Noninvasive prenatal detection of chromosomal aneuploidies using different next generation sequencing strategies and algorithms.

OBJECTIVE: Here we describe the successful application of massively parallel sequencing for noninvasive prenatal detection of trisomy 21. In addition, for the detection of a broader spectrum of fetal aneuploidies, a target enrichment approach was successfully tested. METHODS: The circulating cell-free DNA was prepared from 53 maternal blood samples and analysed using Illumina's sequencing systems Genome Analyzer(IIx) and HiSeq2000, respectively. In a first experiment the SureSelect Target Enrichment System was tested. RESULTS: In our initial study analysing 42 samples on the Genome Analyzer(IIx) , all eight samples from women carrying a trisomy 21 fetus were correctly identified. On the basis of our HiSeq2000 sequence data, we discussed new algorithms for detection of fetal trisomy 21. In addition, we successfully used the combination of a target enrichment system followed by sequencing and were able to identify fetal trisomy 13 and fetal trisomy 21. CONCLUSIONS: Our results confirm previous reports that massively parallel sequencing of cell-free fetal DNA allows the reliably noninvasive detection of trisomy 21 from maternal blood with the potential to enhance test selectivity and specificity by bioinformatic means. According to our preliminary results, targeted sequencing might be an alternative strategy to detect chromosomal aneuploidies besides trisomy 21.

Visual analysis of gel-free proteome data.

We present a visual exploration system supporting protein analysis when using gel-free data acquisition methods. The data to be analyzed is obtained by coupling liquid chromatography (LC) with mass spectrometry (MS). LC-MS data have the properties of being nonequidistantly distributed in the time dimension (measured by LC) and being scattered in the mass-to-charge ratio dimension (measured by MS). We describe a hierarchical data representation and visualization method for large LC-MS data. Based on this visualization, we have developed a tool that supports various data analysis steps. Our visual tool provides a global understanding of the data, intuitive detection and classification of experimental errors, and extensions to LC-MS/MS, LC/LC-MS, and LC/LC-MS/MS data analysis. Due to the presence of randomly occurring rare isotopes within the same protein molecule, several intensity peaks may be detected that all refer to the same peptide. We have developed methods to unite such intensity peaks. This deisotoping step is visually documented by our system, such that misclassification can be detected intuitively. For differential protein expression analysis, we compute and visualize the differences in protein amounts between experiments. In order to compute the differential expression, the experimental data need to be registered. For registration, we perform a nonrigid warping step based on landmarks. The landmarks can be assigned automatically using protein identification methods. We evaluate our methods by comparing protein analysis with and without our interactive visualization-based exploration tool.