dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We have described Visual ModuleOrganizer, a novel graphical interface with a new optimized implementation of the ModuleOrganizer tool. The key features of these tools are: (1) detection of modules within larger sequences and with efficient low memory usage; (2) userfriendly handling of ModuleOrganizer thanks to a graphical interface; and (3) dynamic graphical parameters that tune the visualization of the results based on the user needs. In addition, Visual ModuleOrganizer will be useful to investigate evolutionary and comparative analysis (modules insertions, deletions, rearrangements,. ) from all type of DNA repeats (transposable elements, CRISPR, minisatellites,. ). ModuleOrganizer remains applicable in principle to any set of nucleic sequences sharing some similarities and for which a multiple alignment fails to correctly retrieve the architecture of conserved blocks in the sequences.
Designing a Logic Circuit by ABC We have derived the circuit S for the system of partially specified Boolean functions in Table  2  using the software tool ABC (Berkeley Logic Synthesis and Verification Group, ABC) that is a growing software system for synthesis and verification of binary synchronous sequential logic circuits. ABC provides scalable logic synthesis and optimization based on And-Inverter Graphs (AIGs) and other internal circuit representations. Moreover, ABC includes a number of commands for synthesis, resynthesis, optimization and verification of logic circuits as well as for printing the circuit statistics and parameters. The ABC can be easily downloaded from its official web site and run against a LUT for web service W to synthesize a corresponding circuit S. We have run the ABC tool against the LUT corresponded to Table  2.
Packet loss and jitter have been selected for perturbation where jitter corresponds to the variation in video packet delays and packet_loss is the percentage of packets being lost during video transmitting. In the subjective test, video clips were randomly provided for participants which had five minutes break after watching 20 video clips. Given statistics gathered as described above, we have randomly extracted a sample of 17 user statistical data (Low number of users since we are only dealing a proof of concept experimentation). In this sample, the jitter ranges between 2 ms and 20 ms while the packet_loss ranges from 0,02% to 0,25%. The latter means, five circuit inputs can be used to represent each parameter scores. To represent the QoE value, the corresponding circuit under construction has three outputs.
Some methods consider that the set of expressed isoforms  (Jiang and Wong, 2009)  or a candidate superset  (Huang et al. , 2013; Xing et al. , 2006)  is known in advance, in which case the only problem is to estimate their expression. However, little is known in practice about the possible isoforms of genes, and restricting oneself to isoforms that have been described in the literature may lead to missing new ones. Two main paradigms have been used so far to estimate expression at the transcript level while allowing de novo transcript discovery. On the one hand, the Cufflinks software package  (Trapnell et al. , 2010)  proceeds in two separate steps to identify expressed isoforms and estimate their abundance. It first estimates the list of alternatively spliced transcripts by building a small set of isoforms containing all observed exons and exon junctions.
In a second step, the expression of each transcript is quantified by likelihood maximization given the list of transcripts. Identification and quantification are therefore done independently. On the other hand, a second family of methods  (Behr et al. , 2013; Bohnert and R€ atsch, 2010; Li et al. , 2011a, b; Mezlini et al. , 2013; Xia et al. , 2011)  jointly estimates the set of transcripts and their expression using a penalized likelihood approach. These methods model the likelihood of the expression of all possible transcripts, possibly after some preselection, and the penalty encourages sparse solutions that have a few expressed transcripts. The two-step approach of Cufflinks  (Trapnell et al. , 2010)  is reasonably fast, but does not exploit the observed read density along the gene, which can be a valuable information to identify the set of transcripts.
Statistical model We consider an extension of the model originally introduced by  Jiang and Wong (2009)  and used in NSMAP for estimating isoform expression for a known set of expressed transcripts. Given a gene of interest, we assume that the list of its n exons is known, and that the reads of the RNA-Seq experiments have been mapped to a reference genome. For the purpose of our work, an exon can either be defined by read alignment software as a cluster of reads or from a predefined annotation such as the one provided by the UCSC genome browser (http://genome. ucsc. edu/). In the latter case, exons with alternative 5 0 -donor and 3 0 -acceptor sites are considered as two separate exons. For alternative 5 0 -donor sites, the exon is broken down as one exon ending at the first 5 0 -donor site, and another one starting at this same point and ending at the second 5 0 -donor site (similarly for exons with 3 0 -acceptor sites).
