dataesr/scientific-paragraphs-categorization huggingface dataset:
Reversed-phase capillary LC-MS/MS and LC-MS analyses Capillary LC-MS/MS analysis was used to generate a peptide accurate mass and time (AMT) tag database  [54]  for virus-infected lung homogenates (see below). For this, dried peptide fractions were reconstituted in 30 μL of 25 mM ammonium bicarbonate, pH 7. 8, and analyzed using a 4-column custom-built capillary LC system coupled online to a linear ion trap mass spectrometer (LTQ; Thermo Scientific, San Jose, CA) by way of an inhouse manufactured nanoelectrospray ionization interface, as previously described  [55]. To identify the eluting peptides, the LTQ stage was operated in a data-dependent MS/MS mode as previously described  [53]. Following lung and virus peptide/protein AMT tag database generation, capillary LC-MS analyses were performed on all individual H5N1 and mock-infected samples to generate quantitative data.
Development of the AMT tag database for virus-infected C57BL/6J mice An AMT tag database of identified peptides was developed for virus-infected lungs, using mock-infected and virus-infected samples from two studies: 1) H5N1 data reported here and 2) another with SARS-CoV MA15. The details of study 2 will be described elsewhere. To generate the AMT tag database, aliquots of the H5N1or mock-infected samples were combined to make the following pools: 1) mock-infection (all time points), 2) early infection (1 and 2 d), and 3) late infection (4 and 7 d). Samples from the SARS-CoV MA15 studies were similarly pooled. Each pool was subjected to strong cation exchange fractionation as described above, and each fraction was analyzed by nanocapillary LC-MS/MS. The SEQUEST analysis software  [56]  was used to match the MS/MS fragmentation spectra with sequences from the April 20, 2010 UniProt/Swiss-Prot rodent protein database, containing 16,244 entries (protein TITIN_Mouse was removed due to excessive length).
The data were also matched to sequences from the H5N1 viral proteome. When searching, SEQUEST used a dynamic mass modification on methionine residues corresponding to oxidation (15. 99 Da) and a static mass modification on cysteinyl residues to account for alkylation by iodoacetamide (57. 02 Da). Peptides passing the following filter criteria were stored as AMT tags in a Microsoft SQL Server database: 1) SEQUEST DelCn2 value (normalized Xcorr difference between the top scoring peptide and the second highest scoring peptide in each MS/MS spectrum) ≥ 0. 10 and 2) SEQUEST correlation score (Xcorr) ≥ 1. 6, 2. 4, and 3. 2 for fully tryptic peptides with 1+, 2+, and 3+ charge states, respectively, and Xcorr ≥ 4. 3, and 4. 7 for partially tryptic or non-tryptic protein terminal peptides with 2+, and 3+ charge states, respectively.
Fully non-tryptic peptides were excluded, and a minimum peptide length of 6 amino acid residues was required. These criteria resulted in 39,744 peptides identified with an estimated false discovery rate of <2% based on a target-decoy database search  [57]. The elution times for these peptides were normalized to a range of 0 to 1 using a predictive peptide LC normalized elution time (NET) model and linear regression, as previously reported  [58].
Processing of quantitative LC-MS datasets Quantitative LC-MS datasets were processed using the PRISM Data Analysis system  [59] , which is a series of software tools developed in-house (e. g. Decon2LS  [60]  and VIPER  [61] ; freely available at http://ncrr. pnl. gov/ software/). Individual steps in this data processing approach are reviewed here  [54]. The peptide identities for detected features in each dataset (i. e. a single LC-MS analysis) were determined for features matched to AMT tags with high confidence based upon the accurate measured monoisotopic masses and NETs for each of the 39,744 peptides in the filtered AMT tag database within initial search tolerances of ± 6 ppm and ± 0. 025 NET for monoisotopic mass and elution time, respectively. The peptides identified from this matching process were retained as a matrix for subsequent data analysis.
