dataesr/scientific-paragraphs-categorization huggingface dataset:
It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. ViSEAGO is publicly available on https://bioconductor. org/packages/ViSEAGO.
Introduction Large -omic datasets are nowadays easily produced. While bioinformatical and biostatistical data analyses are quite robust, functional analysis remains a critical step of these high-throughput studies. One essential resource for such analysis is Gene Ontology (GO)  [1, 2] , that provides an unified vocabulary to describe gene functions (GO terms) and relations between them in three categories: biological processes (BP), molecular functions (MF) and cellular components (CC). GO annotation represents the association between a gene and a GO term. For each category, GO is structured in a graph, where each GO term is a node and edges are relations between GO terms. GO term annotations including GO acyclic graph and GO terms association tables are currently maintained and improved in major databases.
However, depending on the database being used, there are important differences between supported species and corresponding genes knowledge. This has a strong impact on GO annotations, enrichment tests and downstream analyses  [3]. This statement is clearly illustrated in Fig. 1  with available annotations for three golden standard models such as human, mouse, zebrafish and five livestock species such as cow, chicken, pig, sheep and rabbit. Numbers of GO annotations by category (MF, BP and CC) vary between databases and between species within database for the eight selected vertebrate species. Globally, GO annotations are more inferred computationally for all species, but experimental annotations represent a good part of human and mouse annotations (Fig. 1 ). Ensembl database contains more annotations than NCBI especially for livestock species due to the use of Ensembl Compara annotation pipeline, which increases the number of terms based on the projection of manually annotated GO terms with experimental evidence type from orthologous genes  [4].
The acronym stands for three major concepts used in the package (Fig. 2 ): Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology (ViSEAGO). By using last current GO annotations, users can at once easily perform multiple enrichment tests on large datasets from complex experimental design. It provides interesting functionalities to organize biological functions into clusters by using GO semantic similarity, an adapted distance computations between GO terms. ViSEAGO captures functional similarity based on GO annotations by respecting the topology of GO terms in the GO graph. Hence, it allows enhancing classical functional GO analysis (Table  1 ). Moreover, through an user-friendly package developed in R language, it facilitates biological interpretation, supported by GO annotations, using several visualizations, like dendrogram of GO terms, MDS of GO terms, heatmap of enrichment p-values.
Methods Through ViSEAGO, a functional analysis is conducted using the following steps: reading one or multiple lists of genes of interest associated with a reference gene set (i. e. gene background) and loading the last current GO terms' annotations from selected database in section "GO annotation", performing functional enrichment tests in section "Enrichment Analysis", computing semantic similarity and visualizing clusters of GO terms in section "Visualization & Semantic similarity" (Fig. 2 ). ViSEAGO offers the advantage of performing all analyses in the same statistical environment R, from DE genes identification to the discovery of biological functions of interest. Although the package was developed with differential expression analysis in mind, it can be used with any list of genes, proteins or genomic features.
Loading experimental data and GO annotations First, the selected genes of interest, i. e. differentially expressed genes (DE), are divided into one or more lists of genes depending on experimental design. The reference list can be customized. In the case of differential analysis, the reference list will be the list of all expressed genes. We highly recommend to perform functional analysis with all genes of interest (i. e. without threshold on fold change) to maintain a continuum between underlying biological functions. Depending on species and databases, GO term annotations are then performed on these lists of genes with category BP, MF or CC. Available species (animals, plant, fungi, bacteria. ) can be displayed from the selected database with ViSEAGO::available_organisms method, before fetching the required GO annotations with the ViSEAGO::annotate method.
Functional enrichment analysis ViSEAGO addresses the problem of functional analysis in the context of complex experimental designs and large lists of genes of interest. Functional enrichment tests are performed for each list of genes of interest compared to the gene background. No threshold is applied and results are combined together. The most popular test to perform a functional enrichment analysis is the Fisher's exact test  [5]. P-values measure the degree of independence between belonging to the GO term and being enriched. They are unadjusted for multiple testing in this exploratory context. ViSEAGO offers all statistical tests and algorithms developed in the Bioconductor topGO R package  [16] , taking into account the topology of GO graph by using ViSEAGO::create_topGOdata method followed by the topGO::runTest method.
