dataesr/scientific-paragraphs-categorization huggingface dataset:
Else a gap "-" is inserted in the consensus sequence at this position. The next step of the probe design strategy consists in incrementing a window of length "l" (l is the length of probes set by the user) along the consensus sequence to find all possible degenerate probes that do not contain gaps ("-") and whose degeneracy does not exceed the threshold value of maximum degeneracy allowed. Subsequently, a parallelization is made to distribute all the extracted degenerate probes into "N" jobs (N is the number of jobs set by the user). For each job, all the degenerate probes are processed. Otherwise, all possible specific and explorative oligonucleotide probes are generated from each degenerate probe, using IUPAC codes  [34]. These oligonucleotides are checked for cross-hybridizations against the reference SSU rRNA database initially built, using BLASTN program with the following parameters: "-W 7 -F F -S 1 -e 100 -b 20000".
Parallelization Method. Selecting probes for a group of nucleic acid sequences and checking the specificity of each possible probe against a complete SSU rRNA database require a very important computation time. Our software allows running this kind of design on a computing grid. First, the user must choose the number of jobs to use. The consensus sequence, constructed from the alignment file   of each group of sequences, is read to extract all possible degenerate probes that do not contain gaps ("-"), based on the probe length set by the user. The degeneracy of each degenerate probe is calculated. If this degeneracy is less than "maximum degeneracy authorized by the user" (MaxDeg), the degenerate probe is saved. A weight value is calculated for each saved degenerate probe based on its degeneracy.
The subfiles created will have almost the same weight (Figure  2 ) and the same number of potential probes. Each subfile represents a job that will be submitted to the EGI computing grid. Moreover, we have developed job monitoring scripts, with resubmission in case of failure to improve the reliability of our grid software. Three cases can be distinguished. (i) The job submission failed: the job is resubmitted when a network route is found. (ii) The job is submitted successfully and failed when executed: a new job is created and submitted. (iii) The job is submitted successfully and done successfully but the other jobs are not finished: the program waits for all jobs and then merges all results in a single output file. For running conditions, the database is copied on grid Storage Elements (SEs) accessible to all the grid jobs of a probe design.
Regarding submission time, it is important not to overload the Workload Management System (WMS). Otherwise, the program may wait until each job is entirely associated with a CE of the EGI grid before submitting the next job. The following elementary configuration files are necessary to submit jobs successfully on the EGI grid. (i) JDL files: each job needs a job description language (JDL) file to be submitted on the Grid. (ii) Script files: such files describe the elementary tasks that will be executed on the grid. The scripts contain operating system commands and Perl scripts called to perform probe design among all extracted degenerate probes. During execution, SSU rRNA database and subfiles containing degenerate probes are copied on the CE in which the job is running, and Blastn analysis is launched to test cross-hybridization.
Database Building. We developed a new algorithm for the construction of a high-quality curated phylogenetic database, as described above. Our algorithm can be easily adapted and used to build high-quality SSU rRNA databases for different taxonomic ranks (genus, family, order, class, etc. ). We used this algorithm to build a SSU rRNA database at the genus level. We obtained about 66,000 16S rRNA gene sequences representing 2,069 prokaryotic genera; each is composed of a set of homogeneous sequences representing the whole diversity. We used PhylGrid 2. 0 and this database to create a complete phylogenetic oligonucleotide database composed of about 20,000 probes targeting 2,069 prokaryotic genera.
The probe selection is equally distributed The Scientific World Journal over a given number of jobs. We have compared our parallelization method with the original algorithm PhylArray  [29]. We have shown that our approach is more efficient and allows a fine load balancing by sharing equitably the processing of probe selection for the input group across jobs. The comparison results of our load balancing method with that used in PhylArray-for a probe design with a mean degeneracy per job equal to 37,132. 25 probes-showed that our software allowed creating jobs with a small load standard deviation of only 0. 5 probe while PhylGrid generated a high load standard deviation of 18,647. 85 probes between jobs. The experimental results obtained have shown that the parallel implementation of our software had significantly increased performance up to 106x when running around 600 jobs on the European Computing Grid (with submission and waiting latency).