This problem can have several solutions yielding the same objective value, and which are typically sparse in the number of transcripts (see Supplementary Fig. S2 ). The non-uniqueness of the solution is not an artifact of our network flow approach, but a property of the ' 1 -penalized estimator. Algorithms such as SLIDE, NSMAP or others that explicitly enumerate the candidates and minimize the parameter in the candidate space also return one of several solutions. In parallel, the stability of the estimator is investigated in Section 4 of the Supplementary Material.
When the two reads of a pair span bins potentially separated by some exons, we use heuristics based on genomic distances to decide whether these exons are spliced. All software programs are used with default parameters, except that for paired-end experiments, we provide fragment length mean and standard deviation to IsoLasso, Cufflinks and FlipFlop. Note that all results can be easily reproduced by following the tutorials available at http:// cbio. ensmp. fr/flipflop/experiments. html.
Simulated human RNA-Seq data Because little is known about the true set of isoforms expressed in real data, we start our experimental validation with a set of simulations. We use the RNASeqReadSimulator software (http://alumni. cs. ucr. edu/$liw/rnaseqreadsimulator. html) to generate single-end and paired-end reads from the annotated human transcripts available in the UCSC genome browser (hg19). We restrict ourselves to the 1137 multi-exons genes on the positive strand for chromosome 1, corresponding to 3709 expressed transcripts. We follow the protocol of Isolasso  (Li et al. , 2011b)  and consider that a transcript from the annotation has been detected by a method if it predicts a transcript that (i) includes the same set of exons, and such that (ii) all internal boundary coordinates (i.
For instance, for the three to four transcripts levels, FlipFlop achieves 75% of precision and 67% of recall, while Cufflinks reaches 74 and 52% and IsoLasso reaches 64 and 51%. This demonstrates that an adapted model for long reads is critical for isoform recovery. NSMAP optimizes a similar Poisson objective function as FlipFlop but only models reads at the exon or exon-exon junction levels; it looses statistical power when the read length increases. Figure  3b  shows paired-end results for 400 bp fragment length, 20 bp SD, 1 million read pairs and read lengths from 100 to 175 bp. Although our model is designed for single-end reads and is particularly adapted to long reads, it shows competitive or better accuracy for pairedend reads. Once again, when the read length increases, FlipFlop performance improves proportionally more than other methods.
In Figure  3c , the read length is set to 150 bp and the number of simulated reads varies from 1 million to 10 million. Increasing the coverage clearly helps FlipFlop, whereas it does not change much for Cufflinks and IsoLasso. Cufflinks constructs its set of transcripts and estimates their abundances in two separate steps, and the construction of the set of returned transcripts does not take read density into account: it intends to find the smallest set of isoforms covering all the observed reads. IsoLasso is based on penalized likelihood maximization like FlipFlop and NSMAP, but starts from a restricted set of isoforms-the same set returned by Cufflinks for single-end data. Consequently, this family of methods discards some information that can help identifying the set of expressed isoforms.
Overall, FlipFlop estimates the set of expressed isoforms for 1137 genes in 59 min, i. e. about two genes per second. Note also that the time for data prepreprocessing (finding exon boundaries and read counts for exons and junctions) is taken into account for all methods except NSMAP. These simulations confirm several facts. First, methods that identify and quantify transcripts as a single penalized maximum likelihood problem show good performances and take clear advantage of an increase in coverage. Second, correctly modeling long reads allows to greatly improve the accuracy of isoform reconstruction. Third, the proposed network flow strategy allows to solve the penalized likelihood approach quickly even when the set of candidate isoforms is extremely large.
