dataesr/scientific-paragraphs-categorization huggingface dataset:
We studied the Anna Karenina effect in our synthetic dataset. Fig. S3  shows that correct pairs have closer correct neighbors than incorrect pairs: for instance, employing a threshold Hamming distance of 0. 15 to define neighbors (see Fig. S3A ), correct pairs have 6. 2 neighbors on average, of which 90% are correct pairs, while incorrect pairs have 0. 63 neighbors on average, of which 33% are correct pairs (see Fig. S3B ). In this case, correct pairs thus have almost 10 times more neighbors than incorrect ones, demonstrating a strong Anna Karenina effect. This favors correct pairs in the IPA, especially at early iterations  [22]. For the parameters used in Fig. 5A , the Mirrortree-IPA performs slightly better than the DCA-IPA, and this difference exists right from the first iteration. How does the performance of these two methods depend on parameters characterizing the dataset? Fig.
(Note that ENVR has regulatory roles on ACRE expression  [62]. ) Indeed, we expect proteins encoded close to one another to share common evolutionary history, because they tend to be horizontally transferred together, and to have similar levels of expression and evolutionary rates  [36, 40]. Fig. 6A  and B  shows that the DCA-, MI-and Mirrortree-IPA are all able to reliably pair LOLC-MACA and ACRE-ENVR homologs that are encoded close to one another on genomes, despite the absence of known direct physical interactions between these protein families. We further compared these results to three pairs of protein families with known direct physical interactions  [41]. Fig. 6C, D  and E  shows that the Mirrortree-IPA performs less well than the DCA and MI-based versions in these cases, especially for the dataset of histidine kinases and response regulators (Fig.
6C ). These proteins feature a strongly coevolving interaction interface  [63] , diversified across many paralogs per species (average number of pairs per species in our dataset: m = 11) to avoid unwanted crosstalk. This argues in the favor of the DCA-IPA and the MI-IPA rather than Mirrortree-based methods in order to predict partnership among physically interacting partners. Interestingly, the MI-IPA slightly outperforms the DCA-IPA for all the real datasets in Fig. 6 , while this is not the case for synthetic data comprising only phylogenetic correlations (see Fig. 3 ). In all panels, the mean number m of pairs per species is indicated, and yellow lines represent the average TP fraction obtained for random within-species pairings. All results are averaged over 50 replicates that differ in their initial random within-species pairings.
Their averages over 500 IPA replicates that differ in their initial random pairings of sequences are shown ranked by decreasing value. Datasets correspond to the same protein family pairs as in Fig. 6 , and the IPA was run with N increment = 50. Outliers in DCA score appear for all three pairs of protein families with known direct physical interactions (BASS-BASR, MALG-MALK, XDHA-XDHC), but not for the other ones (LOLC-MACA, ACRE-ENVR).
The robustness of MI to finite dataset size effects  [41]  and its ability to quantify statistical dependence whatever its origin might make it most appropriate for complex natural data. Finally, we applied the DCA-IPA, the MI-IPA and the Mirrortree-IPA to natural sequence data from several pairs of prokaryotic protein families, with or without known direct physical interactions, but always with genome proximity and thus significant shared evolutionary history. We obtained accurate predictions for all these datasets. Therefore, correlations from evolutionary history can play an important part in the performance of these algorithms in the case of natural sequence data. Interestingly, we found that the Mirrortree-IPA performs significantly less well than the MI-IPA and DCA-IPA on the histidine kinase-response regulator dataset, which features large numbers of paralogs per species and is known to possess strongly coevolving contacts  [5, 63] , and to which DCA is thus particularly well-suited.
", solid lines). Each species comprises 4 chains AB. B: Fraction of pairs correctly identified (TP fraction) versus number of pairs per species, employing the same methods (and same colors) as in panel A, and a training set of 50% of the total dataset. For the Orthology method, results are shown only up to 8 pairs per species, because computations become lengthy. In both panels, and as in Fig. 3 , data was generated using a tree of 10 generations, with exactly 5 mutations per branch, out of 200 bits in each chain AB, thus yielding 1024 chains AB. Species were built randomly, and some of them were chosen randomly to build the training set, the remaining ones making up the testing set. The chance expectation, corresponding to random within-species pairings, is shown for comparison. Results are averaged over 100 replicates in panel A and 20 replicates in panel B, each corresponding to a different realization of the branching process used for data generation.
