ACG, MetaSV and hard cutoff method in NA12878 and HG002.
Besides, we evaluated the performance of our approach in data generated from multiple sequencing platforms. With precision of 91.6–96.8% and recall rates of 84.1–94% (Fig. 5), CNV-P showed similar performance on sequencing data generated by BGISEQ-500. Moreover, in addition to the trained classifiers for the above five software, we provided extra modules in our approach for training and predicting if CNVs were detected by other tools. These results suggest that our approach is suitable for CNVs generated from multiple sequencing platforms and detecting software.
Figure 5: Performance of CNV-P on different sequencing platform.
The precision and recall rate of CNV-P for sample NA12878 using sequencing data generated from BGISEQ-500 and Illumina. (A) The raw precision results; (B) Precision rate; (C) Recall rate.
Detecting CNVs from WGS is error-prone because of short-length reads and library-property-dependent bias (Kosugi et al., 2019). Inflated false positive makes it a big challenge for researchers to identify clinically relevant CNVs, as it is time- and money-consuming to validate a large amount of false positive CNVs. To solve this problem, we develop CNV-P, an effective machine-learning-based framework to acquire high-confident CNVs. Instead of handling the shortcomings of existing methods by developing another detecting algorithm, CNV-P focuses on providing a reliable set of CNVs from existing detection software. We demonstrate that CNV-P can identify a set of high-confidence CNVs with high precision and recall rates. Moreover, CNV-P is robust to the proportion of variants in training sets, size of CNVs and sequencing platforms, indicating the utility of CNV-P in a variety of clinical or research contexts.
Comparing with the conventional method of using hard cutoff, such as a minimum number of supporting reads, to filter CNV results, CNV-P greatly reduces errors caused by lack of expertise and subjective assumptions. Instead of running default multiple software in advance, CNV-P can make accurate predictions for each tool dependently. In addition to the five commonly used software that we have trained prediction models, we provide an extra module in CNV-P including the function of model training and predicting if CNVs are detected by other tools.
However, our models may have weaker power for large-size CNVs, because there are only a small number of large fragments in our training data. Besides of data from healthy individuals, we believe that great improvement could be made to identify large-size true CNVs in the future when more datasets are accumulated.
CNV-P is a well-performed machine-learning framework for accurately filtering CNVs. CNV-P framework can be applied on CNVs from various detection methods and sequencing platforms, making our framework easy to adopt and customize. CNV-P greatly helps to generate a set of high-confident CNVs, benefiting both basic research and clinical diagnosis of genetic diseases.
Gold-standard CNVs data sets using by CNV-P.
Data sets for CNV-P.
Training features for CNV-P.
The authors thank Dr. Jian Guo for constructive comments on this project and Chen Ye for data download and management.
Wen-Jing Wang conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft.
The data is available in the Supplemental Materials and CNV-P is available at GitHub: https://github.com/wonderful1/CNV-P.
This project was supported by the National Key Research and Development Program of China (No.2018YFC1004900), the National Natural Science Foundation of China (No.81300075), the Science, Technology and Innovation Commission of Shenzhen Municipality under grant (No.JCYJ20170412152854656, JCYJ20180703093402288). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Holtgrewe M. 2010. Mason@ a read simulator for second generation sequencing data.
Li H. 2011. wgsim-Read simulator for next generation sequencing. GitHub.
Another typical carnival is held in Fornesighe, picturesque and tiny hamlet of Forno di Zoldo. Main mask of this carnival is the Gnaga (an old lady who brings in the basket the child now grown up). The old lady is obviously tired by the age, while his son is smiling. One interpretation of this unusual character is a good omen for the spring, the carnival obviously takes place between the end of winter and beginning of spring, rich and flourishing (the younger) after a hard and difficult winter (the old lady). It is also a reversal of the typical sequence of events, an aspect present in many carnival representations (in this situation the natural passage of charges and duties from an old mother to an adult son). The Gnaga isn’t a mask with an ancient tradition. It was born in 1897 thanks to Valentino Toldo, emigrant in the Switzerland Romansh, which probably took its cue from the typical characters of the area to create this character. Initially the mask wasn’t designed as you see it today, but it was the same Valentino Toldo to dress up as a woman and to bring in the parade the son.
Beyond the Gnaga in the Fornesighe
carnival there is a Matazin that opens the procession, mask of Iberian
origin that prevailed in Italy and in Europe, with colorful dress and
the traditional parade of wooden faces which is also linked to a
competition. Another unique and quite innovative aspect (even if they
are present for several years) is the presence in the streets of the
village and at the typical wooden rustic almost all uninhabited of
authentic iron sculptures representing people involved in ancient
activities and crafts. This idea was born to give a strong human
presence in these corners of the country now sparsely inhabited. Other
typical Zoldo masks, however now abandoned, were:
- Al Compare: black suit with bowler hat and cane privet.
- Al Coco: character with meta-wool suit, white face made from sheep's skin to make it look old, at who the people gave a donation (often eggs, from which the name was born).
- La Comara: the traditional Zoldo dress;
- Al Nuiz: chosen amongst the conscripts, dressed in black and wore a top hat.
- La Nuiza: often played by a male chosen for its feminine features.
- L’Ampezzana: added later was a woman with traditional ampezzan dress provided by a woman moved by marriage to the Valle d'Ampezzo in Zoldo, gave a touch of originality through an unusual dress.
The carnival was also enriched by the Sonador who played for free. The conscripts, however, remained in a house designated to prepare lunch, after having provided for the preparation of breakfast and having dressed the conscripts.
Return to Carnivals page.// Copyright takubokudori.
// This source code is licensed under the MIT or Apache-2.0 license.
//! An example of parsing the outputs of cmd.exe.
#[cfg(feature = "std")]
use std::process::Command;
#[cfg(feature = "std")]
use windy::AString;

#[cfg(feature = "std")]
fn main() {
    let o = Command::new("cmd")
       .args(&["/c", "ThisCommandDoesNotExist"])
       .output()
       .unwrap();
    let (stdout, stderr) = unsafe {
        (
            AString::new_unchecked(o.stdout),
            AString::new_unchecked(o.stderr),
        )
    };
    println!("stdout: {:?}", stdout);
    println!("stderr: {:?}", stderr);
}

#[cfg(not(feature = "std"))]
fn main() {
    panic!("Use std feature!");
}
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Sponsored AdEDMONTON, ALBERTA — Every drummer is completely certain that someone else will be providing the drum kit for tonight’s basement show. Despite a complete lack of communication on the matter between him and any of the four drummers scheduled to play tonight, Shawn Ruthless, the show’s promoter, confirmed, “It will get figured out.”
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The reoccurring reasons given for the