dataesr/scientific-paragraphs-categorization huggingface dataset:
IS6 nomenclature and initial identification There are at present (December 2020) nearly 160 IS6 family members in ISfinder (https://www-is. biotoul. fr/ scripts/search-db. php) from nearly 80 bacterial and archaeal species, although this represents only a fraction of those present in the public databases. The family was named  [8]  after the directly repeated insertion sequences in transposon Tn6  [9]  to standardize the various names that had been attributed to identical elements (e. g. IS15, IS26, IS46, IS140, IS160, IS176)  [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]  including one isolate, IS15, corresponding to an insertion of one iso-IS6 (IS15Δ) into another  [11]. More recently, there has been some attempt to rename the family as the IS26 family (see  [23] ) because of accumulating experimental data from IS26 itself, and the importance of this IS in accumulation and transmission of multiple antibiotic resistance, although this might potentially introduce confusion in the literature.
As might be expected, transposase length is approximately correlated with the clades. For example, family members from the archaea tend to be slightly smaller, in the range of 700-750 for clades Ai and Aii while members of clades h and i all carry the longest transposase genes (1230 to 1460 bp and 1710 to 1760 bp respectively). The division into clades is also underlined to some extent by the IR sequences where the sequence motifs are more pronounced when each clade is considered separately (Fig. S1 ) (see "Organization" below). Clearly, the ISfinder collection does not necessarily accurately reflect the IS6 family distribution and these groupings should be interpreted with care. For example, although many are not included in the ISfinder database, IS6 family elements are abundant in archaea and cover almost all of the traditionally recognized archaeal lineages (methanogens, halophiles, thermoacidophiles, and hyperthermophiles  [41]  (Fig.
It has been suggested that such variant should be suffixed as IS26. v1,. v2 etc. [42]. This makes sense if the mutation is not functionally neutral and results in a change IS properties or behavior. 309, and their best-fit evolutionary models were predicted with ProTest 3. 2. 4. A maximum likelihood tree was reconstructed with RaxML 8. 2. 9 using a bootstrap value of 1000. The final tree was visualized in FigTree 1. 4. 4 (http://tree. bio. ed. ac. uk/software/figtree) and edited with Inkscape 0. 92. 4 (http://www. inkscape. org).
Finding new family members: the strategy As for all insertion sequences, there are no fully automated methods for identification of IS6 family members in the public databases and ISfinder has not undertaken an extensive database survey. As a general strategy, potential IS sequences can be compared to those in the ISfinder database using the online BLAST tool (https:// www-is. biotoul. fr/blast. php) or TnCentral (https:// tncentral. proteininformationresource. org/tn_blast. html). For genome or plasmid analysis the semiautomatic genome annotation tool (Varani et al. 2011) (ISsaga: http:// issaga. biotoul. fr/issaga_index. php) can be used. This is integrated into ISfinder and compares sequences with those in the ISfinder database. There are number of other software tools available, showing different funcionalities, sensivity and precision that can be used for ab initio IS identification.
We highlight some of these, such as OASIS  [44] , ISEScan  [45] ,and ISQuest  [46]. However, no software performs better than the classic and manual annotation based on strucutural features and genetic characterisitiscs present in each IS family. In fact, there are still many bioinformatic challenges in obtaining a complete and proper IS identification in a given bacterial genome. For instance, it is important to ascertain manually whether the potential IS includes both terminal inverted repeats (IR; see Organization: Terminal Inverted Repeats below) and whether or not the IS is flanked by direct target repeats (DR). This will indicate whether the IS is functional (complete IR) and how it may have moved to its present position (presence or absence of DR; see Mechanism: the state of play below).
The transposase A single transposase orf is transcribed from a promoter at the left end and stretches across almost the entire IS. The putative transposases (Tpases) are between 213 (IS15) and 254 (IS6100) amino acids long with a majority in the 220-250 amino acid range. They are very closely related and show identity levels ranging from 40 to 94% with a helix-turn-helix (HTH) and a typical catalytic motif (DDE) (Fig. 2b, c , Fig. S2 ). Translation products of this frame have been demonstrated for IS240  [35]. However, the 7 members of clade h, all from Clostridia, are somewhat larger (340-350 amino acids) as a consequence of an N-terminal extension in the transposases with a predicted Zinc Finger (ZF) composed of several CxxC motifs (Fig. 2b ; Fig. S2 ). A Blast analysis of the non-redundant protein database at NCBI revealed a large number of IS6 family transposases of this type (data not shown).
