dataesr/scientific-paragraphs-categorization huggingface dataset:
Nowadays, TB molecular surveillance systems rely mainly on multiple interspersed repetitive unit-variable number tandem repeats (MIRU-VNTR) typing for tracing outbreaks and ongoing transmission. Unlike IS6110 RFLP, MIRU-VNTR is a PCR-based genotyping method whose results are expressed in a digital code, enabling their portability and creation of databases  [7]. In its standard 24-locus format, the discriminatory power of MIRU-VNTR was shown to be equal to that of IS6110 RFLP, except in settings where genetically closely related strains predominate  [8]. Compared to whole genome sequencing (WGS), virtually the highest discriminatory typing approach, 24-loci MIRU-VNTR was shown to overestimate recent TB transmission, thus emphasizing the need for an accessible, cost-effective alternative  [9, 10].
Towards this end, we reconsidered IS6110-5'3'FP, a typing approach that we have previously developed  [11] , and which we believe could represent a valuable alternative to 24-loci MIRU-VNTR. IS6110-5'3'FP relies on a single PCR amplification reaction, coupled to simultaneous differential labeling of 5' and 3' IS6110 polymorphic ends, and their automated fractionation on a capillary sequencer. IS6110-5'3'FP addresses all the drawbacks of IS6110 RFLP, while offering increased discriminatory power and flexibility. Indeed, it efficiently and accurately resolves both 5' and 3' polymorphic ends on a capillary sequencer, thus enabling interlaboratory reproducibility, results portability, and hence establishment of databases. Here we introduced major amendments and refinements to the original IS6110-5'3'FP protocol and demonstrated its highest discriminatory power compared to 24-loci MIRU-VNTR.
Data analysis Chromatographs showing FAM-and ATTO 532-fluorescing fragments were imported to Gen-eMapper software V5 (Applied Biosystems). Fluorescent peak data, which were blinded with regard to IS6110 copy number, were analyzed using AFLP default and the advanced mode with selection of a starting point of electrophoresis at 3250 data point. Fluorescent peaks whose RFU> 500 were considered. Background signals from the auto-ligation reaction were automatically subtracted from the chromatographs of typed strains, a new modification which simplifies data analysis, since in the original protocol checkerboard dilution testing was performed to determine the threshold for assigning a specific peak. The size of fragments was determined using GeneScan 600Liz version 2 (Applied Biosystems) with a sizing quality up   [11].
(B). The new protocol of IS6110-5'3'FP. In this new protocol version, aside from using the frequently cutting BstUI enzyme instead of HincII, there is no need for plasmid library amplification in E. coli, a modification that considerably shortens the method turnaround. Moreover, amplification in E. coli could result in the loss of some IS6110-containing plasmid most likely because of clone instability. Therefore, omission of this step increases the sensitivity of the method. https://doi. org/10. 1371/journal. pone. 0197913. g001 than 0. 95, and verified using the plot determined by the software. GeneMapper also recorded the fragment data in a binary format in Excel files which were exported into BioNumerics v6. 1, visualized as virtual electrophoresis gels and analyzed. Two IS6110-5'3'FP profiles were considered to be different if they had at least one peak difference.
Cost estimation of IS6110-5'3'FP We carried out an estimation of the total cost of IS6110-5'3'FP, from DNA extraction to data acquisition, by taking into account direct costs (e. g. , reagents and consumables) and depreciation costs (15%) for those equipments that were directly related to the method and purchased within the last 5 years. This included mainly the automated capillary sequencer used for polymorphic fragments fractionation. In our estimation, labor costs and maintenance fees were included.
Background: The capacity of technologies measuring DNA methylation (DNAm) is rapidly evolving, as are the options for applicable bioinformatics methods. The most commonly used DNAm microarray, the Illumina Infinium HumanMethylation450 (450K array), has recently been replaced by the Illumina Infinium HumanMethylationEPIC (EPIC array), nearly doubling the number of targeted CpG sites. Given that a subset of 450K CpG sites is absent on the EPIC array and that several tools for both data normalization and analyses were developed on the 450K array, it is important to assess their utility when applied to EPIC array data. One of the most commonly used 450K tools is the pan-tissue epigenetic clock, a multivariate predictor of biological age based on DNAm at 353 CpG sites. Of these CpGs, 19 are missing from the EPIC array, thus raising the question of whether EPIC data can be used to accurately estimate DNAm age.