Real RNA-Seq data Our second round of experiments involves two independent real human embryonic stem cell datasets. They both contain about 50 million 75 bp reads, either paired-end or single-end, with respectively NCBI SRA accession number SRR065504 and ERR361241. In the experiments of Section 3. 1, we generated the reads based on a known set of transcripts. In the present case, the reads come from actual human tissues, and we do not have access to the true set of expressed transcripts. Following  Xia et al. (2011)  and  Li et al. (2011a) , we choose to use the UCSC annotation as ground truth in the evaluation. Admittedly, this is not perfect as some expressed transcripts may be missing from the annotation, and some annotated transcripts may not be expressed in this particular experiment.
However, agreement of the prediction with the set of known transcripts could be a good sign. Figure  5  shows precision and recall of each method for different FPKM (Fragments Per Kilobase of exon per Million fragments mapped) levels. When considering all transcripts with predicted abundances higher than 1 FPKM, FlipFlop has a higher precision for both the paired-end and single-end datasets, while Cufflinks has a better recall. For transcripts with more than 5 FPKM abundance, all methods have a similar recall, with a slight advantage to Cufflinks, while FlipFlop shows a much better precision. Section 7 of the supplementary gives additional details on the real RNA-Seq data experiments: Supplementary Figure  S8  shows the running time of the compared methods and Supplementary Figure  S9  corresponds to the precision-recall curves obtained for FlipFop when varying the model selection rule.
Moreover, we show that correctly modeling long reads is of primary importance for isoform recovery. Our approach, which models reads covering any number of exons, outperforms other methods for 300 bp long reads. We believe this is an important improvement as RNA-Seq technologies are moving forward longer reads. Our FlipFlop method has also shown to be competitive with state-of-the-art methods on real single-end and paired-end human stem cells data, especially for transcripts whose abundance was significant. In addition, the runtime of our method was comparable with the runtime of the second set of methods, and orders of magnitude faster than existing software for penalized likelihood maximization. We believe these results have important practical implications. In addition to the obvious gain in time when estimating the expression of transcripts for a single gene and a single sample, our approach makes the task amenable in a reasonable amount of time for all genes in a large number of samples.
Funding: This work was supported by the  European Research Council  ( SMAC-ERC-280032  to J-P. V. , E. B. ); the  European Commission  ( HEALTH-F5-2012-305626  to J-P. V. , E. B. ); the  French National Research Agency  ( ANR-09-BLAN-0051-04  and  ANR-11-BINF-0001  to J-P. V. , E. B. ); the  Stand Up  to Cancer grant ( SU2C-AACR-DT0409  to L. J. ); the  National Science Foundation  ( SES-0835531 ,  CCF-0939370  to J. M. ); and the  CNRS  (project  FlipFlop , under program  PEPS-BMI ).
In recent years, high-throughput molecular tools have led to an exponential growth of available 16S rRNA gene sequences. Incorporating such data, molecular tools based on target-probe hybridization were developed to monitor microbial communities within complex environments. Unfortunately, only a few 16S rRNA gene-targeted probe collections were described. Here, we present PhylOPDb, an online resource for a comprehensive phylogenetic oligonucleotide probe database. PhylOPDb provides a convivial and easy-to-use web interface to browse both regular and explorative 16S rRNA-targeted probes. Such probes set or subset could be used to globally monitor known and unknown prokaryotic communities through various techniques including DNA microarrays, polymerase chain reaction (PCR), fluorescent in situ hybridization (FISH), targeted gene capture or in silico rapid sequence identification.
Several tools were therefore proposed to select phylogenetic probes such as PRIMROSE  (7) , ARB PROBE_DESIGN  (8) , ORMA  (9)  or CaSSiS  (10, 11). Unfortunately, most of these programs are not well-suited for large-scale probe design. Designing probes for a large group of sequences requires considerable computational resources and can take up to few days for only one design. Only CaSSiS was implemented for large-scale sequence data sets. Furthermore, all of these tools allow selecting probes targeting only known microbial communities with available sequences in public environmental databases. However, it is also important to define explorative probes that can detect uncharacterized phylogenetic signatures and anticipate genetic variations  (12). Currently, only four software programs allow the selection of explorative phylogenetic probes: PhylArray (13), PhylGrid  (14) , KASpOD  (15)  and MetaExploArrays  (16).
