dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioreactor samples. Samples originating from the bioreactor, referred to as BR-alginate in the samples overview table, were imaged both with widefield and confocal microscopy. The workflow for both is described below. Widefield images. The widefield images are analyzed using the CellProfiler pipeline in 'Wide-field_bioreactor_lung_rfp. cpproj' and 'Widefield_bioreactor_breast_gfp. cpproj', which are entitled: 'Example CellProfiler pipeline for the image analysis of the breast(Iung) cancer cell bioreactor cultures'. As explained in the introduction the CellProfiler pipelines should be adapted to one's own needs. Confocal stack images. For the confocal stack images first the 3D spheroids in the images can be manually outlined using the RoiManager of FIJI. These ROI's can then be provided to the analysis script in the MATLAB procedure at https://github.
GFP and RFP signals do not overlap In order to make sure the fluorescence proteins were specific for tumor and stromal cells, respectively, increasing numbers of cells were plated in 2D, and the fluorescence signal measured using filter sets for either fluorescent protein. No bleed-through was observed from one fluorescence channel into the other, and a positive correlation between RFP/GFP signals with cell numbers could be demonstrated (Fig. 6a ). Similar correlations were observed when increasing number of cells were embedded into 3D matrix and fluorescence was measured the next day (data not shown).
Acknowledgements The authors would like to thank  Ingrid Verberne  for the construction and testing of lentiviral plasmids as well as cell transduction,  Katja Välimäki  and  Antonio Ribeiro  for the assistance in TMA construction and IHC. We acknowledge support from the  Innovative Medicines Initiative Joint Undertaking  ( IMI grant  agreement  n°115188 ), resources composed of financial contribution from  EU-FP7  and EFPIA companies in kind contribution. We also acknowledge support from  Fundação para a Ciência e Tecnologia (FCT) ,  Portugal  ( EXPL/BBB-IMG/0363/2013 ); MFE is the recipient of a PhD fellowship from  FCT  ( SFRH/BD/52208/2013 ). RG had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Background: The evolution of next-generation sequencing (NGS) technologies has led to increased focus on RNA-Seq. Many bioinformatic tools have been developed for RNA-Seq analysis, each with unique performance characteristics and configuration parameters. Users face an increasingly complex task in understanding which bioinformatic tools are best for their specific needs and how they should be configured. In order to provide some answers to these questions, we investigate the performance of leading bioinformatic tools designed for RNA-Seq analysis and propose a methodology for systematic evaluation and comparison of performance to help users make well informed choices. Results: To evaluate RNA-Seq pipelines, we developed a suite of two benchmarking tools. SimCT generates simulated datasets that get as close as possible to specific real biological conditions accompanied by the list of genomic incidents and mutations that have been inserted.
BenchCT then compares the output of any bioinformatics pipeline that has been run against a SimCT dataset with the simulated genomic and transcriptional variations it contains to give an accurate performance evaluation in addressing specific biological question. We used these tools to simulate a real-world genomic medicine question s involving the comparison of healthy and cancerous cells. Results revealed that performance in addressing a particular biological context varied significantly depending on the choice of tools and settings used. We also found that by combining the output of certain pipelines, substantial performance improvements could be achieved.
For instance, in a recent benchmarking study  [6] , the authors assessed RNA-Seq alignment tools on three human and malaria datasets at the read, junction and base levels. However, their benchmark suffered from limitations that could impact their conclusions. We question the relevance of the metrics used in their comparisons: the way multiple alignments are discarded (those do not use the standard NH tag in the SAM file), the fact that highly-expressed junctions have more weight in the junction-level analysis than under-expressed ones, and the fact the the authors rely solely on the CIGAR string to assess the alignment (while some ambiguous alignments might have distinct CIGAR strings). The authors use the dataset with the highest mutation rate of 3% to tune the parameters. , so RNA-Seq benchmarking efforts to-date have tended to provide a review of the pipeline tools available at a given time, with a focus on individual components such as aligners  [7] [8] [9] [10] , variant callers  [11] [12] [13]  or quantification methods  [7, 14].
