ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA

Abstract Motivation The use of liquid biopsies for cancer patients enables the non-invasive tracking of treatment response and tumor dynamics through single or serial blood drawn tests. Next-generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single-nucleotide variants (SNVs) in circulating cell-free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs. Results We present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples. Availability and implementation ABEMUS is cross-platform and can be installed as R package. The source code is maintained on Github at http://github.com/cibiobcg/abemus, and it is also available at CRAN official R repository. Supplementary information Supplementary data are available at Bioinformatics online.

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ctDNAtools: An R package to work with sequencing data of circulating tumor DNA

10.1101/2020.01.27.912790 ◽

2020 ◽

Author(s):

Amjad Alkodsi ◽

Leo Meriranta ◽

Annika Pasanen ◽

Sirpa Leppä

Keyword(s):

Residual Disease ◽

Fragment Size ◽

R Package ◽

Monte Carlo Sampling ◽

Circulating Tumor Dna ◽

Cancer Diagnostics ◽

Sequencing Data ◽

Liquid Biopsies ◽

Tumor Dna

AbstractSummarySequencing of cell-free DNA (cfDNA) including circulating tumor DNA (ctDNA) in minimally-invasive liquid biopsies is rapidly maturing towards clinical utility for cancer diagnostics. However, the publicly available bioinformatics tools for the specialized analysis of ctDNA sequencing data are still scarce. Here, we present the ctDNAtools R package, which provides functionalities for testing minimal residual disease (MRD) and analyzing cfDNA fragmentation. MRD detection in ctDNAtools utilizes a Monte Carlo sampling approach to test ctDNA positivity through tracking a set of pre-detected reporter mutations in follow-up samples. Additionally, ctDNAtools includes various functionalities to study cfDNA fragment size histograms, profiles and fragment ends patterns.AvailabilityThe ctDNAtools package is freely available under MIT license at https://github.com/alkodsi/ctDNAtools.

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TPES: tumor purity estimation from SNVs

Bioinformatics ◽

10.1093/bioinformatics/btz406 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4433-4435 ◽

Cited By ~ 4

Author(s):

Alessio Locallo ◽

Davide Prandi ◽

Tarcisio Fedrizzi ◽

Francesca Demichelis

Keyword(s):

R Package ◽

Computational Method ◽

Supplementary Information ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Tumor Purity ◽

Whole Exome ◽

Whole Exome Sequencing Data ◽

Fraction Distribution ◽

Tumor Genome

Abstract Motivation Tumor purity (TP) is the proportion of cancer cells in a tumor sample. TP impacts on the accurate assessment of molecular and genomics features as assayed with NGS approaches. State-of-the-art tools mainly rely on somatic copy-number alterations (SCNA) to quantify TP and therefore fail when a tumor genome is nearly euploid, i.e. ‘non-aberrant’ in terms of identifiable SCNAs. Results We introduce a computational method, tumor purity estimation from single-nucleotide variants (SNVs), which derives TP from the allelic fraction distribution of SNVs. On more than 7800 whole-exome sequencing data of TCGA tumor samples, it showed high concordance with a range of TP tools (Spearman’s correlation between 0.68 and 0.82; >9 SNVs) and rescued TP estimates of 1, 194 samples (15%) pan-cancer. Availability and implementation TPES is available as an R package on CRAN and at https://bitbucket.org/l0ka/tpes.git. Supplementary information Supplementary data are available at Bioinformatics online.

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hypeR: An R Package for Geneset Enrichment Workflows

10.1101/656637 ◽

2019 ◽

Cited By ~ 1

Author(s):

Anthony Federico ◽

Stefano Monti

Keyword(s):

High Throughput Sequencing ◽

R Package ◽

Supplementary Information ◽

Sequencing Data ◽

Wide Audience ◽

Popular Method ◽

Link Type ◽

High Throughput Sequencing Data ◽

One Stop ◽

Recent Version

ABSTRACTSummaryGeneset enrichment is a popular method for annotating high-throughput sequencing data. Existing tools fall short in providing the flexibility to tackle the varied challenges researchers face in such analyses, particularly when analyzing many signatures across multiple experiments. We present a comprehensive R package for geneset enrichment workflows that offers multiple enrichment, visualization, and sharing methods in addition to novel features such as hierarchical geneset analysis and built-in markdown reporting. hypeR is a one-stop solution to performing geneset enrichment for a wide audience and range of use cases.Availability and implementationThe most recent version of the package is available at https://github.com/montilab/hypeR.Supplementary informationComprehensive documentation and tutorials, are available at https://montilab.github.io/hypeR-docs.

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neoepiscope improves neoepitope prediction with multivariant phasing

Bioinformatics ◽

10.1093/bioinformatics/btz653 ◽

2019 ◽

Vol 36 (3) ◽

pp. 713-720 ◽

Cited By ~ 5

Author(s):

Mary A Wood ◽

Austin Nguyen ◽

Adam J Struck ◽

Kyle Ellrott ◽

Abhinav Nellore ◽

...

