seGMM: a new tool to infer sex from massively parallel sequencing data

Inspecting concordance between self-reported sex and genotype-inferred sex from genomic data is a significant quality control measure in clinical genetic testing. Numerous tools have been developed to infer sex for genotyping array, whole-exome sequencing, and whole-genome sequencing data. However, improvements in sex inference from targeted gene sequencing panels are warranted. Here, we propose a new tool, seGMM, which applies unsupervised clustering (Gaussian Mixture Model) to determine the gender of a sample from the called genotype data integrated aligned reads. seGMM consistently demonstrated >99% sex inference accuracy in publicly available (1000 Genomes) and our in-house panel dataset, which achieved obviously better sex classification than existing popular tools. Compared to including features only in the X chromosome, our results show that adding additional features from Y chromosomes (e.g. reads mapped to the Y chromosome) can increase sex classification accuracy. Notably, for WES and WGS data, seGMM also has an extremely high degree of accuracy. Finally, we proved the ability of seGMM to infer sex in single patient or trio samples by combining with reference data and pinpointing potential sex chromosome abnormality samples. In general, seGMM provides a reproducible framework to infer sex from massively parallel sequencing data and has great promise in clinical genetics.

Intellectual disability genomics: current state, pitfalls and future challenges

Intellectual disability (ID) can be caused by non-genetic and genetic factors, the latter being responsible for more than 1700 ID-related disorders. The broad ID phenotypic and genetic heterogeneity, as well as the difficulty in the establishment of the inheritance pattern, often result in a delay in the diagnosis. It has become apparent that massive parallel sequencing can overcome these difficulties. In this review we address: (i) ID genetic aetiology, (ii) clinical/medical settings testing, (iii) massive parallel sequencing, (iv) variant filtering and prioritization, (v) variant classification guidelines and functional studies, and (vi) ID diagnostic yield. Furthermore, the need for a constant update of the methodologies and functional tests, is essential. Thus, international collaborations, to gather expertise, data and resources through multidisciplinary contributions, are fundamental to keep track of the fast progress in ID gene discovery.

Using of «bubble sensors» to control the quality of sequencing by the Illumina / Solexa method

In the modern world, sequencing is an integral part of medicine, biology and other scientific fields. The Illimina / Solexa method is a new generation method and relates to methods of mass parallel sequencing. One of the features of using this method is the sequential pumping of various chemicals through the flow cell in which the reaction occurs. For uniformity and high quality of DNA sequencing, it is necessary that the amount of gas in liquids be minimized. Because many it can adversely affect both during chemical reactions and at the stage of recording reaction results. This article will examine the sequencing system using the Illumina\Solexa method using bubble sensors. An algorithm was developed that periodically receives information from bubble sensors in a microfluidic tube. The information received is processed and allows at certain stages to report deviations from the normal conditions for sequencing. The experimental results are presented.