scholarly journals OmicCircos: A Simple-to-Use R Package for the Circular Visualization of Multidimensional Omics Data

2014 ◽  
Vol 13 ◽  
pp. CIN.S13495 ◽  
Author(s):  
Ying Hu ◽  
Chunhua Yan ◽  
Chih-Hao Hsu ◽  
Qing-Rong Chen ◽  
Kelvin Niu ◽  
...  

Summary OmicCircos is an R software package used to generate high-quality circular plots for visualizing genomic variations, including mutation patterns, copy number variations (CNVs), expression patterns, and methylation patterns. Such variations can be displayed as scatterplot, line, or text-label figures. Relationships among genomic features in different chromosome positions can be represented in the forms of polygons or curves. Utilizing the statistical and graphic functions in an R/Bioconductor environment, OmicCircos performs statistical analyses and displays results using cluster, boxplot, histogram, and heatmap formats. In addition, OmicCircos offers a number of unique capabilities, including independent track drawing for easy modification and integration, zoom functions, link-polygons, and position-independent heatmaps supporting detailed visualization. Availability and Implementation OmicCircos is available through Bioconductor at http://www.bioconductor.org/packages/devel/bioc/html/OmicCircos.html . An extensive vignette in the package describes installation, data formatting, and workflow procedures. The software is open source under the Artistic—2.0 license.

2014 ◽  
Vol 13 ◽  
pp. CIN.S19519 ◽  
Author(s):  
Oscar Krijgsman ◽  
Christian Benner ◽  
Gerrit A. Meijer ◽  
Mark A. van de Wiel ◽  
Bauke Ylstra

In order to identify somatic focal copy number aberrations (CNAs) in cancer specimens and to distinguish them from germ-line copy number variations (CNVs), we developed the software package FocalCall. FocalCall enables user-defined size cutoffs to recognize focal aberrations and builds on established array comparative genomic hybridization segmentation and calling algorithms. To distinguish CNAs from CNVs, the algorithm uses matched patient normal signals as references or, if this is not available, a list with known CNVs in a population. Furthermore, FocalCall differentiates between homozygous and heterozygous deletions as well as between gains and amplifications and is applicable to high-resolution array and sequencing data. AVAILABILITY AND IMPLEMENTATION: FocalCall is available as an R-package from: https://github.com/OscarKrijgsman/focalCall . The R-package will be available in Bioconductor.org as of release 3.0.


2021 ◽  
Author(s):  
Qingqing Chen ◽  
Ate Poorthuis

Identifying meaningful locations, such as home or work, from human mobility data has become an increasingly common prerequisite for geographic research. Although location-based services (LBS) and other mobile technology have rapidly grown in recent years, it can be challenging to infer meaningful places from such data, which - compared to conventional datasets – can be devoid of context. Existing approaches are often developed ad-hoc and can lack transparency and reproducibility. To address this, we introduce an R software package for inferring home locations from LBS data. The package implements pre-existing algorithms and provides building blocks to make writing algorithmic ‘recipes’ more convenient. We evaluate this approach by analyzing a de-identified LBS dataset from Singapore that aims to balance ethics and privacy with the research goal of identifying meaningful locations. We show that ensemble approaches, combining multiple algorithms, can be especially valuable in this regard as the resulting patterns of inferred home locations closely correlate with the distribution of residential population. We hope this package, and others like it, will contribute to an increase in use and sharing of comparable algorithms, research code and data. This will increase transparency and reproducibility in mobility analyses and further the ongoing discourse around ethical big data research.


Author(s):  
Xiaoqiang Wang ◽  
Emilie Lebarbier ◽  
Julie Aubert ◽  
Stéphane Robin

Abstract Hidden Markov models provide a natural statistical framework for the detection of the copy number variations (CNV) in genomics. In this context, we define a hidden Markov process that underlies all individuals jointly in order to detect and to classify genomics regions in different states (typically, deletion, normal or amplification). Structural variations from different individuals may be dependent. It is the case in agronomy where varietal selection program exists and species share a common phylogenetic past. We propose to take into account these dependencies inthe HMM model. When dealing with a large number of series, maximum likelihood inference (performed classically using the EM algorithm) becomes intractable. We thus propose an approximate inference algorithm based on a variational approach (VEM), implemented in the CHMM R package. A simulation study is performed to assess the performance of the proposed method and an application to the detection of structural variations in plant genomes is presented.


2019 ◽  
Vol 3 (s1) ◽  
pp. 11-12
Author(s):  
Carolina Schinke ◽  
Eileen Boyle ◽  
Cody Ashby ◽  
Yan Wang ◽  
Davies Christopher Wardell ◽  
...  

