dataesr/scientific-paragraphs-categorization huggingface dataset:
Although methods have been developed to determine germline status by matching to public germline databases like dbSNP or sequence a large number of normal individuals to be surrogates for matched normal  [12] , such methods cannot adequately account for rare germline variants that are private to a family or small population. We present SGZ, a novel computational method for predicting the somatic vs. germline origin of variants discovered in cancer specimens (Fig 1 ) without the need for a matched normal sample. In this method, the cancer specimen is sequenced to high depth (>500x) using MPS, in our implementation by a targeted clinical assay of 394 cancer-related genes and over 3,500 genome-wide SNPs  [1]. SGZ leverages the precise measurement of the allele frequencies of variants of interest offered by deep sequencing and a statistical model of genome-wide copy number and tumor/normal admixture to characterize the mutational state of the variants.
First, an initial fit is assessed using the JAGS software package  [14] , a Gibbs sampling based Markov Chain Monte Carlo algorithm. Assuming a sample has 200 segments after segmentation, the total number of parameters is more than 400. Based on our pipeline design, there are around 10,000 observed SNPs and 50,000 observed median-normalized log-ratios. After checking the convergence of all parameters, the following key MCMC parameters are employed: sampling size at 500, burn-in size at 500, thinning Copy number detection overview. Aligned DNA sequences of the tumor specimen are normalized against a process-matched normal, producing log-ratio and minor allele frequency (MAF) data. Next, whole-genome segmentation is performed using a circular binary segmentation (CBS) algorithm on the log-ratio data.
Method validation datasets We validated SGZ in three different ways, including (1) specimens with matched normal where the true origin of all alterations was known, (2) cell-line admixtures that modeled the impact of varying tumor purity on the inference, and (3) a large set of clinical FFPE specimens with known somatic drivers where real-world somatic variant recovery was assessed. The first dataset consisted of 87 specimens from 30 non-small cell lung and colon cancer patients, wherein each patient we studied three samples: the primary tumor, a metastatic site, and adjacent tissue matched normal (S3 Table ). All DNA were extracted from fresh-frozen clinical specimen. The primary and metastatic tumors uniformly contained a mixture of malignant and benign epithelial, stromal and inflammatory cells.
To assess the robustness of the method to different levels of tumor purity, we examined three cancer cell lines (HCC-1937, HCC-1954, & NCI-H1395), which were titrated with their matched lymphoblastoid normal to six levels of tumor purity (10%, 20%, 30%, 40%, 50%, 75%). A total of 42 unique variants were detected by our pipeline and used for validation (S4 Table ). The third dataset is data from 20,182 clinical FFPE tissue samples sent to Foundation Medicine for FoundationOne testing. The samples were of a variety of tumor types, originating from a wide diversity of cancer centers and community oncology practices. To evaluate SGZ predictions of germline/somatic origin, we examined predictions at 17 known somatic hotspot mutations (e. g. BRAF V600, KRAS G12) and 20 common germline SNPs. To assess SGZ predictions of tumor zygosity, we selected the most frequently mutated somatic variants at oncogenes (BRAF, EGFR, IDH1, KRAS, NRAS, PIK3CA) and tumor suppressor genes (TP53, RB1, PTEN) for analysis.
Method validation results To demonstrate the importance of taking into account the genome-wide copy number profile for somatic/germline prediction, we applied SGZ to the three validation datasets and compared SGZ to a method that does not take tumor aneuploidy into account (referred to as "basic method"), in which a variant is classified as germline if its mutation frequency is near 50% or 100%, or otherwise is classified as somatic  [21]  (S1 Method). SGZ yielded somatic vs. germline calls for 85% of variants in the lung and colon samples, 83% of variants in the three cell lines admixtures, and 84% in the 17 somatic hotspot mutations and 20 common germline variants in the 20,182 Foundation Medicine clinical samples. Among these calls, 95%, 97% and 96% of the somatic mutations were predicted correctly, respectively; 99%, 97%, and 97% of the germline mutations were predicted correctly, respectively.