Semantic similarity between GO terms and sets of GO terms Exploring hundreds of statistically significant GO terms in a "flat" table can be challenging in a complex study with multiple conditions. To catch the group structure from the GO terms data, the choice of measure of similarity between pairs of GO terms is a key criterion. ViSEAGO offers therefore several methods based on semantic similarity (SS) to group together enriched GO terms according to their annotation and their topological position in the GO graph. The ViSEAGO::compute_SS_distance method is based on the Bioconductor GOSemSim R package  [17]  which implements the five common SS methods between GO terms. Four of the SS algorithms use the information content of a GO term (IC), which is computed as the negative log probability of occurrence of the term in a set of GO terms.
Clusters of enriched GO terms are produced by cutting the dendrogram in a static or dynamic mode developed in dynamicTreeCut R package  [20]. In addition to the dendrogram of GO terms with their description, a heatmap plot is produced with -log10(p-value) from functional enrichment test(s) and IC value. The organization of enriched GO terms into clusters respecting the GO graph topology is entirely supported by the dendrogram and enriched by the results of the functional enrichment tests of the study and completed by the value of IC. This clever combination ensures functional coherence and facilitates biological interpretation. In this way, GO terms within the same cluster share similar biological functions. To gain further insights, relationships between sets of GO terms can be explored using a similar approach.
Case 1: role of alveolar luminal cells in the mouse mammary gland during pregnancy To illustrate how ViSEAGO assesses and compares biological themes, we analyzed the publicly available expression dataset (Gene Expression Omnibus with accession number GSE60450) of luminal cells in the mouse mammary gland  [22] , using a generalized linear model and quasi-likelihood tests  [23] , as proposed in edgeR vignette. Among the 15,804 expressed genes, we obtained 7699 DE genes for the comparison pregnant versus lactate, 9583 for the comparison virgin versus lactate and 7302 for the comparison virgin versus pregnant. For each comparison of the study, enrichment of GO BP terms are tested using a Fisher's exact test with the elim algorithm  [16]  (from topGO Package) and mouse GO annotation from EntrezGene database (through Bioconductor org.
Mm. eg. db database). In the mouse GO annotation version Bioconductor 2018-Apr4, 23, 843 genes are annotated with at least one BP GO term. Enrichment tests revealed respectively 198, 151, and 232 BP enriched GO terms for the three comparisons (p-value < 0. 01). A clustering heatmap plot (Fig. 3 ) using SS distance based on Wang's method between enriched GO terms and ward. D2 aggregation criterion allows to data mine GO terms and capture biological meaning. Enriched GO terms are organized in the dendrogram and branches are colored depending on their cluster assignation. Extracting biological insights from this plot highlights three major pathways: signaling pathways, metabolism, and epithelial cell proliferation and morphogenesis. Those pathways are already discussed in the original publication  [8] , but ViSEAGO highlighted other interesting groups revealing biological pathways to further investigate.
Case 2: functional genomics of the digestive tract in broilers The aim of the study  [24]  was to gather knowledge on genes involved in digestive functions in the context of the adaptive capacity of the broilers to changing and even unfavorable dietary conditions. The transcriptome was performed on four digestive tract segments representing the digestive efficiency process (Bioproject accession number PRJNA418230). Differentially expressed genes were identified between the four digestive tract segments by fitting a generalized linear model with edgeR package  [23]. Then, DE genes were organized in seven gene clusters to focus on co-regulated genes throughout the digestive tract and their functions. Enrichment tests were performed with ViSEAGO package independently for each gene cluster containing at least 300 DE Fig.