All results are averaged over 50 replicates that differ in their initial random within-species pairings. Datasets were constructed as described in [22, 41] , starting from the P2CS database [64, 65]  for histidine kinase-response regulator (HK-RR) dataset (C) and using a method adapted from [17]  that relies on finding homologs of Escherichia coli protein pairs in all other cases.
Acknowledgments AFB thanks  Ned S. Wingreen  and  Yaakov Kleeorin  for inspiring discussions. The authors thank  Institut de Biologie Paris-Seine (IBPS) at Sorbonne Université  for funding via a  Collaborative Grant  ( Action Incitative)  to AFB and MW. MW also acknowledges funding by the  EU   H2020 research and innovation program MSCA-RISE-2016  under grant agreement No. 734439  InferNet.
Funding Completion of this article was supported by the  'Région Auvergne-Rhône-Alpes ' and  FEDER 'Fonds Européen de Développement Régional ' (# 23000816 SRESRI 2015 ),  Institut Carnot Plant2Pro  (Project 2017 SyntenyViewer),  AgreenSkills  fellowship (Applicant ID # 4146 ),  ERC   Advanced grant  project  TREEPEACE  (# 339728 ), and  ANR  projects  H2oak  ( ANR-14-CE02-0013 ),  PAGE  ( ANR-11-BSV6-0008 ) and  GENOAK  ( ANR-11-BSV6-0009 ).
SNS data meta-analysis For human and mouse data, we reanalyzed SNS replication data generated in previous studies. Human H9, IMR90, HeLa and iPS data were from  (6)  and K562 data from  (7). The analysis of mouse data was performed on data from (  8 ) and  (9). For the (8) dataset, we kept only two of the three replicates of the experiment due to the apparent low specificity of data collected for the third replicate (as confirmed by personal communication with the authors).
Resampling procedure To correct for the higher sequencing depth of the chicken SNS experiment, we pooled together all human H9 replicates on the one hand, and all mouse mESC replicates, on the other hand, to generate one large human and one large mouse sample. These two datasets showed a similar sequencing depth of 28 reads/kb in human and 24 reads/kb in mouse. We randomly sampled ∼20% of the chicken SNS data to generate three independent datasets in the chicken genome with a sequencing depth of 28 reads/kb, comparable to the datasets in human and mouse. To limit the intercell line variability, we focused on the human H9 cell line, because it is the closest cell type to mESCs in mouse for which SNS data are available.
Cell type specificity To control our analysis of origin cell type specificity (Figure  6 ), we first pooled and subsampled reads of different replicates of each cell type experiment to generate five datasets (one for each cell line) with en equal sequencing depth (32 million mapped reads) and detected origins in these datasets. The sequencing depth of these datasets was much lower than the one previously described such that in the new H9 set, we retrieved 80% of the H9 origins set only.
Polymorphism analysis We collected human polymorphism data from the 1000 Genomes project (phase3): 3. 6 million indels and 84 million SNPs, polarized using great ape genomes as outgroups  (28). The base-specific SNP density shown in Figure  4  and S4 is defined as the number of observed x → y variants divided by the number of x bases, where x and y correspond respectively to the ancestral and derived alleles (A, C, G or T). Human SNPs were categorized according to the frequency of the derived allele (DAF) among the 2500 individuals sequenced by the 1000 Genomes project: rare variants: DAF <1%; common variants: DAF >10%. Mouse and chicken polymorphism datasets (17 and 20 millions SNPs, from db-SNP v142 and v147 respectively (29)) were downloaded directly from the UCSC genome browser  (25). dbSNP entries originate from several studies, with relatively small sample sizes (<20 individuals).
