Testing of patients with genetics-related disorders is in progress of shifting from single gene assays to gene panel sequencing, whole-exome sequencing (WES) and whole-genome sequencing (WGS). Since WGS is unquestionably becoming a new foundation for molecular analyses, we decided to compare three currently used tools for variant calling of human whole genome sequencing data. We tested DeepVariant, a new TensorFlow machine learning-based variant caller, and compared this tool to GATK 4.0 and SpeedSeq, using 30×, 15× and 10× WGS data of the well-known NA12878 DNA reference sample. According to our comparison, the performance on SNV calling was almost similar in 30× data, with all three variant callers reaching F-Scores (i.e. harmonic mean of recall and precision) equal to 0.98. In contrast, DeepVariant was more precise in indel calling than GATK and SpeedSeq, as demonstrated by F-Scores of 0.94, 0.90 and 0.84, respectively. We conclude that the DeepVariant tool has great potential and usefulness for analysis of WGS data in medical genetics.

Diagnosis of primary immunodeficiencies (PIDs) is complex and cumbersome yet important for the clinical management of the disease. Exome sequencing may provide a genetic diagnosis in a significant number of patients in a single genetic test. In May 2013, we implemented exome sequencing in routine diagnostics for patients suffering from PIDs. This study reports the clinical utility and diagnostic yield for a heterogeneous group of 254 consecutively referred PID patients from 249 families. For the majority of patients, the clinical diagnosis was based on clinical criteria including rare and/or unusual severe bacterial, viral, or fungal infections, sometimes accompanied by autoimmune manifestations. Functional immune defects were interpreted in the context of aberrant immune cell populations, aberrant antibody levels, or combinations of these factors. For 62 patients (24%), exome sequencing identified pathogenic variants in well-established PID genes. An exome-wide analysis diagnosed 10 additional patients (4%), providing diagnoses for 72 patients (28%) from 68 families altogether. The genetic diagnosis directly indicated novel treatment options for 25 patients that received a diagnosis (34%). Exome sequencing as a first-tier test for PIDs granted a diagnosis for 28% of patients. Importantly, molecularly defined diagnoses indicated altered therapeutic options in 34% of cases. In addition, exome sequencing harbors advantages over gene panels as a truly generic test for all genetic diseases, including in silico extension of existing gene lists and re-analysis of existing data.

Background: Recently high-throughput sequencing (HTS) using next generation sequencing techniques became useful in digital gene expression profiling. Our study introduces analysis options for HTS data based on mapping to miRBase or counting and grouping of identical sequence reads. Those approaches allow a hypothesis free detection of miRNA differential expression. Methods: We compare our results to microarray and qPCR data from one set of RNA samples. We use Illumina platforms for microarray analysis and miRNA sequencing of 20 samples from benign follicular thyroid adenoma and malignant follicular thyroid carcinoma. Furthermore, we use three strategies for HTS data analysis to evaluate miRNA biomarkers for malignant versus benign follicular thyroid tumors. Results: High correlation of qPCR and HTS data was observed for the proposed analysis methods. However, qPCR is limited in the differential detection of miRNA isoforms. Moreover, we illustrate a much broader dynamic range of HTS compared to microarrays for small RNA studies. Finally, our data confirm hsa-miR-197-3p, hsa-miR-221-3p, hsa-miR-222-3p and both hsa-miR-144-3p and hsa-miR-144-5p as potential follicular thyroid cancer biomarkers. Conclusions: Compared to microarrays HTS provides a global profile of miRNA expression with higher specificity and in more detail. Summarizing of HTS reads as isoform groups (analysis pipeline B) or according to functional criteria (seed analysis pipeline C), which better correlates to results of qPCR are promising new options for HTS analysis. Finally, data opens future miRNA research perspectives for HTS and indicates that qPCR might be limited in validating HTS data in detail.