Investigation of copy number variation in subjects with major depression based on whole-genome sequencing data

2017 ◽  
Vol 220 ◽  
pp. 38-42 ◽  
Author(s):  
Chenglong Yu ◽  
Bernhard T. Baune ◽  
Ma-Li Wong ◽  
Julio Licinio
Keyword(s):  
Major Depression ◽  
Copy Number Variation ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Number Variation

The average copy number variation (CNVA) of chromosome fragments is a potential surrogate marker for TMB in predicting responses to immunotherapy in Non-small cell lung cancer

2020 ◽  
Author(s):  
Yuan yuan Lei ◽  
Guo chao Zhang ◽  
Chao qi Zhang ◽  
li yan xue ◽  
Zhen lin Yang ◽  
...  
Keyword(s):  
T Cells ◽  
Copy Number Variation ◽  
Genomic Instability ◽  
Genome Sequencing ◽  
Copy Number ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Number Variation ◽  
Average Copy

Abstract Background: Genomic instability plays a large role in the process of cancer. Tumor mutational burden (TMB) is closely related to immunotherapy outcome and is an important manifestation of genomic instability. However, the cost of TMB detection is extremely high, which limits the use of TMB in clinical practice. Another new indicator of genome instability, CNVA (the average copy number variation) which calculates the changes of 0.5 Mb chromosomal fragments, requires extremely low sequencing depth, and is expected to replace TMB as a new marker of immune efficacy.Methods: A total of 50 samples (23 of which came from patients who received immunotherapy) were subjected to low-depth (10X) chromosome sequencing on the MGI platform. CNVA was calculated by the formula avg (abs (copy number-2)). Then, we analyzed the relationship between CNVA and immune infiltration or immunotherapy efficacy. In addition, through the analysis of whole genome sequencing data of 509 lung adenocarcinoma in the TCGA database, we compared CNVA with classic marker TMB to evaluate the value of CNVA as an immune evaluation index.Results: Compared with the low CNVA group, the high CNVA group had higher expression of PD-L1, CD39 and CD19, and more infiltration of CD8 + T cells and CD3 + T cells. Among the 23 patients treated with immunotherapy, the average CNVA value of the SD (stable disease)/PR (partial response) group was higher than that of the PD (progressive disease) group (P <0.05). The data of whole genome sequencing data of 509 lung adenocarcinomas from TCGA and real-time quantitative PCR results of 22 frozen specimens found that CNVA was more correlated with CD8 and PD-L1 than TMB. In addition, CNVA showed a specific positive correlation with TMB (r = 0.2728, p < 0.0001).Conclusion: CNVA can be a good indicator of immune infiltration and predicting immunotherapy efficacy. With its low cost and potential clinical application for testing, it is expected to become a substitute for TMB.

scholarly journals Identification of Copy Number Variation in Domestic Chicken Using Whole-Genome Sequencing Reveals Evidence of Selection in the Genome

Animals ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 809
Author(s):  
Donghyeok Seol ◽  
Byung June Ko ◽  
Bongsang Kim ◽  
Han-Ha Chai ◽  
Dajeong Lim ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Breeding Systems ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Genetic Characteristics ◽  
Red Jungle Fowl ◽  
Number Variation

Copy number variation (CNV) has great significance both functionally and evolutionally. Various CNV studies are in progress to find the cause of human disease and to understand the population structure of livestock. Recent advances in next-generation sequencing (NGS) technology have made CNV detection more reliable and accurate at whole-genome level. However, there is a lack of CNV studies on chickens using NGS. Therefore, we obtained whole-genome sequencing data of 65 chickens including Red Jungle Fowl, Cornish (broiler), Rhode Island Red (hybrid), and White Leghorn (layer) from the public databases for CNV region (CNVR) detection. Using CNVnator, a read-depth based software, a total of 663 domesticated-specific CNVRs were identified across autosomes. Gene ontology analysis of genes annotated in CNVRs showed that mainly enriched terms involved in organ development, metabolism, and immune regulation. Population analysis revealed that CN and RIR are closer to each other than WL, and many genes (LOC772271, OR52R1, RD3, ADH6, TLR2B, PRSS2, TPK1, POPDC3, etc.) with different copy numbers between breeds found. In conclusion, this study has helped to understand the genetic characteristics of domestic chickens at CNV level, which may provide useful information for the development of breeding systems in chickens.

scholarly journals Identification of Medium-Sized Copy Number Alterations in Whole-Genome Sequencing

