scholarly journals MitoScape: A big-data, machine-learning platform for obtaining mitochondrial DNA from next-generation sequencing data

2021 ◽  
Vol 17 (11) ◽  
pp. e1009594
Author(s):  
Larry N. Singh ◽  
Brian Ennis ◽  
Bryn Loneragan ◽  
Noah L. Tsao ◽  
M. Isabel G. Lopez Sanchez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mitochondrial Dna ◽  
Big Data ◽  
Next Generation Sequencing ◽  
Next Generation ◽  
Mitochondrial Genetics ◽  
Dna Variants ◽  
Mitochondrial Dna Variants ◽  
Ngs Data ◽  
Generation Sequencing

The growing number of next-generation sequencing (NGS) data presents a unique opportunity to study the combined impact of mitochondrial and nuclear-encoded genetic variation in complex disease. Mitochondrial DNA variants and in particular, heteroplasmic variants, are critical for determining human disease severity. While there are approaches for obtaining mitochondrial DNA variants from NGS data, these software do not account for the unique characteristics of mitochondrial genetics and can be inaccurate even for homoplasmic variants. We introduce MitoScape, a novel, big-data, software for extracting mitochondrial DNA sequences from NGS. MitoScape adopts a novel departure from other algorithms by using machine learning to model the unique characteristics of mitochondrial genetics. We also employ a novel approach of using rho-zero (mitochondrial DNA-depleted) data to model nuclear-encoded mitochondrial sequences. We showed that MitoScape produces accurate heteroplasmy estimates using gold-standard mitochondrial DNA data. We provide a comprehensive comparison of the most common tools for obtaining mtDNA variants from NGS and showed that MitoScape had superior performance to compared tools in every statistically category we compared, including false positives and false negatives. By applying MitoScape to common disease examples, we illustrate how MitoScape facilitates important heteroplasmy-disease association discoveries by expanding upon a reported association between hypertrophic cardiomyopathy and mitochondrial haplogroup T in men (adjusted p-value = 0.003). The improved accuracy of mitochondrial DNA variants produced by MitoScape will be instrumental in diagnosing disease in the context of personalized medicine and clinical diagnostics.

The power of next-generation sequencing and machine learning for causal gene finding and prediction of phenotypes.

2021 ◽  
pp. 401-410
Author(s):  
Anna S. Sowa ◽  
Lisa Dussling ◽  
Jörg Hagmann ◽  
Sebastian J. Schultheiss
Keyword(s):  
Machine Learning ◽  
Next Generation Sequencing ◽  
Single Step ◽  
Gene Finding ◽  
Next Generation ◽  
Causal Gene ◽  
Efficient Manner ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

Abstract The wide application of next-generation sequencing (NGS) has facilitated and accelerated causal gene finding and breeding in the field of plant sciences. A wide variety of techniques and computational strategies is available that needs to be appropriately tailored to the species, genetic architecture of the trait of interest, breeding system and available resources. Utilizing these NGS methods, the typical computational steps of marker discovery, genetic mapping and identification of causal mutations can be achieved in a single step in a cost- and time-efficient manner. Rather than focusing on a few high-impact genetic variants that explain phenotypes, increased computational power allows modelling of phenotypes based on genome-wide molecular markers, known as genomic selection (GS). Solely based on this genotype information, modern GS approaches can accurately predict breeding values for a given trait (the average effects of alleles over all loci that are anticipated to be transferred from the parent to the progeny) based on a large training population of genotyped and phenotyped individuals (Crossa et al., 2017). Once trained, the model offers great reductions in breeding speed and costs. We advocate for improving conventional GS methods by applying advanced techniques based on machine learning (ML) and outline how this approach can also be used for causal gene finding. Subsequent to genetic causes of agronomically important traits, epigenetic mechanisms such as DNA methylation play a crucial role in shaping phenotypes and can become interesting targets in breeding pipelines. We highlight an ML approach shown to detect functional methylation changes sensitively from NGS data. We give an overview about commonly applied strategies and provide practical considerations in choosing and performing NGS-based gene finding and NGS-assisted breeding.

scholarly journals Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3148
Author(s):  
Youngjun Park ◽  
Dominik Heider ◽  
Anne-Christin Hauschild
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Cancer Research ◽  
Data Analysis ◽  
Next Generation Sequencing ◽  
Systems Biology ◽  
Large Scale ◽  
Next Generation ◽  
Ngs Data ◽  
Generation Sequencing

The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.

scholarly journals NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data

2021 ◽  
Author(s):  
Anne Krogh Nøhr ◽  
Kristian Hanghøj ◽  
Genis Garcia Erill ◽  
Zilong Li ◽  
Ida Moltke ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genetic Research ◽  
Likelihood Estimation ◽  
Software Tool ◽  
Estimation Methods ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Ngs Data ◽  
Generation Sequencing

Abstract Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C ++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.

scholarly journals A Method for Next-Generation Sequencing of Paired Diagnostic and Remission Samples to Detect Mitochondrial DNA Mutations Associated with Leukemia

2017 ◽  
Vol 19 (5) ◽  
pp. 711-721 ◽  
Author(s):  
Ilaria S. Pagani ◽  
Chung H. Kok ◽  
Verity A. Saunders ◽  
Mark B. Van der Hoek ◽  
Susan L. Heatley ◽  
...  
Keyword(s):  
Mitochondrial Dna ◽  
Next Generation Sequencing ◽  
Next Generation ◽  
Mitochondrial Dna Mutations ◽  
Dna Mutations ◽  
Generation Sequencing

scholarly journals Mining and Development of Novel SSR Markers Using Next Generation Sequencing (NGS) Data in Plants

Molecules ◽  
2018 ◽  
Vol 23 (2) ◽  
pp. 399 ◽  
Author(s):  
Sima Taheri ◽  
Thohirah Lee Abdullah ◽  
Mohd Yusop ◽  
Mohamed Hanafi ◽  
Mahbod Sahebi ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Ssr Markers ◽  
Next Generation ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing
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