The vast majority of these were from Clostridial species. In addition, the transposases of members of clade i (450 amino acids) have both the ZF domain and a supplementary N-terminal extension (Fig. 2b ). Several members (e. g. ISRle39a, ISRle39b and ISEnfa1) apparently require a frameshift for Tpase expression. It is at present unclear whether this is biologically relevant. However, alignment with similar sequences in the public databases suggests that ISEnfa1 itself has an insertion of 10 nucleotides and is therefore unlikely to be active.
Acknowledgements We would like to thank  Jian Zhang  ( Protein Information Resource PIR, Georgetown University ),  Hongzhan Huang  ( Protein Information Resource PIR, University of Delaware ),  Cathy Wu  ( Protein Information Resource, PIR Georgetown University and University of Delaware ),  Erik Snesrud  and  Patrick McGann  ( Walter Reed) Army Institute of Research ) and  Danillo Oliveira Alvarenga  ( Universidade Estadual Paulista, Jaboticabal, Sao Paulo, Brazil ) for providing essential expert assistance and both conceptual and practical support for TnCentral,  Fred Dyda  and  Alison Hickman  ( NIDDK, NIH, Bethesda, Maryland, USA ) for reading the manuscript and for suggestions and  Sally Partridge  ( The University of Sydney and Westmead Hospital, Australia ) for critical comments.
Background: DNA methylation is an epigenetic mechanism involved in human development. Numerous epigenome-wide association studies (EWAS) have investigated the associations of DNA methylation at single CpG sites with childhood outcomes. However, the overall contribution of DNA methylation across the genome (R 2 Methylation ) towards childhood phenotypes is unknown. An estimate of R 2 Methylation would provide context regarding the importance of DNA methylation explaining variance in health outcomes. We therefore estimated the variance explained by epigenome-wide cord blood methylation (R 2 Methylation ) for five childhood phenotypes: gestational age, birth weight, and body mass index (BMI), IQ and ADHD symptoms at school age. We adapted a genome-based restricted maximum likelihood (GREML) approach with cross-validation (CV) to DNA methylation data and applied it in two populationbased birth cohorts: ALSPAC (n = 775) and Generation R (n = 1382).
2% of variance. More recently, Zhang et al. [7]  tested the validity of the GREML approach in methylation data using simulations and real data in a sample of adults. The authors estimated that concurrent blood DNAm levels explained 6. 5% of the variance in BMI but were not associated with height, when controlling for genetic effects. In contrast, using a Bayesian approach not relying on similarity matrices, Banos et al. [8]  estimated the proportion of BMI variance explained by concurrent DNAm to be 75. 7% in adulthood. The CpG-level effects estimated by this model explained up to 30. 8% in adult replications cohorts, but only 3. 3%, 2. 05% and 9. 65% at birth, age 7 and age 15 respectively, with BMI and DNAm measured at the same time-points. The results suggest highly age specific effects depending on when both BMI and DNAm were measured.
Please note that the study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool (http:// www. brist ol. ac. uk/ alspac/ resea rchers/ our-data/). Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Consent for biological samples has been collected in accordance with the Human Tissue Act (2004). Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. Generation R invited all pregnant women living in the city of Rotterdam, the Netherlands, with an expected delivery date between April 2002 and January 2006 to participate in the study, of which 9,778 were enrolled.
DNA methylation DNAm was measured in cord blood at birth. Bisulfite conversion was performed with the EZ-96 DNAm kit (shallow) (Zymo Research Corporation, Irvine, USA). DNAm levels were then measured with the Illumina Infinium HumanMethylation450 BeadChip array (Illumina Inc. , San Diego, USA). Preprocessing in ALSPAC was performed with the meffil package  [23]. Quality control check included on a sample levels mismatched genotypes, mismatched sex, incorrect relatedness and on a CpG level low concordance with other time points, extreme dye bias, and poor probe detection. In Generation R, pre-processing was performed with the CPACOR workflow  [24]. Quality control exclusion criteria included on a CpG level failed bisulfite conversion, hybridization or extension, and on a sample level sex mismatches and call rate < = 95%.
