dataesr/scientific-paragraphs-categorization huggingface dataset:
Note that the scale of the y-axis is logarithmic Even though the GBS approach has higher resolution for homozygous deletions, it was still able to detect large hemizygous deletions and duplications with high accuracy as demonstrated by both aCGH and ddPCR assays. A caveat of the validation approach used here, however, is that hemizygous deletions and duplications were called visually and not computationally from the aCGH data, such that the actual number of such events that are detectable by aCGH but not by GBS may be higher than reported here. In two cases, CNVs initially called as hemizygous turned out to be homozygous deletions as assessed by ddPCR, but this is not a major problem since any follow-up studies on such erroneously called mutants should generate independent data that would provide a correct picture.
Our method therefore fills a gap in the range of methods available for screening irradiation-induced CNVs by providing a low-cost method with reasonable resolution, Fig. 5  aCGH and GBS log 2 ratio profiles showing an unusual succession of events on chromosome 1 of individual FNMN0066. Duplications and hemizygous deletions are indicated in green (top arrows) and orange (bottom arrows) respectively. Arrows indicate the starting points of the events and solid horizontal lines indicate the span (x-axis) and mean log 2 ratio (y-axis) of the segment. Note that the automated GBS approach missed the middle duplication around position 15 Mb and split the last duplication into two separate events. Both datasets, however, outline the same series of duplications and hemizygous deletions whereas previously existing computational methods either relied on high-depth WGS which is hardly affordable for large populations (e.
Using the SNP genotyping capabilities of GBS Fast neutron irradiation has been shown to also induce single-base substitutions and small indels (e. g. [7, 15] ). In this context, it should be noted that the aCGH technology will likely miss these lesions as well as deletions up to a few kb, which similarly limits its applicability to forward genetics. On the other hand, the sequencing technology employed by GBS has the ability to discover SNPs and indels. While calling mutations presents a few challenges to avoid confounding them with sequencing errors, the fact that ~30,000 bins were covered by a mean number of reads > 7 in our dataset suggests that there are plenty of well-covered fragments from which Fig. 6  Copy number of target regions in control and mutant samples as estimated by ddPCR for putative (a) duplications, (b) hemizygous deletions and (c) homozygous deletions.
An interesting outcome related to the use of GBS data in this study is that SNP calls were useful for identifying contamination and genetic heterogeneity in the mutant population. The SNP markers called by GBS identified individuals that were not of the M92-220 genetic background as well as regions of natural heterogeneity among the M92-220 seed stock originally subjected to mutagenesis. Although aCGH data can also be used to achieve these goals (e. g. [14, 31] ), SNP genotyping through sequencing allows for a more straightforward way to do so.
Modularity of the GBS approach The GBS approach described here is modular in the sense that sequencing data generated from different libraries or different sequencing runs can be sequentially added to reach the resolution required for a given application. In this study, we sequenced a first library ranging from ~50-225-bp restriction fragments on three Ion Proton chips and a second library ranging from ~150-350-bp fragments on a single Ion Proton chip. These data allowed us to assess the relative contributions of sequencing depth and library content on the performance of CNV calling. In our validation using aCGH data, the addition of a second library improved the resolution of the GBS approach by enabling two additional aCGHconfirmed homozygous deletions and one additional hemizygous deletion to be detected.
The cost of performing additional sequencing on libraries that are ready for sequencing is also lower than going through the whole library preparation and sequencing process. One advantage of this modularity is that results can be reassessed as soon as new data is generated, such that researchers can evaluate the needs for further sequencing in a stepwise manner and thus progress smoothly towards a final dataset that suits their needs. Although there comes a point where WGS will be cheaper than adding "modules" (different libraries or additional chips), we argue that this modularity allows for flexibility in adjusting to the needs of a particular application. Moreover, increased sequencing of a GBS library guarantees high coverage of particular positions in the genome, which is likely more useful in calling duplications and hemizygous deletions than low-depth WGS (e.
