dataesr/scientific-paragraphs-categorization huggingface dataset:
Hospital biobank CRB (Biological Resources Center). 156 samples were then selected for DNA extraction and shotgun sequencing, Fecal metagenomic data from IBD patients among them samples from 53 patients with CRC, 42 adenoma In addition to study population H, we investigated 25 published patients, and 61 randomly chosen controls. Colonic neoplasia status fecal metagenomes of patients with an inflammatory bowel disease was determined by colonoscopy (Table 1, Supplementary Table S1 (IBD) (Qin et al, 2010) (see Fig 2), but these were not used as and Supplementary Dataset S1 for participant metadata). For a controls for CRC classification in study population H (see Supple- subset of 129 patients, 16S rDNA could be amplified from the same mentary Dataset S1 for participant metadata). DNA extracts and subjected to 16S amplicon sequencing (see below and Supplementary Dataset S2).
Fecal samples were collected and conserved under anaerobic conditions in a sealed bag, kept at À20°C for short-term storage, and stored at À80°C upon arrival in the laboratory (see Supplementary Table S1 and Supplementary Dataset S1 for participant metadata). ). The first principal component (PC1) accounting for 43% of the variation in CRC-associated functional changes correlated with cancer progression (Spearman correlation of 0. 45, P < 1E-8) and stratified non-neoplastic, adenoma, and early-and late-stage CRC patients (color-coded). Shown below are boxplots of each patient's PC1 value grouped by cancer progression. Significant differences between groups were established using pairwise Wilcoxon tests (see bottom right legend). Boxes denote interquartile ranges (IQR) with the median as a black line and whiskers extending up to the most extreme points within 1.
The HTSstation analysis portal is a suite of simple web forms coupled to modular analysis pipelines for various applications of High-Throughput Sequencing including ChIP-seq, RNA-seq, 4C-seq and re-sequencing. HTSstation offers biologists the possibility to rapidly investigate their HTS data using an intuitive web application with heuristically pre-defined parameters. A number of open-source software components have been implemented and can be used to build, configure and run HTS analysis pipelines reactively. Besides, our programming framework empowers developers with the possibility to design their own workflows and integrate additional third-party software.
Design HTSstation workflows rely on interconnected modules revolving around the sequence mapping which uses Bowtie  [16]  (upgraded to Bowtie2 since September 2013) to map sequencing reads to a reference genome or sequence database and calculates genome-wide coverage profiles. This mapping module is preceded by an optional demultiplexing step, and followed by applicationspecific modules (ChIP-seq, RNA-seq, 4C-seq and SNP calling, see Figure  1 ). Each module returns a table of output files that can be downloaded, sent to external tools for viewing and further processing. The overall design of HTSstation strives to accommodate three types of user experiences (see Figure  2 ). Biologists will mostly interact with the web interface made of intuitive and simple web forms requiring minimal knowledge of the specifics of the underlying programs and algorithms (Figure  3  and File S2, panel A).
Bioinformaticians will typically prefer a scripting approach with detailed control over the back-end process. Each module has its own command-line Python program with a number of specific flags and options. Lastly programming enthusiasts will be able to download and extend our source code from our public repository (programs are at https://github. com/bbcf/bbcfutils/ and libraries at https://github. com/bbcf/bbcflib/). Each workflow script consists in a succession of elementary operations defined as library functions. Other workflows can be built by combining some of those operations with new ones and in particular incorporate third-party tools via system calls. Many functionalities developed for HTSstation are useful in a broader context and have been designed as independent applications: our generic workflow manager, Bein, handles tracking files, logging and dispatching program executions to (in our case) a computing cluster.
The GenRep application (http:// bbcftools. epfl. ch/genrep) manages genome references and their associated files (Bowtie indexes, FASTA sequence files and GTF annotation files) with a versioning system. We also integrated a customized genome browser (GDV: http://gdv. epfl. ch) and a collection of statistical analysis and data mining algorithms provided under the Bioscript collection (http://bbcftools. epfl. ch/ bs); see Methods section and File S1 for more details.