On the contrary, the basic method was able to make predictions for 100% of the variants in the three datasets, but only predicted somatic variants correctly 67%, 92% and 95% of time, and germline variants correctly 87%, 41% and 51% of the time, which are significantly lower than the accuracy of SGZ. Importantly, in none of the three datasets did the basic method achieve satisfactory performance in both germline mutations and somatic mutations simultaneously (Table  1 , S1 Table ). In the cell line dataset, out of a total number of 184 short variants that are correctly classified by SGZ, 63 short variants are incorrectly classified by the basic method due to local copy number deviation from 2 and/or zygosity deviation from the heterozygous state, strongly suggesting the necessity to take copy number variation into account in order to make accurate predictions (S5  To characterize the performance of SGZ as a function of tumor purity, we captured the call rate and prediction accuracy of SGZ in each tumor purity level in the cell-line dataset (Table  2 ).
Overall, the call rate is between 75% to 94%, and the prediction accuracy ranges from 88% to 100%. As expected, the call rate at 10% tumor purity is the highest among all dilution levels, due to the large difference between expected germline and somatic AF (S1 Fig). Though not available in this dataset, it is expected that call rate would rapidly drop to 0% as the tumor purity exceeds 90% due to much smaller differences between somatic and germline variant AF expectations. For germline and somatic prediction accuracy, a high level of accuracy is maintained from 10% through 75% tumor purity. It is also expected that the prediction accuracy would drop as tumor content exceeds 90%. To assess SGZ predictions of tumor zygosity, we examined data from the most frequently mutated somatic variants at oncogenes (BRAF, EGFR, IDH1, KRAS, NRAS, PIK3CA) and tumor suppressor genes (TP53, RB1, PTEN) in the Foundation Medicine clinical sample set.
Alterations in oncogenes are expected to be mostly heterozygous, as a single mutation is required for activation, whereas the tumor suppressor genes are expected to have the first functional copy inactivated via mutation, and the second inactivated through loss-of-heterozygosity (LOH)  [22]. Our predictions of tumor zygosity are concordant with the roles these genes play: TP53 and RB1 were to determined have >90% of mutations under LOH, while BRAF V600 and KRAS G12 mutations showed no significant enrichment for LOH (Table  3 ). To assess our ability to detect subclonal mutations, we examined EGFR T790M in the nonsmall cell lung carcinoma subset of our dataset, where the mutation would be expected to occur in tyrosine kinase inhibitor resistant subclones. Indeed, we discovered a significant enrichment of subclonal somatic vs.
Cancer database application Despite best efforts of cancer investigators leveraging matched normal controls, germline variants may erroneously get nominated and recorded as somatic mutations in the literature and public catalogues of somatic variation, due to the challenges inherent in large scale sequencing studies and MPS data analysis. These variants may divert scarce resources needed for functional follow-up or potentially mislead therapeutic choice if pursued clinically. It would thus be beneficial if these false somatic variants could be collectively flagged and potential interpretation and application corrected. To discover mutations that may be misclassified as somatic in a public database, we applied the SGZ method to the 20,182 clinical specimens to identify variants predicted to be germline but annotated in COSMIC (v62) as somatic.
We used conservative parameters e G = 0. 05 and e S = 0. 10, which are higher than the error rates from Fig 2A. P(G|n G , n S ) can be readily obtained as 1 -P(S|n G , n S ). Table  4  shows the top 10 variants present in COSMIC, but strongly predicted by our method as germline. Each variant was predicted germline in at least 45 samples. Although 9 of 10 variants were annotated as confirmed somatic, the number of entries in the database were all low ( 4), reinforcing that the somatic annotation is likely inaccurate. Further evidence of germline origin was that most variants had an entry in dbSNP, though few were classified as common SNPs. The full list of seventy COSMIC variants predicted to be germline is given in S2 Table.