Both cohorts were normalized using a combined dataset, using meffil functional normalization with ten control probe principal components and slide included as a random effect, see Mulder et al. [25]  for further details. To lessen the influence of methylation outliers while retaining a consistent sample size, extreme values were winsorized. Per CpG site, DNAm levels exceeding three times the interquartile range above the third or below the first quartile (3*IQR criterion) were replaced by the maximum or minimum value, respectively, of the sample below the exclusion criterion. Only autosomal probes were considered in this study for consistent interpretation of effects between sexes. This resulted in 470,870 and 473,864 CpG probes in ALSPAC and Generation R, respectively, which were used for the computation of the methylation similarity matrix.
Covariates In ALSPAC, mothers were asked about their smoking during pregnancy, and these data were used to generate a binary variable of any smoking during pregnancy. Maternal education was collapsed into whether they had achieved a university degree or not. White cell proportions were estimated with the Houseman method using a combination of four reference panels specific for cord blood  [31]. In Generation R, maternal age was obtained at enrollment. Maternal smoking was defined as either "Never smoked", "Quit smoking in early pregnancy", "Continued smoking during pregnancy". Maternal education during pregnancy was categorized as "no education", "primary education", "secondary education first phase", "secondary education second phase", higher education first phase", higher education second phase".
XX' was then standardized by dividing the matrix with the mean of the diagonal, resulting in an average value of 1 for the diagonal of M. We used the R package BGData 2. 1. 0 [32]  to compute the similarity matrix. The next step is to regress the outcomes on M and covariates using a mixed effects model fitted with REML. Fixed effects covariates included several variables known to be associated with DNAm levels: sex, maternal age, maternal smoking, maternal education, cell type proportions, gestational age, birth weight (unless a variable was the outcome). M and batch were defined as random effects. Multiple imputation was used to avoid potential bias due to missing data and to make analyses more comparable between outcomes by including the same set of participants. We used the covariate and outcome variables to predict missing variables in 10 imputations with 30 iterations using MICE  [33]  in R.
Methylation ). (7)  Step 1-6 are repeated to have results for 100 random training-testing splits (Monte-Carlo cross-validation). (8)  The mean estimate of ΔR 2  Methylation , with standard deviation across the cross-validation splits are extracted. (9)  Steps 1-8 are repeated for each imputed dataset (n = 10) and then pooled using Rubin's rule. (10)  Results of both cohorts are averaged, weighted by the inverse of the cross-validation variance. We reran all analyses with an M matrix based on independent methylation features only. To construct such a matrix, we used EnMCB 1. 7. 3 to identify MCBs  [35]. We aggregated neighboring CpG sites with r = > 0. 3 correlation and maximum 1000 bp gap into MCBs. We then averaged the methylation betas of all CpGs within one MCB resulting in a single value, representing the methylation status of a MCB.
The MCB methylation levels were then combined with independent CpG sites (r < 0. 3) and used to compute an M matrix. ALSPAC used 60,722 MCBs and 167,440 independent CpGs to produce an independent M matrix and Generation R used 71,175 MCBs alongside 206,062 independent CpGs sites. These analyses were run with the qgg package in R, which has implemented GREML models with crossvalidation  [36]. We wrote additional functions, which can be found in the omicsR2 package: https:// github. com/ aneum ann-scien ce/ omics R2. The provided functions simplify the process of comparing the predictive performance of DNA methylation compared to a covariates-only baseline model and add support for multiply imputed data.
Acknowledgements We thank all the children and families who took part in this study, as well as the support of general practitioners, hospitals, midwives and pharmacies. ALSPAC We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. GENR The Generation R Study is conducted by  Erasmus MC  in close collaboration with the  Erasmus University Rotterdam , Faculty of Social Sciences, the  Municipal Health Service Rotterdam area , the  Rotterdam Homecare Foundation, Rotterdam  and the  Stichting Trombosedienst & Artsenlaboratorium Rijnmond  ( STAR-MDC ),  Rotterdam.