Although numerous oligonucleotide probes have been reported, available collections of rRNA-targeted oligonucleotide probes are rare. The oligonucleotide probe database (OPD)  (17)  was proposed in 1996 to collect tested phylogenetic oligonucleotide probes. The last data set of OPD listed 96 primers and probes targeting small and large subunit rRNA. However, OPD has not been updated since 1997 and is no longer available online. More recently, 'probeBase'  (18) , an online resource for published rRNA-targeted oligonucleotide probes and associated information, was established in 2002. It currently includes 2788 probes (status January 2014). Here, we present PhylOPDb ('phylogenetic oligonucleotide probe database'), a comprehensive phylogenetic OPD targeting 16S rRNA gene sequences. We used two highthroughput probe design software, PhylGrid and KASpOD, to select both regular and explorative 16S rRNA gene-targeted oligonucleotide probes.
Database construction and development Probe design using PhylGrid PhylGrid (  14 ) is a large-scale probe design software linked to the EGI grid. It is an improvement of the PhylArray algorithm presented in Militon et al. (13)  that allows defining regular and explorative oligonucleotide probes targeting SSU rRNA genes at any phylogenetic level (Figure  1 ). The PhylGrid probe design was based on a custom 16S rDNA-curated sequence database originating from the EMBL. All SSU rDNA sequences downloaded from the prokaryotic (PRO) and environmental (ENV) divisions of the EMBL nucleotide sequence database were used as a reference to build this database. First, 16S rDNA gene sequences were extracted and filtered according to their quality and size. Only sequences that met the following criteria were kept: (i) sequence length is between 1200 and 1600 nucleotides, (ii) sequence is assigned to the genus level in EMBL database, (iii) the percentage of ambiguous nucleotides is <1% and (iv) the maximum number of consecutive unknown bases must not exceed five.
Then, extracted sequences were grouped at the genus taxonomic rank according to the NCBI taxonomy database. For each genus, sequence orientation of all sequences was checked using BLASTN  (19)  and redundancy was eliminated using BLASTCLUST  (19). Finally, a K-means approach was implemented to check for badly annotated sequences that may prevent selecting specific probes for this group. Eventually, 66 075 rDNA (16S) gene sequences representing 2069 prokaryotic genera were obtained. Using this custom 16S rRNA gene sequences database, a total of 3 553 975 degenerate probes of 25 mer were selected. The probe length of 25 nucleotides offers the balance of highest sensitivity and specificity in the presence of a complex background  (20, 13). Coverage and specificity tests were performed against the input database using a BLASTN  (19)  allowing up to two mismatches between a probe and its target.
Putative cross-hybridizations were therefore defined when a non-targeted sequence harbours at most two mismatches with a probe. The number of mismatches allowed was chosen taking into the destabilization effect on the probe-target complex and the loss of signal  (21). A stringent threshold of two mismatches was set to limit putative cross-hybridizations. It should be noticed that ambiguous nucleotides are considered as mismatches by BLASTN. Five non-overlapping probes showing the best specificity and coverage were then selected for each genus. During this step, some cross-hybridizing probes may have been selected. Nevertheless, the program ensured that the simultaneous analysis of the five selected probes could not cause misleading interpretations of hybridization data. A set of 19 874 25-mer probes corresponding to 10 320 degenerate probes was obtained.
Probe design using KASpOD KASpOD (  15 ) is a fast k-mer-based software dedicated to the design of group-covering oligonucleotide probes. It allows selecting highly specific and explorative probes based on large data sets (Figure  2 ). The KASpOD probe design was based on the Greengenes database  (22). The May 2011 release containing 406 997 sequences was downloaded and extracted from Greengenes. Then, using a custom PERL script, only the sequences assigned to a genus were retained for further analyses. These 310 575 sequences were then sorted by genus into different FASTA files. For each genus, a clustering step was performed at a 100% identity threshold using CD-HIT  (23)  to remove any redundancies. Moreover, only high-quality sequences were retained: (i) sequence length >1200 nucleotides and (ii) <1% of ambiguous nucleotides.
