dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We developed a method to rapidly screen for novel endogenous lipid molecules in complex biological extracts featuring notably improved sensitivity compared to "gold standard" targeted mass spectrometric analyses by MRM and PRM. Our method is unbiased and extendable to any known or plausible combination of lipid structural modules. AIF LC-MS/MS provides a rich dataset that comprises all fragment ions produced from all ionisable precursors, including fragments originating via novel or unexpected fragmentation pathways and intermolecular rearrangements. Conceivably, it might be possible to implement a multi-layer datamining strategy that considers alternative combinations of fragments linked by boolean logic operations  24. We underscore that the major bottleneck of the identification of picogram amount of lipids is not the acquisition of informative MS/MS spectra or database searches, but rather in recognizing and selecting the appropriate precursor ion for the subsequent fragmentation.
Rat tissues were provided by the animal facility of the Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany in compliance with German animal welfare legislation and in strict pathogen-free conditions. Animal handling protocols were approved by the Institutional Animal Welfare Officer (Tierschutzbeauftragter), and necessary licenses were obtained from the regional Ethical Commission for Animal Experimentation of Dresden, Germany (Tierversuchskommission Landesdirektion Sachsen). Software for AIF LC-MS/MS discovery screening. AIF LC-MS/MS spectra were processed by Arcadiate 2. 0 software, a tool for visualization and analysis of LC-MS/MS data. Arcadiate is available for download at: https://itunes. apple. com/de/app/arcadiate/id585258468?mt= 12&uo= 4 Arcadiate operates under a Macintosh OS v.
Identification of ERC by AIF LC-MS/MS. The acquisition cycle consisted of FT MS and FT-MS/MS scans in positive ion mode. FT MS spectra were acquired in the m/z range of 225 to 725 at the mass resolution of R m/z 200 = 140,000; automated gain control (AGC) value of 1 × 10 6 ; maximum injection time of 1024 ms. All-ions FT MS/MS were acquired from the same m/z range of precursors. Fragments were detected within m/z 50 to 725 at R m/z 200 = 17,500 with AGC value 3 × 10 6 ; maximum injection time of 1024 ms; nCE was step increased at 20, 25 and 35%. Spectra in. raw format were converted to mzML and interpreted by Arcadiate software. It aligned XIC peaks of putative precursors and fragments (Supplementary Dataset S1) and sorted the alignments according to their quality (see Supplementary Text for algorithm details).
Acknowledgements MM was partly supported by  PRIN   2010-2011  grant from the  Italian Ministry of Education, University and Research. Work in AS, CS and SE laboratories was supported by  TRR83  grant (Projects  A17 ,  A2  and  A19 , respectively) from  Deutsche Forschungsgemeinschaft (DFG). AS, SE, and AN are members of the  Lipid Center program of Max Planck Gesellschaft.
Genetic analysis Genomic DNA was isolated using standard methods. Primer pairs were used to amplify the 28 coding exons of NEBL with flanking intronic sequences according to Arimura et al. [19]  based on the published sequence (GenBank accession number NM_006393. 2). Polymerase chain reaction amplifications were performed using standard protocols (available on request). The amplicons were analyzed by direct sequencing using ABI Big Dye Terminator chemistry and run on an ABI 3100 Avant device (Applied Biosystems, Darmstadt, Germany) as per the manufacturer's instructions. Detected variants in a sample were confirmed at least in two independent PCRs and sequencing runs. Sequencher software version 4. 8 (Gene Codes, Ann Arbor, MI, USA) or PhredPhrap/Consed free Softs was used to facilitate data analysis and mutation identification followed by visual inspection of individual sequencing traces.
Genetic variants were annotated according to the cDNA and protein reference sequence (Ensembl ID ENST00000377122 and UniProtKB/Swiss-Prot O76041). We further analyzed the detected variants by checking the presence or absence of these variants in large population-based databases (dbSNP and Exome Sequencing Project/ESP5400 dataset) and the possible impact of the amino acid substitution. Further information about the bioinformatic analysis can be found in the Supplemental Methods. Briefly, the significance of the identified variants was assessed by considering the conservation of the affected amino acid, the nature and location of the change, its rarity assessed in own controls and population-based datasets, and cosegregation in the family when available.
