A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce

A Chamber-Based Digital PCR Based on a Microfluidic Chip for the Absolute Quantification and Analysis of KRAS Mutation

The Kirsten rat sarcoma virus gene (KRAS) is the most common tumor in human cancer, and KRAS plays an important role in the growth of tumor cells. Normal KRAS inhibits tumor cell growth. When mutated, it will continuously stimulate cell growth, resulting in tumor development. There are currently few drugs that target the KRAS gene. Here, we developed a microfluidic chip. The chip design uses parallel fluid channels combined with cylindrical chamber arrays to generate 20,000 cylindrical microchambers. The microfluidic chip designed by us can be used for the microsegmentation of KRAS gene samples. The thermal cycling required for the PCR stage is performed on a flat-panel instrument and detected using a four-color fluorescence system. "Glass-PDMS-glass" sandwich structure effectively reduces reagent volatilization; in addition, a valve is installed at the sample inlet and outlet on the upper layer of the chip to facilitate automatic control. The liquid separation performance of the chip was verified by an automated platform. Finally, using the constructed KRAS gene mutation detection system, it is verified that the chip has good application potential for digital polymerase chain reaction (dPCR). The experimental results show that the chip has a stable performance and can achieve a dynamic detection range of four orders of magnitude and a gene mutation detection of 0.2%. In addition, the four-color fluorescence detection system developed based on the chip can distinguish three different KRAS gene mutation types simultaneously on a single chip.

Competitive SNP-LAMP probes for rapid and robust single-nucleotide polymorphism detection

In this work, we developed a simple and robust assay to rapidly detect SNPs in nucleic acid samples. Our approach combines loop-mediated isothermal amplification (LAMP)-based target amplification with fluorescent probes to detect SNPs with high specificity. A competitive "sink" strand preferentially binds to non-SNP amplicons and shifts the free energy landscape to favor specific activation by SNP products. We demonstrated the broad utility and reliability of our SNP-LAMP method by detecting three distinct SNPs across the human genome. We also designed an assay to rapidly detect highly transmissible severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants from crude biological samples. This work demonstrates that competitive SNP-LAMP is a powerful and universal method that could be applied in point-of-care settings to detect any target SNP with high specificity and sensitivity. We additionally developed a publicly available web application for researchers to design SNP-LAMP probes for any target sequence of interest.

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

BACKGROUND AND OBJECTIVES

This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges.

METHODS

We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail.

RESULTS

It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes.

CONCLUSIONS

Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.