This information can be used to annotate variants for which somatic/germline status is unknown or in doubt. When applied over a large cohort of samples, SGZ can aid in the discovery of novel recurrent somatic mutations, along with their clonality and zygosity status  [23, 24]. Conversely, SGZ can also identify germline variants not yet catalogued in public databases such as dbSNP and flag them from further consideration as cancer drivers. In this report, we describe the computational approach, which may be implemented on any cancer deep sequencing platform with copy number modeling support and provide both the methodology and a detailed worksheet to ease implementation (S1 Fig). We also apply the method to generate a proposed re-annotation of a large number of variants currently believed to be somatic, in the hope of improving the reliability of publicly availably cancer information.
706], were obtained for the lower and upper DM particle mass bounds respectively (the upper index 'cr' stands for the critical configuration). 6. For a relatively low compact core as the one calculated for mc 2 = 10. 4 keV, we have λ B = 3. 1 l c ; while in the most compact one (black solid curve in fig. 3  for mc 2 = 345 keV) we have λ B = 4. 0 l c. This justifies our use of the quantum fermion statistical treatment. 7. The DM contribution to the Galactic halo rotation curves becomes necessary above ∼ 7 kpc (see  figs. 4 and 5). This is in agreement with the DM modelindependent observational analysis by  Iocco et al. (2015). 8. Interestingly, in the mass range mc 2 = 10 -345 keV the RAR DM distribution predicts Keplerian rotation curves at r 2 pc (see fig. 4 ). This feature is in agreement with the apparent Keplerian trend observed in the innermost gas data points in  Sofue (2013) , with the caveat that the tracers in the region 1 -100 pc might not follow the gravitational potential due to the more complex bar pattern speed present in the Milky Way central region (see also the discussion section in Sofue (2013)).
Methods Whole exome sequencing  (cases 1, 4, 5, 9, 10, 12, 13, 14)  WES was undertaken in 24 previously undiagnosed UK/French eye anomaly patients (12 males and 12 females). Case 4 had exome sequencing because of the association of developmental delay, dysmorphic features and posterior embryotoxon. Exome capture was performed using the Nimblegen V3 Enrichment kit following the manufacturer's protocol. The captured libraries were sequenced with an Illumina HiSeq2000 with 100-bp paired-end reads. We used PolyPhen-2, SIFT and Mutation Taster software tools to predict the functional effects of variants  (Adzhubei, Schmidt, et al, 2010; Ng, Hennekoff et al, 2003; Schwarz, Cooper, et al, 2014). This strategy allowed the identification of BCOR variants in cases 1, 5 and 9. Case 12 had similar WES, but not captured and instead using an Illumina HiSeq2500 and 125-bp paired end sequencing.
Sanger sequencing All pathogenic BCOR variants retained after filtering from the whole exome or targeted resequencing data were confirmed by Sanger sequencing. Parental studies were performed to determine whether these variants were inherited or appeared de novo. Direct BCOR (NM_001123385. 1, 1755 aa) sequencing was performed in the other female cases  (3, 6, 7, 8, 11, 13, 14, 15)  and one male case (2) because of suggestive features and to confirm WES findings. Case 16 had a copy number variant detected by array Comparative Genomic Hybridisation (CGH) (Agilent 60k ISCA) and confirmed by qPCR.
While his array CGH was normal, WES revealed the variant c. 4807A>C; p. (Ser1603Arg) in BCOR, inherited from his asymptomatic mother. This variant involves a highly conserved amino acid, is absent from the general population (gnomAD database)  (Lek, Karczewski, et al, 2016)  and is predicted to be deleterious by in silico software Polyphen-2, SIFT and Mutation Taster.
Background: Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems and/or purposes and (ii) adaptation to different environments. As a result, approximately 600 goat breeds have developed worldwide; they differ considerably from one another in terms of phenotypic characteristics and are adapted to a wide range of climatic conditions. In this work, we analyzed the AdaptMap goat dataset, which is composed of data from more than 3000 animals collected worldwide and genotyped with the CaprineSNP50 BeadChip. These animals were partitioned into groups based on geographical area, production uses, available records on solid coat color and environmental variables including the sampling geographical coordinates, to investigate the role of natural and/or artificial selection in shaping the genome of goat breeds.