Analysis of Kap1 repression of endogenous retroviruses in Mouse ES cells The transcriptional repressor Kap1 was shown to mediate silencing of endogenous retro-viruses (ERV) in mouse embryonic stem (ES) cells  [7] , as deduced from comparing the transcriptomes of wild-type and Kap1 knockout ES cells. Two replicates of the KO condition and one of the WT were generated producing three FASTQ files containing a total of 47 M Illumina GAII sequencing reads. We submitted these files to HTSstation mapping module to generate three BAM files of mappings to the mm 9 mouse genome and genome-wide coverage densities as BIGWIG files. The default options use a seed of 28 bp with 2 mismatches and a maximum of 5 hits per read. These mappings were then transferred to the RNA-seq module to compute differential isoform expression analysis.
This proceeds through every Ensembl  [17]  annotated exon and computes its average read coverage, then infers expression of every Ensembl transcript from its exon composition (see File S1). Differential expression analysis between the two conditions was performed using the R package DESeq  [18]  and the results can be viewed as an interactive MA-plot highlighting interesting genes such as Alox15, Col4a1 and Col4a2 which are over-expressed in the KO condition as can be verified by direct inspection in the UCSC genome browser and, obviously, the Kap1 gene itself stands out as the most down-regulated gene on the MA-plot (Figure  5A ). A complementary analysis was performed with the same reads by mapping them to the Repbase collection of repetitive DNA elements (rodent section, version 16. 08  [19] ) and analyzed similarly to genes for differential expression, to reveal induction of ERV elements in the KO condition.
The phylogeography of Mycobacterium leprae The phylogeography of the human obligate pathogen Mycobacterium leprae was established by genotyping more than 400 samples including 5 complete genomes  [3, 8]. We have mapped the Illumina GAII reads for all 5 strains (directly from their SRA urls) against the reference TN strain (refseq NC_002677. 1) obtaining an average coverage between 0. 36 and 26. The resulting BAM files were subsequently analyzed with HTSstation's SNP module, to extract high-confidence single nucleotide polymorphisms (SNPs) using SAMtools  [20]. Only high-coverage regions were analyzed and statistical support for each polymorphism is taken into account to filter out false positives. Then each SNP is linked to its genomic context using annotations available for the target genome.
Discussion The HTSstation portal has over its two years lifetime processed in excess of 500 jobs per year with an average of 15 distinct users per month. Complete analyses with complex experimental designs through several HTSstation modules typically take less than two days (ignoring queuing time on the computing cluster). Beyond the tool itself we believe that HTSstation represents an innovative framework for high-throughput data analysis methods that combines high flexibility using 'off the shelf' modules. The framework mostly relies on external software packages that are widely accepted standards in the field (Bowtie, SAMtools, DESeq, MACS, SOAPsplice, Exonerate, etc. ) but takes care of scaling with increasing data volumes, of consistency between inputs and outputs of unrelated tools, and allows us to seamlessly integrate specific workarounds and additions for missing or inadequate   1 ), e.
g. from a demultiplexing task. Users provide inputs as URLs and organize them hierarchically in groups and runs  (2)  where groups represent distinct experimental conditions (3) and runs are distinct replicates  (4). When relevant, some groups can be selected as representing the control condition. Selected runs can be moved between groups  (5). All modules have a section with general parameters (e. g. email and analysis name) (  6 ) and modulespecific options  (7). doi:10. 1371/journal. pone. 0085879. g004 features of existing products (see Methods). Moreover the currently selected tools will be easily replaced in the future with their potential successors. It also integrates in its core design the normal process of bioinformatics development cycle: command-line tools with specifically tuned options are integrated into Bash, Perl or Python scripts that, when stabilized and in need of scaling to a large number of executions, are packaged into modules.