Conclusion Our study provides a proof of concept that GBS can be used to assess copy number variation in a set of fast neutron mutants. This method could be used to allow population-scale assessments in this and other collections of mutants, an endeavour that is cost-prohibitive using previous technologies. Improvements and adjustments to the GBS workflow described here could also be made to enable the use of standard GBS data to assess CNVs in elite cultivars or advanced lines. This would be especially powerful, as it might enable researchers to revisit the tremendous amount of already-existing GBS data and extract the CNV information that remains undiscovered.
Array comparative genomic hybridization Array CGH was performed at the University of Minnesota for 19 of the mutant DNA samples using a 940 K CGH array developed for soybean (Agilent Technologies, Santa Clara, CA, USA). The detailed information about this array platform can be found in accession number GPL22907 in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) (http://www. ncbi. nlm. nih. gov/geo). For each of the 19 hybridizations, the DNA of the fast neutron line to be assessed was labeled with Cy3 whereas control (wildtype M92-220) DNA was labeled with Cy5. Following hybridization, arrays were read with a scanner and fluorescence values were used to compute the log 2 of the Cy3/Cy5 fluorescence ratio, which we will refer to as the aCGH log 2 ratio. The methods used to scan and compute the log 2 ratios were performed as previously described  [18].
Calling copy number variation using aCGH data The aCGH data output provided log 2 ratio profiles that could readily be used for copy number variation calling. Homozygous deletions were detected by segmenting the log 2 ratio profiles using a robust segmentation algorithm implemented in the R package robseg  [32]  (additional details in Additional file 1). A segment was labeled as a deletion if its mean log 2 ratio was below -3 as this value (representing a ratio of 1/8) was heuristically found to accurately separate true positives from false positives. Contiguous deletions that had been partitioned in segments with different mean log 2 ratios were merged and considered as a single deletion. The span of a homozygous deletion was defined as extending from the 3′ end of the first probe supporting the deletion to the 5′ end of the last probe supporting it.
Homozygous deletion calls were filtered out if the event was located in regions of naturally occurring (i. e. not fast neutron-induced) heterogeneity among the M92-220 seedstock originally subjected to mutagenesis (see Additional file 1). Homozygous deletion calls supported by less than three probes were also filtered out as they potentially represented false positives and were not expected to be detected by the GBS approach in any case. Hemizygous deletions and duplications proved more challenging to call computationally from aCGH data due to their less extreme log 2 ratio values. Indeed, we found it hard to find a unique set of parameters that would yield results that matched what could be observed from the log 2 ratio profiles, so we called them visually from the log 2 ratio profiles instead.
In the context of this study, the term "duplication" refers to any increase in copy number. Calling copy number variation using GBS data CNV calling from GBS data was more challenging as the sequencing data could not be readily converted to log 2 ratio-like values. As a consequence, we devised a custom analysis pipeline for calling CNVs from GBS data. Raw sequences were processed using the Fast-GBS pipeline  [33] , which includes the following steps: demultiplexing with Sabre  [34] , adapter trimming with Cutadapt  [35] , alignment to the reference genome using BWA  [36] , and finally SNP-and indel-calling using Platypus  [37]. At the alignment step, we used a composite reference genome combining the version 2 assembly of the Glycine max genome (Gmax_275_v2. 0) [38, 39]  and the mitochondrial and chloroplastic DNA sequences retrieved in August 2017 from SoyBase  [40].
We included the mitochondrial and chloroplastic reference sequences in order to avoid the numerous reads originating from the organellar genomes (10. 5 and 4. 0% of the ~130 M mapped reads aligned to the chloroplastic and mitochondrial genomes, respectively) from aligning to potential homologous sites in the nuclear genome. CNV calling from GBS data relied entirely on the position and count of reads aligned to the nuclear reference genome. SNP calls were not used for CNV calling per se but were useful in revealing 11 putative mutant lines that showed signs of contamination by other germplasm. The 11 "contaminated" fast neutron lines (see Additional file 1: Figure  S12 ) were not considered further in this study as their CNV calls might be due to naturally occurring variation rather than fast neutron irradiation.
