not only tumor suppressors (EGR, 14 KLF6  15  ), but also broad-spectrum inflammatory regulators, like the ubiquitin-editing enzyme TNFAIP3.
16 The early program drives the temporal induction of downstream NF-κB-mediated programs known to link inflammation and cancer (REL, RELB, NFKB, NFKB2),  17  and the cytokine-  ). Thus, the secretome of senescent cells activates a plethora of gene regulators that apparently substitute for the oncogene-mediated activities in the stepwise model of human primary cell transformation. (i) (ii) (iii) 1 1 1 1 1 1 1 0 -1 1 -1 1 1 0 0 -1 1 -1 -1 1 As final example, we used TETRAMER to reconstitute TF wiring implicated in the trans-differentiation of B-lymphoma cells to primary macrophages by over-expressing CEBPA  19  (Fig. 2a ). In fact, GRN reconstruction revealed the downstream TF regulatory cascade initiated by C/EBPα, thus providing a comprehensive view of the master regulators implicated in this process.
This TF regulatory wiring was reconstituted by integrating TF-TGs relationships from three GRN databases (CellNet 1 , CAGE 3 , qcChIP-seq  6  ; Fig. 2b ). Note both the redundancy (CellNet vs. qcChIP-seq, TFs depicted at top right; CellNet vs. CAGE, TFs specified on left) and the unique retrieval of some TFs (e. g. , HIF1A, MAFF) from different databases, illustrating the advantage of incorporating multiple sources of TF-TG relationships. Indeed, albeit in the predicted C/EBPα cistrome (Fig. 2c ), FOSL1 was only retrieved from CAGE, SPI1 only from CellNet, and JUNB only from the qcChIP-seq database, independent ChIP-seq assays demonstrated C/EBPα binding proximal to each gene, but beyond the proximity criterion of the qcChIP-seq collection (enhancer regions < 10 kb distance to TSS), thus explaining why FOSL1 and SPI1 are not predicted as C/EBPα targets by this collection (Fig.
To date, few molecular resources have been developed on Lavandula angustifolia and no SNP markers have been reported. As of June 2017, 204 nucleotide sequences had been deposited in the NCBI's GenBank database for Lavandula species, with more than 50% being sequences of Lavandula angustifolia. Most of them (~25%) are related to synthesis or regulation of essential oils  [14] [15] [16] [17] [18] [19] [20] [21] ; or are isolated from the chloroplast genome (~30%). Moreover, EST-derived SSR markers have been recently developed and successfully tested for transferability between Lavandula species  [22]. In this study, we firstly developed genomic reference sequences thanks to de novo RNA-seq assembly and transcriptome guided DNA-seq assembly. Secondly, we used these resources for SNP mining and finally tested the SNP within the scope of a phylogeny analysis of lavender clones.
Since reads 1 were of better quality than read 2, they were used to build the primary De Bruijn graph and reads 2 were used to resolve 8 bubbles in the graph. The k-mer size values used were 64 bases for CLC and 25 bases for TRINITY (fixed default value). The quality of the raw assemblies was assessed with TRANSRATE  [31]. TRANSRATE maps paired-end reads back to the raw contigs and calculate metrics (percentage of reads mapping back to contigs in proper pairs, number of contigs with a predicted open reading frame, estimation of fragmented transcripts…), in order to assess how well the assembled contigs are supported by sequencing data. On this basis, a score is determined for each contig and the set of contigs with best scores are gathered into an "optimized assembly". TRANSRATE also computes a global score that allows comparing two or more assemblies performed with the same initial dataset but with different tools or settings.
To evaluate the completeness of the assemblies, we performed a BLASTx+ (e-value 1e-20) alignment of the optimized assemblies against SwissProt database (http://www. uniprot. org/) using a script developed by the TRINITY developer's team (https://github. com/trinityrnaseq/trinityrnaseq/wiki/Counting-Full-Length-Trinity-Transcripts). This analysis allowed to determine (i) the number of unique best blast hit (BBH) found with contigs from each optimized assembly and (ii) the percentage of coverage of the hit sequence by the contig. We finally took advantage of both assemblies to build the reference Unigene. This Unigene is compound of the sequences assembled with TRINITY or CLC that covered more than 70% of their BBH in SwissProt database with more than 30% identity. In case of redundancy within an assembly (i.
