Next Generation Sequencing Data Analysis Evaluation in Patients with Parkinsonism from a Genetically Isolated Population

Parkinson's disease (PD) can be caused by genetic changes in a lot of genes. The effect of these changes is determined by the nature of the mutation and ranges from weak associations to pathogenic mutation which leads to loss of protein function. Our study is based on epidemiological data which show significantly increased prevalence of PD (2.9 %) in an isolated population of South-Eastern Moravia in the Czech Republic. We compared two different Next Generation Sequencing (NGS) data analysis approaches in DNA from 28 PD patients in the genes responsible for Parkinsonism (ADH1C, ATP13A2, EIF4G1, FBXO7, GBA + GBAP1, GIGYF2, HTRA2, LRRK2, MAPT, PARK2, PARK7, PINK1, PLA2G6, SNCA, UCHL1 and VPS35) using: 1) already described missense rare variants or pathogenic mutations 2) twelve control DNA samples from the same isolated population. Ion Torrent NGS data processing and trimming from Fastaq through “bam” to “vcf” files was done parallely by Torrent Suite/Ion Reporter and NextGENe software. After filtering out, three missense mutations were found in LRRK2 gene: rs33995883 in 6/0 patients/control (p/c); rs33958906 in 1/1p/c; rs781737269 in 3/0p/c; one missense mutation in MAPT gene rs63750072 in 6/1p/c; and one mutation in HTRA2 gene rs72470545 in 3/1p/c. Both the results from NextGENe with Ion Torrent adaptation and from Ion Reporter significantly correlated in variant calling. Our study may contribute to further explain the genetic background of Parkinsonism.

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Evaluation and Comparison of Multiple Aligners for Next-Generation Sequencing Data Analysis

BioMed Research International ◽

10.1155/2014/309650 ◽

2014 ◽

Vol 2014 ◽

pp. 1-16 ◽

Cited By ~ 28

Author(s):

Jing Shang ◽

Fei Zhu ◽

Wanwipa Vongsangnak ◽

Yifei Tang ◽

Wenyu Zhang ◽

...

Keyword(s):

Data Analysis ◽

Next Generation Sequencing ◽

Real Life ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Computational Performance ◽

Alignment Algorithms ◽

Ngs Data Analysis ◽

Ngs Data ◽

Generation Sequencing

Next-generation sequencing (NGS) technology has rapidly advanced and generated the massive data volumes. To align and map the NGS data, biologists often randomly select a number of aligners without concerning their suitable feature, high performance, and high accuracy as well as sequence variations and polymorphisms existing on reference genome. This study aims to systematically evaluate and compare the capability of multiple aligners for NGS data analysis. To explore this capability, we firstly performed alignment algorithms comparison and classification. We further used long-read and short-read datasets from both real-life andin silicoNGS data for comparative analysis and evaluation of these aligners focusing on three criteria, namely, application-specific alignment feature, computational performance, and alignment accuracy. Our study demonstrated the overall evaluation and comparison of multiple aligners for NGS data analysis. This serves as an important guiding resource for biologists to gain further insight into suitable selection of aligners for specific and broad applications.

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PM4NGS, a project management framework for next-generation sequencing data analysis

GigaScience ◽

10.1093/gigascience/giaa141 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Roberto Vera Alvarez ◽

Lorinc Pongor ◽

Leonardo Mariño-Ramírez ◽

David Landsman

Keyword(s):

Project Management ◽

Data Analysis ◽

Next Generation Sequencing ◽

Organizational Structure ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Management Framework ◽

Ngs Data Analysis ◽

Ngs Data ◽

Generation Sequencing

Abstract Background FAIR (Findability, Accessibility, Interoperability, and Reusability) next-generation sequencing (NGS) data analysis relies on complex computational biology workflows and pipelines to guarantee reproducibility, portability, and scalability. Moreover, workflow languages, managers, and container technologies have helped address the problem of data analysis pipeline execution across multiple platforms in scalable ways. Findings Here, we present a project management framework for NGS data analysis called PM4NGS. This framework is composed of an automatic creation of a standard organizational structure of directories and files, bioinformatics tool management using Docker or Bioconda, and data analysis pipelines in CWL format. Pre-configured Jupyter notebooks with minimum Python code are included in PM4NGS to produce a project report and publication-ready figures. We present 3 pipelines for demonstration purposes including the analysis of RNA-Seq, ChIP-Seq, and ChIP-exo datasets. Conclusions PM4NGS is an open source framework that creates a standard organizational structure for NGS data analysis projects. PM4NGS is easy to install, configure, and use by non-bioinformaticians on personal computers and laptops. It permits execution of the NGS data analysis on Windows 10 with the Windows Subsystem for Linux feature activated. The framework aims to reduce the gap between researcher in experimental laboratories producing NGS data and workflows for data analysis. PM4NGS documentation can be accessed at https://pm4ngs.readthedocs.io/.

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

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab174 ◽

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.

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