RNA-seq challenge: The need for standard data-sets and benchmarking methodology Combining a set of tools to create a complete analysis pipeline requires expertise and familiarity with the characteristics of each candidate software component of the workflow. This has implications for researchers and clinicians alike, as using the right pipeline, correctly configured, limits false positives and ensures that relevant genomic events are surfaced. This emphasizes the need for a benchmarking methodology that provides concrete information on the pipeline and configuration settings that yield best results for a given biological question. Traditional simulation approaches like BEERS  [10]  or Flux Simulator (Flux)  [18]  aim to model RNA-Seq experiments in silico by sequencing reads from a reference genome according to annotated transcripts and by modeling different experimental behaviors.
Implementation We have developed SimBA (Simulation & Benchmarking Analysis), a software suite designed to evaluate the performance of an entire RNA-Seq pipeline in the context of a specific biological question. SimBA provides an integrated environment (see Fig. 1 ) that lets the users generate benchmark reports that evaluate the workflow of their choice. It is comprised of two components: SimCT A configurable generator of simulated RNA-Seq data that can emulate specific biological mechanism (ie. SNVs, indels, fusions) and provide robust data sets covering cases such as fusion genes (or fusions). BenchCT A qualitative evaluation tool which assess any pipeline results against a simulated dataset to obtain a clear understanding of its performance characteristics in answering a particular biological question.
In this paper, we illustrate how this vision applies in genomic medicine by building a set of RNA-Seq data simulations of a somatic condition and propose an automated workflow using Snakemake  [20]  to compare tools in three qualitative RNA-Seq use cases: SNVs, indels and fusion genes. The Snakefile used to generate all results presented in the article is available at https://github. com/jaudoux/ simba-publication-pipeline.
SimCT: RNA-Seq operation SimCT is a complete and modular workflow to simulate RNA-Seq data from a reference genome and known transcript annotations. SimCT works in three steps (see Fig. 2 ): 1. The first step introduces a set of variants in the reference genome. SimCT then generates a haploid mutated genome in FASTA format and a set of GTF annotations whose coordinates are converted for this modified reference. 2. We then process the modified reference files using FluxSimulator, that offers good performance in generating a random expression profile and simulating reads by reproducing a complete "in silico" RNA-Seq protocol. 3. In the final step, the reads and corresponding alignments produced by FluxSimulator are sent to post-processing where alignment coordinates are converted to the coordinates of the original reference genome.
Gene fusions Gene fusions (fusions) are generated by randomly picking two exons from the annotations, and combining their two parent genes into a fusion gene. This is accomplished by merging the upstream part of the parent gene of one exon with the downstream part of the parent gene of the other exon. Both fused exons are then merged to force the fusion junction between the two ends. The new fusion gene is saved in a separate FASTA file, and annotations of the fusions transcripts are added to the GTF file. Fig. 1  Overview of the SimBA benchmarking procedure. A benchmarking pipeline implemented with SimBA is composed of three components, i/ Simulation of synthetic data using SimCT, ii/ Processing of the synthetic data using a pipeline manager (i. e Snakemake  [20] , iii/ Qualitative evaluation of the results using BenchCT Fig.
Dataset characteristics In addition to the FASTQ file containing reads with their alignments, SimCT also saves splice and fusion junctions and mutations supported by the sequenced reads in separate files. Fusion junctions are defined as either non colinear junctions (junctions on distinct chromosomes, or in reverse order), or colinear junctions with a junction length longer than a pre-defined threshold (300kb by default). This means, that colinear fusion junctions are not necessarily produced by the fusions introduced, but can also result from very large splice junctions of unfused genes.
BenchCT: RNA-Seq qualitative analysis assessment To assess the RNA-Seq analysis results based on simulated data, we have developed BenchCT. It loads the reference "truth database" of known dataset characteristics and compares it to the output files (SAM, BED, VCF, software specific files) of the pipelines being evaluated. For each characteristic, BenchCT classifies predictions as TP (true positives), FP (false positives) or FN (false negatives). We then compute two metrics for qualitative analysis: (precision = TP/(TP + FP) and recall = sensitivity = TP/(TP + FN)). BenchCT uses genomic interval-trees available as CracTools-core modules, useful for quickly checking whether a position is at a given distance of the known position in the "truth database".