Our framework provides a structured method running through each of these steps and eventually provides a web interface for the benefit of the biologist looking for a swift processing of his/her data. The web interface of HTSstation was deliberately designed for user-friendliness rather than exhaustiveness. We focused on biologically telling options (such as ''remove PCR duplicates'') rather than algorithmically inclined options (''length of alignment seed'') for which we have heuristically set predefined values, through the development process described above. However, default program option can be modified by the user: this requires uploading a configuration file (a simple human-readable text file, similar in structure to the INI file format) through the job submission form.
Accessibility and reproducibility of the results The choice of a web-based client-server architecture presents an immediate advantage for sharing data: the results of an analysis are presented as web pages with obfuscated URLs. These pages include detailed description of the analysis setup, downloadable file bundles and summary reports. The complete results or any individual file can be shared with collaborators or uploaded to external viewing and processing software via their URLs. Reproducibility of analyses is facilitated by a cloning functionality which copies parameters and input files from a previous run. Users can then provide new inputs and parameter values. From the system administration point of view, the workflow manager (Bein) stores all execution parameters and links to input and output files in a database.
Mapping This module uses Bowtie  [16]  to map raw sequences to a reference genome and returns the alignments as BAM files  [20] , as well as the associated genome-wide read densities. We implemented an optional removal of PCR duplicates at the level of BAM files  [24, 26]. This uses a Poisson distribution to estimate the likelihood of obtaining n reads at the same position and limits this number to the 95% probability range. Using this option helps reduce the number of false positives in peak detection for ChIP-seq applications. Similarly, we post-process Bowtie's BAM output to include the optional ''NH'' tag that indicates the number of alignments found for each given read. This is used as a weight when computing genome-wide read densities (using our own Bam2wig application). A mapping report is generated to provide informative diagnostics such as total number of reads mapped, proportion of alignment by strand, number of mismatches, genome coverage statistics.
RNA-seq The RNA-seq procedure uses an original optimization procedure to properly infer transcript expression from quantification at the exon level. The proposed model optimizes the distribution of reads among alternative transcripts, then runs DESeq  [18]  to infer differential expression of genes and their isoforms between samples. In the results page of the RNA-seq module, an interactive MA-plot can be generated for all pairwise comparisons including replicates of the same condition (see File S2, panels D-E). Discovery of new splice junctions is provided by running SOAPsplice  [28]  on unaligned reads.
Post-processing Bioscript is a Python library of genomic data processing algorithms that can be run from the command line or imported in custom Python scripts. A web service was set up to automatically generate HTML forms for each algorithm. These can be embedded within third party web application, in this case HTSstation, or accessed directly at http://bbcftools. epfl. ch/bs. The client (HTSstation) provides file URI (from the output of, e. g. a ChIP-seq or an RNA-seq analysis) to the service (Bioscript) and collects its outputs for download or further processing. On the server side Bioscript deals with the job queues and processing. To name but a few Bioscript currently includes tools to N Generate descriptive and quantitative statistics and graphics such as heatmaps, correlation plots and MA-plots (see File S2, panel E).
N A set of file manipulation tools including a file format converter (e. g. , from WIG to BIGWIG) and a BAM to density generator to create normalized coverage profiles from read alignments. N A number of analysis tools to annotate (i. e. , associate features to genome annotations), cluster, transform (e. g. , data smoothing or log transform) or manipulate (e. g. , intersect) genomic datasets. This also includes the BEDtools  [31]  suite, the R packages TopGo  [32]  (Gene Onotlogy), DEseq (differential expression statistics) and DNA motif scanning on selected genomic regions (typically ChIP-seq peaks).
Backend We have designed an SQLite format to accommodate the various file formats used in genomics  [15] , with transparent import and export functionalities to and from these files (BED, GFF/ GTF, WIG, BIGWIG, BAM). Together with the two backbone applications (GenRep and Bein), they not only ensure the reliability and reproducibility of the analysis, but also improve the long-term maintenance and extension to new modules within the platform. In addition, our workflow manager (Bein) completely decouples program executions from their environment (cluster, local workstation), making it easy to adapt the entire system to a new run-time environment, such as a cloud.