A175T, p. S747L) (Table  I ). The mutations p. I652L and p. P916L were subsequently annotated in dbSNP with low allelic frequencies of 0. 3% and 0. 08%, respectively (Table  I ). While four mutations were absent from the ESP5400 dataset (European Americans), two (p. A175T and p. I652L) occurred at a very low frequency of < 0. 01%. Five amino acid substitutions altered a non-conserved residue (the exception was p. I652L). Two independent mutation analysis tools predicted damaging effects for the six mutations (either PolyPhen2 and/or Mutation Taster; see Supplemental methods). Mutation p. P916L is located in the N-terminal serine-rich linker region; the other five mutations are located in the central nebulin-like repeats (NLRs). These are important actin-binding modules of the protein (Figure  1 B ).
Exome Sequencing Project (ESP) and dbSNP datasets confirmed the rarity/ novelty of all mutations, which is a major criterion for pathogenicity in cardiomyopathies with so many "private" mutations  [1, 2, 4]. Norton et al. proposed using the ESP dataset as a new resource for guidance in DCM patients  [21]. They found an allele frequency of 0. 04% as a conservative cutoff point for pathogenic mutations based on their bioinformatic analysis of DCM mutations against the ESP dataset. Using the same approach for NEBL, all mutations found by us are below that threshold, confirming their extreme rarity (Table  I ). We are aware that a non-synonymous mutation causing a genetic disease increases in evolutionarily highly conserved sites  [22]. The affected amino acids of the identified mutations were highly conserved in different species.
Supplemental data Supplemental data contain Supplemental methods, Supplemental results, Supplemental Table SI and Supplemental Figures  S1, S2,  and S3. no ventricular dilatation and hypertrophy as shown by echocardiography and ventriculography and/or angiography). All of these individuals were older than 50 years (in order to exclude late onset cardiomyopathy). The second control cohort B included 192 Caucasian sex-matched blood donors from the local blood transfusion service without heart disease. None of the control individuals was hypertensive.
Genetic analysis A bioinformatic approach for detected variants was used which was based on presence or absence of these in population-based databases. First, we searched dbSNP (build 135, at http:// www. ncbi. nlm. nih. gov/snp), which also includes data from the 1000 Genomes project. Second, we searched the Exome Variant Server (EVS) provided by the Exome Sequencing Project (ESP) of the National Heart, Lung and Blood Institute (http:// evs. gs. washington. edu/EVS/, Tennessen et al. [4] ). The EVS data release ESP5400 is taken from 5379 samples drawn from multiple ESP cohorts and represents the first data freeze of the ESP exome variant data (EVS release version: v. 0. 0. 12). The ESP dataset includes exome data for 3510 individuals of European descent, which matched our patients. Third, we performed mutation prediction by the two most widely used analysis tools to predict the possible impact of an amino acid substitution.
Acknowledgments This study was supported by a  Charité research grant ,  Assistance Publique -Hôpitaux de Paris  (PHRC programme hospitalier de recherche clinique  AOM95082 ), the  CONNY-MAEVA charitable foundation , the  Leducq Foundation (Fondation Leducq)  ( Eurogene Heart Failure network ), a  European Union   FP7 grant  ( INHERITANCE network ), the  Italian Ministry of University and Research  (grant  20083EWHYR_002 ), and the  Czech Ministry of Health  (project for conceptual development of research organization 00064203). We are grateful to the patients and their relatives for their participation in the study. We thank  Andrea Behm ,  Uzma Aslam ,  Laetitia Dubosq-Bidot , and  Peng Xu  for excellent technical support.
For a perfectly conducting plane, r Q κ, κ = χ Q = ±1 for electric and magnetic modes, respectively. This expression is now suitable for numerical evaluation, which we carry out in Mathematica, using routines for computing spheroidal functions  [35, 41]  based on the package created by Falloon  [42]. This code provides all the necessary spheroidal functions, as well as the expansion coefficients A m ν,n (iγ). Since we are carrying out this calculation via a conversion to the spherical basis, we are restricted to configurations with d > R, so that a sphere enclosing the disk does not intersect the plane  [21]. Our calculation shows the corresponding numerical instabilities for d < R.