To accommodate advancements in DNAm array technology and the increasing volume of data, many pipelines for data preprocessing, normalization, and analyses have been developed to streamline data handling  [1, 5, 25, 28, 30]. Here, we refer to "preprocessing methods" as algorithms commonly performed on DNAm data prior to probe-type normalization, including methods to reduce background fluorescence or adjust for dye bias, which if unaddressed can reduce the dynamic range of beta values  [31]. Probe-type normalization is a necessary adjustment for Illumina microarray DNAm data, as there are two different probe designs that possess differential beta distributions  [2]. Tools such as the R function 'preprocessNoob' in the minfi package subtract background based on the out-of-band intensities (for example, Infinium I probes fluorescing in the color channel opposite their designed base extension).
Color or dye bias adjustment is applied to account for the two color channels that type II probes employ, one for methylated and one for unmethylated CpGs, since residual dye can introduce unwanted variation. Tools to account for the color bias include in the Bioconductor package 'methylumi, ' which is based on smooth quantile normalization, or the Illumina GenomeStudio software which implements a shift-and-scaling normalization  [6]. Although these methods have been reviewed in comparison to one another  [33, 35, 36] , a mixed variety of pipelines are used across the literature and the influence of method selection on detecting true positives or generating accurate predictions should be investigated both within and across array technologies. One tool that could be compromised by different preprocessing methods or the lack of certain 450K CpG sites on the EPIC array is the pan-tissue epigenetic clock, a popular predictive model that estimates an individual's biological age, irrespective of tissue type, using DNAm at 353 CpGs  [12].
DNA methylation quantification All microarrays were scanned with an Illumina HiScan system. For the EPIC array data, we used the most current manifest file, "Infinium MethylationEPIC v1. 0 B4 Manifest File," released by Illumina on May 26, 2017 and consisting of 865,918 probes, whereas for the 450K we used the "HumanMethylation450 v1. 2 Manifest File" with 485,577 probes. Both manifest files are available at https://support. illumina. com/downloads. html. In addition to unprocessed (raw) data, we used data preprocessed in three different ways (1) color corrected/background subtracted in Genome Studio (GS), (2) quantile-normalized using "preprocessQuantile"  [29] , (3) normal-expontential out-of-band (noob)-normalized with "preprocessNoob"  [30]. Raw data and data that were to be quantile or noob normalized were uploaded directly into R from IDAT files using the 'minfi' package function "read.
DNA methylation age We calculated DNAm age for each sample by using a modified version of the publicly available R code at https://dnamage. genetics. ucla. edu, with the normalization feature set to "TRUE"  [12]. We focused our inquiry on data preprocessing only, and not probe-type normalization methods, as the epigenetic clock code applies an imputation of missing values and performs a calibrated version of a beta-mixture quantile normalization  [12, 28]. The 71-CpG Hannum method age estimates were generated using methods described previously  [10].
Data preprocessing methods affected the calculated DNA methylation age, but within error margins of the epigenetic clock Given that there is not an accepted standard practiced method of preprocessing data prior to calculating DNAm age, we assessed the potential effects of different commonly used data preprocessing methods on the DNAm age estimates. We compared DNAm age estimates calculated from raw data as well as after applying three separate standard data preprocessing methods: color-correction and background-normalization with GenomeStudio software (abbreviated GS), quantile-normalization, or noobnormalization. Imputation and a probe-type normalization were performed the same way across preprocessing methods using the R code supplied with the epigenetic clock method  [16, 28, 32]. Using monocyte-derived data from 172 subjects on both the 450K and EPIC, we found that DNAm age was highly correlated across both raw data and data after three different preprocessing methods (r > 0.