In silico functional annotation of the reference Unigene 9 To further describe biological functions related to the Unigene sequences, we ran the Trinotate pipeline (https://trinotate. github. io/) which is a comprehensive annotation suite adapted to in silico annotation of de novo assembled transcriptome. The analysis includes homology searches (evalue 1e-5) to SwissProt database, protein domain identification using HMMER and Pfam databases, protein signal peptide and transmembrane domain prediction with Signalp and tmHMM servers, and search for Gene Ontology (GO) terms and KEGG pathways  [32, 33]. Insofar as the ability to annotate sequences based on similarity search against databases depends on the completeness of these databases, we completed the annotations using supplementary databases and tools.
2 ). We stopped the iterations when the maximum number of sequences from the reference Unigene were re-built in the draft Genespace (in our case, at the 8ith iteration) thus getting our reference Genespace (step 6, Fig. 2 ). CLC Assembly Cell (https://www. qiagenbioinformatics. com/) v 5. 0 was used for reads mapping (CLC_ref_assemble_long tool) and read extraction (sub_assembly tool) steps. Mapping was less stringent at the first iteration (step 1, Fig. 2 : 100% of the read length with a similarity percent of 95%) than for other iterations (step 4, Fig. 2 : 100% of the read length with a similarity percent of 98 %) in order to take into account exons/introns junctions at the first step of mapping. For assembly steps, we used idba_ud v. 1. 0. 9 software  [34]. This tool offers the advantage to use the new sequences assembled at the iteration i-1 to 'guide' the assembly at the iteration i (--input-long-read option) and thus allowing to keep the information build at the iteration i-1 while assembling new sequences at iteration i.
SNP discovery Filtered DNA-seq paired-end reads of the 15 lavender clones and of the lavandin 'Grosso' were mapped against the Genespace sequences with CLC software. To be included in the mapping, at least 95 % (90% for 'Grosso') of the read (length fraction = 0. 95 or 0. 90) must be aligned to the reference sequence with at least 90% identity (80% for 'Grosso') (similarity fraction =0. 90 ou 0. 80). Moreover, reads with non-11 specific matches (i. e. reads mapping equally well at several alignment positions) were excluded from the mapping. SNP detection was performed with CLC and GATK v 3. 6  [35] [36] [37]  tools. It has been demonstrated that a significant improvement of SNP calling -in terms of further successful genotyping of the SNP-can be obtained by focusing on SNP discovered by more than one method  [38].
Assessment of the lavenders genetic relationship From the filtered putative SNPs set, we selected SNPs that generated no missing genotype data. The analyses were based on the 'Population Structure' workflow from the Grundwald laboratory (https://grunwaldlab. github. io/Population_Genetics_in_R/Pop_Structure. html). The R packages 'poppr' version 2. 7. 1  [39, 40] , 'adegenet'  [41, 42]  and 'ade4'  [43]  were used to calculate absolute genetic distances with 'Prevosti's method'  [44]  (a method suited to SNP data), to perform a principal component analysis (PCA) and built an Neighbor joining phylogenetic tree with 1000 bootstrap.
RNA-seq and DNA-seq trimming A total of 169,082,890 (84,541,445 pairs) of RNA paired-end (PE) reads were sequenced reaching 25. 5 Gigabases (Gb) (S2 Finally, a pool of 147,672 contigs from CLC raw assembly and 194,706 contigs from TRINITY raw assembly were selected as two optimized assemblies for downstream analyses. To assess the completeness of the optimized contigs sets, we used a TRINITY utility (see Methods), to perform a BLASTX alignment against SwissProt database (e-value 1e-20) (Table  1.