With this new version, gene annotation has improved considerably and the position of the SNPs on the CaprineSNP50 BeadChip has been updated. With the availability of genomic and highthroughput SNP tools, there is an increased interest in identifying and exploring signatures of selection and the genomic diversity resulting from adaptation to environment and human selective pressure. These genomic tools were used to identify signatures of selection in circumscribed datasets. The CaprineSNP50 BeadChip was used to investigate and compare several Swiss goat breeds and genomic signatures of selection were detected in regions that affect variation in coat color, growth, and milk composition  [11]. The combination of analyses of runs of homozygosity (ROH), F ST (fixation index), XP-EHH (cross population extended haplotype homozygosity), and the use of Bayesian methods allowed the detection of signatures of selection in a region that contains genes related to the immune system in another mountain breed raised in the North of Italy, the Valdostana Italian goat breed  [12].
Genomic changes that result from climate changes and are linked to adaptation to different environments can be analyzed with a landscape genomic approach, which was successfully applied in other livestock species, for example in Ugandan cattle  [18]. To date in goats, this approach was applied only on a reduced number of SNPs or amplified fragment length polymorphism (AFLP) markers  [19, 20]. However, it would be interesting to apply it at the genomic level since it provides useful information on the environmental factors that shaped the genome. In this work, we used different approaches to identify regions under artificial and environmental selection across the AdaptMap goat dataset, which is composed of data from more than 3000 animals that were collected worldwide and genotyped with the CaprineSNP50 Bead-Chip.
For this purpose, we considered several groups of animals that were partitioned based on geographical area, as done in Colli et al. [21] , production uses, available records on solid coat color and environmental variables in relation to the geographical coordinates of sampling. For the first time, we applied FLK and hapFLK analyses on goat data, which have been successfully used in analyses of high-throughput data from sheep  [22, 23]. These methods increase the power of detection for signatures of selection, and they enable detection of soft or incomplete selective sweeps. Finally, we also applied landscape genomic approaches to this large goat dataset, to investigate the role of natural selection in shaping the goat genome.
Methods Prior to the application of methods for detecting signatures of selection, we applied several filtering steps to the AdaptMap goat dataset, which originally contained 4563 animals from 144 breeds that were collected worldwide and genotyped with the CaprineSNP50 Bead-Chip. This dataset was first edited by removing mixed breeds, related animals, SNPs that were monomorphic, unmapped, mapped to sex chromosomes or with low call rate, which resulted in a working dataset of 46,654 SNPs and 3197 animals  [21]. Additional filtering steps were applied depending on the analysis performed, as described below. The main analyses are summarized as follows: (1) detection of signatures of selection based on the genetic diversity of subcontinental populations or breeds, and (2) detection of signatures of selection associated with specific phenotypes, traits or external variables (e.
Signatures between and within subcontinental groups To detect signatures of major differentiation between populations, we used the FLK  [25]  and hapFLK  [26]  approaches. Briefly, these methods account for population structure and differences in effective population size by modelling the genetic divergence between populations as derived from drift and population division. Because these methods are not completely robust to strong bottlenecks and large admixture events, subsets of populations were selected starting from the working dataset, removing admixed animals and strongly inbred populations. This was done first by identifying genetic sub-structure in the initial diversity analysis which defined sub-continental groups  [21]. Then, an admixture analysis was carried out to identify and remove admixed populations within each sub-continental group, which generated sub-continental filtered groups.
This last step was performed using the Treemix software  [27] , allowing for up to three migration events within each group. Graphical representations of the results of all Treemix analyses are in Additional file 1. The number of animals included in each sub-continental group is in   Following filtering of the data, FLK and hapFLK analyses were carried out on each of the sub-continental filtered groups, using the wild ancestor of domestic goat (Bezoar, Capra aegagrus) as an outgroup to root population trees. For hapFLK, the cross-validation procedure was performed with the fastPHASE software  [28] , which determined that 30 haplotype clusters were needed to capture haplotype diversity. For both hapFLK and FLK analyses, p-values were computed as explained in the hapFLK software documentation.