Evaluation procedures With BenchCT, we propose standardized procedures for the binary classification of alignments, splice junctions, indels, SNVs and fusions predictions. For each of these events, we have proposed a means of comparison of results with the "truth database" (see Fig. 3 ). In the case where several alignments can correctly be made, it may be possible to find multiple event/mutation scenarios that explain the simulated data-set. In such cases, BenchCT attempts to score these solutions as correct using a heuristic approach. Alignment evaluation BenchCT alignment evaluation is performed as follows: if a proposed alignment overlaps the real alignment by at least one base (encoded in the read name), it is considered as true. Parameters can be set to ignore some alignments (ie. multi-mapped reads) that are counted as false positives (FP).
Indels and SNVs evaluation The mutations are evaluated with the same GI procedure were start and end Fig. 3  BenchCT evaluation procedures. Each event is evaluated with benchCT with a specific procedure that allow approximate matching. For alignement, only overlap between the prediction and the truth is evaluated. For Splice junctions and Fusions we expect an overlap between the prediction and a candidate in the truth database with a limited agreement distance according to the threshold. For mutation (SNV and Indel), similar procedure is used, as well as the verification of the mutation. For SNVs we evaluate the mutated sequence and for insertions and deletions, the length of the mutation positions are equal, and the strand is left undefined. The GI p used to search the GI-tree is defined as p = (p chr , p posthreshold, p pos + threshold) For indels we evaluate the distance between the predicted indel length and the observed one according to the threshold.
Simulated dataset Simulated dataset used in the results were generated using the primary assembly of the version GRCh38 of the human genome. Annotations were downloaded from Ensembl FTP (version 86) in GTF format. SNVs were introduced at a rate of 0. 0014 and 0. 0016 ('-s' option) for normal and somatic condition respectively. Insertions and deletions were introduced at a rate of 0. 0001 ('-d' and '-i' options) in both conditions with max indel length set to default  (15). SimCT was used with --vcf-file and --vcf-ratio 0. 95 options to randomly take 95% of the mutations from the VCF file of common SNP provided by dbSNP (version 20160527). Finally 100 fusions (number chosen arbitrarily) were introduced for the somatic condition. This does not mean that all of the fusions genes are expressed and found in the RNA-Seq data as this depends on the expression profile randomly generated by FluxSimulator.
Expression profiles were generated using the following parameters for FluxSimulator: k = -0. 7, x 0 = 15, 000, 25, 000 for normal and somatic conditions respectively, x 1 = x 2 0 , NB_MOLECULES= 10 6. NB_MOLECULES is the number of expressed transcript molecules. Parameters k and x 0 are part of a mixed power and exponential law of the expression profile, where k is the exponent of the rank of gene expression. This means that the smaller k the smaller the decay of the expression resulting in highly expressed transcripts having closer expression levels. x and x 1 govern the parameter of the exponential decay by multiplying the rank of gene expression k. This mostly affects the tail of the gene expression spacing the differences further out between lowly expressed genes. Parameter ranges were estimated for mammalian cells by nonlinear fitting to expression levels observed in experimental results by the developers of FluxSimulator  [18]  and were chosen accordingly in the suggested ranges.
Mutation discovery pipelines Twelve mutation discovery pipelines were defined: with Freebayes, (8) Hisat2_2pass GATK (HaplotypeCaller), and (9) Hisat2_2pass SAMtools (mpileup and bcftools). 4. One using CRAC mapping software  (10)  that integrates its own calling algorithm. 5. Two "meta-calling" pipelines that are the union of the results of two pipelines using bcftools merge command; (11) A combination of STAR-GATK and CRAC variants, and (12) A combination of STAR-GATK and Hisat2_2pass-GATK. Mapping software settings and versions are described in the Table  1 , they are identical for all datasets. Hisat2_2pass consists in running Hisat2 a second time on the same dataset using splice junctions discovered at the first pass. The variant calling pre-processing procedure has been adapted from GATK best practices  [24].