Large-scale variations in replication initiation landscapes To study the evolution of replication initiation landscapes, we generated the first map of replication origins in a bird genome, by sequencing short nascent strands (SNS) in the chicken DT40 cell line (derived from a bursal lymphoma, see Materials and Methods). For a comparison with mammalian origins, we re-analysed all previously published human and mouse SNS datasets (6-9) using the same detection pipeline, with a resolution of 500 bp (see Methods). This method  (7)  detects genomic segments on which the accumulation of reads is significantly higher than the background, which depends on the global read depth of the experiment (Supplementary Table  S1 ). In the following comparisons, we were particularly careful to differences in sequencing depth between experiments, since any detection method based on reads enrichment is more sensitive when the global coverage is high (Supplementary Figure  S1 ).
Thus, we sub-sampled SNS reads so that all three species reached the same sequencing depth (see Methods section). In human and mouse, SNS datasets are available for different cell lines (human: IMR90, H9, K562, HeLa and iPS; mouse: mESC10, MEFs). To limit differences due to tissue-specific origin activity, evolutionary comparisons are based on origins detected in H9 (human) and mESC10 (mouse) cell lines, both of which correspond to embryonic stem cells. As expected given the differences in genome sizes (1. 2 Gb in chicken versus ∼3 Gb in mammals), we identified fewer origins in chicken (33 500) than in human (155 395) or mouse (205 881) (Supplementary Table  S2 ). However, variations in the number of origins are not entirely explained by genomes size since we observe a 2. 5-fold variation in origin density, from 34 origins per Mb in chicken to 84 origins per Mb in mouse (55 origins/Mb in human).
5% of the replication initiation activity, compared to 39. 1% in human and 39. 2% in chicken. Thus, origin activity is more uniformly distributed in mouse than in human and chicken (Figure  2 ). It should be noted that for both human and mouse, we obtain consistent results with SNS datasets corresponding to distinct cell lines generated by different laboratories (Supplementary Figure  S2 ). Thus, the differences in replication initiation landscapes observed between human and mouse are reproducible, and are not explained by the cell type variability. Interestingly, the more uniform replication initiation landscape that we observe in mouse compared to human coincides with a more uniform GC content distribution  (31). In agreement with previous observations in human  (5, 6) , we observe strong positive correlations between the origin density and regional genomic GC content in all three species (Supplementary Figure  S3A-C ).
Polymorphism data show evidence of selective pressure on replication origins To investigate selective constraints impacting replication origins, we first analysed intraspecific sequence polymorphism. In humans, we used data from the 1000 genomes project (based on 2500 individuals), focusing on single nucleotide polymorphisms (SNPs) for which the nature of the ancestral and derived alleles could be inferred by comparison with great apes genomes  (28). To detect signatures of selection, we analysed SNPs with different derived allele frequencies (DAFs). Common variants (DAF > 10%) generally correspond to relatively old mutations that have spread in the population as a result of genetic drift, selection or GC-biased gene conversion (gBGC)  (35). Rare variants (DAF < 1%) predominantly correspond to recent mutations, which are less affected by selection or gBGC and hence are expected to more accurately reflect the prevalence of mutational events  (36).
Overall, the density in common SNPs within SNS peaks is 25% lower than in flanking regions (Figure  4-A ). Although this deficit is weaker than the 40% depletion observed in coding exons (Figure  4C , dotted line), it clearly indicates that the sequence of human replication origins is subject to quite strong purifying selection. Hence, the ∼40bp region centered on the SNS peak (hereafter referred to as the c"ore region of origins), includes genetic elements that are important for their function. In chicken we observed a clear depletion of SNPs specifically in the core region as compared to the surrounding region (Supplementary Figure  S6 ), which suggests that this region might be subject to purifying selection, as in humans. Unfortunately, polymorphism datasets currently available in mouse and chicken are based on relatively small population samples, which does not allow the comparison of rare and common variants.
We next annotated each core region of origins to a background cluster depending on their 5-mer contents and then searched for motifs cluster-wise to adjust the procedure to a heterogeneous background. Using the DREME software in the MEME suite (37), we selected motifs with strong e-values (≤1e -200 ) and further inspected motifs that were most enriched in the core region of origins compared with random segments. The detected motifs are all very short (≤6 bp), and strong enrichments are detected cluster-wise in motifs that are rich in CG dinucleotides for all species (Figure  5 ). The AC (GT) repeats previously identified in mouse (  8 ) are confirmed and detected in human origins with 1. 85-fold enrichment. Then, CCC(GGG) trinucleotides emerge as a shared sequence signature among species, with a two-fold cluster-wise enrichment in origins compared with random sequences.