2014 ◽  
Vol 13s3 ◽  
pp. CIN.S14023
Author(s):  
Hatice Gulcin Ozer ◽  
Aisulu Usubalieva ◽  
Adrienne Dorrance ◽  
Ayse Selen Yilmaz ◽  
Michael Caligiuri ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Copy Number Alterations ◽  
Sequencing Data ◽  
Entire Genome ◽  
Multiple Challenges ◽  
Cost Efficient

The genome-wide discoveries such as detection of copy number alterations (CNA) from high-throughput whole-genome sequencing data enabled new developments in personalized medicine. The CNAs have been reported to be associated with various diseases and cancers including acute myeloid leukemia. However, there are multiple challenges to the use of current CNA detection tools that lead to high false-positive rates and thus impede widespread use of such tools in cancer research. In this paper, we discuss these issues and propose possible solutions. First, since the entire genome cannot be mapped due to some regions lacking sequence uniqueness, current methods cannot be appropriately adjusted to handle these regions in the analyses. Thus, detection of medium-sized CNAs is also being directly affected by these mappability problems. The requirement for matching control samples is also an important limitation because acquiring matching controls might not be possible or might not be cost efficient. Here we present an approach that addresses these issues and detects medium-sized CNAs in cancer genomes by (1) masking unmappable regions during the initial CNA detection phase, (2) using pool of a few normal samples as control, and (3) employing median filtering to adjust CNA ratios to its surrounding coverage and eliminate false positives.

scholarly journals CNView: a visualization and annotation tool for copy number variation from whole-genome sequencing

2016 ◽  
Author(s):  
Ryan L. Collins ◽  
Matthew R. Stone ◽  
Harrison Brand ◽  
Joseph T. Glessner ◽  
Michael E. Talkowski
Keyword(s):  
Copy Number Variation ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Software Tool ◽  
Whole Genome ◽  
Data Types ◽  
Recent Emergence ◽  
Number Variation ◽  
Population Scale

AbstractSummaryCopy number variation (CNV) is a major component of structural differences between individual genomes. The recent emergence of population-scale whole-genome sequencing (WGS) datasets has enabled genome-wide CNV delineation. However, molecular validation at this scale is impractical, so visualization is an invaluable preliminary screening approach when evaluating CNVs. Standardized tools for visualization of CNVs in large WGS datasets are therefore in wide demand.Methods & ResultsTo address this demand, we developed a software tool, CNView, for normalized visualization, statistical scoring, and annotation of CNVs from population-scale WGS datasets. CNView surmounts challenges of sequencing depth variability between individual libraries by locally adapting to cohort-wide variance in sequencing uniformity at any locus. Importantly, CNView is broadly extensible to any reference genome assembly and most current WGS data types.Availability and ImplementationCNView is written in R, is supported on OS X, MS Windows, and Linux, and is freely distributed under the MIT license. Source code and documentation are available from https://github.com/RCollins13/[email protected]

scholarly journals A Bioinformatics Pipeline for Estimating Mitochondria DNA Copy Number and Heteroplasmy Levels from Whole Genome Sequencing Data

2021 ◽  
Author(s):  
Stephanie L Battle ◽  
Daniela Puiu ◽  
Eric Boerwinkle ◽  
Kent Taylor ◽  
Jerome Rotter ◽  
...  
Keyword(s):  
Mitochondrial Genome ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Dna Molecules ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Accurate Identification

Mitochondrial diseases are a heterogeneous group of disorders that can be caused by mutations in the nuclear or mitochondrial genome. Mitochondrial DNA variants may exist in a state of heteroplasmy, where a percentage of DNA molecules harbor a variant, or homoplasmy, where all DNA molecules have a variant. The relative quantity of mtDNA in a cell, or copy number (mtDNA-CN), is associated with mitochondrial function, human disease, and mortality. To facilitate accurate identification of heteroplasmy and quantify mtDNA-CN, we built a bioinformatics pipeline that takes whole genome sequencing data and outputs mitochondrial variants, and mtDNA-CN. We incorporate variant annotations to facilitate determination of variant significance. Our pipeline yields uniform coverage by remapping to a circularized chrM and recovering reads falsely mapped to nuclear-encoded mitochondrial sequences. Notably, we construct a consensus chrM sequence for each sample and recall heteroplasmy against the sample's unique mitochondrial genome. We observe an approximately 3-fold increased association with age for heteroplasmic variants in non-homopolymer regions and, are better able to capture genetic variation in the D-loop of chrM compared to existing software. Our bioinformatics pipeline more accurately captures features of mitochondrial genetics than existing pipelines that are important in understanding how mitochondrial dysfunction contributes to disease.

Sign in / Sign up

Export Citation Format

Share Document