OBJECTIVES/SPECIFIC AIMS: 1) Determine the mutational landscape, including translocation, mutations and mutational signatures as well as copy number variations of pPCL and identify significant differences to non pPCL MM. 2) Determine whether genetic changes pertinent to pPCL could be explored as therapeutic targets to improve the dismal prognosis of this patient population. METHODS/STUDY POPULATION: Samples from overall 19 pPCL patients that presented to the Myeloma Center, UAMS between 2000-2018 were used for this study. We performed gene expression profiling (GEP; Affymetrix U133 Plus 2.0) of matched circulating peripheral PCs and bone marrow (BM) PCs from 13 patients. Whole exome sequencing (WES) was performed on purified CD138+ PCs from BM aspirates from 19 pPCL patients with a median depth of 61x. CD34+ sorted cells, taken at the time of stem cell harvest from the same 19 patients, were used as controls. Translocations and mutations were called using Manta and Strelka and annotated as previously reported. Copy number was determined by Sequenza. RESULTS/ANTICIPATED RESULTS: 1) GEP from the BM and circulating peripheral PCs showed that the expression patterns of the two samples from each individual clustered together, indicating that circulating PCs and BM PCs in pPCL result from the same clone and are biologically clearly related. 2) The clinical characteristics from the patient cohort used for WES analysis were as follows: median age was 58 years (range 36–77), females accounted for 74% (14/19), an elevated creatinine level was found in 78% (14/18) and an elevated LDH level in 71% (10/14). All patients presented with an ISS stage of III. Median OS of the whole dataset was poor at 22 months, which is consistent with OS from previously reported pPCL cohorts. 3) Primary Immunoglobulin translocations were common and identified in 63% (12/19) of patients, including MAF translocations, which are known to carry high risk in 42% (8/19) of patients [t(14;16), 32% and t(14;20), 10%] followed by t(11;14) (16%) and t(4;14) (10%). Furthermore, 32% (6/19) of patients had at least one MYC translocation, which are known to play a crucial role in disease progression. 4) The mutational burden of pPCL consisted of a median of 98 non-silent mutations per sample, suggesting that the mutational landscape of pPCL is highly complex and harbors more coding mutations than non-pPCL MM. 5) Driver mutations, that previously have been described in non-pPCL MM showed a different prevalence and distribution in pPCL, including KRAS and TP53 with 47% (9/19) and 37% (7/19) affected patients respectively compared to 21% and 5% in non-PCL MM. PIK3CA (5%), PRDM1 (10%), EP300 (10%) and NF1 (10%) were also enriched in the pPCL group compared to previously reported cases in non-pPCL MM. 6) Biallelic inactivation of TP53 – a feature of Double Hit myeloma - was found in 6/19 (32%) samples, indicating a predominance of high risk genomic features compared to non-pPCL MM. Furthermore, analysis of mutational signatures in pPCL showed that aberrant APOBEC activity was highly prevalent only in patients with a MAF translocation, but not in other translocation groups. DISCUSSION/SIGNIFICANCE OF IMPACT: In conclusion we present one of the first WES datasets on pPCL with the largest patient cohort reported to date and show that pPCL is a highly complex disease. The aggressive disease behavior can, at least in part, be explained by a high prevalence of MAF and MYC translocations, TP53 and KRAS mutations as well as bi-allelic inactivation of TP53. It is of interest that only KRAS but not NRAS mutations are highly enriched in pPCL. From all highly prevalent genomic alterations in pPCL, only KRAS mutations offer a potential for already available therapeutically targeting with MEK inhibitors, which should be further explored.


2015 ◽  
Author(s):  
Endre Sebestyén ◽  
Babita Singh ◽  
Belén Miñana ◽  
Amadís Pagès ◽  
Francesca Mateo ◽  
...  

AbstractAlternative splicing is regulated by multiple RNA-binding proteins and influences the expression of most eukaryotic genes. However, the role of this process in human disease, and particularly in cancer, is only starting to be unveiled. We systematically analyzed mutation, copy number and gene expression patterns of 1348 RNA-binding protein (RBP) genes in 11 solid tumor types, together with alternative splicing changes in these tumors and the enrichment of binding motifs in the alternatively spliced sequences. Our comprehensive study reveals widespread alterations in the expression of RBP genes, as well as novel mutations and copy number variations in association with multiple alternative splicing changes in cancer drivers and oncogenic pathways. Remarkably, the altered splicing patterns in several tumor types recapitulate those of undifferentiated cells. These patterns are predicted to be mainly controlled by MBNL1 and involve multiple cancer drivers, including the mitotic gene NUMA1. We show that NUMA1 alternative splicing induces enhanced cell proliferation and centrosome amplification in non-tumorigenic mammary epithelial cells. Our study uncovers novel splicing networks that potentially contribute to cancer development and progression.


2020 ◽  
Vol 5 ◽  
pp. 252
Author(s):  
Jim R. Broadbent ◽  
Christopher N. Foley ◽  
Andrew J. Grant ◽  
Amy M. Mason ◽  
James R. Staley ◽  
...  

The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added to the package or updated in recent years. These features can be divided into four categories: robust methods for Mendelian randomization, methods for multivariable Mendelian randomization, functions for data visualization, and the ability to load data into the package seamlessly from the PhenoScanner web-resource. We provide examples of the graphical output produced by the data visualization commands, as well as syntax for obtaining suitable data and performing a Mendelian randomization analysis in a single line of code.


