dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to reduce false positive, we combined our miRNA and mRNA expression data sets. Of the 4802 potential miRNAtargeted genes, 1933 were differentially expressed between the four B cell to plasma cell populations (adjusted P-value ≤ 5%, fold change ≥ 2). To obtain a high-confidence list of candidate miRNA-target interactions, we performed Pearson's correlation analysis between the mean expression values of each miRNA and the mean expression values of its deregulated target genes using miRTarget R package developed by our group (https://github. com/kassambara/ miRTarget). Negative correlation between a given miRNA and a predicted target mRNA could be the first indicator of a potential direct interaction. Finally, the combination of target prediction, differential expression (adjusted P-value ≤ 5%, fold change ≥ 2) and correlation filters (r < -0.
6, Supplementary Figure  S4 ) yielded 1596 negative miRNAs-mRNAs interactions involving 51 unique miRNAs and 948 unique mRNAs (Supplementary Table  S4 ). At least 316 of the 1596 putative target interactions have experimental support in other cell types (comprising 272 unique genes and 35 miRNAs) according to miRecords and miRTarbase databases (Supplementary Table  S4 ). Next, we ranked the miRNAs based on the number of inversely correlated mRNA targets (Figure  3A ). The first 15 miRNAs with most negatively correlated events includes miR-16, miR-30e, miR-30d, miR-30b, let-7c, let-7b, miR-26a, miR-342-3p, miR-29C, let-7g and miR-140-3p belonging to miRNA cluster 1; miR-106b and miR-15b in cluster 2; and miR-148a and miR-34a belonging to cluster 3 (Figure  3A ). Figure  3B  shows the mean expression of these top 15 associated miRNAs.
Functional analysis of dynamically regulated miRNAs To better understand which and how biological functions are affected by the differentially expressed miR-NAs during PCD, we performed functional annotations using the clusterProfiler R package  (35)  and MSigDB (34) canonical pathway gene set collections. We inferred the functions of deregulated miRNAs from their target genes. miRNAs that were more abundantly expressed in MBCs and down-regulated during PCD (cluster 1 miR-NAs) are predicted to interact with genes coding for proteins involved cell cycle (CCND2, CCNE1/2, CDC25A, CDK6, CHEK1, E2F3, HDAC2 and RB1), MYC activation pathway (BCAT1, CAD, CCND2, CDC25A, E2F3, EIF4E, HMGA1, MTDH, POLR3D, SHMT1 and TAF12), E2F pathway (CCNE1/2, CDC25A, E2F3/6/7, MYBL2, RB1, RRM2 and TFDP1), membrane trafficking (AP2M1, AP4E1, ARCN1, CLTC, COPZ1, CPD, DNAJC6, IGF2R, MYO6, TPD52, TXNDC5, VPS25 and YIPF6), Golgi associated vesicle budding and biogenesis (AP4E1, CLTC, CPD, DNAJC6, IGF2R, TPD52, TXNDC5 and YIPF6), amino sugar and nucleotide sugar metabolism (GALE, GFPT1, GNPNAT1, NANP, PGM1, UGDH and UGP2) (Figure  3C  and Supplementary Table  S5 ).
Furthermore, 10 of these miRNAs have been reported to be overexpressed in high-risk MM patients, including (let-7c/i, miR-106b, miR-148a, miR-16, miR-18a, miR-19a, miR-20a, miR-21 and miR-29b)  (20). One miRNA (miR-155) was described to be associated with t (4,14) cytogenetic group; one (miR-21) with t  (11, 14)  and 11 miRNAs with del13q, including miR-16 and miR-34a  (21, 22)  (Supplementary Table  S7 ). Additionally, using publicly available miRNA expression data (ArrayExpress accession number: E-MTAB-1363), we found that 31 out of the 63 PCD miRNAs were significantly differentially expressed in primary multiple myeloma tumor plasma cells and/or in myeloma cell lines compared to the normal bone marrow plasma cells (Supplementary Table  S8 ). These miRNAs include members of miR-30 family, miR-16 and miR-106b.