DISCUSSION Despite the central role of DNA replication in the life cycle of living organisms, the molecular mechanisms inducing the firing of replication origins are still poorly understood. To track evolutionary signatures that could unravel sequence features controlling replication initiation, we conducted the first comparative analysis of high-resolution maps of replication origins in chicken, human and mouse. Our analyses rely on published and original data from short nascent strand sequencing, a strategy for the fine mapping of replication origins that has been validated by other independent high-throughput protocols  (7, 23, 24). Our analyses show the reliability of such detections, since more than 90% of the most active origins (intensity >7. 5 RPKM, Figure  6 ) in the H9 human, cell lines are also detected in other datasets from different cells and different laboratories.
Intriguingly, we showed that this selective pressure is not due to the presence of G4 motifs, which tend to be located further upstream of the SNS peak (Figure  3 ). There is evidence that G4 motifs are functionally important, but their presence is not sufficient to initiate replication  (19). One striking result of the present analysis is that despite this evidence of purifying selection, which is clearly visible in human polymorphism datasets, inter-species comparisons revealed very limited conservation of replication origins on larger evolutionary scales. Only 10-20 % of mammalian origins are functionally conserved in chicken, and 30% of human origins are functionally conserved in mouse. The number of functionally conserved origins ranged from    23 ) that do not overlap HeLa origins detected by SNS-seq.
We analyzed this dataset of dormant origins, and found that their level of sequence conservation is much lower than that of active origins (Table  2 ), which suggests that functional constraints are even weaker for dormant origins. One possible hypothesis to reconcile the contrasting patterns of conservation observed on different time-scales is a selective pressure to maintain a sufficient density of replication origins along chromosomes, but no constraint on the precise location of these origins. According to this model, if the number and intensity of origins in a given chromosome are just sufficient to ensure its proper replication, any mutation that would disrupt an origin would be counter-selected. However, a mutation that creates a new origin might occasionally occur (e. g. by converting a dormant origin into a firing origin).
FUNDING Association pour la Recherche sur le Cancer [Labellisation   PGA120150202272  to M. -N. P. , F. P. ];  Agence Nationale de la Recherche  [ ANR-15-CE12-0004 , OriMolMech to F. M. , M. -N. P. , F. P. ];  Spanish Ministry of Economy and Competitiveness  [ BFU2016-78849-P , co-financed by the  European Union FEDER  funds to J. M. F. -J. , M. G. ]. Funding for open access charge:  Association pour la Recherche sur le Cancer  [Labellisation PGA120150202272].
DATA AVAILABILITY The high-throughput sequencing data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO; http://www. ncbi. nlm. nih. gov/geo/) under accession number GSE119488. Processed Data resulting from the meta-analysis of replication origins are available on GitHub (https://github. com/Flomass/EvolutionReplicationOrigins) together with dedicated scripts to perform the analysis described herein.
To this end, we induced de novo L1 retrotransposition in cultured cells by transfecting a plasmid-borne active L1 element, and we mapped novel L1 target sites by a dedicated deep-sequencing approach  (Philippe et al. , 2016). We compared the new integration sites with a large collection of publicly available genomic datasets and with the distribution of existing endogenous L1 copies. Our data confirm the primordial role of the local DNA sequence at the target site and reveal how pre-existing biases in the genomic distribution of L1 target motifs skew the profile of new integration events. Overall, we find that new L1 insertions broadly target all the regions of the human genome, being insensitive to chromatin organization or transcriptional activity, although with a bias for early-replicating genomic domains.
