dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study we describe an intracellular, non-canonical mode of action for C1s in tumor cells. Multivariate Cox analysis revealed the negative prognostic value of C1s to be independent of other complement activation markers such as C1q, and C4d and C3d deposits, highlighting a cascadeindependent role for C1s. C1S silencing altered the transcriptional program of ccRCC cells and reduced cell proliferation and viability. This phenotype was independent of VHL mutation, as it was observed in Caki-1 cells, which are VHL wild-type and A498 cells, which are VHL mutated. Noncanonical functions for C1s have also been reported in cSCC, although the genes differentially expressed after C1S silencing were different  [15]. Moreover, reduced proliferation and viability were observed in the lung adenocarcinoma cell line A549, suggesting the general relevance of C1s as a proliferation-and cell survival-promoting factor.
Acknowledgements We are grateful to  Veronique Rossi  and  Isabelle Bally  ( IBS, Grenoble ) for providing the pcDNA3. 1 C1sexpressing vector, described in [52]. We are grateful to the team of the  platform «Centre d'Histologie, d'Imagerie et de Cytometrie», (CHIC) at our Centre de Recherche des Cordeliers UMRS1138 (Paris, France ) for the technical assistance. CHIC is a member of the  Sorbonne University Flow Cytometry Network (RECYF). We thank  Isabelle Sauret  and  Simon Lefranc  from "Centre de Ressources Biologiques " and the  Pathology Department at the IMM  for their help in the sample storage and collection. Authors contribution Designed the study: LTR, MVD, IC,  CSF ,  WHF. Performed research: MVD, MR, JR, MADD, TRR, VP, AG, GL, AB, MM. Analysed and discussed the data: MVD, MR, JR, MADD, CG, TRR, VP, AG, GL, AB, MM, VFB, IC, DD,  CSF ,  WHF ,  LTR.
This work was supported by grants from the  Ligue Regionale Contre le Cancer and Fondation ARC Pour La Recherche Sur Le Cancer  to LTR. This work was also supported also by grants from  CARPEM (ExhauCRF program ),  INCa  ( HTE program ),  Canceropole Ile de France  ( R17054DD ), Association pour la recherche en thérapeutiques innovantes en cancérologie (ARTIC) to CSF,  French National Research Agency  grant ( ANR-16-CE91-0004-01 ) to CG. The  Labex Immuno-Oncology Excellence Program ,  INSERM, University of Paris and Sorbonne University  also supported this work. MVD received a PhD fellowship from  La Fondation ARC  pour la recherche sur le cancer. AG received a PhD fellowship from  Fondation pour la Recherche Médicale -FRM.
e. transfer, replication and expression of selectable markers  [4] , but were useless for non conjugative plasmids. On the other hand, PCR-based detection methods  [5]  could detect the number of copies for specific plasmid sequences, but did not allow to understand the ecological characteristics of mobile genetic elements, as for example their host range. The fast development of sequencing technologies and reduction of sequencing costs led to the rapid increase of available genomic and metagenomic data sets. This material contains a vast amount of information on plasmid diversity, plasmid host-range, resistance conferred to specific host taxa, etc. that could allow to better understand the circulation and spread of plasmids and the genes they carry. Accessing this information requires new tools to process partially assembled datasets to identify plasmids sequences.
Well-defined plasmids can be identified through homology search (e. g. , Plasmid-Finder  [6] , PLACNET, PLACNETw  [7] ). These programs basically look for similarities between a query sequence and a local database. Query and subject sequences are usually quite long (at least several hundreds of bases), so the probability of finding similarities by chance (and thus to wrongly identify a sequence as plasmid) is very low. Homology search is usually very precise and reliable. However, current data limitations can substantially decrease the sensitivity of this method. Indeed, it requires an exhaustive database composed of a wide variety of genomics data, enough to cover many taxa, and even if this is the case, rare plasmids may not be identified. In particular, the range of taxa in which a plasmid can be replicated and maintained (i.