A technical replicate pair represented an identical DNA sample quantified twice for quality control purposes; specifically, the sample was divided into two separate tubes after bisulfite conversion and DNAm was quantified separately. Technical replicate sample identity was confirmed by examining the 59 SNP probes present on the EPIC array (Additional file 1: Figure  S8 ). We focused on the 24 technical replicates (12 pairs) from the EPIC array, calculating DNAm age for each technical replicate from data subjected to each separate preprocessing method: raw, GS color corrected and background subtracted, quantile normalized, or noob normalized. We calculated the median absolute difference between each technical replicate pair's DNAm age estimates in each dataset. We found that the GS color correction and background subtraction had the least deviation across replicates (error GS = 2.
17 years), followed by noob normalization (error Noob = 2. 41 years), Fig. 3  DNA methylation age acceleration variation across preprocessing methods. a Scatter plot of EPIC DNA methylation (DNAm) age calculated from raw and data from three different preprocessing methods: quantile, GenomeStudio (GS), and normal exponential out-of-band (noob) normalization. Colored regression lines and surrounding shaded gray areas represent 95% confidence interval for each group. b Boxplot of estimated DNA methylation (DNAm) age-chronological age (acceleration difference) for each preprocessing method. c Boxplot of residuals from a linear regression (DNAm age~chronological age) across methods. The median is illustrated by horizontal line with upper and lower hinges representing the 25th and 75th percentiles, upper and lower whiskers extend no further than the inter-quartile range multiplied by 1.
Discussion This study had two primary aims (1) to investigate whether using EPIC methylation data to calculate DNAm age is an appropriate approach, given the 19 and 6 missing probes used in the pan tissue 353 CpG model  (Horvath)  and 71 CpG model  (Hannum) , respectively, and (2) to evaluate the effect of various data preprocessing methods prior to calculating DNAm age, as a standard pipeline for processing data prior to calculating DNAm age does not exist. By analyzing monocyte DNAm from 172 individuals quantified on both the 450K and EPIC arrays, we demonstrated that the lack of the clock-CpGs on the EPIC array did not compromise the utility of the epigenetic age predictors. We also evaluated the performance of the EPIC pan-tissue epigenetic clock (334-CpG model) on another EPIC dataset, consisting of three tissues from 13 individuals, finding comparable correlations with age to those reported with the 450K array (353-CpG model).
Furthermore, we found small differences in the DNAm age estimate between data preprocessing methods, implying that although the methods assessed here differed in mean values, the trends in respect to chronological age were consistent across methods. Finding that preprocessing method influenced mean values of DNAm age is important for the interpretation of future analyses, as we demonstrated that variation in DNAm age can be introduced by how the data are preprocessed. Our work here provides supporting evidence for the DNAm age acceleration residual measure, since this value is reflective of inter-individual variability within a measured dataset, and is, therefore, more comparable across studies. In contrast, the DNAm age difference, the crude difference between estimated DNAm age and chronological age, can be reflective of global DNAm shifts due to preprocessing methods.
Whichever measure of DNAm age is used (acceleration difference, residual, or age itself ), there is an additional consideration that small effect sizes should be interpreted with caution. To highlight this point, we calculated DNAm age for technical replicates from raw data and three different preprocessing methods. We found that while there was some variability in DNAm age across technical replicates, regardless of preprocessing methods, the observed median absolute error in DNAm age for each method (2. 7-3. 14 years) was lower than the reported error of the epigenetic clock (3. 6 years)  [17]. GS-preprocessed data produced the tightest replicates, followed by noob and then quantile normalization, and the consistency between replicates was lowest when DNAm age was calculated from raw data.
These findings may suggest using preprocessed data rather than raw data, but overall we emphasize the importance of considering the technical error of the epigenetic clock and caution interpretation of changes of less than 3. 6 years. To examine the appropriateness of using EPIC data to calculate DNAm age for future research, we took advantage of a cohort with DNAm data on the same individuals on both the 450K and EPIC arrays. It is crucial to examine whether the epigenetic clock can continue to be used on EPIC data, as the 450K platform is no longer available. There was high consistency between 450K and EPIC DNAm age estimates, and the lack of 19 CpG sites did not significantly affect the prediction accuracy of the epigenetic clock. Probe-wise correlation coefficients of the 334 common clock CpG sites across the 450K and EPIC were lower than anticipated; however, previous reports have demonstrated that the majority of EPIC probes are not well correlated to those of the 450K and that this is most prevalent at invariable probes  [21].