Proteomics data processing, missing values imputation, and normalization Quality control processing was performed to identify and remove contaminant proteins, redundant peptides, peptides with an insufficient amount of data across the set of samples  [62] , and LC-MS datasets that showed significant deviation from the standard behavior of all LC-MS analyses  [63]. The peptide abundance values were normalized across the technical replicates using a global median centering of the data. [64]. The normalized log10 abundance values were averaged across the technical replicates within each biological sample. Peptide level significance patterns were used for protein roll-up to select appropriate peptides for protein quantification. Proteins were quantified using a standard R-Rollup method using the most abundant reference peptide  [65] , after filtering the peptides that were redundant, had low data content, or were outside the dominant significance pattern.
Identification of over-represented biological functions, canonical pathways and upstream regulators Functional enrichment of biological functions, canonical pathways and upstream regulators was performed using Ingenuity Pathways Analysis (Ingenuity Systems, Inc. ). IPA examines differentially expressed transcripts and proteins in the context of known biological functions, mapping each gene identifier to its corresponding molecule in the Ingenuity Pathways Knowledge Base (IPKB). For all analyses, the p-values were generated using the right-tailed Fisher's Exact Test  [69]. Upstream Regulator Analysis in IPA incorporates downstream target genes from the experimental dataset and compiled knowledge of reported relationships between regulators and their known target genes within IPKB. This analytical tool was used to predict the upstream regulators in our analysis.
each eigentranscript identified in the kinetics of the host response to H5N1 VN1203 wild-type, the number of correlating transcripts is indicated. For each set of transcript the top 5 associated over-represented biological functions, canonical pathways, and upstream regulators are indicated. Ingenuity pathway analysis was used to determine the top 5 bio function categories, canonical pathways, and upstream regulators. The functions annotation p-value range represents the range of p-values for the functions annotations associated with each bio function category. The p-value of overlap associated for each upstream regulator indicates the significance of the overlap between the genes targeted by the upstream regulator in the IPKB database and the experimental dataset. *Upstream regulators with an apteryx signify a chemical reagent or chemical drug.
in the kinetics of the host response to H5N1 VN1203 wild-type, the number of correlating transcripts and proteins are indicated. For each set of proteins the top 5 associated over-represented biological functions, canonical pathways, and upstream regulators are indicated. Ingenuity pathway analysis was used to determine the top 5 bio function categories, canonical pathways, and upstream regulators. The functions annotation p-value range represents the range of p-values for the functions annotations associated with each bio function category. The p-value of overlap associated for each upstream regulator indicates the significance of the overlap between the genes targeted by the upstream regulator in the IPKB database and the experimental dataset. *Upstream regulators with an apteryx signify a chemical reagent or chemical drug.
A NET average and standard deviation were assigned to each identified peptide if the same peptide was observed in multiple analyses. Both calculated monoisotopic masses and observed NETs of identified peptides were included in the AMT tag database. Identified MS/MS spectra corresponding to peptides in the AMT tag database are available at the PRoteomicsIDEntification (PRIDE) database (http://www. ebi. ac. uk/pride/) under the project name A Systems Biology Approach to Emerging Respiratory Viral Diseases in the PRIDE Public Projects folder and corresponding to PRIDE Accession numbers 19855-19860.
Acknowledgements This project has been funded in whole or in part with Federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under Contract No. HHSN272200800060C. The authors thank  Maria L. Luna ,  Athena A. Schepmoes ,  Robert A. Heegel , and  Anil K. Shukla  of  Pacific Northwest National Laboratory (PNNL ) for preparing and analyzing proteomics samples. Proteomics data were obtained using capabilities developed under the support of the  National Institute of General Medical Sciences  ( 8 P41 GM103493-10 ),  National Institutes of Health , and analyses were performed in the  Environmental Molecular Sciences Laboratory , a national scientific user facility sponsored by the  Department of Energy's Office (DOE) of Biological and Environmental Research  and located at PNNL.
For each eigentranscript identified in the kinetics of the host response to H5N1 VN1203 wild-type, the number of correlating transcripts is indicated. For each set of transcript the top 5 associated over-represented biological functions, canonical pathways, and upstream regulators are indicated. Ingenuity pathway analysis was used to determine the top 5 bio function categories, canonical pathways, and upstream regulators. The functions annotation p-value range represents the range of p-values for the functions annotations associated with each bio function category. The p-value of overlap associated for each upstream regulator indicates the significance of the overlap between the genes targeted by the upstream regulator in the IPKB database and the experimental dataset. *Upstream regulators with an apteryx signify a chemical reagent or chemical drug.