Keyword(s):

False Negative ◽

Supplementary Information ◽

Supplementary File ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Somatic Variant ◽

Negative Results ◽

Multiple Datasets ◽

False Negative Results

Abstract Motivation The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for the co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false-positive and false-negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels). Results Herein, we illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to ∼5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible and supports several major histocompatibility complex binding affinity prediction tools. Availability and implementation neoepiscope is available on GitHub at https://github.com/pdxgx/neoepiscope under the MIT license. Scripts for reproducing results described in the text are available at https://github.com/pdxgx/neoepiscope-paper under the MIT license. Additional data from this study, including summaries of variant phasing incidence and benchmarking wallclock times, are available in Supplementary Files 1, 2 and 3. Supplementary File 1 contains Supplementary Table 1, Supplementary Figures 1 and 2, and descriptions of Supplementary Tables 2–8. Supplementary File 2 contains Supplementary Tables 2–6 and 8. Supplementary File 3 contains Supplementary Table 7. Raw sequencing data used for the analyses in this manuscript are available from the Sequence Read Archive under accessions PRJNA278450, PRJNA312948, PRJNA307199, PRJNA343789, PRJNA357321, PRJNA293912, PRJNA369259, PRJNA305077, PRJNA306070, PRJNA82745 and PRJNA324705; from the European Genome-phenome Archive under accessions EGAD00001004352 and EGAD00001002731; and by direct request to the authors. Supplementary information Supplementary data are available at Bioinformatics online.

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schex avoids overplotting for large single-cell RNA-sequencing datasets

Bioinformatics ◽

10.1093/bioinformatics/btz907 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2291-2292 ◽

Cited By ~ 1

Author(s):

Saskia Freytag ◽

Ryan Lister

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

R Package ◽

Supplementary Information ◽

Supplementary Data ◽

Sequencing Data ◽

Single Cell Rna Sequencing

Abstract Summary Due to the scale and sparsity of single-cell RNA-sequencing data, traditional plots can obscure vital information. Our R package schex overcomes this by implementing hexagonal binning, which has the additional advantages of improving speed and reducing storage for resulting plots. Availability and implementation schex is freely available from Bioconductor via http://bioconductor.org/packages/release/bioc/html/schex.html and its development version can be accessed on GitHub via https://github.com/SaskiaFreytag/schex. Supplementary information Supplementary data are available at Bioinformatics online.

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MAGOS: Discovering Subclones in Tumors Sequenced at Standard Depths

10.1101/790386 ◽

2019 ◽

Cited By ~ 1

Author(s):

Navid Ahmadinejad ◽

Shayna Troftgruben ◽

Carlo Maley ◽

Junwen Wang ◽

Li Liu

Keyword(s):

Copy Number Variants ◽

R Package ◽

Computational Method ◽

Intratumor Heterogeneity ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Real World Data ◽

Computation Efficiency ◽

Whole Exome ◽

Whole Exome Sequencing Data

ABSTRACTUnderstanding intratumor heterogeneity is critical to designing personalized treatments and improving clinical outcomes of cancers. Such investigations require accurate delineation of the subclonal composition of a tumor, which to date can only be reliably inferred from deep-sequencing data (>300x depth). To enable accurate subclonal discovery in tumors sequenced at standard depths (30-50x), we develop a novel computational method that incorporates an adaptive error model into statistical decomposition of mixed populations, which corrects the mean-variance dependency of sequencing data at the subclonal level. Tested on extensive computer simulations and real-world data, this new method, named model-based adaptive grouping of subclones (MAGOS), consistently outperforms existing methods on minimum sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports subclone analysis using single nucleotide variants and copy number variants from one or more samples of an individual tumor. Applications of MAGOS to whole-exome sequencing data of 331 liver cancer samples discovered a significant association between subclonal diversity and patient overall survival. MAGOS is freely available as an R package at github (https://github.com/liliulab/magos).

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EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning

Bioinformatics ◽

10.1093/bioinformatics/btz435 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4827-4829 ◽

Cited By ~ 6

Author(s):

Xiao-Fei Zhang ◽

Le Ou-Yang ◽

Shuo Yang ◽

Xing-Ming Zhao ◽

Xiaohua Hu ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Ensemble Learning ◽

R Package ◽

Supplementary Information ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

The Individual ◽

Downstream Analysis ◽

Shiny Application

Abstract Summary Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis. Availability and implementation The R package and Shiny application are available through Github at https://github.com/Zhangxf-ccnu/EnImpute. Supplementary information Supplementary data are available at Bioinformatics online.

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gpart: human genome partitioning and visualization of high-density SNP data by identifying haplotype blocks

Bioinformatics ◽

10.1093/bioinformatics/btz308 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4419-4421 ◽

Cited By ~ 3

Author(s):

Sun Ah Kim ◽

Myriam Brossard ◽

Delnaz Roshandel ◽

Andrew D Paterson ◽

Shelley B Bull ◽

...

