Dataset Information

Graphkernels: R and Python packages for graph comparison.

ABSTRACT: Summary:Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C?++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. Availability and implementation:The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. Contact:mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch. Supplementary information:Supplementary data are available online at Bioinformatics.

SUBMITTER: Sugiyama M

PROVIDER: S-EPMC5860361 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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graphkernels: R and Python packages for graph comparison.

Sugiyama Mahito M Ghisu M Elisabetta ME Llinares-López Felipe F Borgwardt Karsten K

Bioinformatics (Oxford, England) 20180201 3

<h4>Summary</h4>Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfei ...[more]

PMID: 29028902

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Project description:The Genome Analysis Toolkit (GATK) is commonly used for variant calling of single nucleotide polymorphisms (SNPs) and small insertions and deletions (indels) from short-read sequencing data aligned against a reference genome. There have been a number of variant calling comparisons against GATK, but an equally comprehensive comparison for VarScan not yet been performed. More specifically, we compare (1) the effects of different pre-processing steps prior to variant calling with both GATK and VarScan, (2) VarScan variants called with increasingly conservative parameters, and (3) filtered and unfiltered GATK variant calls (for both the UnifiedGenotyper and the HaplotypeCaller). Variant calling was performed on three datasets (1 targeted exon dataset and 2 exome datasets), each with approximately a dozen subjects. In most cases, pre-processing steps (e.g., indel realignment and quality score base recalibration using GATK) had only a modest impact on the variant calls, but the importance of the pre-processing steps varied between datasets and variant callers. Based upon concordance statistics presented in this study, we recommend GATK users focus on "high-quality" GATK variants by filtering out variants flagged as low-quality. We also found that running VarScan with a conservative set of parameters (referred to as "VarScan-Cons") resulted in a reproducible list of variants, with high concordance (>97%) to high-quality variants called by the GATK UnifiedGenotyper and HaplotypeCaller. These conservative parameters result in decreased sensitivity, but the VarScan-Cons variant list could still recover 84-88% of the high-quality GATK SNPs in the exome datasets. This study also provides limited evidence that VarScan-Cons has a decreased false positive rate among novel variants (relative to high-quality GATK SNPs) and that the GATK HaplotypeCaller has an increased false positive rate for indels (relative to VarScan-Cons and high-quality GATK UnifiedGenotyper indels). More broadly, we believe the metrics used for comparison in this study can be useful in assessing the quality of variant calls in the context of a specific experimental design. As an example, a limited number of variant calling comparisons are also performed on two additional variant callers.

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Graphkernels: R and Python packages for graph comparison.

Publications

graphkernels: R and Python packages for graph comparison.

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