il/molgen/Sorek/listeria_browser/). Thanks to an increasing number of high-resolution transcriptomic studies  [13, 15, [18] [19] [20] , referenced Listeria ncRNAs now include 154 trans-acting sRNAs, 46 cis-regulatory RNAs and 104 asRNAs  [21]. These ncRNAs constitute a pool of putative regulatory mechanisms for bacterial gene expression, most of which remain to be characterized. Detailed study of some of these transcripts has revealed an amazing versatility in their regulatory properties, which we will illustrate below. Novel riboswitch-mediated regulations. Among the new ncRNA elements discovered in the above-described studies, some had been previously annotated in databases as putative riboswitches. Riboswitches are cis-regulatory RNA elements, which act most often as sensors of metabolites, but can also bind a variety of specific ligands including metal ions or cyclic-di-GMP.
Acknowledgements We are grateful to  Fabrizia Stavru  for critical reading of the manuscript and, together with  Javier Pizarro-Cerdá , for sharing immunofluorescence images. We also thank  Nina Sesto  for helpful remarks about bacterial RNA processes. Work in the Cossart laboratory received financial support from  Pasteur Institute, INSERM  and  INRA , from the  European Research Council (ERC)  advanced grant  233348 , from  Agence Nationale de la Recherche  ( ANR BacNet 10-BINF-0201  and  EpiLis 11-BSV3-003-01 ), from  Laboratoires d'Excellence  ( IBEID ANR-10-LABX-62-01 ), from  Fondation Jeantet and Fondation le Roch Les Mousquetaires. P. Cossart is a  Senior International Research Scholar  of the  Howard Hughes Medical Institute.
5×10 -9 , FDR<0. 001), and other musculoskeletal, cardiovascular, and endocrine tissue-types (FDR<0. 05) (Fig. 3 ; Supplementary Fig. 8 ; Supplementary Table  15 ). We also showed that a subset of the 697 height associated SNPs that represented lead cis-eQTLs in blood defined 75 genes that were collectively enriched for expression in cartilage (P=0. 008) (Supplementary Note and Supplementary Table  8 ). We used DEPICT to prioritize 649 genes (at FDR<0. 05) within height-associated loci (Table  3  and Supplementary Table 16 ). Of these 649 genes, 202 genes (31%) were either significant in the GRAIL analysis (Supplementary Tables  13  and 16 ) and/or overlapped with a list of abnormal skeletal growth syndromes that we assembled from the OMIM database  12  (n=40; Supplementary Tables  9  and 16 ).
These include genes newly predicted to be in pathways related to cartilage or bone development (FAM101A, CRISPLD1 and the noncoding RNA LINC00476), collagen or extracellular matrix (GLT8D2, CCDC3, and ZCCHC24), histone demethylation (ATAD2B and TSTD2) and other genes predicted to have skeletal phenotypes but not currently annotated as belonging to relevant pathways (ARSJ, PSKH1, COPZ2, ADAMTS17 and the microRNA cluster MIR17HG). Of note, mutations in both ADAMTS17 and MIR17HG have been identified as causes of syndromic short stature in humans  15, 16. As suggested by the prioritization of ADAMTS17 and MIR17HG, it is possible that some of the newly highlighted genes may also underlie new syndromes of abnormal skeletal growth. As a further proof of principle, the second entry on our list of prioritized genes (Table  3  and Supplementary Table 16 ), CHSY1, was not a known monogenic gene in the OMIM database  12  when we assembled our list, but mutations in this gene have since been shown to cause a syndrome including brachydactyly and short stature  17, 18.
Validation -linear mixed model (LMM) based association analysis Each of 15 studies (59,380 individuals) used genome-wide SNP information to calculate a genomic relationship matrix (GRM) for all pairs of individuals and used this to correct association statistics for cryptic relatedness and population stratification. Each study used a linear mixed model as implemented in the software EMMAX  23. Meta-analysis was performed as described for the standard GWAS and using a single GC correction. Each study additionally repeated the analyses for each chromosome using a GRM generated from the remaining 21 chromosomes, or in the case of the largest study (WGHS) repeating the analysis for all odd numbered chromosomes using a GRM generated from the even numbered chromosomes and vice versa. Each study then combined association results from the 22 or 2 parts of the genome into one set of data and we repeated the single GC metaanalysis.