Acknowledgments This work is based on preliminary studies of the scattering problem for a disk by  Alexej Weber  in an earlier stage of this project. His contribution is acknowledged. We thank  G. Bimonte ,  R. L. Jaffe ,  M. Kardar , and  M. Krüger  for helpful discussions, and  M. T. H. Reid  for carrying out comparisons with the methods of Ref. [32]. N. G. was supported in part by the  National Science Foundation (NSF)  through grant  PHY-1520293.
Motivation: Most computational approaches for the analysis of omics data in the context of interaction networks have very long running times, provide single or partial, often heuristic, solutions and/or contain user-tuneable parameters. Results: We introduce local enrichment analysis (LEAN) for the identification of dysregulated subnetworks from genome-wide omics datasets. By substituting the common subnetwork model with a simpler local subnetwork model, LEAN allows exact, parameter-free, efficient and exhaustive identification of local subnetworks that are statistically dysregulated, and directly implicates single genes for follow-up experiments. Evaluation on simulated and biological data suggests that LEAN generally detects dysregulated subnetworks better, and reflects biological similarity between experiments more clearly than standard approaches.
A strong signal for the local subnetwork around Von Willebrand Factor (VWF), a gene which showed no change on the mRNA level, was identified by LEAN in transcriptome data in the context of the genetic disease Cerebral Cavernous Malformations (CCM). This signal was experimentally found to correspond to an unexpected strong cellular effect on the VWF protein. LEAN can be used to pinpoint statistically significant local subnetworks in any genomescale dataset.
Introduction The organization of the molecular machinery of cells is thought to be inherently modular  (Alon, 2003; Hartwell et al. , 1999). When studying large-scale datasets, once gene-level scores have been computed, a common next step is thus to aggregate them to the level of gene sets. Pathway analysis focuses on enrichment in annotated gene sets, such as genes involved in a common biological process. For the remainder of this article, we will use the terms pathway and gene set interchangeably in the above sense of a set of genes sharing a common functional annotation. Significant scores for a particular pathway suggest specific higher-level functional interpretations of the dataset  (Khatri et al. , 2012). The MSigDB database  (Subramanian et al. , 2005)  for example includes pathways corresponding to genes that share functional annotations, chromosomal locations or cisregulatory motifs, or are part of specific molecular (e.
Secondly, experimental methods typically do not allow the study of subnetworks in their entirety, so that a non-trivial prioritization step is required before further validation. As a consequence, comparative studies on medically relevant data have reported poor consistency between individual methods  (Jiang and Gribskov, 2014)  and questioned the merit of applying subnetwork-based methods altogether  (Staiger et al. , 2013)  We present here a novel network-based approach, termed local enrichment analysis (LEAN). LEAN is designed to avoid computational and statistical issues through the use of a strongly constrained subnetwork model. The strong constraint permits a combination of fundamental advantages, relative to existing network analysis approaches. In particular, the underlying optimization problem is efficiently solvable, allows to survey all solutions (not just the optimal one), it has no parameters that have to be tweaked by the user, and LEAN subnetworks of interest imply specific genes for experimental follow-up.
Supplementary table  S1  compares key features of LEAN with a range of other subnetwork analysis approaches. We evaluated the performance of LEAN by comparing it to previously published subnetwork detection methods on simulated pathways and showed that it is able to extract biologically meaningful common pathways even on a relatively small number of publicly available datasets. An application to a transcriptomic dataset of the response to invalidation of CCM genes in mouse models led to the discovery of the previously unknown involvement of Von Willebrand Factor (VWF) in the pathophysiology of CCM disease.