After this processing pipeline, the 16S rDNA database contained 252 250 high-quality sequences. The clustering of the whole database at high-identity thresholds (99, 98 and 97%) coupled with manual curation, allowed removing of potentially badly assigned sequences. Furthermore, some microbial genera were clustered together, as they were hardly distinguishable on the basis of their sequences. Eventually, 252 183 16S rDNA sequences were fed to KASpOD to perform the probe design. A total of 3 242 105 degenerate candidate probes of 25 mer were designed for 1295 prokaryotic genera. The maximum number of mismatches between a probe and its target was set to two mismatches, and any ambiguous character will be counted as a match if the aligning base is one of the nucleotides represented by the ambiguity code.
The minimal probeset harbouring the best coverage and specificity was then defined. First, the non-overlapping probes were selected within the probes showing no crosshybridizations. Subsequently, while there were some 16S rDNA sequences, which were not covered by at least three probes, the program selected additional probes with increasing numbers of cross-hybridizations. During this step, the program ensured that no more than two probes show significant cross-hybridization with the same non-targeted genus, thereby avoiding misleading interpretations of hybridization data. Finally, after the removal of redundant probes with the PhylGrid probes, 54 129 16S rRNA gene-targeted oligonucleotide probes were added to PhylOPDb.
PhylOPDb web interface To make all the phylogenetic oligonucleotide probes easily available, we developed a web interface (Figure  3 ) to fetch and download the 74 003 probes that compose our oligonucleotide database (PhylOPDb). The website, freely available (http://g2im. u-clermont1. fr/phylopdb/), was implemented using PHP, HTML5, CSS3, JavaScript, jQuery, JSON and MySQL. PhylOPDb will be updated annually adding newly designed probes, removing deprecated probes and re-computing coverage and specificity. Moreover, the 16S rDNA sequences databases used for the probe design can be downloaded through the PhylOPDb web interface. Based on the EMBL taxonomy, the PhylOPDb web interface provides a hierarchical browse of the database contents. When a taxonomic group is selected, corresponding to a kingdom, phylum, class, order, family or genus, the oligonucleotide probes of this group are then displayed.
Discussion Over the past decades, high-throughput molecular tools have opened an unprecedented opportunity for microbiology by enabling the culture-independent genetic study of complex microbial communities, which were so far largely unknown. Among these tools, environmental microarrays, including POAs, are key technologies that are well adapted to profiling environmental communities  (21, (24) (25) (26) (27) (28) (29). For instance, the PhyloChip (  28 ) is currently the most widely used high-density POA. Nevertheless, 16S rRNA genetargeted probesets were poorly described and updated. Therefore, PhylOPDb is the most comprehensive SSU rRNA oligonucleotide database by overcoming the currently existing 16S rRNA gene-targeted probe collections. For instance, the entire probeset provided through the PhylOPDb web interface could be used to build a comprehensive POA allowing monitoring of >2000 microbial genera in one experiment.
PhylOPDb provides a free and convivial web interface to browse and download a complete 16S rRNA gene-targeted oligonucleotide database composed of 74 003 regular and explorative 25-mers probes covering 2178 prokaryotic genera. PhylOPDb is also well adapted for other molecular tools using primers or probes (PCR, quantitative PCR, FISH and gene capture) with the availability of groupspecific signatures. One of the goals of our database is to exhaustively provide the most specific probes or primers at a fine phylogenetic level. When biologists are interested in specific microbial taxa, it is difficult to reveal them using 'universal' probes or primers. For such biological applications, we consider that our database will be helpful. Furthermore, thermodynamics of nucleic acids hybridizations is not fully understood, molecular approaches such as FISH, PCR, DNA microarrays and gene capture are still empiric and need biological validations.