Validation -within family (transmission) association analyses A pure transmission based analysis was performed in seven cohorts for SNPs representing 416 signals of association (Supplementary Note), selected after repeating meta-analysis excluding these studies, with single GC correction. Filtering of low imputation quality SNPs in the studies was followed by inverse variance method of meta-analysis of the family based results. Because of the presence of related individuals, family based studies have lower power at a given sample size. For each study, we calculated the effective sample size (the size of a sample of unrelated individuals that would have the equivalent power; see Supplementary Note and Winkler et al. 24  ). Estimation of winner's curse in our data set was performed by repeating the meta-analysis excluding either the family-based studies or excluding random sets of studies from GIANT matched by effective sample size to the family based studies.
Within-family prediction analysis We used the SNPs selected from GCTA-COJO analysis to create a genetic predictor (also called "genetic profile score") for each of all the full sibs using PLINK  25. We then adjusted the genetic predictor by the first 20 principal components (PCs) generated from the principal component analysis (PCA)  26. By comparing the predictors within and between families, we partitioned the variance in the predictor analysis into components due to real SNP effects (V g ), errors in estimating SNP effects (V e ), and population structure (C g + C e ), as described in the Online Methods below. We calculated the weighted average of each of the four (co)variance components over the three cohorts by their sample size, i. e. Σ i (V g(i) n i )/Σ i (n i ) with the subscript i indicating the cohort and n being the sample size.
To assess the prediction accuracy, we usually perform a regression analysis of the real phenotype against the predictor, i. e. [17]   so that the regression slope is actually [18]   with the regression R 2 being [19]   In the absence of population structure, [20]   Variance explained by SNPs in proximity to the top associated SNPs We performed analyses to quantify the variance explained by SNPs in close physical proximity to the top associated SNPs in 9,500 unrelated individuals (pairwise genetic relatedness < 0. 025) from a combined dataset of the QIMR and TwinGene cohorts. As in previous analyses, to avoid sample overlap between discovery and validation studies, we repeated the discovery meta-analysis excluding the QIMR and TwinGene cohorts, and identified 643 genome-wide significant SNPs from the GCTA-COJO analysis of the summary statistics using ARIC data for LD estimation.
Acknowledgments A full list of acknowledgments appears in the Supplementary Note. (ADVANCE)  D. Absher , T. L. A, L. L. W. ; (AMCPAS) S. Kanoni; (ARIC)  S. Buyske , A. E. J. , K. E. N. ; ( B1958C ) T. Ferreira; (BHS) D. Anderson; (CARDIOGENICS) S. Kanoni; (DESIR)  S. Cauchi , L. Y. ; ( DGE DietGeneExpression ) I. H. C. ; (DIAGEN) A. U. J. , G. M. ; (DILGOM) K. K. ; (DUNDEE) T. Ferreira; (EAS) J. L. B. , R. M. F. ; (EGCUT) T. E. , K. F. , E. M. ; (ELY) J. Luan; (EMIL (SWABIA)) B. O. B. ; ( EPIC-Norfolk )  J. Luan , (FBPP) A. C. , G. B. E. ; (FENLAND) J. Luan; (GLACIER) F. Renstrom,  D. Shungin ; (GxE) C. D. P. , (HNR)  S. Pechlivanis ,  A. Scherag ; (IMPROVE) L. Folkersen, R. J. S. ; ( KORA S3 ) J. S. R. ; ( KORA S4 ) E. A. ; (Leipzig) A. Mahajan, I. P. ; (LURIC)  G. Delgado , T. B. G. , M.
Definition of insertion variants In this work, we will focus on insertion variants: sequences that are present at one site (position) in the donor genome but are absent from the reference genome at this site. We divide insertions into three mutually exclusive types: (i) novel insertions in the donor genome that have no match in the reference, (ii) duplicated insertions, which are found at two or more sites in the donor and a strict subset of those in the reference and (iii) transpositions, which are sequences in the reference that moved to a different site in the donor. Duplicated insertions include mobile element insertions (MEI), for which databases of known sequences have been created to facilitate discovery  (Stewart et al. , 2011). All three types of insertions are difficult to detect using short reads.
g. BreakDancer and GASV  (Chen et al. , 2009; Sindi et al. , 2009). NovelSeq  (Hajirasouliha et al. , 2010)  and SOAPindel  (Li et al. , 2013)  detect sites of long, novel insertions by clustering paired reads for which one mate is unmapped. Alternatively, tools based on read coverage can detect duplicated insertions of any length by finding reference segments that have higher read depth than expected. While insertion sites cannot be determined by this method alone, the Reprever  (Kim et al. , 2013)  software identifies low-copy duplicated insertions by combining paired-end mapping with read depth analysis. Finally, several methods detect sites of mobile element insertions using collections of known transposable element sequences, by searching for read pairs where one mate is mapped to a known element and the other to a unique part of the reference genome  (Ewing and Kazazian, 2011; Hormozdiari et al.