For each eigenprotein identified in the kinetics of the host response to H5N1 VN1203 wild-type, the number of correlating transcripts and proteins are indicated. For each set of proteins the top 5 associated over-represented biological functions, canonical pathways, and upstream regulators are indicated. Ingenuity pathway analysis was used to determine the top 5 bio function categories, canonical pathways, and upstream regulators. The functions annotation p-value range represents the range of p-values for the functions annotations associated with each bio function category. The p-value of overlap associated for each upstream regulator indicates the significance of the overlap between the genes targeted by the upstream regulator in the IPKB database and the experimental dataset. *Upstream regulators with an apteryx signify a chemical reagent or chemical drug.
In silico PREDICTION OF TAL EFFECTORS TARGETS One major output of the modular TAL effector-DNA recognition code discovery is the possibility to predict through computer programs, the DNA binding sites of a TAL effector within a whole plant genome or promoterome (i. e. , the sequences immediately upstream of the transcriptional start sites) of any sequenced organism. Four bio-informatic tools are currently available and enable to scan genomes for TAL effectors binding sites, rapidly providing users with lists of potential S or R targets. Target Finder from the TALE-NT 2. 0 suite (https://talent. cac. cornell. edu/, Doyle et al. , 2012) , Talvez (http://bioinfo. mpl. ird. fr/cgi-bin/ talvez/talvez. cgi, Pérez-Quintero et al. , 2013)  and Storyteller (http://bioinfo-prod. mpl. ird. fr/xantho/tales, Pérez-Quintero et al.
, 2013)  algorithms are available as web interface and/or standalone software. For these three examples, predictions rely on the use of a RVD-nucleotide association matrix based on known TAL effectortarget pairs, to convert a sequence of RVDs of a given TAL effector into a positional weight matrix (PWM). These PWM are regularly updated based on novel experimental insights into TAL-DNA binding or the availability of experimentally confirmed TAL target sequences. Target Finder and Talvez both use the PWM to scan and score all possible binding sites in a promoter region with a log-likelihood function. In contrast, Storyteller uses this matrix to generate a set of possible binding sequences and takes the advantage of a faster pattern-search algorithm based on Hidden Markov models. Moreover, Talvez incorporates a position correction parameter, which enables to tolerate RVD-nucleotide mismatches toward the C-terminal end of RVD sequences and improves target sites prediction.
ACKNOWLEDGMENTS We are grateful to  A. Pérez-Quintero  for providing us with the predicted targets of Hax4 obtained with Talvez and Storyteller and  E. Doyle  for providing us with Hax4 targets with  Target Finder. This work was in part supported by the  LABEX TULIP  ( ANR-10-LABX-41 ) and a  Jeunes Chercheurs grant  from the  Agence Nationale de la Recherche  (Xopaque  ANR-10-JCJC-1703-01 ) to  Laurent D. Noël.
g. Illumina Genome analyzer v1, hereafter referred to as 'GA1') to >3 billion (HiSeq2000) reads per flow cell. As a consequence, the public databases hosting NGSgenerated data sets are populated with ChIP-seq profiles presenting a large variety in sequencing depth. Importantly, previous studies have demonstrated that by increasing the sequencing depth, the number of discovered binding sites increases accordingly. Intuitively, it is expected that the number of sequenced reads required to discover all binding events is directly related to their total number and to their binding pattern (i. e. 'broad' regions covering large parts of a genome will require more reads to be properly identified than 'sharp' patterns with few target sites). When evaluating the quality of NGS-based profiling, it is therefore important to assess if a given ChIP-seq profile is performed under optimal sequencing conditions, including the minimal sequencing depth required to discover most of the relevant binding events of a given factor.