Interestingly, at least 316 of the 1596 putative target interactions (comprising 272 unique genes and 35 miR-NAs) have experimental support in the literature according to miRecords and miRTarbase databases (Supplementary Table  S4 ). Pathway analysis revealed that these miRNA target genes are mainly involved in cell cycle, MYC activation pathway, membrane trafficking, golgi associated vesicle biogenesis and budding, and signaling by TGF-beta. Overall, our approach provides valuable information for identification of potent miRNA target genes as well as the elucidation of dynamic gene expression changes and the underlying related biological processes. We next searched to extract, from the 1596 miRNA-mRNA target interactions, the network of miRNAs regulating critical genes associated with plasma cell differentiation, critical genes being defined as genes that have at least 1 citation index associated with PCD.
FUNDING French  INCA (Institut National du Cancer) Institute  [ 2012-109/087437  and  PLBIO15-256 ];  Languedoc Roussillon CRLR  ( R14026FF ),  Fondation de France  [201400047510];  ITMO Cancer (MM&TT)  and the  France Génomique National  infrastructure, funded as part of the  Investissements d'Avenir program  managed by the  Agence Nationale pour la Recherche  [ ANR-10-INBS-09 ]. Funding for open access charge:  INCA (Institut National Du Cancer), France.
IV. SIMULATION RESULTS ON SYNTHETIC DATA This section evaluates the performance of the proposed algorithms with synthetic data. This enables the performance of the algorithms to be compared on data with a known ground truth. All simulations have been implemented using MATLAB R2015a on a computer with Intel(R) Core(TM) i7-4790 CPU@3. 60GHz and 32GB RAM. The section is divided into three parts whose objectives are: 1) introducing the criteria used for the evaluation of the unmixing quality, 2) description of the synthetic images considered in the experiments, and 3) evaluating and comparing the proposed NUSAL-K and RUSAL algorithms with other state-of-the-art algorithms.
V. RESULTS ON REAL DATA This section illustrates the performance of the proposed algorithms when applied to three real hyperspectral images. The first hyperspectral image has received much attention in the remote sensing community  [16] ,  [46]. This image was acquired over Moffett Field, CA, in 1997 by AVIRIS. The dataset contains 100 × 100 pixels, L = 152 spectral bands (after removing water absorption bands) acquired in the interval 0. 4-2. 5µm, has a spatial resolution of 100m and is mainly composed of three components: water, soil, and vegetation (see Fig. 3  (a)). This image is interesting since it is known to include bilinear scattering effects  [16] ,  [21] ,  [32]  which makes it suitable for the assessment of the NUSAL-K and RUSAL algorithms presented in this paper. The second image, denoted as Madonna, was acquired in 2010 by the Hyspex hyperspectral scanner over Villelongue, France (00 03'W and 4257'N).
The dataset contains L = 160 spectral bands recorded from the visible to near infrared (400 -1000nm) with a spatial resolution of 0. 5m  [47]. It has previously been studied in  [21] ,  [48] ,  [49]  showing NL effects (between the trees and the soil), EV effects (mainly for the vegetation) and shadow effect. The subimage considered contains 160 × 200 pixels and is composed of R = 4 components: tree, grass, soil and shadow (see Fig. 3 (b) ). For these two images, the VCA algorithm  [5]  was used to extract the corresponding endmembers, i. e. , R = 3 endmembers for the Moffett image and R = 4 endmembers for the Madonna image. The third image was acquired by the AVIRIS sensor, in 1998, over Salinas Valley, California (see Fig. 3 (c) ). The dataset contains 86 × 83 pixels, 204 spectral bands with the same spectral resolution and spectral range as the Moffett image (the water absorption bands were removed) and a spatial resolution of 3.
Introduction Next Generation Sequencing (NGS) data provide an unprecedented access to life mechanisms. In particular, these data enable to assess genetic differences between chromosomes, individuals or species. Such polymorphisms represent a fundamental source of information in many aspects of biology with numerous applications in agronomy, environment or medicine. With the democratization of sequencing provided by the NGS technologies, determining genetic differences such as SNPs or indels has now become a routine task. There exist numerous software designed for predicting such polymorphisms. Mostly, these methods are based on the use of a reference genome by mapping sequenced reads as this is the case for GATK  [6]  or SamTools  [14]  or by mapping partial assemblies as for DISCOVAR  [22]  or FERMI  [12]  to cite a few.