Our contribution We propose a new tool, MINDTHEGAP, for detecting and assembling insertions. MINDTHEGAP has several novel features that are not found in other tools. First, a mapping-free site detection algorithm has been designed to detect insertions of any size. Second, an improved method for insertion assembly enables the reconstruction of long insertions of all three types. Third, a memory-efficient data structure enables high scalability. We evaluated MINDTHEGAP on simulated and real Illumina sequencing data. Among 1 kbp simulated homozygous insertions, a large fraction were found and correctly assembled (recall values between 65-98. 4%, precision 497%). Simulated heterozygous 1 kbp insertions proved to be more challenging to assemble (60% recall for Caenorhabditis elegans, 35% for human chromosome 22); however, precision remained high (93% and 89%, respectively).
METHODS The input of MINDTHEGAP is a set of reads and a reference genome. The software performs three steps: (i) construction of the de Bruijn graph of the reads, (ii) detection of insertion breakpoints on the reference genome (find module) and (iii) local assembly of inserted sequences (fill module). Both the detection step and the assembly step rely solely on the constructed graph. The output of the second step is a set of putative insertion positions on the reference genome, whereas the output of the last step is, for each insertion site, one or several assembled sequences.
Fill module: assembly of inserted sequences The third step of MINDTHEGAP is called the fill module. Starting from a known insertion site represented by flanking k-mers (L, R), the module performs de novo assembly to attempt to reconstruct the inserted sequence between L and R. In a nutshell, a graph of contigs is constructed by performing breadth-first traversal of k-mers, starting from L. The traversal is halted when graph becomes too complex. Then, all the contigs in the graph are searched for the presence of R. All paths between L and the contigs containing R are enumerated, and one or more putative insertion sequences are returned (see Fig. 2 ). More specifically, insertions are assembled using the algorithm of Minia  (Chikhi and Rizk, 2013; Salikhov et al. , 2013). Assembly is performed by traversing the graph from a given starting k-mer in a breadthfirst fashion.
Simulated datasets To evaluate MindTheGap, we generated artificial read datasets and reference genomes based on real genomes. First, we simulated sequencing reads for a real genome (the donor). Then, another genome (the reference) was obtained by simulating nonoverlapping deletions from a copy of the donor genome. Deletion locations were sampled uniformly along the sequence. These deletions correspond to homozygous insertions in the donor. To simulate heterozygous insertions, reads were sampled in equal numbers from the donor and the reference genomes. Sequencing was simulated with Wgsim from the Samtools package  (Li et al. , 2009)  using the following parameters: paired-end mode with 2 Â 100 bp reads, an insert size of 300 bp (std = 50) and a base error rate of 0. 01. Coverage was set to 40Â for homozygous datasets and 60Â for heterozygous datasets.
Real sequencing data Paired-end sequencing data from C. elegans strain N2 were downloaded from SRA (accession SRX026594). This dataset is composed of 33. 8 M Illumina 2 Â 100 bp read pairs (insert size of 350 bp), representing roughly 70Â of coverage on the 100. 3 Mb reference sequence of C. elegans (downloaded from NCBI version WBcel235). As we did not find any validated dataset of known large insertions for this genome, we simulated insertion variants following the protocol of simulated data: 1000 regions of a given size (here 1-100 bp or 1 kb) were deleted in the reference genome, corresponding to homozygous insertion variants in the N2 donor genome. Sequencing data of human individual NA12878 from DePristo et al. , 2011, consisting of 2. 8 G Illumina 2 Â 101 bp read pairs, was downloaded from EBI (ftp://ftp-trace.