For all the above reasons, we have developed a bioinformatics-based quality control (QC) system that uses raw NGS data sets to (i) infer a set of global QC indicators (QCis), which reveal the comparability of different enriched-NGS data sets, (ii) provide local QCis to judge the robustness of cumulative read counts ('peaks or islands') in a particular region, (iii) provide guidelines for the choice of the optimal sequencing depth for a given target and, finally, (iv) to have quantitative means of comparing different antibodies and antibody batches for ChIP-seq and related antibody-driven studies. In addition, we have established a QC indicator database that will be expanded to cover virtually all publicly available enrichment-related NGS profiling assays. Thus, users can compare the quality indicators computed by the NGS-QCi Generator for a given ChIP-seq experiment with the quality indicators for published data sets present in the QC indicator database.
This information will guide users toward optimization of the ChIP-seq process, if the QC is lower than that achieved previously by others and/or with other antibodies. Moreover, this QC system will be useful for antibody development and certification. We discuss the simplicity and versatility of the present QC method and database in view of currently existing QC assessment procedures and guidelines. The NGS-QC Database of QC indicators for publicly available profiles and the NGS-QC Generator tool are freely accessible through a customized Galaxy instance at http://igbmc. fr/Gronemeyer_NGS_QC.
Assessment of the inherent robustness of ChIP-seq profiles Based on the rationale that beyond a sequencing depth threshold a ChIP-seq profile changes only in amplitude but not in pattern, we evaluated this property by monitoring the changes of its RCIs after read-subsampling. For this, aligned reads were randomly sampled at three distinct densities (90, 70 and 50%; referred to as s90, s70 and s50 subsets, respectively). To avoid bias, random sampling was performed without replacement; each separately sampled density subset was generated from the original read data set. RCI profiles were constructed by counting the overlaps within a defined window ('bin'). With the aim of having no more than one binding event per bin, it is currently fixed to 500 bp. An empirical evaluation of the influence of this parameter on the assessment of the quality indicators confirmed our initial choice (Supplementary Figure  S1d ).
Thus, for providing a quantitative assessment of the changes of RCI dispersion in a given data set, we have evaluated the fraction of bins displaying a dRCI within in a given interval, which has been defined as the global density QC indicator 'denQCi'. This global indicator-evaluated in conditions where only a half of the initial sequenced reads are available (s50)-is systematically used in this study to measure the degree of robustness of the evaluated profile to the readsubsampling treatment (i. e. high denQCi corresponds to low RCI dispersion). In addition, the changes in robustness on subsequent read subsampling has been evaluated by comparing the denQCi for the sampling closest to the original profile (s90) with that sampling only half of the sequenced reads (s50). This is defined as the similarity QC (simQCi) indicator, computed as ratio between denQCis for the s90 and s50 sampling subsets.
QC-STAMP ¼ denQCiðs50Þ simQCi To evaluate the divergence of this global descriptor over all enrichment-related NGS profiles currently compiled in the NGS-QC database, the QC-STAMP distributions assessed for three different RCI dispersion intervals were subdivided in four quantiles to which the following grades have been attributed: 'D', lower quartile (<25%); 'C', interquartile 25-50%; 'B', interquartile 50-75% and 'A' upper quartile (>75%). The NGS-QCi Generator database associates these grades for 2. 5, 5 and 10% dRCI to each profile as a three-letter symbol, such that, for example AAA ('triple A') reveals an A grade for all three dRCIs. All available profiles are displayed as a dynamic QC-STAMP versus TMR scatterplot, which allows judging of their QCi similarities in the context of the sequencing depth.
Furthermore, their corresponding global QCis assessed from 5 random sampling assays are displayed (average ± standard deviation). ChIP-seq profiles established from similar TMRs can lead to variable quality patterns as revealed by visual inspection of three ChIP-seq profiles of the tri-methylation of lysine 4 of histone 3 (H3K4me3) generated with antibodies obtained from the same supplier and with similar ($19 millions) TMR levels (Figure  1C ). Yet, they present major differences of global RCIs and background levels (note the different scales). Indeed, the computing of the QCis provides quantitative descriptors (denQCi, ds50 5% and simQCi, ds90/s50 5%) for the relative quality of the three profiles, which fully comply with the visual quality assessment, thus illustrating the usefulness of this approach in providing quantitative QC values for comparing different ChIP-seq data sets.