NEED FOR BIOINFORMATICS SOLUTIONS TO SUPPORT PM The availability of high-throughput technologies dedicated to clinical applications makes it very attractive for cancer centers to use these new tools on a daily basis. However, establishing such a clinical facility is not a trivial task due to the aforementioned complexity of PM framework along with the overwhelming amount of data. Indeed, the field of oncology has entered the so-called big data era as the particle physics did several years ago. From the big data 4 V's perspective, data integration issue (i. e. , merging heterogeneous data in a seamless information system) in oncology can be formulated as follows: a large Volume of patients' data is disseminated across a large Variety of databases which increase in size at a huge Velocity. In order to extract most of the hidden Value from these data we must face challenges at: (i) the technical level to develop a powerful computational architecture (software / hardware), (ii) the organizational and management levels to define the procedures to collect data with highest confidence, quality and traceability, and (iii) the scientific level to create sophisticated mathematical models to predict the evolution of the disease and risks to the patient.
Third, the architecture must warrant the reproducibility of the results. If many recent publications point out the key role of the bioinformatics for PM today (see Simon and Roychowdhury, 2013 for a review), clinical trials usually do not detail the complete bioinformatics environment used in practice to assess the quality and the traceability of the generated data. Different software platforms such as transMART  (Athey et al. , 2013) , G-DOC  (Madhavan et al. , 2011)  or the cBio Cancer Genomics Portal  (Cerami et al. , 2012)  have been recently developed to promote the data sharing and analysis of genomics data in translational research. Canuel et al. (2014)  reviewed the different solutions available and compared their functionalities. One of the most interesting features of these platforms relies on their analytical functionalities.
In the context of the SHIVA clinical trial, high-throughput and IHC data are sent by the different biotechnological platforms to the bioinformatics platform using standardized procedures for transfer and synchronization. Data are then integrated into the KDI system within ad-hoc repositories and databases. Metadata describing the data are stored in the KDI core database such as the patient identifier, the type of data (e. g. , mutation screening, clinical data, DNA copy number profile) and the technology used (e. g. , Affymetrix microarray, Ion Torrent TM PGM sequencing). Each type of data is then processed by dedicated bioinformatics pipelines in order to extract the relevant biological information such as the list of mutations and the list of amplifications/deletions. Therefore, the KDI core database acts as a hub allowing referencing all data through the use of web services.
The KDI core database knows exhaustively which data is available for a given patient and where the raw and processed data are physically stored. It thus offers the possibility for clinicians to make queries through a web application and to extract the list of available information for a given patient. In addition, the system is also used to manage and perform automatic integrative analysis required for the therapeutic decision. From a technical point of view, the KDI system consists of different modules dedicated to the storage, processing, analysis and visualization of each type of data (clinical, biological, microarray, NGS, etc. ). High modularity associated with an efficient interoperability makes our system able to retrieve any relevant information. To facilitate the developments of these modules, we have retained a classical n-tiers architecture implemented with the JAVA/J2EE language.
Data layer Data are stored in a relational database using the Entity-Attribute-Value (EAV) pattern. This conceptual modeling provides a data model plasticity required to handle the heterogeneity and the scalability of the variables of interest. Therefore, with EAV modeling, same concepts managed by different projects (with specific requirements by project) can be stored in a unique database without any modification of the data model. MySQL has been chosen as database provider for all web applications of the system. Complementary solutions such as NoSQL databases are currently evaluated for particular requirements (ontologies storage, specific queries, etc. ).
Front-end layer Presentation layer is based on JSF (Java Server Faces) which is a component oriented framework for building user interfaces for web applications. To enrich the basic component set provided by JSF, we use additional component libraries such as Apache Trinidad and Primefaces. By this systematic approach for each user interface, we aim to build a visual identity, ergonomic, easily usable, for the whole information system. All data available within KDI can be browsed and retrieved from a user-friendly bioinformatics web portal.