Keyword(s):

Clustering Algorithms ◽

R Package ◽

Supplementary Information ◽

Visualization Tool ◽

Sequencing Data ◽

Haplotype Blocks ◽

Snp Data ◽

Computing Environments ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing

Abstract Summary For the analysis of high-throughput genomic data produced by next-generation sequencing (NGS) technologies, researchers need to identify linkage disequilibrium (LD) structure in the genome. In this work, we developed an R package gpart which provides clustering algorithms to define LD blocks or analysis units consisting of SNPs. The visualization tool in gpart can display the LD structure and gene positions for up to 20 000 SNPs in one image. The gpart functions facilitate construction of LD blocks and SNP partitions for vast amounts of genome sequencing data within reasonable time and memory limits in personal computing environments. Availability and implementation The R package is available at https://bioconductor.org/packages/gpart. Supplementary information Supplementary data are available at Bioinformatics online.

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SimRVSequences: an R package to simulate genetic sequence data for pedigrees

Bioinformatics ◽

10.1093/bioinformatics/btz881 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2295-2297

Author(s):

Christina Nieuwoudt ◽

Angela Brooks-Wilson ◽

Jinko Graham

Keyword(s):

Sequence Data ◽

R Package ◽

Supplementary Information ◽

Supplementary Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Genetic Sequence ◽

Large Numbers

Abstract Summary We present the R package SimRVSequences to simulate sequence data for pedigrees. SimRVSequences allows for simulations of large numbers of single-nucleotide variants (SNVs) and scales well with increasing numbers of pedigrees. Users provide a sample of pedigrees and SNV data from a sample of unrelated individuals. Availability and implementation SimRVSequences is publicly-available on CRAN https://cran.r-project.org/web/packages/SimRVSequences/. Supplementary information Supplementary data are available at Bioinformatics online.

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Genomic alterations in appendiceal carcinoma using circulating DNA.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.4_suppl.658 ◽

2019 ◽

Vol 37 (4_suppl) ◽

pp. 658-658

Author(s):

Walid Labib Shaib ◽

Ali Roberts ◽

Mehmet Akce ◽

Christina Wu ◽

Olatunji B. Alese ◽

...

Keyword(s):

Low Frequency ◽

Circulating Tumor Dna ◽

Plasma Samples ◽

Single Nucleotide Variants ◽

Liquid Biopsies ◽

Genomic Alterations ◽

Synonymous Mutations ◽

Molecular Alterations ◽

Uncertain Significance ◽

Brca1 Brca2

658 Background: Appendiceal cancers (AC) comprise around 0.5% of all gastrointestinal neoplasia. The genomic landscape of AC has not been well studied. The yield of circulating tumor DNA (ctDNA) from the plasma of patients with AC has not been reported. The aim of this study is to confirm the feasibility of NGS using ctDNA and characterize common alternations in the genomic profile of AC. Methods: The molecular alterations in 372 plasma samples from 303 patients with AC using clinical-grade NGS of ctDNA (Guardant 360) across multiple institutions, was evaluated. The test detects single nucleotide variants in 54 -73 genes, copy number amplifications, fusions, and indels in selected genes. Results: A total of 303 AC patients were evaluated; 169 female (56%). Median age was 56.8 (range: 25-83). ctDNA NGS testing was done on 372 plasma samples; 48 patients had testing performed twice, 9 three times, and 1 was tested four times. Genomic alterations were defined in 207 (55.6%) samples with a total of 288 alterations identified after excluding variants of uncertain significance (VUSs) and synonymous mutations. TP53 associated genes were most commonly altered (n = 96, 33.3%), followed by KRAS (n = 41, 14.2%), APC (n = 19, 6.6%), EGFR (n = 15, 5.2%), BRAF (n = 13, 4.5%), NF1 (n = 13, 4.5%), MYC (n = 9, 3.1%), GNAS (n = 8, 2.7%), PI3CA (n = 7, 2.4%), MET (n = 6, 2.08%), ATM in 6 (1.6%). Other genomic alterations of low frequency, but clinically relevant: AR (n = 4, 1.39%), TERT (n = 4, 1.39%), ERBB2 (n = 4, 1.39%), SMAD4 (n = 3, 1.04%), CDK4 (n = 2, 0.69%), NRAS (n = 2, 0.69%), FGFR1 (n = 2, 0.69%), FGFR2 (n = 2, 0.69%), PTEN (n = 2, 0.69%), RB1 (n = 2, 0.69%), and CDK6, CDKN2A, BRCA1, BRCA2, JAK2, IDH2, MAPK, NTRK1, CDH1, ARID1A, and PDGFRA were all reported once. Conclusions: Evaluation of ctDNA was feasible among individuals with AC. The frequency of genomic alterations in ctDNA testing is similar to those previously reported in tissue NGS. Liquid biopsies are non-invasive methods that can provide personalized options for targeted therapies in patients with AC.

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