10.29007/hb5r ◽  
2019 ◽  
Author(s):  
Mohammad Alkhamis ◽  
Amirali Baniasadi

cn.MOPS is a frequently cited model-based algorithm used to quantitatively detect copy-number variations in next-generation, DNA-sequencing data. Previous work has implemented the algorithm as an R package and has achieved considerable yet limited performance improvement by employing multi-CPU parallelism (maximum achievable speedup was experimentally determined to be 9.24). In this paper, we propose an alternative mechanism of process acceleration. Using one CPU core and a GPU device in the proposed solution, gcn.MOPS, we achieve a speedup factor of 159 and reduce memory usage by more than half compared to cn.MOPS running on one CPU core.


2017 ◽  
Author(s):  
Enrique Vidal ◽  
François le Dily ◽  
Javier Quilez ◽  
Ralph Stadhouders ◽  
Yasmina Cuartero ◽  
...  

AbstractThe three-dimensional conformation of genomes is an essential component of their biological activity. The advent of the Hi-C technology enabled an unprecedented progress in our understanding of genome structures. However, Hi-C is subject to systematic biases that can compromise downstream analyses. Several strategies have been proposed to remove those biases, but the issue of abnormal karyotypes received little attention. Many experiments are performed in cancer cell lines, which typically harbor large-scale copy number variations that create visible defects on the raw Hi-C maps. The consequences of these widespread artifacts on the normalized maps are mostly unexplored. We observed that current normalization methods are not robust to the presence of large-scale copy number variations, potentially obscuring biological differences and enhancing batch effects. To address this issue, we developed an alternative approach designed to take into account chromosomal abnormalities. The method, called OneD, increases reproducibility among replicates of Hi-C samples with abnormal karyotype, outperforming previous methods significantly. On normal karyotypes, OneD fared equally well as state-of-the-art methods, making it a safe choice for Hi-C normalization. OneD is fast and scales well in terms of computing resources for resolutions up to 1 kbp. OneD is implemented as an R package available at http://www.github.com/qenvio/dryhic.


Sarcoma ◽  
2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Silke Brüderlein ◽  
Joshua B. Sommer ◽  
Paul S. Meltzer ◽  
Sufeng Li ◽  
Takuya Osada ◽  
...  

Immortal tumor cell lines are an important model system for cancer research, however, misidentification and cross-contamination of cell lines are a common problem. Seven chordoma cell lines are reported in the literature, but none has been characterized in detail. We analyzed gene expression patterns and genomic copy number variations in five putative chordoma cell lines (U-CH1, CCL3, CCL4, GB60, and CM319). We also created a new chordoma cell line, U-CH2, and provided genotypes for cell lines for identity confirmation. Our analyses revealed that CCL3, CCL4, and GB60 are not chordoma cell lines, and that CM319 is a cancer cell line possibly derived from chordoma, but lacking expression of key chordoma biomarkers. U-CH1 and U-CH2 both have gene expression profiles, copy number aberrations, and morphology consistent with chordoma tumors. These cell lines also harbor genetic changes, such as loss of p16, MTAP, or PTEN, that make them potentially useful models for studying mechanisms of chordoma pathogenesis and for evaluating targeted therapies.


Cancers ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Bartosz Wojtas ◽  
Bartlomiej Gielniewski ◽  
Kamil Wojnicki ◽  
Marta Maleszewska ◽  
Shamba Mondal ◽  
...  

Gliosarcoma is a very rare brain tumor reported to be a variant of glioblastoma (GBM), IDH-wildtype. While differences in molecular and histological features between gliosarcoma and GBM were reported, detailed information on the genetic background of this tumor is lacking. We intend to fill in this knowledge gap by the complex analysis of somatic mutations, indels, copy number variations, translocations and gene expression patterns in gliosarcomas. Using next generation sequencing, we determined somatic mutations, copy number variations (CNVs) and translocations in 10 gliosarcomas. Six tumors have been further subjected to RNA sequencing analysis and gene expression patterns have been compared to those of GBMs. We demonstrate that gliosarcoma bears somatic alterations in gene coding for PI3K/Akt (PTEN, PI3K) and RAS/MAPK (NF1, BRAF) signaling pathways that are crucial for tumor growth. Interestingly, the frequency of PTEN alterations in gliosarcomas was much higher than in GBMs. Aberrations of PTEN were the most frequent and occurred in 70% of samples. We identified genes differentially expressed in gliosarcoma compared to GBM (including collagen signature) and confirmed a difference in the protein level by immunohistochemistry. We found several novel translocations (including translocations in the RABGEF1 gene) creating potentially unfavorable combinations. Collected results on genetic alterations and transcriptomic profiles offer new insights into gliosarcoma pathobiology, highlight differences in gliosarcoma and GBM genetic backgrounds and point out to distinct molecular cues for targeted treatment.


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