Local enrichment analysis LEAN is based on two ingredients: A list of measures of statistical significance (P-values) for some or all genes and an interaction network. In many applications, P-values originate from a statistical test for differential expression, such as limma  (Smyth, 2004). While the approach is readily applicable to other types of datasets, we will describe it in the following using the example of its application to the results of a transcriptomic experiment evaluating differential gene expression (input P-values). Analysis is performed using the given interaction network restricted to genes for which an input P-value has been calculated based on the transcriptomic data. A local subnetwork N g consists of a subset of genes formed from a designated center gene g and its direct interactors in the given network.
Interaction network For use in this study, we employed the murine STRING interaction network  (Franceschini et al. , 2013). STRING represents one of the largest publicly available collections of protein-protein interaction data (for details on the STRING networks used see supplementary methods). STRING gene interactions originate from different sources of evidence, such as experimental studies of physical interactions, co-expression in public datasets, co-citation in literature and evidence of functional or physical interaction extracted from public databases. Each interaction has an associated global score that reflects the overall strength of evidence for functional interaction. STRING has been used in numerous previous network-based analyses  (Abadie et al. , 2011; Choudhary et al. , 2009; Gaballa et al.
LEAN detects simulated deregulation of subnetworks To assess the capacity of the local subnetwork model to detect deregulated pathways, we compared the performance of LEAN to other common pathway analysis methods on simulated data from a statistical model of transcriptomic pathway deregulation. Starting with a high-confidence STRING functional network, we randomly selected a small number of subnetworks as hypothetical pathways, and assigned lower P-values to the genes contained in these subnetworks (see supplementary methods for details). We then evaluated the capacity of different methods to recover these simulated pathways. We verified that the graph radius of the simulated subnetworks was substantially larger than 1 (mean subnetwork radius: 2. 68 6 0. 7 SD) to ensure that our evaluation dataset is not biased towards overly compact subnetworks, which would confer an advantage to the local subnetwork model (see Supplementary Figs.
S1 and S2 for a more detailed evaluation of how the methods perform dependent on subnetwork compactness). We evaluated the performance of seven approaches: a gene-bygene approach not using network information, LEAN, the GSEA  (Subramanian et al. , 2005)  enrichment score applied to our definition of local subnetworks ('local GSEA'), KeyPathwayMiner (KPM)  List et al. (2016) , the jActiveModules method  (Ideker et al. , 2002) , GiGA  (Breitling et al. , 2004)  and RegMOD  (Qiu et al. , 2010). Performance was measured using Receiver-Operator-Characteristic (ROC) analysis comparing true positive rates (TPR) and false positive rates (FPR) over all possible detection cutoffs with simulated pathway genes defined as positives and genes not contained in simulated pathways as negatives. We varied the characteristics of the simulated pathways using two parameters: To generate low input P-values for genes within pathways, the P-values of these genes were redrawn from [0, p scale ] with probability p enr per individual gene.
In this model, p scale governs the difference in significance of a pathway gene in comparison to a background gene and p enr is the expected proportion of pathway genes exhibiting smaller P-values than the background genes. Figure  3A  shows ROC curves obtained on pathways with medium P-value scaling and medium proportion of significant pathway genes (p enr ¼ 0:5; p scale ¼ 0:1). Since the KeyPathwayMiner, jActiveModules and GiGA methods do not score individual genes, but entire subnetworks, we could not evaluate them in the same way. For these two methods, we thus obtained the ten highestscoring subnetworks and computed the TPR and FPR obtained by selecting all genes contained in the highest scoring subnetwork, the five or the ten best scoring subnetworks, respectively. On these data, LEAN P-values provide significantly better separation between pathway genes and background genes than most other tested methods (p 0:05 for comparisons between LEAN and any other ROC evaluation permitting method, DeLong's method, see supplementary methods for details).
LEAN P-values reflect similarity of biological conditions If statistical profiles resulting from an experiment are reflective of the underlying biology, similar experiments should be expected to lead to similar detections. To examine how much gene-and local subnetwork-level P-values conform to this ideal, we evaluated the overlap of genes detected as significant in six publicly available experimental datasets whose biological interpretation we were familiar with. Four datasets measure the response of cell lines upon stimulation with transforming growth factor beta (TGFb) and tumor necrosis factor alpha (TNFa) in comparison to appropriate controls. The remaining two datasets respectively compare basal gene expression between two tissue types and a common reference tissue, murine cerebellum.