The minimum mapping quality was set to 20 for calling homozygous deletions and duplications, and to 35 for calling hemizygous deletions (see Additional file 1 for details on how and which parameters were optimized for different categories of CNVs). Three filtering steps are then applied to reduce the dataset to a set of bins deemed informative for the purposes of CNV calling. The first filtering step consists in removing bins that have a mean count of reads per individual > 150, so as to remove bins that present exceptionally high read counts, possibly due to paralogous loci or organellar sequences that would have mapped to the nuclear genome despite our precautions. The second filtering step consists in removing bins with a mean read count per individual < 7 (< 8 for hemizygous deletions), as these bins do not provide enough power for discriminating true variation in copy number from random variation.
The third and last step consists in removing bins with a very high variance in the mean read count: when the ratio of the variance of the number of reads per individual to the mean number of reads per individual is > 3, we eliminate these as the high variance in these bins might lead to spurious CNV calls. In order to translate the read count data into information that could be used for CNV calling, we computed a log 2 ratio value analogous to the one obtained from aCGH for every informative bin of every individual. This log 2 ratio was computed by dividing the number of reads (+ 1 to avoid undefined logarithms) observed for an individual in a given bin by the overall mean number of reads (+ 1) observed per individual in this bin, and then computing the base 2 logarithm of this ratio. Because this approach compares relative read depth across samples rather than to the reference, any structural differences between the reference genome (Williams 82) and the M92-220 genome will have a minimal impact on CNV calling, with the exception of translocations which may cause regions that are contiguous in Williams 82 to be far apart in M92-220 or vice-versa.
Validation of the GBS approach using aCGH data We assessed the performance of the GBS approach by comparing the set of events found by GBS and aCGH in the 19 fast neutron individuals for which aCGH data was available. For this purpose, any GBS event that overlapped with an event of the same type in the aCGH dataset was considered a true positive, whereas any event detected by GBS but not by aCGH was considered a false positive. Events that were found by aCGH but not by GBS were simply considered undetected by GBS. Although aCGH has its own disadvantages and issues (such as inconsistent hybridization efficiencies across probes and possible cross-hybridization among paralogous sequences), it was taken here as a gold standard given that its resolution was expected to be much higher than that of GBS, such that any event that is detectable by GBS should also be detected by aCGH.
Validation of the GBS approach using PCR and ddPCR In order to provide an independent assessment of the accuracy of CNV calls near the detection threshold, we selected the smallest four duplications, eight hemizygous deletions, and 14 homozygous deletions among the set of lines that were assessed by GBS but not by aCGH and assessed them with PCR and/or ddPCR assays; the number of events selected from each type reflected the relative frequency of these types of events in our dataset. In addition, we sought to validate two homozygous deletions which were each found in two replicates of a control line (M92-220-Long R6-1 and M92-220-Long R6-2). Up to two primer pairs targeting each event were designed using an in-house pipeline which combined Pri-mer3  [41]  for primer design and Exonerate  [42]  for specificity checking (see Additional files 1, 4, 5, 6, 7 and 8 for detailed methods and results related to this section).
Analysis of the number of genes affected To estimate the proportion of gene disruptions that are found by GBS among those identified by aCGH, we performed a simple analysis to compute the number of genes that are affected (by any type of CNV) for each event. Gene model coordinates for the genome assembly version 2 of soybean were retrieved from SoyBase  [40]  in January 2019. We then simply computed the number of gene models overlapping with each of the lesions and tallied them by type and according to whether or not they were found by the GBS dataset generated from the use of the combined libraries. A given gene model was allowed to be counted more than once if it was affected independently by CNV events occurring in different lines.
