Cost-effective and accurate method of measuring fetal fraction using SNP imputation.

Motivation: With the discovery of cell-free fetal DNA in maternal blood, the demand for non-invasive prenatal testing (NIPT) has been increasing. To obtain reliable NIPT results, it is important to accurately estimate the fetal fraction. In this study, we propose an accurate and cost-effective method for measuring fetal fractions using single-nucleotide polymorphisms (SNPs). Results: A total of 84 samples were sequenced via semiconductor sequencing using a 0.3× sequencing coverage. SNPs were genotyped to estimate the fetal fraction. Approximately 900 000 SNPs were genotyped, and 250 000 of these SNPs matched the semiconductor sequencing results. We performed SNP imputation (1000Genome phase3 and HRC v1.1 reference panel) to increase the number of SNPs. The correlation coefficients (R2) of the fetal fraction estimated using the ratio of non-maternal alleles when coverage was reduced to 0.01 following SNP imputation were 0.93 (HRC v1.1 reference panel) and 0.90 (1000GP3 reference panel). An R2 of 0.72 was found at 0.01× sequencing coverage with no imputation performed. We developed an accurate method to measure fetal fraction using SNP imputation, showing cost-effectiveness by using different commercially available SNP chips and lowering the coverage. We also showed that semiconductor sequencing, which is an inexpensive option, was useful for measuring fetal fraction. Availability and implementation: python source code and guidelines can be found at https://github.com/KMJ403/fetalfraction-SNPimpute. Contact: kangskim@ajou.ac.kr or sunshinkim3@gmail.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Synthesis and size characterization of silica nanospheres using sedimentation field-flow fractionation (SdFFF).

Silica nanoparticles were synthesized by a conventional emulsion polymerization by mixing ethanol, ammonium hydroxide, water and tetra ethyl orthosilicate (TEOS). A new reaction apparatus was assembled for a large scale synthesis of silica nanospheres in the laboratory, which was designed for uniform mixing of the reactants. The apparatus was equipped with a disc type agitator with six rectangular propellers. The new apparatus allowed high reproducibility in terms of the mean size and the size distribution of the silica nanoparticles with the relative standard deviation of less than about 6%. Sedimentation field-flow fractionation (SdFFF) was employed for determination of the size distribution of the silica nanoparticles. SdFFF provided size-based separation of the silica nanoparticles, with the retention time increasing with the size. When SdFFF analysis was repeated three times for the same sample, the standard deviation was less than 4%, showing reliability of SdFFF in size measurement. SdFFF seems to provide more accurate size distribution than DLS, particularly for those having broad and multimodal size distributions. Change in the agitation speed resulted in significant change in the mean diameter of the silica nanoparticles. Agitation speed of 400 rpm in 3 L reaction vessel yielded silica particles of about 100 nm in diameter, while at 200 rpm in 1 L vessel yielded those of about 500 nm.

Exploring protein fold space by secondary structure prediction using data distribution method on Grid platform.

MOTIVATION: Since the newly developed Grid platform has been considered as a powerful tool to share resources in the Internet environment, it is of interest to demonstrate an efficient methodology to process massive biological data on the Grid environments at a low cost. This paper presents an efficient and economical method based on a Grid platform to predict secondary structures of all proteins in a given organism, which normally requires a long computation time through sequential execution, by means of processing a large amount of protein sequence data simultaneously. From the prediction results, a genome scale protein fold space can be pursued. RESULTS: Using the improved Grid platform, the secondary structure prediction on genomic scale and protein topology derived from the new scoring scheme for four different model proteomes was presented. This protein fold space was compared with structures from the Protein Data Bank, database and it showed similarly aligned distribution. Therefore, the fold space approach based on this new scoring scheme could be a guideline for predicting a folding family in a given organism.