The generation and management of the Illumina 450K methylation array data (EWAS data) for the  Generation R Study  was executed by the  Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC , the  Netherlands. We thank  Mr. Michael Verbiest ,  Ms. Mila Jhamai ,  Ms. Sarah Higgins ,  Mr. Marijn Verkerk  and  Dr. Lisette Stolk  for their help in creating the EWAS database. We thank  Dr. A. Teumer  for his work on the quality control and normalization scripts.
Availability of data and materials The datasets generated and analyzed during the current study are not publicly available to ensure participant privacy and compliance with Dutch and UK law, but are available on reasonable request. For Generation R data, please contact management (datamanagementgenr@erasmusmc. nl) and the corresponding author. For ALSPAC, please see http:// www. brist ol. ac. uk/ alspac/ resea rchers/ for instructions on data access.
ACKNOWLEDGEMENTS We wish to thank  G. Giuliani  and the  ICTP/ESP section , for maintaining and developing the RegCM4 code and providing useful assistance. We also thank  A. Sayer  for providing the MODIS based ACAOD observations used in Supplementary Information. We also wish to thank the AEROCLO-sA, ORACLES, and  CLARIFY-2017 scientific community  for providing a background to this work and promoting ideas and discussion during meetings. This work was supported by the  French National Research Agency  under Grant Agreement  ANR-15-CE01-0014-01 , AEROCLO-sA. This work was granted access to the HPC resources of CALMIP supercomputing center under the allocation  2019-p19062.
Neuroimaging studies across genomic variants are scarce. An investigation of 49 unaffected carriers of SZ-associated CNVs across five genomic loci in the UK biobank showed smaller volumes of the thalamus, hippocampus, and nucleus accumbens  [26]. Functional connectivity similarities have also been demonstrated between 16p11. 2 and 22q11. 2 deletions as well as with idiopathic ASD and SZ  [27]. Alternatively, a recent study suggests a relatively distinct association between neuroimaging alterations and six different CNVs  [28]. In this study, we aimed to characterize shared and distinct neuroanatomical alterations associated with eight CNVs at four genomic loci. We analyzed high-resolution structural brain scans from the largest multi-site dataset of CNV carriers (n = 484, of which 87 have not yet been published) and controls (n = 1296) to date.
METHODS Participants Deletions and duplications carriers' neuroimaging data included in the study were selected on the following breakpoints (hg 19): 16p11. 1  and supplementary materials). Signed consents were obtained from all participants or legal representatives prior to the investigation. Of note, data of 87 CNV carriers have never been published. Clinically ascertained CNV carriers were recruited as either probands referred for genetic testing, or as relatives. Controls were either noncarriers within the same families or individuals from the general population. We pooled data from five cohorts. CNVs from non-clinical populations were identified in the UK Biobank  [31, 32].
MRI data processing Data for Voxel-Based Morphometry were preprocessed and analyzed with SPM12 (http://www. fil. ion. ucl. ac. uk/spm/software/spm12/)  [33] [34] [35]  running under MATLAB R2018b (https://www. mathworks. com/products/ new_products/release2018b. html). For surface-based feature extraction, we used FreeSurfer 5. 3. 0 (http://surfer. nmr. mgh. harvard. edu [36,37,] ). Quality control was performed using standardized ENIGMA quality control procedures (http://enigma. ini. usc. edu/protocols/imaging-protocols/).
Statistical analysis for global brain measures Global brain aggregate measures (TIV, total gray matter (GM) volume, total SA, and mean CT) were adjusted for age, age 2 , and sex as fixed effects and scanning site as random factor. Non-clinically ascertained subjects from the UKBB are on average 30 years older than the clinically ascertained subjects. Because of this age difference we used age matched control groups for univariate analysis. Global measure z-scores for each CNV for clinically and non-clinically ascertained CNVs were calculated using 331 and 965 controls, respectively. All statistical analyses were performed in R, version 3. 4. 4 (https://www. r-project. org/), or in MatlabR2018b.
