Manipulating base quality scores enables variant calling from bisulfite sequencing alignments using conventional Bayesian approaches

AbstractCalling germline SNP variants from bisulfite-converted sequencing data poses a challenge for conventional tools, which have no inherent capability to dissociate true polymorphisms from artificial mutations induced by the chemical treatment. Nevertheless, SNP data is desirable both for genotyping and for resolving the interaction between genetic and epigenetic effects when elucidating the DNA methylome. The confounding effect of bisulfite conversion can be resolved by observing differences in allele counts on a per-strand basis. Herein, we present a computational pre-processing approach for adapting such data, thus enabling downstream analysis in this way using conventional variant calling software such as GATK or Freebayes.

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NGSEP3: accurate variant calling across species and sequencing protocols

Bioinformatics ◽

10.1093/bioinformatics/btz275 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4716-4723 ◽

Cited By ~ 7

Author(s):

Daniel Tello ◽

Juanita Gil ◽

Cristian D Loaiza ◽

John J Riascos ◽

Nicolás Cardozo ◽

...

Keyword(s):

Short Tandem Repeats ◽

Tandem Repeats ◽

High Throughput Sequencing ◽

Variant Calling ◽

Real Data ◽

Supplementary Information ◽

Sequencing Data ◽

Comparative Accuracy ◽

Downstream Analysis ◽

Short Tandem

Abstract Motivation Accurate detection, genotyping and downstream analysis of genomic variants from high-throughput sequencing data are fundamental features in modern production pipelines for genetic-based diagnosis in medicine or genomic selection in plant and animal breeding. Our research group maintains the Next-Generation Sequencing Experience Platform (NGSEP) as a precise, efficient and easy-to-use software solution for these features. Results Understanding that incorrect alignments around short tandem repeats are an important source of genotyping errors, we implemented in NGSEP new algorithms for realignment and haplotype clustering of reads spanning indels and short tandem repeats. We performed extensive benchmark experiments comparing NGSEP to state-of-the-art software using real data from three sequencing protocols and four species with different distributions of repetitive elements. NGSEP consistently shows comparative accuracy and better efficiency compared to the existing solutions. We expect that this work will contribute to the continuous improvement of quality in variant calling needed for modern applications in medicine and agriculture. Availability and implementation NGSEP is available as open source software at http://ngsep.sf.net. Supplementary information Supplementary data are available at Bioinformatics online.

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LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis

10.1101/444711 ◽

2018 ◽

Cited By ~ 1

Author(s):

Maia Malonzo ◽

Viivi Halla-aho ◽

Mikko Konki ◽

Riikka J. Lund ◽

Harri Lähdesmäki

Keyword(s):

Dna Methylation ◽

Data Analysis ◽

Bisulfite Sequencing ◽

Bisulfite Conversion ◽

Sequencing Data ◽

Dna Libraries ◽

Conversion Rates ◽

Bisulfite Sequencing Data ◽

Low Efficiency ◽

Sequencing Data Analysis

AbstractDNA methylation is measured using bisulfite sequencing (BS-seq). Bisulfite conversion can have low efficiency and a DNA sample is then processed multiple times generating DNA libraries with different bisulfite conversion rates. Libraries with low conversion rates are excluded from analysis resulting in reduced coverage and increased costs. We present a method and software, LuxRep, that accounts for technical replicates from different bisulfite-converted DNA libraries. We show that including replicates with low bisulfite conversion rates generates more accurate estimates of methylation levels and differentially methylated sites.AvailabilityAn implementation of the method is available at https://github.com/tare/LuxGLM/tree/master/[email protected]

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Large-Scale Uniform Analysis of Cancer Whole Genomes in Multiple Computing Environments

10.1101/161638 ◽

2017 ◽

Cited By ~ 12

Author(s):

Christina K. Yung ◽

Brian D. O’Connor ◽

Sergei Yakneen ◽

Junjun Zhang ◽

Kyle Ellrott ◽

...

Keyword(s):

Large Scale ◽

Variant Calling ◽

Sequencing Data ◽

Coding Regions ◽

Whole Genomes ◽

Portable Software ◽

Working Groups ◽

Genome Consortium ◽

Data Portal ◽

Downstream Analysis

AbstractThe International Cancer Genome Consortium (ICGC)’s Pan-Cancer Analysis of Whole Genomes (PCAWG) project aimed to categorize somatic and germline variations in both coding and non-coding regions in over 2,800 cancer patients. To provide this dataset to the research working groups for downstream analysis, the PCAWG Technical Working Group marshalled ~800TB of sequencing data from distributed geographical locations; developed portable software for uniform alignment, variant calling, artifact filtering and variant merging; performed the analysis in a geographically and technologically disparate collection of compute environments; and disseminated high-quality validated consensus variants to the working groups. The PCAWG dataset has been mirrored to multiple repositories and can be located using the ICGC Data Portal. The PCAWG workflows are also available as Docker images through Dockstore enabling researchers to replicate our analysis on their own data.