For all mapping procedures we applied the following procedure: 1. Mark duplicates with PicardTools. 2. Apply SplitNTrim (GATK command) procedure which splits reads into exon segments (removing Ns but maintaining grouping information) and hard-clip any sequences overhanging into the intronic regions. 3. Apply ReassignOneMappingQuality (GATK command) read filter to reassign all good alignments to the default value of 60. This re-calibration pipeline produced a BAM file used by the variant calling software whose versions and settings are described in Table  1. For the CRAC pipeline we have used the cractools extract command that produces a VCF file from a BAM file produced with CRAC.
These two lengths were chosen to mimic the Illumina Hiseq 2500 sequencing platform which typically produces these read lengths. The deep sequencing was used to create a base dataset that can be further sub-sampled for other applications. The Table  2  is a summary of the four data-sets that constitute our genomic medicine corpus; a detailed description of SimCT and FluxSimulator parameters is described in "Simulated dataset" section of the method.
Aligner selection The first step in the bioinformatic analysis process is alignment. For this reason, we selected three aligners for the benchmark; (i) CRAC  [29]  for its high precision, (ii) STAR  [30]  for its sensitivity and, (iii) Hisat2  [31]  which offers a good compromise between precision and recall (Additional file 1: Figures  S2  and S3 ). We then evaluated the predictions of twelve variant calling pipelines based on these three aligners and three variant callers (see "Mutation discovery pipelines" section). Then, we used SimBA to optimize mapping software predictions for gene fusions by independently tuning their parameters (see "Gene fusions discovery pipeline" section). In the following, we discuss only part of results, by selecting representative dataset and evaluation metrics.
Benchmarking performance in SNV detection This benchmarking resulted in the following observations for SNV detection (see Fig. 4 ). Performance in identifying SNVs varies significantly depending on the pipeline. An example of this is the STAR-GATK pipeline which is the most accurate (> 97%) while all other pipelines provide precision of between 90 and 95%. The SAMtools  [32]  caller has a much higher recall than GATK  [33]  and Freebayes  [34]  regardless of the aligner (∼ 92% vs ∼ 85%). We observed significant differences in calling pipeline performance between "normal" (Fig. 4a ) and "somatic" (Fig. 4b ) data-sets. For example, Hisat2_2pass-GATK results vary from 91 to 94% of precision between normal and tumoral data-sets (datasets A and B), implying that there is background noise that is progressively smoothed by the addition of new real biological events.
Evaluation of gene fusions detection pipelines on the GRCh38-101bp-160-somatic dataset. Fusions were splited in two category with an individual evaluation. a Colinear fusion where the fusion involves to genomic locations that are located on the same strand of the same chromosome with a distance superior to 300kb. b non-colinear fusions wich does not satisfy the colinear criteria of STAR and CRAC are very different. STAR, with 30 chim_segment, yields a better Fscore (0. 65) than CRAC (0. 25) with a 0. 85 chim_value (see Fig. 6 ). It is very unlikely that these differences are due to the length of sequences, but rather to the nature of fusions in each data-set (see Additional file 1: Figures  S7 to S8 ). We also observe that colinear fusion predictions are more accurate than in the case of non-colinear fusions for both aligners, (see Fig.
6 a  and b ). Nevertheless, sensitivity for both types of fusions is high when using both CRAC and STAR with an average value of between 80% and 90% of fusions detected on all datasets examined. In conclusion, it can be said that the principal challenge in exploring fusions is to correctly configure the tools for the specific data-set being examined as this significantly reduces the number of false-positives produced during the alignment phase. Indeed, eliminating false chimeric alignments at the very first step of a fusion gene detection pipeline is much safer than using empirical filters based on annotations later on, as true fusions could be incorrectly filtered out.