e. , its host range  [5] ) can greatly vary: some being restricted to a few close species, and others consisting in a wide range of taxa across the phylogeny. Thus, plasmids with a narrow host range (especially when only present in uncultivated species) could be nearly impossible to identify through homology. In the absence of an exhaustive database, an accurate identification method requires to define broader associations with plasmids. This can be achieved by the reconstruction of plasmids through homology-based clustering (PLACNET, PLACNETw  [7] , MOBrecon  [8] ): the query sequences which are homologous to the same reference plasmid sequences are likely to be part of the same plasmids. This approach can be successful in two cases:  (1)  if the generated graphs are manually pruned by an expert user (PLACNET, PLACNETw  [7] , but this strongly impedes the possibility to apply this method to large datasets) and (2) if the query sequences are also compared to complementary databases (e.
Overall, the current plasmid identification methods are limited, in either precision or sensitivity. These limitations are not problematic for certain applications: for example, k-mer-based methods are well adapted to detect plasmid sequences in metagenomics samples containing many uncultivable species and homology-based approaches are well suited if users focus only on a few plasmids of interest. Yet, no tool is currently available to classify, with both high precision and sensitivity, a large data set of sequences as plasmid or chromosome. Such a tool would be very useful for example to monitor the spread of a gene family (e. g. , antibiotic resistance genes) using partially assembled genomic datasets. Indeed, 90% of available assemblies on public repositories (up to 100% for species with few sequenced genomes) are partially assembled genomes (contigs or scaffolds).
It is thus not possible to rely on the size of the genomic elements and their content in specific sequences (as ribosomal RNA genes or origin of replications) to classify them as chromosome or plasmids and an accurate plasmid identification method is required. Here, we present PlasForest, a new tool for classifying contigs in partially assembled genomic datasets as plasmid or chromosome. Contrary to classical homology-based tools, our method does not attempt to assign query contigs to specific subject sequences, but rather to sort contigs in plasmid and chromosome sequences through a machine learning algorithm fed with parameters extracted from an homology search against an exhaustive plasmid database, as well as other variables (e. g. , contig size and %G + C content). PlasForest can discriminate plasmids from chromosome sequences with an overall F1 score of 0.
950 for any bacterial contig in genomic datasets. In particular, PlasForest is able to detect up to 77. 4% of plasmid contigs under 1 kb with only 2. 8% false-positives and up to 99. 9% of plasmid contigs over 50 kb with less than 2. 2% false-positives. Compared to other currently available tools, PlasForest has a significantly better capacity to correctly identify plasmids from chromosomes in partially assembled genomes. We implemented this tool in a user-friendly pipeline able to identify plasmids in large datasets in a reasonable amount of time.
Pipeline description A pipeline executing all analyses required for PlasForest was encoded in Python 3. 6. This pipeline performs all the different steps described in Fig. 1. First, it filters query contigs by their annotations, to avoid re-identification of sequences already described as chromosome or plasmid (step 1). If the user wants to re-assign contigs with Plasforest, this step can be skipped with option -r. Feature acquisition (step 2) consists of submitting the filtered sequences to BLASTn against a local copy of the plasmid database, and calculation of the overlaps between query and subject sequences. Features are then computed for each query contig on its sequence and the distribution of overlaps. The pipeline thus extracts seven features: average overlap, contig size, G + C content, max overlap, median overlap, number of hits, variance overlap (see Fig.
2A ). The classification (step 3) passes features to the random forest classifier, which outputs the predicted identification for each query contig. The output can also include the best hits that were found in the plasmid database with option -b and/or the features used for classification with option -f. Progression of the pipeline can be displayed if run with option -v. Predictions can be Fig. 1  General method of classification implemented in PlasForest split to several threads with the multithreading option -threads and/or to several batches with the option -size_of_batch. We tested runtime and memory usage for the pipeline on a subset of 1000 contigs randomly drawn from the COMGENOME dataset (see Methods). For a single thread, PlasForest takes on average 490 s and uses a maximum of 2. 3 gigabytes of memory (compared to respectively 300 s and 600 megabytes for PlasClass-a k-mer-based tool-on the same dataset).