Results on simulated datasets MINDTHEGAP was applied on several simulated datasets to precisely estimate its recall and precision. This enabled to quantify the impact of different levels of genome complexity, to independently evaluate each module and modes (detection versus assembly, homozygous versus heterozygous) and to analyze the range of insertion sizes MINDTHEGAP is able to detect and assemble. 3. 1. 1 High recall and precision in homozygous mode For insertions of 1 kb, MINDTHEGAP recovered between 65 and 98. 4% of the simulated insertions, depending mainly on the complexity of the studied genome (Table  1 ). Almost all predicted homozygous insertions are true-positive results, resulting in high precision (consistently above 97%). Table  1  shows that almost all insertion sites were detected by the find module in homozygous mode.
Varying insertion lengths Figure  3  shows that MINDTHEGAP can detect and assemble insertions of any size. We observed that the performance of the find module is independent of the size of the insertions: recall of the find module never fell below 98. 5% (data not shown), without any false positive, even for the human chromosome 22 dataset. However, lower recalls are due to the fill module failing to assemble longer insertions. For small insertions (5100 bp), MINDTHEGAP obtained high recall and precision for all simulated datasets. Only 650 over 1000 insertions of 1 kb could be assembled in the chromosome 22, and among these, 646 showed 490% identity with the original deleted sequences. This was likely because of the high repeat content of this chromosome. We observed that the insertions MINDTHEGAP fails to assemble generally correspond to complex graph of contigs, containing many exact repeats longer than ðk À 1Þ.
Heterozygous mode To evaluate the heterozygous mode of MindTheGap, we simulated datasets with only heterozygous insertions (see Section 2. 4). Our analysis in Methods showed that heterozygous insertion sites were likely to be more difficult to detect and distinguish from genomic repetitions than heterozygous insertions sites. Table  2  shows that for the human and C. elegans simulated datasets, both recall and precision are significantly below those in homozygous mode. Further investigation showed that the low recall is owing to poor performance of the find module. We found that the results in this module were sensitive to the values of parameters k, r (maximal repeat size at fuzzy sites) and h (maximal number of occurrences of flanking kmers in the reference genome). Setting k to a higher value and r and h to smaller values (here: k = 51, r = 2, h = 1) enabled to reach a precision $97%, at the cost of a noticeably lower recall.
Comparison with SOAPindel On insertions of size 1-100 bp, SOAPindel shows similar recall and precision to MINDTHEGAP (Fig. 3 ). However, SOAPindel is limited in the Fig. 2. Fill module. A graph of contig is constructed from the left flanking kmer L, in a breadth-first search order. Construction stops when a maximum number of nodes is reached, or when a branch becomes too deep. The right flanking kmer R is searched within all nodes, finally all paths (in blue) between L and R are outputted as putative insertions size of detectable insertions, depending on the insert size of the reads: given our simulation parameters, we observed that SOAPindel recall decreased for insertions larger than 175 bp, and the largest insertion detected was of length 189 bp. Noticeably, the performance of SOAPindel was independent of the genotype of insertions.
Evaluation on a real sequencing dataset of C. elegans To evaluate the impact of real reads and a real donor genome with some degree of polymorphism in the reference genome, MINDTHEGAP was run on a C. elegans strain N2 read dataset against the reference genome containing simulated deletions. This is to simulate homozygous insertion variants in the donor genome. Additional insertions variants are likely to exist C. elegans strain. Thus, the number of FP could not be evaluated, as the true set of insertions present in these reads is unknown. For 1 kb insertion variants, 81. 1% were correctly predicted and assembled by MINDTHEGAP (Table  1 ). Compared with the fully simulated dataset on the same simulated insertions, the find module missed more insertion sites, whereas the fill module had a better recall of inserted sequences.
Application on real insertions of human individual NA12878 To evaluate the ability of MINDTHEGAP to recover real insertions in real data, we executed it on a human individual NA12878 dataset containing 2. 8 G 100 bp reads. As the coverage was high, parameter k was empirically set to 63 and t to 5 (k-mers with less than five occurrences were discarded). Predictions were then compared with a set of 74 large insertions predicted by alignment of fosmid sequences to the reference hg18 genome (see Section 2. 4). 20 insertion sites were recovered by the find module. No heterozygous insertions were predicted. We set r = 15, which enabled to find twice more sites than with r = 5. This suggests that real insertions contain longer repeated sequences at their breakpoints than expected in a random simulation.