Efficient de novo assembly of highly heterozygous genomes from whole-genome shotgun short reads.

Although many de novo genome assembly projects have recently been conducted using high-throughput sequencers, assembling highly heterozygous diploid genomes is a substantial challenge due to the increased complexity of the de Bruijn graph structure predominantly used. To address the increasing demand for sequencing of nonmodel and/or wild-type samples, in most cases inbred lines or fosmid-based hierarchical sequencing methods are used to overcome such problems. However, these methods are costly and time consuming, forfeiting the advantages of massive parallel sequencing. Here, we describe a novel de novo assembler, Platanus, that can effectively manage high-throughput data from heterozygous samples. Platanus assembles DNA fragments (reads) into contigs by constructing de Bruijn graphs with automatically optimized k-mer sizes followed by the scaffolding of contigs based on paired-end information. The complicated graph structures that result from the heterozygosity are simplified during not only the contig assembly step but also the scaffolding step. We evaluated the assembly results on eukaryotic samples with various levels of heterozygosity. Compared with other assemblers, Platanus yields assembly results that have a larger scaffold NG50 length without any accompanying loss of accuracy in both simulated and real data. In addition, Platanus recorded the largest scaffold NG50 values for two of the three low-heterozygosity species used in the de novo assembly contest, Assemblathon 2. Platanus therefore provides a novel and efficient approach for the assembly of gigabase-sized highly heterozygous genomes and is an attractive alternative to the existing assemblers designed for genomes of lower heterozygosity.

In silico prediction of major drug clearance pathways by support vector machines with feature-selected descriptors.

We have previously established an in silico classification method ("CPathPred") to predict the major clearance pathways of drugs based on an empirical decision with only four physicochemical descriptors-charge, molecular weight, octanol-water distribution coefficient, and protein unbound fraction in plasma-using a rectangular method. In this study, we attempted to improve the prediction performance of the method by introducing a support vector machine (SVM) and increasing the number of descriptors. The data set consisted of 141 approved drugs whose major clearance pathways were classified into metabolism by CYP3A4, CYP2C9, or CYP2D6; organic anion transporting polypeptide-mediated hepatic uptake; or renal excretion. With the same four default descriptors as used in CPathPred, the SVM-based predictor (named "default descriptor SVM") resulted in higher prediction performance compared with a rectangular-based predictor judged by 10-fold cross-validation. Two SVM-based predictors were also established by adding some descriptors as follows: 1) 881 descriptors predicted in silico from the chemical structures of drugs in addition to 4 default descriptors ("885 descriptor SVM"); and 2) selected descriptors extracted by a feature selection based on a greedy algorithm with default descriptors ("feature selection SVM"). The prediction accuracies of the rectangular-based predictor, default descriptor SVM, 885 descriptor SVM, and feature selection SVM were 0.49, 0.60, 0.72, and 0.91, respectively, and the overall precision values for these four methods were 0.72, 0.77, 0.86, and 0.98, respectively. In conclusion, we successfully constructed SVM-based predictors with limited numbers of descriptors to classify the major clearance pathways of drugs in humans with high prediction performance.

In silico classification of major clearance pathways of drugs with their physiochemical parameters.

Predicting major clearance pathways of drugs is important in understanding their pharmacokinetic properties in clinical use, such as drug-drug interactions and genetic polymorphisms, and their subsequent pharmacological/toxicological effects. In this study, we established an in silico classification method to predict the major clearance pathways of drugs by identifying the boundaries of physicochemical parameters in empirical decisions for each clearance pathway. It requires only four physicochemical parameters [charge, molecular weight (MW), lipophilicity (log D), and protein unbound fraction in plasma (f(up))] that were predicted from their molecular structures without performing any benchwork experiments. The training dataset consisted of 141 approved drugs whose major clearance pathways were determined to be metabolism by CYP3A4, CYP2C9, and CYP2D6, hepatic uptake by OATPs, or renal excretion in an unchanged form. After grouping by charge, each drug was plotted in a three-dimensional space according to three axes of MW, log D, and f(up). Then, rectangular boxes for each clearance pathway were drawn mathematically under the criterion of "maximizing F value (harmonic mean of precision and recall) with minimum volume," yielding to a precision of 88%, which was confirmed through two types of validation: leave-one-out method and validation using a new dataset. With further modification toward multiple pathways and/or other pathways, not only would this in silico classification system be useful for industrial scientists at the early stage of drug development, which can lead to the selection of candidate compounds with optimal pharmacokinetic properties, but also for regulators in evaluating new drugs and giving regulatory requirements that are pharmacokinetically reasonable.