False discovery rates (FDR) were estimated using the qvalue R package  [29]  and SNPs corresponding to an FDR of 0. 15 or less were considered significant. A genome scan for signatures of selection between sub-continental groups was also performed using FLK analysis as described by Fariello et al. [22]. Briefly, the frequency of the ancestral allele of each group was estimated from the within-group analysis. These ancestral alleles were then used to perform a new genome scan using FLK analysis. Frequencies of Bezoar alleles were used to root the population tree. Only the groups that corresponded to clear geographical clustering of goat populations were considered for this ancestral analysis, i. e. the Southwestern, Southeastern, Northern and Alpine European groups, the Central Asian group and the Northwestern, Easter and Southern African groups.
Signatures of selection within breeds The above-mentioned filtered dataset that was used for FLK and hapFLK analyses was also used to detect signatures of selection within the breeds using: (i) ROH and (ii) iHS statistic  [30]. ROH analyses were performed using the Zanardi software  [31]  by considering a minimum of 15 SNPs per ROH, a minimum ROH length of 1 Mb and allowing for one heterozygous SNP within an ROH to account for the possibility of genotyping errors. For each SNP, the proportion of animals that displayed a homozygous region at that SNP was calculated. Then, this measure was transformed by its empirical quantile for all SNPs across the genome (i. e. the proportion of all SNPs with a higher or equal proportion of homozygous animals in the breed considered). The iHS analyses were performed in populations with at least 10 genotyped individuals.
Within each population, phasing of individuals was performed using SHAPEIT2  [32]. For SNPs with a minor allele frequency higher than 0. 05, the ancestral alleles were randomly assigned. The iHS statistics were calculated using the rehh v2. 0 R package  [33]. Standardized iHS values were computed in allele frequency bins of 0. 05 and then further corrected using robust estimations of their mean and variance using the rlm function from the MASS R package  [34]. Because assessing significance of ROH and iHS values is less robust, ROH and iHS signals were reported only for significant FLK or hapFLK signatures of selection.
External phenotypes and production traits Reduced subsets of the dataset were investigated to detect genomic regions associated with specific phenotypes. Reductions were performed according to the availability of the information for each animal or breed included in the dataset. Therefore, two reduced panels were created corresponding to differences related to production purposes and phenotypes (solid coat colors). Panel 1: production purposes A questionnaire that contained four possible production assignments (milk, meat, fiber, and leather) was circulated to all AdaptMap members who provided the samples, to obtain information on the main purpose of each breed. Pre-filtering was performed by considering only breeds with known and prevalent purposes. Since none of the breeds was described as being solely raised for leather production, this purpose was excluded from subsequent analyses.
Then, another filtering step was performed on the working dataset to remove breeds that are raised for more than one purpose by using an in-house script that discarded animals belonging to different purpose groups that shared the same coordinates of the first component ± 1. 5 of the overall quantile distribution (ee Additional file 2: Figure  S1 ). The final filtered dataset included 192 animals (three breeds) for fiber, 241 animals (12 breeds) for meat and 818 animals (23 breeds) for milk. Details on breeds and sample sizes are in Table  2a. Three types of analyses were carried out on the final filtered dataset: ROH, F ST and XP-EHH. ROH analyses were performed for each separate purpose group using the Zanardi software  [31]  by considering a minimum of 15 SNPs per ROH, a minimum ROH length of 1 Mb and allowing for one heterozygous SNP within an ROH to account for the possibility of genotyping errors, as previously mentioned.
The F ST and XP-EHH analyses were carried out by comparing each group against all the others and using the script described by Talenti et al. [12]  in which 1-Mb windows with an overlap of 500 kb were considered for window-based F ST and the Selscan software  [35]  for XP-EHH. The results were normalized with the norm normalization tool included in the software suite. For all three analyses (ROH, window-based F ST and XP-EHH), the top 0. 5% of SNPs based on marker or window distribution was retained as relevant. The Bedtools software  [24]  was used to find consensus regions between two or all three approaches. Only regions detected by at least two approaches were considered for further analyses. Panel 2: solid coat colors Pictures representative of the breeds for each animal of the working dataset were provided by the AdaptMap members and were inspected to find common and unique patterns across each breed.