DNA COPY NUMBER ANALYSIS PIPELINE The use of the Affymetrix CytoscanHD microarray allows both the detection of DNA copy number alterations and the loss of heterozygosity events. The analysis workflow is presented in Figure  4A. Raw data are normalized with the Affymetrix Power Tools software package 2. Then, the log R ratio is segmented in order to detect breakpoints and assign copy number status using Colibri  (Rigaill, 2010)  and GLAD  (Hupé et al. , 2004 ) software. A similar process is applied on the allele difference profile using the GAP software  (Popova et al. , 2009). Both profiles (DNA copy number and LOH) allow the estimation of absolute copy number for each probe taking into account the sample cellularity and tumor ploidy estimated by the GAP algorithm. Each gene status (normal, gained, amplified, lost, deleted, loss of heterozygosity) can then be assessed.
Copy number alterations are defined as follows: deletion = 0 copy, loss = 1 copy, normal = 2 copies, gain = 3, 4 or 5 copies and amplification ≥ 6 copies for diploid tumor, and deletion = 0 copy, loss = 1 or (B) Mutation analysis pipeline. The sequenced reads are aligned on the human reference genome, and centered on the targeted genomic regions. Single nucleotide variations (SNVs) and insertion/deletion (indels) are then called. The filtered variations can then be annotated using additional databases in order to lead to a final list of potential druggable variants. 2 copies, normal = 3 or 4 copies, gain = 5 or 6 copies and amplification ≥ 7 copies for tetraploid tumors. Additional steps in the analysis are performed to distinguish between large scale events such as chromosome arm gain and focal events targeting single oncogene or tumor-suppressor gene.
MUTATION ANALYSIS PIPELINE The bioinformatics pipeline presented in the Figure  4B  was applied to detect somatic mutations from the Ion Torrent TM PGM sequencer using the Ampliseq TM cancer panel. Ion Torrent TM PGM raw reads are aligned on the reference human genome hg19 using the TMAP aligner (v0. 3. 7 Life Technologies). The best mapping score for each read is used to detect misalignment. The standalone package of the Torrent Variant Caller (v2. 2 Life Technologies) is then used to call variants (SNVs and indels) from the mapped reads. In the context of clinical trial, variants have to be filtered to promote a high specificity, in order to avoid any false positive mutations. Thus, detected variants are filtered according to their frequency (≥4% for SNVs and 5% for indels), strand ratio (≥0.
2), and reads coverage (≥30X for SNVs and 100X for indels). In addition, SNVs and mainly insertions and deletions detected in the context of a repeated region or a homopolymer are double checked. Homopolymer and repeated regions are prone to contain recurrent false positive, because of the limitation of the Ion Torrent TM PGM technology. In most cases, the variant is discarded if also detected in other patients from the same sequencing run. Otherwise, variations specific to a sample, even within a repeat context, are reported. To facilitate the interpretation of individual patient data for clinical trials, the filtered list of variants is then annotated using the ANNOVAR software  (Wang et al. , 2010). Common polymorphisms found on more than 1% of the ESP or 1K Genome project population as well as recurrent and neutral variants on hotspots are reported.
SUMMARY OF THE DATA INTEGRATION WORKFLOW FOR THE SHIVA CLINICAL TRIAL WITHIN KDI In the context of the SHIVA trial, the clinical data needed for the MBB are first imported in a dedicated module of the KDI system, named ClinicalDB (Clinical Database, Figure  3A ). This step is performed weekly and updates the system by creating the patients recently included in the trial into the KDI core database. At the same time, an anonymous identifier is generated by the system for each new patient. Conversion between the different patient identifiers is guaranteed by the KDI core database and is accessible through the Bioinfo-Portal web application. Once available, the raw data generated by the biotechnological platforms are transferred to the bioinformatics platform for analysis (using rsync system).