Software used and implementation Unless otherwise stated, all analyses were performed in R version 3. 4. 3 or 3. 5. 1  [43]. Bioconductor packages Rsamtools  [44]  and GenomicAlignments  [45]  were used for reading and manipulating the aligned reads. The R package that we developed for implementing the CNVcalling pipeline described here, named delgbs, is publicly available on GitHub (github. com/malemay/delgbs). Starting from sorted and indexed. bam files, the segmentation and CNV calling of the data generated from the first GBS library took 10 min to run on a single CPU and required 2 Gb of RAM, and it thus amenable to run on any personal computer. mutant; mutant_n: the copy number of the templates amplified by primer_pair in the mutant as determined by dividing mutant_conc by mutant_ref; control_conc: the concentration (in number of DNA templates per μl) measured in the control for that assay; control_ref: the concentration (in number of DNA templates per μl) measured using reference primer pair BY in the control; control_n: the copy number of the templates amplified by primer_pair in the control as determined by dividing control_conc by control_ref; ratio: the ratio of the copy number of the mutant to the copy number of the control for the templates amplified by primer_pair.
Acknowledgments The authors would like to thank  Gaétan Légaré  for his contribution to generating the sequencing data,  Chloé Dussault-Benoit  for help with the PCR assays, as well as  Sébastien Bélanger ,  Martine Jean  and  Marie-Laure Martin-Magniette  for valuable comments regarding the methodology. The authors are grateful to  Jeff Roessler  for growing the soybean fast neutron plants and tissue collection, and to  Jean-Michel Michno  for helping to upload the data to GEO and SRA.
Smoking is associated with shorter leucocyte telomere length (LTL), a biomarker of increased morbidity and reduced longevity. This association is widely interpreted as evidence that smoking causes accelerated LTL attrition in adulthood, but the evidence for this is inconsistent. We analysed the association between smoking and LTL dynamics in 18 longitudinal cohorts. The dataset included data from 12 579 adults (4678 current smokers and 7901 non-smokers) over a mean follow-up interval of 8. 6 years. Meta-analysis confirmed a cross-sectional difference in LTL between smokers and non-smokers, with mean LTL 84. 61 bp shorter in smokers (95% CI: 22. 62 to 146. 61). However, LTL attrition was only 0. 51 bp yr 21 faster in smokers than in non-smokers (95% CI: 22. 09 to 1. 08), a difference that equates to only 1.
Here we present the first quantitative meta-analysis aimed at estimating the effect of smoking on the rate of LTL attrition. This analysis was based on all published datasets that we were able to identify that contained relevant longitudinal measurements of LTL and information on smoking status, regardless of whether the association between smoking and LTL dynamics had been previously described. LTL c s = c n c s c n t 0 t 0 t 0 c s c n m s > m n m s > m n m s = m n baseline follow-up years LTL LTL (a) ( b) ( c).
Search strategy and data eligibility requirements We sought longitudinal cohort studies in which baseline and follow-up measurements of LTL were obtained from each participant and in which data on smoking behaviour were also available. To allow sufficient time for biologically meaningful changes in LTL to be observed, we required a followup interval of at least 4 years  [27]. Electronic supplementary material, figure  S1  provides a PRISMA diagram summarizing how the data were obtained. We performed a systematic literature search using ISI Web of Knowledge (Thomson Scientific Technical Support, New York) for articles published prior to 2017 using the search terms: 'telomere*' and 'longitudinal' and 'smoking'. Additional relevant articles were identified by snowballing  [28]. Articles were screened to identify potentially eligible cohorts.
In many cases, determining the effect of smoking on LTL attrition was not the primary aim of an article, but smoking status was mentioned among the list of control variables. In articles that reported estimates of the association between smoking and LTL attrition, the latter estimates usually came from multiple regression models that had been adjusted for baseline LTL. This practice is nearly universal due to the strong association present in most datasets between baseline LTL and rate of LTL attrition arising largely as a consequence of regression to the mean  [29]. We have demonstrated that such adjusted estimates are likely to be biased in a direction that exaggerates the true effect of smoking on LTL attrition  [30]. Therefore, we sought unadjusted estimates of the difference in LTL dynamics between smokers and non-smokers.