Error refers to the median absolute difference between DNAm age and chronological age age  [26]. The consistency in estimated age lends support to the strength of this predictive model on the EPIC platform and will allow users to continue applying this bioinformatic tool to continue to calculate DNAm age. To further examine the application of EPIC array data to predict DNAm age, we estimated DNAm age in an independent EPIC array cohort. We observed correlations between EPIC DNAm age and chronological age that were comparable to previous reports, specifically in PBMCs and BAL samples. The strong association in PBMCs is consistent with previous reports of DNAm age in PBMCs as generated from 450K data  [13, 18]. We observed less consistency in the brush samples; however, this tissue was not included in the training data of the 353-CpG epigenetic clock and so performance may not be reflective of EPIC array but rather be a property of clock itself.
This is reinforced by our experiment removing the 19 clock CpGs not present on the EPIC array from the 450K data, where we observed a nearly perfect correlation with the 353-CpG data, suggesting that the loss of the 19 clock CpG sites did not influence the accuracy of the epigenetic clock. There are limitations to this study that should be taken into consideration when interpreting the results. The primary datasets we investigated when comparing EPIC versus 450K estimated DNAm age were from monocyte samples, and although we found that the lack of 19 CpGs did not affect the pan-tissue DNAm age estimate in this specific cell type, those 19 CpGs may be important to estimate age in other tissues. Their importance to other tissues remains to be explored. Additionally, the methods applied in the current study should not be generalized across all studies.
For example, global normalization methods, such as quantile normalization, are not appropriate in all cases as interesting biological information can be removed in datasets with large variation across samples, such as cancer compared to normal or multiple-tissue projects. Instead, the use of these data transformation methods should be considered on a study-by-study basis  [11]. Furthermore, while we are cognizant there are several other available preprocessing options, for the purposes of our exploration and presentation of these data, we only assessed three of the most common methods. In summary, we have investigated and confirmed that two commonly used methods of DNAm-based age estimation, the 353 CpG Horvath model and the 71-CpG Hannum model, were not compromised when using the latest human DNAm microarray platform, the EPIC array, which is lacking 19/353 CpG and 6/71 CpG targets, respectively.
We have also tested whether DNAm age estimates were influenced by the preprocessing stage; for example, whether raw data generated differing results than normalized data. We assessed raw data and three different preprocessed inputs (noob-, quantile-, and GS-normalized) and found age estimates were different, but less than that of the reported error of the model. Related, we finally also provided support for using the age acceleration residual metric rather than the age acceleration difference in studies applying the epigenetic clock. Our work will provide researchers the confidence to investigate DNAm age using the EPIC array, as well as encourage users to critically consider the technical error of the epigenetic clock when interpreting future findings.
Funding LMM is supported by a  CIHR   Frederick Banting and Charles Best Doctoral Research Award  ( F15-04283 ). CFR is supported by fellowships from the  BC Lung Association, MITACS , and the  Michael Smith Foundation for Health Research. CC is the  Canada Research Chair in Occupational and Environmental Lung Disease. SH was supported by  NIH/NIA   U34AG051425-0. MSK is the  Canada Research Chair in Social Epigenetics , Senior Fellow of the  Canadian Institute for Advanced Research , and  Sunny Hill BC Leadership Chair  in  Child Development. Support  for the DE3 study was provided by  AllerGen NCE, CIHR , and the  BC Lung Association. Support for the EVOIMMUNOPOP study was funded by the  Institut Pasteur , the  CNRS  and the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/  ERC  grant agreement  281297  (to LQ-M).
A second data set was acquired in high/high resolution mode with both precursor and fragment ion detection in the Orbitrap (at 60 000 and 30 000 resolution, respectively). MS/MS spectra were searched against a database containing all 40S ribosomal subunits, initiation factors and nine contaminant proteins (including keratins). xProphet  (38)  was used to adjust the false discovery rates (FDR) to <5% with the corresponding xQuest score cut-offs at 23. 9 for inter-protein cross-links and 16. 0 for intra-protein crosslinks in high/low mode, and 30. 0 for both inter-and intraprotein cross-links in high/high mode. Supplementary Table S1 includes all confident identifications and the scores as reported by xQuest. The closer the score to the threshold, the more likely it is to be a false positive.