From 1321 ERa sites in the overlap between GA1 and the full channel HiSeq2000 profile, >80% of them (1096 sites) comply with ds50 5% (Figure  2C ). Similarly, the number of ERa binding sites overlapping with the GA2X or half channel HiSeq2000 data sets increased strongly over that obtained with GA1, as did the number of robust peaks. The above comparison revealed also a significant number of nonoverlapping sites (Figure  2C ). While it is reasonable to assume that the outliers of the HiSeq2000 profile (red) result mainly from the incomplete binding site recovery from the other profiles, those outliers that are seen in the low TMR profiles but not in the HiSeq2000 are more likely 'false positives'. Indeed, the number of such sites is variable and does not follow a common trend as the increase of the overlap population with increasing sequencing depth; in this respect, the GA2X data set is suboptimal with 4-to 5-times more outliers (green) than the GA1 (gray) and 1/2Hiseq (blue) ones.
Importantly, when considering only the robust peak population, the GA2X outliers were significantly reduced to about the level seen with GA1 and 1/2Hiseq ones. In addition, the nonoverlapping sites, including those of the full channel HiSeq2000, showed consistently lower peak intensities and weaker confidence P-values relative to overlapping population (Figure  2D ). Considering the full channel HiSeq data set as 'gold standard', the number of recovered 'true' ERa binding sites increased from <5% for the GA1 data set to $60% for the half channel HiSeq2000 profile (Figure  2E ). Importantly, 80% 'true positive' binding sites were recovered when only robust ERa sites are considered, indicating that the denQCi criterion identifies the highly reliable sites when comparing ChIP-seqs with largely differing sequencing depths.
The QCis are universally applicable to all ChIP-seq and enrichment-related NGS profiling assays While in previous studies profile saturation has been defined after peak calling  (5, 29, 30) , the present QC evaluation system evaluates robustness directly from the raw pattern of genome-aligned reads. Therefore, QCis can be established for any type of enrichment-related NGS profiles, including ChIP-seq, RNA-seq, GRO-seq and others, making this methodology a universal tool for multidimensional quality profile comparison. Indeed, we have computed QCis for several types of publicly available NGS-generated profiles and observed a high variability between the corresponding QCis even when data sets with similar TMRs were compared (Figure  3A  and Supplementary Figure  S3 ). RNA-seq, which does e. Genome Analyser 1 (GA1); GA2X and Hiseq2000] are illustrated.
It has been implemented in the NGS-QCi Generator, a computational tool that is accessible at a customized cloud of the web-based platform Galaxy (31-33) (Supplementary File S1). The NGS-QCi Generator provides a comprehensive report summarizing the global QCis (Supplementary Figure  S4 ) and provides access to the computed RCI dispersion per 500-bp bins (wiggle or BED format) defined as local QCis, which can be used to identify the robustness of specific regions of interest (Figure  3B  and Supplementary Figure  S5 ). Using the NGS-QCi Generator we have created a QCis database, which comprises at present the QC analysis of >5600 NGS data sets, including ChIP-seq profiles of histone modifications and variants, transcription factors, as well as GRO-seq and RNA-seq profiles (Figure  4A ). This QCi database will be expanded to cover virtually all of the publicly available NGS profiles.
To facilitate and simplify the recognition of QCi divergence between profiles we have defined QC-STAMP, a global descriptor that combines the information provided by denQCi and simQCi. The QC-STAMP corresponds to a three-letter code composed of A, B, C and D that is derived from the position of a given profile QCi within the distribution of compiled QCis in the database. The first letter reveals this position for a dRCI dispersion threshold of 2. 5%, the second and third letter for 5% and 10% dRCI, respectively. A to D grading was done to specify the following intervals: D, lower quartile (<25%); C, interquartile (25-50%); B, interquartile (50-75%); A, upper quartile (>75%) (Figure  4B ). As an example, the H3K4me3 profile derived from 10 007 440 TMRs [arrow (3) in Figure  4A ] classified as 'triple A' profile, while nonenriched WCE profiles were, as expected, of the lowest possible quality, 'triple D' (Figure  4C ).