BenchCT is a complete solution, featuring an easy installation, advanced algorithms, fast execution, multi-core architecture support. BenchCT is also highly configurable thanks to its YAML setup file. BenchCT was designed to provide an easy way to benchmark tools with interpretable metrics, sensitivity and precision. However, we plan to incorporate the evaluation of expression estimates into SimBA as the community defines more standardized comparison metrics  [7, 14]. In future, we also intend to include other qualitative evaluation cases, such as transcript reconstruction.
Conclusion We propose SimBA, an open benchmarking solution that can be used to generate simulated data-sets with high configurability as well as a means to evaluate the performances of analysis pipelines as they relate to various qualitative questions such as read alignment, variant calling and gene fusion detection. In this paper, we have created a means for comparison of results related to questions relevant to genomic medicine by producing reference data-sets that mimic cancerous cells and the investigation of important events in this type of pathology such as mutations, indels and fusions. The results described in our paper emphasize the need for a benchmarking tool that can: (i) Simulate reference datasets emulating a specific biological context, (ii) Calibrate a pipeline for a data-set specific to a biological question, (iii) Aggregate and compare results of a variety of bioinformatic tools to explore how the combination of various tools impacts the overall performance.
, 2007) , as well as light absorption and scattering by the skull and the cerebrospinal fluid  (Okada and Delpy, 2003). Furthermore, fNIRS does not permit imaging of the underlying brain anatomy, meaning that it is not possible to localise the precise anatomical origin of cortical activations, or to account for individual differences. However, ongoing technological advancements that address such drawbacks, including the use of 3D digitizers and spatial-registration software, now enable more accurate estimation of the anatomical source of cortical activation measured using fNIRS and help to reduce variability across subjects  (Singh et al. , 2005; Tak and Ye, 2014; Tsuzuki et al. , 2012; Tsuzuki and Dan, 2014). In addition to these temporo-spatial characteristics, fNIRS offers several pertinent advantages for use in both adult and paediatric populations.
For instance fNIRS has been applied to the study of paediatric sensory function and perceptual development  (Taga et al. , 2003; Watanabe et al. , 2013) , language acquisition  (Gervain et al. , 2008; Homae et al. , 2011) , social-cognitive function  (Csibra et al. , 2004; Minagawa-Kawait et al. , 2009; Southgate et al. , 2014)  and the assessment and early identification of neurodevelopmental disorders  (Lloyd-Fox et al. , 2013; Negoro et al. , 2010; Zhu et al. , 2014). Moreover, these advantageous features of fNIRS have enabled bed-side monitoring of cerebral haemodynamics and the assessment of clinical outcomes in a range of fields including, but not limited to, psychiatry, oncology, and clinical neurology  (Ehlis et al. , 2014; Hirsch et al. , 2009; Jean-Pierre, 2014; Obrig, 2014). Pertinent to this review, it has been demonstrated that fNIRS is fully compatible with a CI.
Acknowledgements C. A. A is funded by the  National Institute for Health Research (NIHR) Biomedical Research Unit Program , and was also funded by  Cochlear Europe Ltd. during the writing of this article. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR , or the Department of Health. The authors thank  Dr Ian Wiggins  for his comments on earlier versions of the manuscript. The authors also thank the two anonymous reviewers for their helpful comments and suggestions.
A common intriguing feature about these proteins is their very large size and ability to mediate a range of phenotypes, including (i) adhesion to abiotic and biotic surfaces, (ii) cell-to-cell interactions, (iii) biofilm formation, (iv) interaction with host epithelial cells and (v) the capacity to mediate invasion  22, 31. Several studies have also shown these large extracellular adhesins also represent therapeutic targets, either as potential vaccines or as targets for anti-adhesion strategies  32, 33. In this study, we took advantage of the large number of E. coli genomes available on public databases to analyze the prevalence and conservation of the yeeJ gene. Our in silico analyses revealed the existence of two distinct variants of YeeJ that share similar functional properties. We show that YeeJ is both surface-located and present in the bacterial supernatant and that its LysM domain binds to peptidoglycan and is required for optimal YeeJ cell-surface localization and biofilm formation.