The overlaps for the high/high and high/low experiments were as follows: calculated from unique peptidepairs: 50. 4% for intra-protein cross-links (70 out of 139; 31 unique to high/high, 38 unique to high/low) and 37. 7% for inter-protein cross-links (26 out of 69; 34 unique to high/high, 9 unique to high/low). The lower overlap and the contribution of more unique cross-links by the high/high dataset for inter-protein cross-links can be explained by the stronger discriminatory power of high mass accuracy MS/MS data for the much larger search space. Absence of a cross-link in one dataset may also be explained by the fact that a cross-link was identified, but fell below the 5% FDR threshold.
This tentative assignment is based on structural data of the archaeal homologues of eIF2  (39)  and XL-MS experiments (see below) detecting cross-links between eIF2␤ and eIF2␥. Likely, eIF2␤ is flexibly bound to the TC since its EM density largely disappears during refinement. The position of eIF3 in the h-48S structure resembles that in the mammalian 43S pre-IC structure (15): the eight eIF3 core subunits (a, c, e, f, h, l, k and m), eIF3d and the WD40 ␤-propeller domain of eIF3b are bound to the solvent-exposed side of 40S (Figures  1  and 2A ). Notably, the h-48S map prior to refinement comprises additional, low-resolution density next to eIF3b. We attribute this density to the eIF3b RRM, the eIF3i WD40 domain and the C-terminal part of eIF3a (Figure  2B  and C ). This is corroborated by our XL-MS results (see below) and by the fact that density at the same position, of virtually identical size and shape was attributed to these subunit domains in the mammalian 43S pre-IC  (15).
ACKNOWLEDGEMENTS We thank  Holger Stark  for collecting the h-48S cryo-EM data set on the Titan Krios at the  Max Planck Institute for Biophysical Chemistry  in G öttingen. We thank  Tatyana Pestova  and  Christopher Hellen  for the plasmids encoding initiation factors and  Alain Miller  ( Cilbiotech S. A. , Mons, Belgium ) for providing HeLa cytoplasmic extracts. We thank all members of the Schaffitzel and Berger teams for discussions and advice. This work used the platforms of the Grenoble Instruct Center (ISBG:  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB). The electron microscope facility is supported by the  Rh ône-Alpes Region , the  Fondation Recherche Medicale (FRM) , the fonds  FEDER , the  Centre National de la Recherche Scientifique (CNRS) , the  CEA , the  University of Grenoble, EMBL  and the  GIS-Infrastrutures en Biologie Sante et Agronomie (IBISA).
Many of the genes identified have putative functions consistent with a role in determining their associated trait as well as the complex Central African pygmy phenotype. Conclusions: These findings highlight the potential of modestly sized datasets of Pygmies and non-Pygmies to detect biologically meaningful associations with traits contributing to the Central African pygmy phenotype. Moreover, they provide new insights into the phenotypic and genetic bases of the complex pygmy phenotype and offer new opportunities to facilitate our understanding of its complex evolutionary origins.
A number of recent studies explored the genetic basis of adult standing height variation in Central Africans, comparing genome-wide SNP data on ≤3 Pygmy populations and their non-Pygmy neighbors  [33, 34]. These studies uncovered signatures of differential polygenic adaptation between Pygmies and non-Pygmies primarily in genomic regions containing genes associated with immunity and metabolism, as well as evidence of differential signatures in Western and Eastern Pygmies suggesting a partially convergent origin of the pygmy phenotype in these groups. Perry et al. [34]  further identified genetic associations with standing height that included four regions previously implicated in adult standing height variation in Europeans. However, no association was detected with the ~100 European standing-heightassociated SNPs present in these modestly sized datasets of 67-169 Pygmies and 58-61 non-Pygmies, strongly suggesting that the differing stature of Pygmies and non-Pygmies is attributable at least in part to genetic factors that have arisen since the ancestral split between Central Africans and Europeans.