A B We carried out limma gene-by-gene analysis of differential expression, as well as LEAN (for details see supplementary methods). To ensure comparable sensitivity, the same cutoff of 0. 05 was applied on Benjamini Hochberg-corrected limma P-values and equally corrected LEAN P-values to obtain 6 lists of significant genes or local subnetwork centers in each case. Figure  4  shows heat maps showing the number of overlapping genes among each pair of lists (see supplementary methods for details). Clustering based on their degree of overlap using limma P-values did not yield groups of biologically similar datasets (Fig. 4A ), possibly in part due to vastly different number of genes detected as significant in the individual datasets. However, LEAN P-values separated stimulation from tissue comparison datasets and-within the stimulation experiments-separated the TGFb pathway and TNFa pathway related datasets into two groups, irrespective of array platform design used in individual datasets (Fig.
4B ). Enrichment analysis using Enrichr  (Chen et al. , 2013)  of the significant local subnetwork centers detected in both TNFa stimulation datasets returned GO terms and pathways relevant to the studied stimulus: The 65 proteins contained in the overlap showed enrichment of GO biological processes 'regulation of I-jB kinase/NF-jB signaling' (P-value 4 Ã 10 À40 , GO:0043122) as well as 'activation of innate immune response' (P-value 1 Ã 10 À33 , GO:0002218), congruent with the known role of TNFa in the induction of inflammatory responses via the NFjB signaling cascade  (Pober, 2002). Enrichment analysis for the TGF-b and tissue comparison datasets also yielded results congruent with the underlying biology (see supplementary methods and supplemental files 1 and 2).
To this end, gene-by-gene analysis of differential expression between CCM-invalidated and control mice was carried out and resulting non-multiple-testing adjusted P-values were subjected to LEAN using the high interaction confidence STRING network (interaction confidence score ! 0:9). This resulted in 68, 211 and 143 local subnetworks significantly enriched for deregulation (LEAN P-value 0:05) in the comparison of veins invalidated for CCM1, CCM2 and CCM3 versus control tissue, respectively (corresponding lists of local subnetwork centers are supplied in file Supplement3). Since features of the disease are similar independently A Fig. 4. Overlap of significant gene/local subnetwork center lists detected on publicly available datasets: (A) Limma gene-by-gene analysis, (B) LEAN. Numbers inside cells reflect absolute overlap, color corresponds to Jaccard index (JI).
Information about the perturbed pathway and used platform technology are shown as color strips on the top and right of the heat map, respectively. Hierarchical complete linkage clustering of the datasets based on Euclidean distance of Jaccard index profiles is represented as a dendrogram of the CCM gene ablated (in patients as well as in the mouse models), we examined the subnetworks common to the three groups to determine shared targets and signaling pathways. Two candidate subnetworks, centered on Coagulation-factor VIII (FVIII) and Von Willebrand Factor (VWF), respectively, were the only ones detected in all three of the CCM invalidation experiments. Applying the approach on a medium-confidence version of the STRING database (interaction confidence score ! 0:4) failed to identify FVIII, but confirmed significance of the VWF candidate subnetwork.
Among the other tested subnetwork methods, only the jActiveModules greedy search strategy led to a result containing VWF, but only if a suitable setting of five or more top-scoring networks were used, which results in a large set of at least 82 unprioritized genes (see Supplementary methods and Supplementary Table  S2  for details). Both FVIII and VWF are mostly known for their role in hemostasis. Given the above results and the fact that both the biological functions and the local subnetworks of VWF and FVIII overlap significantly, we focused our further evaluations on VWF. Figure  5  displays the most significantly deregulated genes in the local subnetwork around VWF (genes showing an input P-value p k Ã in at least one of the three CCM mouse models). They are mainly composed of genes involved in angiogenesis, blood coagulation and hemopoiesis pathways (GO biological process terms: GO:0001525, GO:0007596 and GO:0030097 respectively).