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LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis

BMC Bioinformatics ◽

10.1186/s12859-021-04546-1 ◽

2022 ◽

Vol 23 (1) ◽

Author(s):

Maia H. Malonzo ◽

Viivi Halla-aho ◽

Mikko Konki ◽

Riikka J. Lund ◽

Harri Lähdesmäki

Keyword(s):

Dna Methylation ◽

Bisulfite Sequencing ◽

Probabilistic Method ◽

Computation Time ◽

Bisulfite Conversion ◽

Sequencing Data ◽

Whole Genome Analysis ◽

Dna Libraries ◽

Conversion Rates ◽

Bisulfite Sequencing Data

Abstract Background DNA methylation is commonly measured using bisulfite sequencing (BS-seq). The quality of a BS-seq library is measured by its bisulfite conversion efficiency. Libraries with low conversion rates are typically excluded from analysis resulting in reduced coverage and increased costs. Results We have developed a probabilistic method and software, LuxRep, that implements a general linear model and simultaneously accounts for technical replicates (libraries from the same biological sample) from different bisulfite-converted DNA libraries. Using simulations and actual DNA methylation data, we show that including technical replicates with low bisulfite conversion rates generates more accurate estimates of methylation levels and differentially methylated sites. Moreover, using variational inference speeds up computation time necessary for whole genome analysis. Conclusions In this work we show that taking into account technical replicates (i.e. libraries) of BS-seq data of varying bisulfite conversion rates, with their corresponding experimental parameters, improves methylation level estimation and differential methylation detection.

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Increased yields of duplex sequencing data by a series of quality control tools

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab002 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Gundula Povysil ◽

Monika Heinzl ◽

Renato Salazar ◽

Nicholas Stoler ◽

Anton Nekrutenko ◽

...

Keyword(s):

Low Frequency ◽

Variant Calling ◽

Data Loss ◽

Sequencing Data ◽

Bioinformatics Pipeline ◽

Consensus Sequences ◽

Sequencing Errors ◽

Data Output ◽

Reverse Strand ◽

Duplex Sequencing

Abstract Duplex sequencing is currently the most reliable method to identify ultra-low frequency DNA variants by grouping sequence reads derived from the same DNA molecule into families with information on the forward and reverse strand. However, only a small proportion of reads are assembled into duplex consensus sequences (DCS), and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics toolset that analyses the tag and family composition with the purpose to understand data loss and implement modifications to maximize the data output for the variant calling. Specifically, our tools show that tags contain polymerase chain reaction and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which likely reflect barcode collisions. Finally, we also developed a tool that re-examines variant calls from raw reads and provides different summary data that categorizes the confidence level of a variant call by a tier-based system. With this tool, we can include reads without a family and check the reliability of the call, that increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.

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Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases

Genetics in Medicine ◽

10.1038/s41436-020-01084-8 ◽

2021 ◽

Author(s):

Shilpa Nadimpalli Kobren ◽

◽

Dustin Baldridge ◽

Matt Velinder ◽

Joel B. Krier ◽

...

Keyword(s):

Online Survey ◽

Variant Calling ◽

Theoretical Method ◽

The United States ◽

Genomic Sequencing ◽

Biomedical Data ◽

Sequencing Data ◽

Multimodal Data ◽

Undiagnosed Diseases ◽

Undiagnosed Diseases Network

Abstract Purpose Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. Methods We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. Results We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. Conclusion The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

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BiSulfite Bolt: A bisulfite sequencing analysis platform

GigaScience ◽

10.1093/gigascience/giab033 ◽

2021 ◽

Vol 10 (5) ◽

Author(s):

Colin Farrell ◽

Michael Thompson ◽

Anela Tosevska ◽

Adewale Oyetunde ◽

Matteo Pellegrini

Keyword(s):

Data Aggregation ◽

Bisulfite Sequencing ◽

Low Complexity ◽

Sequencing Analysis ◽

Command Line ◽

Sequencing Data ◽

Bisulfite Sequencing Data ◽

Analysis Platform ◽

Python Package ◽

Bisulfite Sequencing Analysis

Abstract Background Bisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries. Findings We evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy. Conclusion BSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.

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Estimating sequencing error rates using families

BioData Mining ◽

10.1186/s13040-021-00259-6 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Kelley Paskov ◽

Jae-Yoon Jung ◽

Brianna Chrisman ◽

Nate T. Stockham ◽

Peter Washington ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Exome Sequencing ◽

Genome Sequencing ◽

Variant Calling ◽

Error Rates ◽

Sequencing Error ◽

Whole Genome ◽

Sequencing Data ◽

Sequencing Platform ◽

Whole Exome

Abstract Background As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. Results We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method’s versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. Conclusion Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

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