For nine cohorts there were some published findings available relating to the effect of smoking on LTL attrition rate (for a narrative review see  [17] ), but for the other nine cohorts the first author was blind to the effect of smoking at the time of requesting the data (see table  1  for details). The following data were obtained for each cohort included in the meta-analysis: the number of participants (smokers and non-smokers), mean age at baseline measurement (in years), mean followup interval (in years), LTL measurement method (TRF or qPCR), LTL measurement units (base pairs or T/S ratios), mean LTL at baseline and follow-up for smokers and non-smokers, standard deviation of LTL length at baseline and follow-up for smokers and non-smokers, annual rate of LTL attrition for smokers and non-smokers, standard deviation of LTL attrition per year for smokers and non-smokers, and the Pearson correlation between LTL at baseline and follow-up measurements.
Participants were only included if they had LTL data at both baseline and follow-up; participants lost to follow-up were excluded. In cohorts in which LTL was measured at more than two time-points (LBC1921 and LBC1936) we only used the two LTL measurements that gave the longest follow-up interval for each participant, designating the first as baseline and the second as follow-up. LTL attrition rate for a participant was calculated via the following formula: LTL attrition rate (bp yr 21 ) ¼ (LTL baseline 2 LTL follow-up )/follow-up years. We sought to only include data from participants who were either consistent never smokers or consistent current smokers at baseline and follow-up; where possible, we excluded participants who had quit smoking prior to baseline or between baseline and follow-up.
We did not attempt to explore the effects of amount smoked, since consistent data were not available for all cohorts. For the majority of cohorts (15/18), smokers were defined as consistent current smokers at both baseline and follow-up and non-smokers were defined as consistent never-smokers at baseline and follow-up; individuals who changed smoking status between baseline and follow-up were excluded. However, for one cohort (BRUNECK), smokers and non-smokers were defined based on baseline status only, and for the two cohorts, for which data were extracted from published papers (BHS and ESTHER), it was not clear whether the smoker/non-smoker classification was based on status at baseline, follow-up or both.
Statistical analysis We analysed the data in the statistical programming language R, v. 3. 3. 3 [37] , using the meta-analysis package 'metafor'  [38]. Since some cohorts reported LTL measurements in T/S ratios and others in base pairs, we computed standardized mean differences (SMDs) between smokers and non-smokers for LTL and LTL attrition. We used random-effects meta-analysis models throughout, because we deemed the cohorts too different to justify assuming that there are common true effect sizes to estimate  [39]. Unless otherwise stated, we estimated parameters using inverse-variance weighting of cohorts (the default) and REML. In one of our sensitivity analyses we instead weighted cohorts by the correlation between baseline and followup LTL measurements (r). The rationale for this decision is that r has been argued to be a good proxy for LTL measurement error  [27, 40] , an assumption supported by the observation that datasets measured with more precise TRF always have higher correlations than those measured with less precise qPCR (as shown by the values in electronic supplementary material, table  S1 ).
To address the fact that we had two LTL measurements from each cohort, and in some analyses we needed to either compare the difference in SMDs between time-points (table 2, model 4), or combine SMDs across time-points (table 2, model 5), we used the methods outlined in Borenstein et al. [39, chapter 24]  for computing effect sizes from complex data structures involving multiple time-points. To facilitate interpretation of the summary SMDs derived from the meta-analyses we transformed these effects into base pairs. To make these calculations, estimates of the standard deviations of either LTL or LTL attrition rate were required, as appropriate. We estimated these standard deviations from the four cohorts measured with TRF (ADE, BHS, ERA and JLRCS), because these cohorts were the only ones for which LTL was originally measured in base pairs.