Pipeline (1) The dBG of the input dataset(s) is built. This step relies on the GATB library  [7]. This step includes notably the removal of erroneous k-mers (see Section 2. 3. 2). (2) a module detects bubbles generated by the presence of SNPs (close or not) and indels; This module implements Algorithms 1 and 2. It outputs a fasta file containing the variant sequences together with the contig (or unitig or nothing, depending on the user choice) they belong to (see Section 2. 3. 3). (3) For keeping the RAM usage very low, no other information than k-mer presence is stored in the dBG. Thus the variant sequences do not contain any information about abundances in read dataset(s). A module maps back the reads from all input read sets on the variant sequences, mainly in order to determine the read coverage per allele and per read set of each variant.
This module also computes a rank per variant, indicating whether the variant shows discriminant allele frequencies between compared datasets. When two datasets are compared, this rank is the Phi coefficient of the contingency table of read counts, computed as follows: χ 2 n , with n being the sum of read counts. When more than two datasets are compared, the rank is the maximal Phi coefficient obtained among all possible pairs of considered datasets. When two or more read sets are used, this score offers a way to filter out putative false positive variants due to inexact repeats as they are likely to have similar allele frequencies in all datasets. Optionally, as presented Section 2. 3. 4, a genotype per read set is computed for each predicted variant. (4) The last module generates a VCF of the predicted variants.
If no reference genome is provided, this step is simply a change of format from fasta to VCF. If a reference genome is provided, this module maps predicted variants on this reference using BWA-mem  [13]. This enables to output for each variant its localization in the genome. If a variant has more than one best mapping position, then one of those positions is randomly output, and a VCF field indicates a multiple mapping. A variant with multiple mapping positions is likely to be due to inexact repeats and numerous false positive variants can be easily filtered out using this information. The detailed VCF content is described in the additional file.
Predicting genotypes For each prediction and each dataset, a genotype is provided assuming each dataset corresponds to a diploid individual. Genotypes are inferred independently for each individual, based on the read coverage of each allele of the bubble. To do so, the likelihoods of the three possible genotypes (homozygous 0/0 or 1/1 or heterozygous, 0/1) are computed based on a simple binomial model as described in the Nielsen 2011 review  [17] : prior : heterozygous prior(0. 33) like 0/0 = -10 log 10 (1 -err) cu × err c l × C cu cu+c l × 1 -prior 2 like 1/1 = -10 log 10 err cu × (1 -err) c l × C cu cu+c l × 1 -prior 2 like 0/1 = -10 log 10 1 2 cu+c l × prior min(like 0/0 , like 1/1 , like 0/1 ) ⇒ 0/0, or 1/1, or 0/1 These computations rely namely on the error probability parameter, that is the probability that a read maps to a given allele erroneously, assuming it is fixed and indepentent between all observations.
It was fixed to 0. 01, referring to classical sequencing error rates. Finally, the genotype showing the largest likelihood is chosen and all three likelihoods are also output (-log10 transformed) as additional information. Notably, only the probability of observing the data (ie. the read counts for each allele) given the genotype is computed, no prior is used so as to compute the posterior probabilities of each genotype given the data. Users may deactivate genotyping, for instance when the input datasets are not coming from diploid individuals. For a given read set, if a variant has a read coverage lower than the solidity threshold (see Section 2. 3. 2) for both alleles, its genotype is annotated as missing and denoted by ". |. ".
Real data, saccharomyces cerevisiae experiment We used a set of biologically validated variants predicted from an artificial evolution study on Saccharomyces cerevisiae  [9]. In this study, three glucose-limited, chemostat-evolved populations of the haploid yeast strain S288c, named E1, E2 and E3, were sequenced every ≈ 70 generations, giving eight samples per population. Using a reference-based mapping approach, 110 mutations were discovered, among which only 33 have a minor allele frequency (MAF) > 10% and 32 were confirmed by Sanger sequencing. DiscoSnp++ was run independently on populations E1, E2 and E3 (reads downloaded from the NCBI Sequence Read Archive, accession number SRA054922). For each population, DiscoSnp++ was applied on the eight read sets corresponding to the eight time points, with the default parameters and T sol = 11, D = 60 (searching for indels of length at most 60 bp) and P = 4 (authorizing up to 4 close SNPs in a unique bubble).
Real data, human platinum We used reads from the Platinum study  [? ]. More precisely, we downloaded the reads of individuals NA12877 and NA12878, sequenced with 200x coverage depth on a HitSeq 2000 system. These two datasets each benefit from a VCF file 1  containing variants called by a set of nine independent methods. We call those files NA12877. vcf and NA12878. vcf. Thus each variant may be found by any number of methods between one and nine. We ran DiscoSnp++ on the two read sets, using the human genome (ref. hg19) for mapping predictions and also as an input set for predicting variants distinct from the reference but identical in the two sets. We validated the predictions by comparing the VCF generated by DiscoSnp++ with the two VCF files NA12877. vcf and NA12878. vcf limited to variants found by at least half of the methods employed in the platinum study.