3  Visualization of ViSEAGO's functional analysis from mouse RNA-seq with three different transcriptomic datasets. Clustering heatmap plot that combines a dendrogram based on Wang's semantic similarity distance and ward. D2 aggregation criterion, a heatmap of -log10(p-value) from functional enrichment tests and information content (IC). Focus is made on cholesterol biosynthetic process, a major pathway involved in the study genes up to more than 3000. Annotation by GO for BP was used and Fisher's exact test with the elim algorithm was performed  [16]. This algorithm improves the enrichment analysis by taking into account local dependencies between GO terms in GO graph, especially in the case of large data sets. Among the 12,656 expressed genes, 7159 possessed at least one functional GO term in the Ensembl version 94 database (56%).
Hence, ViSEAGO provides an interactive graphic support to facilitate biological interpretation. Semantic similarity between GO enriched terms are computed using Wang's SS method to connect together related GO terms by their annotation and position in the GO graph. Enriched GO terms are organized in a dendrogram built from a hierarchical clustering using ViSEAGO::GOterms_heatmap based on SS distance between enriched GO terms and ward. D2 aggregation criterion (Fig. 4b ). This interactive visualization allows the user to discover at a glance the general data structure of GO terms and to find out best functions that will explain the digestive efficiency process. For instance, clear groups of organized GO terms related to fatty-acid metabolism are found in the intestine (Fig. 4b ). Those functions are essential in the intestine and must work properly for effective digestion.
Case 3: Hypomethylation in bull sperm targets specific genomic functions We re-used and re-analyzed Methylated DNA immunoprecipitation (MeDIP) dataset provided by  [25]  to explore ViSEAGO's functionalities and focus on relationships between sets of GO terms (Gene Expression Omnibus database accession number GSE102960). One aim of the study was to identify hypomethylated CpGs genomic regions and their associated functions from MeDIP datasets in bull sperm in comparison to bovine somatic cells (fibroblasts and liver cells). Among the 1632 and 3109 hypomethylated regions identified in bull sperm in comparison to fibroblast (FvsS comparison) and liver (LvsS comparison) cells respectively, 732 and 1229 unique genes with a match in regulatory elements (promoter: -1 kb to + 0. 1 kb along TSS; downstream: + 1 kb along TES) have been identified using Genome-Features R package (https://forgemia.
In complement, we highlighted several GO terms that were not previously found in the study (Fig. 5a ) to allow a deeper analysis of the datasets. For instance, the GO term "cellular process involved in reproduction in multicellular organism" (GO:0022412) is the most significant term in both comparisons (1. 07 10 -07 and 6. 76 10 -06 for FvsS and LvsS respectively). A hierarchical clustering using Wang's SS method and ward. D2 aggregation criterion that was dynamically cut led to the identification of 21 functional groups of GO terms (Fig. 5a ). To easily interpret the biological functions carried out by the 21 groups, SS between the 21 sets of GO terms were computed using BMA distance. Proximities of these functional groups were shown in a MDS plot (Fig. 5b ) and a heatmap plot (Fig. 5c ).
Conclusions Functional enrichment analysis remains a major challenge especially on large datasets and complex experimental designs. ViSEAGO R package is a generic tool for functional analysis based on Gene Ontology that meets this challenge. The novelty of ViSEAGO is providing by the association of the semantic similarity and visualization to focus on biological interpretation with respect for GO graph. ViSEAGO's functionalities are extended compared to most functional analysis tools in three major aspects: (1) emphasize functional coherence by aggregating closely related biological themes based on the GO graph topology; (2) reliability of the functional interpretation using the last current GO annotations; (3) interactive visualization both synthetic and detailed to facilitate biological interpretation.