Reliability of the classification method PlasForest was trained on artificial draft genomes, created by randomly cutting completely assembled genomes into contigs following a size distribution observed in actual draft genomes (Additional file 4: Table  S1 ), that we called balanced training set (Additional file 5: Table  S2A ). When run on the testing set (Additional file 6: Table  S2B ) with all seven features (Fig. 2A ), PlasForest presented a good ability to discriminate between plasmid and chromosome sequences, with both a high sensitivity (92. 7%) and a high precision (97. 3%). We tested the sensitivity of PlasForest qualitative changes by evaluating the change in performance when adding or removing features in the training of the classifier and when resampling the training dataset and the plasmid database.
The classifier was trained several times on the balanced training set, by feeding it with different combinations of features. Predictions of the classifiers were made on the testing set. The classification showing the highest MCC (Matthews correlation coefficient) was the one using all the features considered (average overlap, contig size, G + C content, max overlap, median overlap, number of hits, variance overlap), and the three variables that showed the highest feature importance were the maximal overlap, the number of hits, and contig size (Fig. 2B ). Bootstrapping the training set and the plasmid database showed that the performance of PlasForest presents a very low sensitivity to the resampling of the training set (see Fig. 3A ). However, the performance of PlasForest decreases substantially when the composition of the plasmid database is resampled (see Fig.
Benchmark of plasmid identification methods We compared the predictions of PlasForest on our testing dataset (see Table  1  and Fig. 4 ), with those of three classification methods based on genomic signatures (PlasFlow  [9] , PPR-Meta  [13] , and PlasClass  [12] ) and one classical homology-based classification method (MOB-recon  [8] ). PlasmidSeeker  [11]  was not included in our analysis, because it requires to know the species from which the genome comes and therefore cannot be used on a wide scale. PlasForest is overall the most sensitive classifier, being able to predict 92. 7% of plasmid contigs. However, for sequences below 2 kb, PlasForest is less sensitive than PlasFlow with down to 77. 4% of plasmids correctly predicted. PlasForest had the highest precision, followed by MOB-recon (with respectively 2.
7% and 6. 1% of sequences incorrectly predicted as plasmids). The precision of PlasForest is lower than that of MOB-recon only for contigs below 1 kb (respectively 2. 8% and 0% of sequences incorrectly predicted as plasmids). On these two indices, PlasForest is therefore globally the best software, though other classifiers can have higher sensitivity or precision for short contigs. However, PlasFlow suffers from low precision, especially below 5 kb (up to 70. 6% of false positives between 1 and 2 kb), and the sensitivity of MOB-recon can be very low (down to 0. 3% of true positives below 1 kb, down to 50% above 50 kb). PlasForest therefore   2 kb where it is outperformed by MOB-recon. Even for very short contigs (under 1 kb) for which k-mers-based methods usually have poor results, PlasForest remains a reliable classifier, with MCC = 0.
Applicability of PlasForest to other datasets We tested further the ability of PlasForest to predict contigs of plasmid origin on three additional datasets. We also compared the results obtained with PlasForest to the results obtained with the four other programs on these three datasets. The COMGENOME dataset gathers artificial contigs, randomly cut with the same size distribution as the testing set, but drawn from complete genomic assemblies that were released after the training and testing sets were constituted (Additional file 7: Table  S3 ). The CONTIG dataset contains partially assembled genomes which were present neither in the training nor in the testing datasets (Additional file 2: List S1), and the METAGENOME dataset contains genomes that were partially assembled from metagenomic short reads (extracted from the datasets of  [14]  and  [15] , Additional file 3: List S2).
Contrary to the COMGENOME dataset, the genomes used to create the CONTIG and the METAGENOME dataset were not annotated, so we could not measure the accuracy of the predictions of the different programs on these datasets. On the COMGENOME dataset (see Table  2 ), PlasForest performances are reduced compared to its performances on the testing set, notably in sensitivity (only 54. 3% true positives, compared to 85. 7% true positives detected by PlasFlow). Yet, PlasForest remains as precise, with only 11. 3% false positives (compared to 9. 3% false positives detected by MOB-recon). Overall, on this dataset, it remains the best classifier, with MCC = 0. 663 and F1 = 0. 674 (respectively MCC = 0. 522 for MOB-recon, and F1 = 0. 538 for PlasFlow). On the CONTIG dataset (see Fig. 5A ), PlasForest and PlasFlow agree to predict that 14,427 contigs originate from plasmids (80.