Results validation In all the performed tests, we analyzed the VCF file containing predicted variants of DiscoSnp++ localized afterwards on the reference genome. We compared it to a reference VCF, containing either the simulated variants (ground truth) or variants validated by other methods (for real data). Within this approach the number of True Positives (TPs): correctly predicted variant; of False Positives (FPs): wrongly predicted variant; of False Negatives (FNs): real variant, non predicted, can be easily computed. Precision of a prediction set is then defined by precision = numberT P numberT P + numberF P and the recall is defined by recall = numberT P numberT P + numberF N. Note that in the case of real datasets, the precision is meaningless as the reference sets are not exhaustive.
Results on synthetic datasets We first propose a bench of results based on synthetic datasets. These datasets are derived from real genomes, either from Escherichia Coli or from the human chromosome 1. We tested DiscoSnp++, cortex  [8]  and an hybrid method composed of SOAPdenovo2  [16]  for generating the assembly, Bowtie2  [10]  for mapping the reads on the assembly, and GATK  [6]  for calling variants. Presented results were obtained using UnifiedGenotyper option of GATK. As presented in the Additional File, when following the GATK guidelines (including read realignment and using the HaplotypeCaller option), the result quality is similar but at the expense of a much longer execution time (almost 3 times longer, from approximately 19h to 54h for the human chromosome 1 experiment).
Two human datasets To evaluate DiscoSnp++ on a diploid and more complex genome, we propose an experiment based on the human chromosome 1, assembly GRCh37. Using variants from the 1000 genomes project, we simulated two individuals, generating 25,928 indels in the range 1 to 10 bp (average 2. 1 bp) and 288,069 SNPs. Results considering all predictions are presented Table  2. Precision and recall of SNP calling are quite similar between compared tools, with DiscoSnp++ achieving the best recall and overall the best compromise between both values. Surprisingly, tools show more contrasted results on indels. In particular, DiscoSnp++ finds almost twice as much true indels than the hybrid approach (72 vs 41 % recall). This comes with a slightly lower precision, but filtering DiscoSnp++ predictions on the basis of the rank score enables to reach a much better precision without losing too much in recall.
Finally, including symmetrically branching bubbles and keeping all k-mers raises the recall to 99. 06% (with a laughable precision of 0. 09%). These results show that the k-mer filtration is a critical step for avoiding false calls. As previously mentioned, DiscoSnp++ provides for each predicted variant, its genotype estimation for each individual. On this dataset, 247,065 true positive variants were predicted thus generating 494,130 predictions (one per individual). Over those predicted genotypes, 485,673 (98. 29%) were correct. When mapping results on a reference genome, among other pieces of information, the number of mapping positions is given for each variant in the vcf file. Considering only variants having a unique mapping position in the reference genome enables to select those that are less likely to be due to inexact repeats.
Saccharomyces cerevisiae experiment This experiment aims to demonstrate the recall permitted by DiscoSnp++ on an experiment where a subset of called variants have been biologically validated. Among the 32 experimentally validated SNPs, 29 were predicted by DiscoSnp++, leading to an estimated recall of 90. 7%. Among those 29 SNPs, four SNPs were close. Detecting all bubbles including symmetrically branching ones leads to the detection of the three remaining unpredicted SNPs. The fact that these SNPs were not detected with the default parameter (detection of non branching and non symmetrically branching bubbles) suggests that these SNPs are located in complex regions of the genome. Note that in the study from  [9] , no SNP with a MAF < 10% was validated and no indel was validated, so we could not assess the recall of these types of DiscoSnp++ predictions on this dataset.
Platinum experiment We ran DiscoSnp++ on two real datasets from two human individuals, NA12877 and NA12878, summing to 400x coverage. The whole process took 22h and required only two command lines. We compared results obtained by DiscoSnp++ with those obtained in the Platinum study, found by at least half of the used methods, that is to say at least five methods. This represents a total of 3,174,898 SNPs and 148,194 indels. DiscoSnp++ predictions retrieved 2,605,955 (82. 08%) of those SNPs, and 133,994 (90. 42%) of those indels. When limiting Platinum variants to those validated by at least six methods among nine, 1,714,222 SNPs remain. DiscoSnp++ found 1,681,626 (98. 10%) of them. Interestingly, no Platinum indel are predicted by at least six methods. This highlights the lack of methods for predicting indels with a high accuracy, even among the reference-based methods.