Genetic distances analysis of 16 lavender clones The 9,449 polymorphic sites generating no missing data were used to calculate genetic distance between the 16 clones with the Provesti method (S6 Table, S7 Fig). Rare alleles within the genotyping matrix were deliberately preserved in the dataset in order to maximize the discriminations between the clones. The pairwise genetic distance matrix was used to perform a neighbor joining tree with 1000 bootstraps (Fig  6 ). Some notable clusters were observed. Firstly, and as expected, we observe that 'Grosso' appeared to be divergent from the lavender clones. Secondly, some tight clusters were observed: C15. 50-B7, Diva-Maillette, Ruffinato-5. 90 and Gabelle-Frisée (the two "blue lavender" of the dataset). The bootstrapped trees suggested that the clone 77.
13 shared a common ancestor with Ruffinato and 5. 90, though 77. 13 seems to have more diverged than the two later from the shared ancestor. Finally, it seems that the known geographical origin France / Bulgaria was not structuring in our dataset as B7 was genetically closer to C15. 50 than to B6, the other Bulgarian clone. Results obtained on the PCA with the 16 clones were analogous to the hierarchical clustering result (S8.
It is difficult to compare this value since this metric is not yet widely used. Similar studies only provide the global percent of reads mapping back to the reads -which, in our case, indicate that the assemblies are well supported by reads data. Moreover, we obtained TRANSRATE scores of 0. 184 and 0. 198 for optimized assemblies obtained with CLC and TRINITY, respectively. This indicated that our assembly was better than 25 % of the published assemblies tested in Smith-Unna et al. [31]. To assess the recovery of genes, we chose to perform a BLASTX alignment analysis against the SwissProt database. To build the reference Unigene, we only selected assembled sequences that covered at least 70% of their hit in the database with at least 30% of similarity. With these supplementary selective conditions, we restricted ourselves to a narrow annotatability of the reference Unigene and we may have 20 missed out some well assembled sequences or biased our reference Unigene towards well-conserved sequences between species.
However, according to our goal that was to develop molecular resources with a high level of reliability, this choice was appropriate to prevent from keeping sequences with assembly errors, which could lead to poor mapping quality for SNP detection  [49, 28]. Despite filtering steps and due to the database selected for BLASTX alignments, contaminant sequences were identified in the 10,060 selected sequences of the Unigene and removed to obtain a cleaned reference set of 8, 640 coding sequences. Gathering the results obtained from two or more assemblers allow to reduce the number of sequences due to bioinformatics artefacts and thus, to increase the assembly quality  [28]. In our reference Unigene, almost 90 % of selected sequences where assembled with both Trinity and CLC indicating the highquality and reliability of the Unigene developed herein.
In this study, DNA assembly for introns insertion allowed the reconstruction of 81 % of the sequences initially present in the reference Unigene versus 88% in the initial study of Aluome et al. [27]. Among these reconstructed sequences, we found that the size of some sequences was shorter in the Genespace than in the Unigene. There are different hypotheses to explain this partial reconstruction. The first one would be that there might be transcripts with introns in the reference Unigene since intron retention is a common splicing mechanism in plant  [50, 51]. By selecting the longest form of sequences, we might have enriched our reference Unigene with sequences where the introns were retained and thus that could not be improved with the DNA assembly. Another cumulative hypothesis would be related to sequencing depth and the high polymorphism frequency in the clone used in this study.
Since the sequencing depth reached in the RNA-seq was larger than in the DNA-seq, some complex sequences may have been built in the Unigene but not in the Genespace due to a lower sequencing depth and thus lead to assembly premature stops. It would be interesting to conduct a structural annotation of the sequences with an appropriate software, in order to have a finer information on the intron/exon organization of the assembled sequences. However, results obtained herein (gene sequences with a mean of 3 exons and 2 introns) are in concordance with mean values observed in plants. Likewise, the size of intronic and exonic regions is in the order of magnitude of what is observed in perennial plants such as Arabidospsis lyrata  [50] , Fragaria vesca  [52]  or trees  [53].