9% of plasmids predicted by PlasForest). However, 52,999 contigs that PlasFlow detects as plasmids are identified as chromosomes by PlasForest (39. 6% of chromosomes predicted by PlasForest). PlasFlow is thus the plasmid identification method whose predictions resemble less those of PlasForest (Cohen's κ = 0. 187). In general, plasmid identification methods based on k-mers disagree with the predictions of PlasForest on this dataset (Cohen's κ = 0. 232 between PlasClass and PlasForest, and Cohen's κ = 0. 205 between PPR-Meta and PlasForest). On the contrary, though PlasForest and MOB-recon only agree to predict that 7350 contigs originate from plasmids (41. 2% of plasmids predicted by PlasForest), only 3783 contigs that On the METAGENOME dataset (see Fig. 5B ), PlasForest and MOB-recon globally identify very few contigs as plasmids (respectively 0.
Discussion PlasForest is a homology-based approach, combined with machine learning, which detects plasmids in contig and scaffold genomes. Its operating principle is to seek homologies between query contigs and a large plasmid database, and then to assign a plasmid/chromosome identity to queries with a random forest classifier. Here we showed that PlasForest is able to deal with large datasets without prior knowledge of the taxonomic background, and that it identifies plasmids with both a high sensitivity and a high precision in unassembled genomes. All the plasmid identification software tested had either a higher false negative error rate or a higher false positive error rate than PlasForest, especially on very short contigs (below 1 kb). PlasForest did not always have the lowest individual error rates, but it optimized the tradeoff between sensitivity and precision, and as a consequence had the highest values of the composite indices F1 and MCC for the vast majority of the size classes.
K-mer-based approaches are thus valuable only when used on long contigs or in some very specific contexts: for example, Plas-midSeeker  [11]  achieves up to 100% sensitivity and 99. 8% specificity in whole genome sequencing reads by ruling out as chromosomal the k-mers which are shared with a complete, reference assembly. However, the use of PlasmidSeeker on broad diverse datasets is practically infeasible: it requires to know the species from which the genome comes from and that at least one genome has been assembled for this species  [11]. The high precision of PlasForest is inherited from its homology-seeking basis. Though homology-based approaches may traditionally show little sensitivity, due to their inability to detect unrelated plasmids, the performances of PlasForest and MOB-recon have significantly improved plasmid identification.
The pipeline of PlasForest offers the possibility to identify which plasmids from the database have the strongest homology with the query. It should be noted though, that the taxonomic assignment can strongly depend on (1) the plasmid host range and (2) horizontal gene transfer events that are yet massively undetermined in bacteria. Any attempt (from PlasForest or from any other method) of taxonomic assignment for broad host range plasmids is thus at least imprecise and can sometimes be impossible  [17]. Finally, PlasForest is the best alternative for plasmid identification in genomic assemblies. However, as PlasForest mostly relies on homologies with a plasmid database, the vast majority of plasmids it is able to detect are related to those of its database. It thus showed reduced performances when resampling its database or when testing it on the COMGENOME dataset, which included genomes published after the construction of the training and test sets.
Unrelated plasmids, especially with narrow host ranges or when the host is uncultivable, are difficult to detect through this method so far. Thus, in its current state, PlasForest cannot be applied to detect plasmids in metagenomic assemblies. Both PlasForest and MOB-recon, two identification methods that were initially designed for genomic assemblies, detected very few contigs as plasmids in the METAGENOME dataset. Though it is very difficult to predict the actual frequency of contigs originating from plasmids, it is e. g. 12. 7% in the COMGENOME dataset. It is thus far higher than 0. 2% so PlasForest and MOB-recon most probably have a very low sensitivity for metagenomic assemblies. However, we argue that investing in homologybased approaches for plasmid detection in metagenomic assemblies is crucial.