Conclusion Small variant prediction remains a fundamental task that is usually a preliminary step, vital to most downstream bioanalyses on sequenced data. Despite the sequencing "democratization" and the impressive current technological improvements, when working on complex species such as eukaryotic ones, there is still only a few reference species for which one disposes of an assembled genome, good enough to serve as support for predicting efficiently and extensively variants of interests. At the same time, there were still an important lack of user-friendly reference-free variant callers able to scale-up large datasets and to provide results quality similar to or better than reference based approaches. With this work we hope we bridged this gap with DiscoSnp++, a new easy to use tool, using low resources and offering high quality results.
The plants produced as triplicate for experiments were mycorrhized at 59, 61, and 58% for M. truncatula and 39, 48, and 39% for B. distachyon by R. irregularis, respectively. The colonization rate of all triplicate plants mycorrhized by G. rosea reached over 80% (detailed informations at NCBI GEO portal GSE67911). Plants were grown with 16 h light (25 • C)/8 h (22 • C) cycles and fertilized twice a week with 0. 5x Long Ashton solution. Lunularia cruciata were collected in the Pyrenees Mountains (France). Gemmae were sterilized  (Fonseca et al. , 2006)  and grown on KNOP medium  (Reski and Abel, 1985)  at 22 • C with a 16 h photoperiod. L. cruciata was mycorrhized according to  Fonseca et al. (2006). As the intergrid method used to assess mycorrhizal rate in roots was not appropriate, we checked by staining that the fungus highly colonized the central part of the thalli.
RNA Production and Sequencing Total RNA extraction and sequencing were performed according to  Tisserant et al. (2013)  for R. irregularis and  Tang et al. (2016)  for G. rosea. Apart from ERM of G. rosea where short paired-end sequencing reads were obtained from Illumina Miseq1000 protocols (2 × 151 bp), all libraries were obtained fromIllumina Hiseq2000 protocols (2 × 101 bp). Library constructions and sequencing were performed on the GeT-PlaGe facility (Toulouse, France), according to standard Illumina protocols. Data are available at NCBI GEO portal (GSE67906 and GSE67911) for G. rosea, and at NCBI Sequence Read Archive (SRR1027885) and at NCBI GEO portal (GSE67926) (-see details on libraries in Table  S1A ) for R. irregularis. Number of reads per libraries, representativeness of fungal reads in symbiotic tissues and variability of data are presented in Tables  S1A, S5A  for R.
Bioinformatic Analysis GMAP analysis  (Wu and Watanabe, 2005)  was performed with the standard parameters between the two genomic assemblies of R. irregularis  (Tisserant et al. , 2013; Lin et al. , 2014). When different gene definitions were present for the same locus, only ORFs supported by RNAseq data were selected and protein lacking a start or a stop codon were discarded. Because of a lack of complete coverage by RNAseq reads, SP7  (Kloppholz et al. , 2011)  was at first absent from the analysis. We therefore used the second assembly  (Lin et al. , 2014)  that contains a SP7 gene definition (Rir018650) to recover this protein in the RiSPs set. In order to be easily identified in the study, this gene is not designated by a RiSP number but keeps its original name. SIS1, a previously characterized putative secreted protein that was described to be essential for mycorhizae establishment  (Tsuzuki et al.
, 2016 ) is absent from our set of 872 RiSPs. Indeed, in our pipeline, a transmembrane domain was identified in the protein sequence. RiSPs and GrSPs were identified using the following pipeline with standard software parameters. SignalP3. 0 (Bendtsen Dyrløv et al. , 2004)  allowed the identification of a signal peptide, TMHMM  (Krogh et al. , 2001) , and Phobius  (Käll et al. , 2004)  excluded proteins presenting a transmembrane domain. TargetP  (Emanuelsson et al. , 2007)  was used to remove proteins with a mitochondrion targeting signal. We selected mature proteins as small as 15 amino acids after the cleavage of the peptide signal and discarded protein sequences with duplicates. No upper size limit was applied. This screen resulted in a list of 872 proteins for R. irregularis and 2633 for G.