e. ,. fits) image file that could be processed and analyzed by conventional microarray software. Thus, after the immunoassay were completed, and the slides were analyzed in the laboratory by illuminating them with 635 nm light and imaging their fluorescent emission at 650 nm with a GenePix 4100A scanner. The images were quantitatively analyzed using GenePix Pro software (Genomic Solutions). A blank was always run in parallel with only a buffer as the "antigenic" sample and then revealed with the same fluorescent antibody or antibody cocktail as the real samples. This image was used as a baseline to calculate spots intensities in the tested sample. The final fluorescence intensity (F) for each antibody spot was calculated with the equation F = (F sample -F blank -2. 5F avcontrol spots ), where F is the fluorescent intensity at 635 nm minus the local background, as quantified by the software (GenePix Pro); F sample is the total fluorescence signal of the sample; F blank is the total fluorescence signal of the blank; and F avcontrol spots is the average fluorescent signal of the control spots.
Biomarker Profiles of MASE Sites Anaerobic water and sediment samples from each selected MASE site (Regensburg, Germany; Gaenvatn Lake, Iceland; Boulby Mine, UK; and Kaunertal Glacier, Austria) were processed in the laboratory by sonication (material and methods section) to obtain the extracts or epitopes that were analyzed using the LDChip, which included different antibodies (small peptides and molecules, proteins, exopolysaccharides and cell extracts; Table  S1 ). The fluorescent images obtained from the assay were analyzed, and the fluorescence intensity of each antibody spot on the microarray was quantified. Data from this analysis were plotted as immunograms or biomarker profiles (Figure  2 ). The immunograms showed several positive antigen-antibody signals in all the samples analyzed: Boulby Mine-hypersaline environment (Bou.
This reference database is implemented as list of k-mers stored explicitly, that requires 24GB when gzipped. Similarly, GeneCodeq  [11]  also has a list of k-mers as ground truth, but the algorithm involved during smoothing is more complex than Quartz. Each base has its associated error probability recalculated using a Bayesian framework and the smoothing takes place only if the new quality is greater than the old one. Both Quartz  [12]  and GeneCodeq  [11]  require a machine with at least 32GB of RAM, because of the size of the reference database. In this paper we present YALFF (Yet Another Lossy Fastq Filter), a reference-based quality score compressor based on k-mers and the Burrows-Wheeler Transformation (BWT)  [21] , that is capable to improve compression while introducing low distortion into the processed data.
Implementation details YALFF is written in C/C++. The C parts are from BWA. In particular, the source code of the aln utility has been recycled to handle the query operations during smoothing. The FM-indexed version of a dictionary string is obtained through the index command of BWA. This opportunistic choice was made to ensure a widespread adoption of YALFF. Because BWA is the recommended aligner in most applications, it is extremely probable that a user who wants to compress some datasets will already have some sort of indexed reference genome which can be used as a dictionary. The indexing procedure and the data produced can be shared between our software and BWA leading to less time and storage required. In Fig. 5  is shown an overview of YALFF. Because each read can be processed independently from the others, YALFF can be easily parallelized using more than one thread.
Datasets, pipeline and parameters The dataset used in this study is a set of real reads (NA12878) from the 1000 Genomes Project http://www. internationalgenome. org/data-portal/sample/NA12878. Only the two paired end archives were used (namely SRR62246 1_1. filt. fastq. gz and SRR622561_2. filt. fast. gz) for the evaluation, while the third containing unpaired reads were discarded. All tests have been done from scratch using the two paired end reads to evaluate the tools, without using previous results from other papers in order to make the comparison as clear as possible. This dataset has been Fig. 5  An overview of YALFF's inner structure widely used for benchmarking in other papers, because the list of known SNPs is available and it can retrieved from ftp://ussd-ftp. illumina. com/2017-1.