Comparison of ranked Cohen's d maps across CNVs To adjust for the unequal power to detect alterations across different CNV groups, which have different sample and effect sizes, we ranked the Cohen's d values of all voxels (/vertices) for each statistical maps (CNV versus controls contrast). We then tested for spatial overlap between maps across CNVs after thresholding the tails of the distribution at the 15th & 85th quantiles. Dice index was calculated using publicly available Matlab scripts (https://github. com/rordenlab/spmScripts).
Quantifying shared variation across CNVs using principal components (PC) PCs were derived to quantify shared morphometry variation across CNVs. We used Cohen's d values of 130 neuroanatomical GM regions (neuromorphometrics atlas) of eight CNVs as input-variables (z-scored Cohen's d contrasts adjusted for total GM and nuisance variables; FactoMineR package in R). The variance explained (coefficient of determination, R-squared) for each CNV-associated Cohen's d map by PCs was obtained by running a linear model (lm) in R; with PC1 and PC2 as independent explanatory variables and the CNV Cohen's d map as a dependent variable.
Limitations Multiple sites included in the study may have introduced noise, but previous studies have shown that site effects do not influence the neuroanatomical patterns associated with CNVs at the 16p11. 2, 22q11. 2, and 15q11. 2 loci  [12, 19, 23]. While shared variation could have been influenced by clinical ascertainment or psychiatric diagnoses, our sensitivity analyses showed that this is not the case. The effect of medication on CNVs brain alterations could not be investigated in the current study as medication information was not available for the whole dataset. We were underpowered to properly investigate potential sex-related effects of 1q21. 1 and 15q11. 2 on brain morphometry. Of note, previous neuroimaging studies of large 22q11. 2 and 16p11. 2 samples were unable to identify any sex-related effects  [19, 25].
For these reasons, there is a practical need for efficient methods that can profile microbial communities at high genomic resolution. In contrast to 16S rRNA gene sequencing, shotgun metagenomics has the potential to resolve microbial communities to the strain level. This is because it generates reads from throughout the genomes of all the community members. It also has the additional advantages of reduced levels of bias and the capability to reconstruct genomes. There are many methods for reference-based strain resolution from metagenome data  [1, 42, 49] , but they are, and will continue to be, limited by the challenge of comprehensively isolating and sequencing the genomes of diverse microbial strains. Comprehensive reference genome databases may be possible for a few slowly evolving species or particularly well studied pathogens but for the entirety of a complex community it is unlikely to ever be tractable.
For example, in a recent de novo large-scale binning study of the relatively well-studied human gut microbiome, it was found that 77% of the species recovered did not have a reference genome in public databases  [38]. This suggests that even less of the strain-level diversity in those samples would be represented in a genome database. These observations motivate the need for de novo methods of metagenomic strain resolution. In the metagenomics context, we adopt the definition of a 'metagenome strain' as a clonal subpopulation with sufficiently low levels of recombination with other strains, that it can be distinguished genetically from them. This does not require that recombination between strains does not occur, rather that either because of physical separation or selection, it has not been sufficiently strong relative to the rate of mutation  [47] , to generate a continuum of diversity throughout the genome.
For the 108 MAGs that had at least two strains with relative frequencies determined in five or more samples we used permutation ANOVA to determine whether strain proportions depended on sampling time. In total 13 of the MAGs had an adjusted p-value <0. 05 i. e. 12. 0%. For these same MAGs 37 had a total coverage that changed significantly over time with an adjusted p-value <0. 05 i. e. 34. 2%. Therefore the intra-species dynamics are more stable than inter-species, with strain proportions remaining fixed as the MAG coverages vary, this was true for 33 of the 37 MAGs that changed significantly in coverage. In Fig. 8 , we use the Anvi'o program  [16, 17]  to summarise information on phylogeny, taxonomy, normalised coverages in the ten samples, and whether the MAGs changed significantly in total abundance, together with the number of strains resolved by STRONG and if those strain relative proportions changed significantly with time.