In vitro, target DNA structure and assembly into nucleosomes can impact L1 EN activity  (Cost et al. , 2001). To examine whether nucleosome occupancy influences L1 local integration site selection in vivo, we examined publicly available MNase-seq data from HeLa S3 at and around pre-integration sites  (Lacoste et al. , 2014). Because the L1 machinery preferentially targets a specific AT-rich motif (Figure  1C -D), we computationally constructed three distinct types of control datasets, each comprising the same number of sites as the experimental data (  1565 ): (i) 1000 purely random control datasets (termed Random); (ii) 1000 base-composition-matched control datasets (termed BMC), also generated randomly but with a base-composition around the target site (±5 bp) identical to the experimental dataset; and (iii) 1000 motif-matched control datasets (termed MMC) consisting of a random collection of sites with an L1 target motif.
, 2004) ). New insertions are overrepresented in chromosome 1, indicating that L1 integration is not perfectly random (Figure  3A ). We  Sultana, van Essen et al. 2019 -p. 7/37  next examined the overall spacing of de novo L1 insertions (Figure  3B ), and compared it to each simulated control dataset, or to 1000 random sampling of 1565 reads from HeLa S3 WGS (Random, BMC, and MMC controls, see above; and WGS). New L1 insertions are more clustered than expected, particularly at inter-insertion site distances in the 10kb-1Mb range. This clustering is observed irrespective of the control dataset used, indicating that it does not result from an uneven distribution of AT-rich sequences, from clusters of L1 target motifs in the human genome, or from aneuploidy of the HeLa S3 cell line. To further test whether some genomic locations are preferred, we scanned the genome by 0.
L1 integration is only modestly influenced by local chromatin states and histone marks To identify potential regulatory features of L1 integration, we measured the association of new L1 insertion profiles with chromatin segmentation states and histone modifications obtained from the ENCODE project  (Ernst and Kellis, 2012; Hoffman et al. , 2013). For comparison, we also analyzed simulated background datasets (Random, BMC and MMC), existing endogenous L1HS-Ta found in HeLa S3 cells  (Philippe et al. , 2016)  (referred as L1 endo), and previously published genome-wide integration profiles of retroviruses and other transposable elements  (Gogol-Döring et al. , 2016; LaFave et al. , 2014; Wang et al. , 2007). De novo L1 insertions show a modest but significant association with weak enhancer and weak transcription chromatin segments (Figure  4A ), and with histone modifications that are characteristic of enhancers (H3K4me1) or transcription over gene bodies (H3K36me3) (Figure  4C ).
However, the levels of these associations are low (excess overlaps represent only 2-3% of all L1 insertions; see Figure  4B ), and it is noteworthy that L1 retrotransposons show the least deviation from randomness, as compared to other transposable elements and retroviruses (Figure  4  and Figure  S4A ). The profile obtained with de novo insertions recovered in the absence of G418 selection (L1 neo-unsel) is similar, although we could only test the association with the most abundant chromatin segments due to the smaller dataset size (Figure  S4B ). The L1 integration profile is in sharp contrast to that observed for murine leukemia virus (MLV) and PiggyBac (PB), which strongly accumulate in chromatin regions related to strong enhancers, promoters and transcription start sites  (Gogol-Döring et al.
, 2016; Sultana et al. , 2017)  (Figure  4  and Figure  S4A ). L1 also strongly differs from human immunodeficiency virus (HIV) and Sleeping Beauty (SB), which rather integrate into transcribed regions with different levels of preference strength (Figure  4  and Figure  S4A ), consistent with previous reports  (Gogol-Döring et al. , 2016; Sultana et al. , 2017). To ensure that the chromatin segmentation model does not obscure any more-significant associations between L1 insertion and any individual genomic feature(s), we also compared L1 insertion sites to each of the entire ENCODE collection of 346 publicly-available ChIP-seq, DNase-seq, and FAIRE-seq datasets, as well as to annotated features from the UCSC genome browser. In line with the results above, de novo L1 insertions are under-represented in GC-rich regions (which disfavor the AT-rich insertion motif), but exhibit negligible or low associations with almost all assayed genomic features (which encompass transcription factors, histone variants and modifications, chromatin enzymes, and biochemical properties of DNA and chromatin; Figure  S5A-C ), and the few significant associations -which primarily correspond to features typical of enhancers (DNaseI hypersensitive sites and H3K4me1) -are nevertheless characterized by modest z-scores, which are far below the levels of associations exhibited by other transposons or viruses.