0/hg38. The reference genome used during alignment, and as a dictionary string for smoothing, is the human genome reference FASTA file hg38. fa downloaded from http:// hgdownload. cse. ucsc. edu/goldenpath/hg38/bigZips/. The FM-index of the human genome is computed only once, in about 1h, and then it can be used for multiple runs of quality compression and reads alignment. The genotyping pipeline is implemented as a single bash script which uses bwa mem for alignments, bcftools for SNP calling and vcfeval for evaluation. All scripts can be found at https://github. com/yhhshb/yalff/tree/master/scripts. Although YALFF can be run on a normal laptop, as opposed to Quartz, for all tools all tests were performed on a 14 lame blade cluster DELL PowerEdge M600 where each lame is equipped with two Intel Xeon E5450 at 3.
Genotyping Accuracy The performance evaluation of the algorithms compares the number of retrieved SNPs from a smoothed file to the ground truth associated with the original dataset. Each set of variants (stored in the output VCF file) is compared against the consensus set of variants. The benchmarking tools output the following values. • True Positives (T. P. ): All those variants that are both in the consensus set and in the set of called variants. • False Positives (F. P. ): All those variants that are in the called set of variants but not in the consensus set. • False Negatives (F. N. ): All those variants that are in the consensus set but not in the set of called variants. These values are used to compute the following three metrics: • Recall: This is the proportion of called variants that are included in the consensus set; that is, R = T.
Discussion The low compressibility of quality values is one of the main problem of sequencing reads compression. Several lossy smoothing strategies have been proposed, all with the intent to improve compressibility without altering the information carried by quality value for downstream analysis. Here, we propose YALFF, a tool that smooths quality scores based on a dictionary of k-mers from a reference genome. The YALFF smoothing algorithm can achieve low distortion of the processed datasets with a small degradation of precision during SNP calling, but with an overall improvement of F-measure. We developed this program with consumer application of genome sequencing in mind. For example, one of the current hot topic is personalized medicine, which requires huge databases to store as many genomic information as possible and new methods to allow common users to share their genetic code.
The variability sources are the random failure of the fiber and the volumetric fraction of fibers. For the sake of simplicity, only the deterministic version is used in this paper. The model is first implemented in the Finite Element software ABAQUS by the means of a subroutine UMAT, which allows to simulate any mechanical behaviour of a structure by using a specifically designed model. In a second step, this UMAT subroutine is used to identify the model parameters by simulating tensile loadings performed on three different types of specimens made from wound pipes ([0 8 ], [90 8 ], [±45 4 ] s ). These tubes are first manufactured by filament winding on a cylindrical mandrel. Two types of plies are manufactured: circumferential plies used for layers oriented at 0 ° and 90 ° (with no interlacing) and helical plies with interlaced fibers.
For the n-th (last) interval, J 7 j X Y =J m X Y. This method is applied to identify the values of the acoustic energy associated with the three damage modes: fiber breakage, fiber / matrix interface failure and diffuse damage. Acoustic data from two different tests are used in order to identify the different energy levels. It is important to keep in mind the qualitative nature of this approach. These two tests are: • Tensile test on [± 45 4 ]s samples, characterized by an early initiation of the diffuse damage, followed by a contribution of the fiber / matrix interface failure. The results are presented in Figure  3. • Tensile test on a sample containing fibers oriented at 0°. Here, data from notched [0 2 ,± 20 2 ] s samples have been selected, which undergo fiber breakage (Figure  5 ). -fiber / matrix interface failure is associated to only one acoustic level.
The peak-negative pressure (PNP) in this condition is -294 kPa, and the ISPTA is 385 mW/cm 2. Based on the resolution model developed in [Desailly et al 2015]  [15] , it was possible to calculate the maximum resolution expected after the localization process : ( ) 1 where   is the standard deviation of arrival time. This standard deviation can be either calculated with the Cramer-Rao lower bound or can be measured on experimental data as the error extracted from the distribution of residuals around the hyperboloid observed on reference echoes. We chose the latter method as it takes into account all types of sampling error. We simply imaged a needle with a tip finer than the wavelength. We recorded the echo from this point-like scatterer multiple times and calculated the residuals from these echoes around the reference hyperboloid.