Indeed, methods based on detection of k-mers probably allow to detect a much higher number of plasmid contigs in unknown species, but they also have a high false-positive rate: they detected up to 25% of plasmid contigs in the METAGENOME dataset. We also showed that their predictions are in high disagreement with one another, therefore rendering them hard to rely on. That is why assembling methods such as metaPlasmidSPAdes  [18]  also rely on homology search, but these methods only apply to newly sequenced datasets and in general substantially increase the running time of assembly. As novel methods to detect plasmids in metagenomes need to rely on homology search, new datasets will be required. Some sequences from assembled plasmidomes (some being already available on MG-RAST database  [19] ) could be incorporated in the plasmid database of PlasForest.
Conclusions In its current state, among all softwares tested, PlasForest is the best identification method for plasmids in contigs and scaffolds. As shown with the COMGENOME dataset, PlasForest still outcompetes other methods on recently released genomes. We released PlasForest as a user-friendly pipeline, including the trained classifier and the plasmid database, directly available on GitHub (https:// github. com/ leaem iliep radier/ PlasF orest). Further releases (at least on a half-year basis) will include a plasmid database updated with the new plasmid sequences submitted to public repositories, and a new classifier trained on this database. This complemented database should be regularly trimmed, in order to keep it of reasonable size without decreasing the performance of PlasForest. Especially in order to identify plasmids in metagenomic data with better accuracy than k-mer-based approaches, plasmidomes should be included in the plasmid database and the classifier should also be trained on annotated metagenomic assemblies.
Methods The aim of PlasForest is to combine both the high precision of homology search with the broad sensitivity of signature-based classifiers in order to discriminate contigs of plasmid origins from contigs of chromosomes. We trained a classifier for which decision relies on the homology of sequences with a large database of plasmid sequences. We simulated contigs by randomly cutting assembled genomes, to construct both a dataset to train the classification algorithm (the training set) and a dataset to measure the classification performance (the testing set). We then compared the classification performance to other plasmid identification tools.
Plasmid database All bacterial plasmid sequences were downloaded from the NCBI RefSeq Genomes FTP server (ftp:// ftp. ncbi. nlm. nih. gov/ genom es/ refseq; September 1st, 2019). This database is composed of 36,450 sequences that we used as reference for homology seeking via BLAST tool  [21]  (e-value < 10 -3 ).
Training and testing datasets To train the classifier and measure its performance, we randomly sampled 10,152 bacterial genomes classified as 'complete' , downloaded from the NCBI Refseq Genomes FTP server (ftp:// ftp. ncbi. nlm. nih. gov/ genom es/ refseq). To mimic the sequence material on which PlasForest will be applied (contigs from unassembled whole genomes), the empirical distribution of contig sizes was established from more than 100,000 Refseq unassembled genomes (see Additional file 4: Table  S1  for the chosen distribution). This distribution was then artificially recreated from complete genomes in the training and testing sets by cutting plasmids and chromosomes at random locations and keeping a defined number of each contig size in plasmids and chromosomes. Only contigs larger than 50 bp were kept, since most current sequencing approaches do not produce shorter reads  [22].
We ended up with approximately 70% of the generated contigs (552,410 contigs coming from 7,400 genomes) to train PlasForest. Genome annotations were used to identify contigs as plasmids or chromosomes (see Fig. 6A ). The remaining 30% of the generated contigs (108,175 contigs coming from 2,752 genomes) were used as a testing set. In these two datasets, plasmid contigs were not at the same frequencies for all the contig sizes (e. g. , > 30% under 1 kb and 2% over 100 kb). This could have led to an artificial detection bias based on contig size (e. g. , a better identification of small plasmid contigs). Thus, we split the initial training dataset into contig size categories (50 bp to 1 kb, 1 to 2 kb, 2 to 5 kb, 5 to 10 kb, 10 to 50 kb, 50 to 100 kb, and over 100 kb), and randomly removed plasmid or chromosome contigs from each category to keep the fraction of plasmid contigs constant (around 10%) across contig sizes.