Single nucleotide variants and InDels identified from whole-genome re-sequencing of Guzerat, Gyr, Girolando and Holstein cattle breeds

AbstractSummaryWhole-genome sequencing (WGS) is a cornerstone of precision medicine, but portable and reproducible open-source workflows for WGS analyses of germline and somatic variants are lacking. We present Sarek, a modular, comprehensive, and easy-to-install workflow, combining a range of software for the identification and annotation of single-nucleotide variants (SNVs), insertion and deletion variants (indels), structural variants, tumor sample heterogeneity, and karyotyping from germline or paired tumor/normal samples. Sarek is implemented in a bioinformatics workflow language (Nextflow) with Docker and Singularity compatible containers, ensuring easy deployment and full reproducibility at any Linux based compute cluster or cloud computing environment. Sarek supports the human reference genomes GRCh37 and GRCh38, and can readily be used both as a core production workflow at sequencing facilities and as a powerful stand-alone tool for individual research groups.AvailabilitySource code and instructions for local installation are available at GitHub (https://github.com/SciLifeLab/Sarek) under the MIT open-source license, and we invite the research community to contribute additional functionality as a collaborative open-source development project.

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Identification of Whole-Genome Significant Single Nucleotide Polymorphisms in Candidate Genes Associated With Serum Biochemical Traits in Chinese Holstein Cattle

Frontiers in Genetics ◽

10.3389/fgene.2020.00163 ◽

2020 ◽

Vol 11 ◽

Cited By ~ 2

Author(s):

Kerong Shi ◽

Fugui Niu ◽

Qin Zhang ◽

Chao Ning ◽

Shujian Yue ◽

...

Keyword(s):

Single Nucleotide Polymorphisms ◽

Candidate Genes ◽

Holstein Cattle ◽

Whole Genome ◽

Nucleotide Polymorphisms ◽

Biochemical Traits ◽

Single Nucleotide ◽

Chinese Holstein ◽

Chinese Holstein Cattle ◽

Serum Biochemical

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Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls

Heredity ◽

10.1038/s41437-020-0301-4 ◽

2020 ◽

Vol 124 (5) ◽

pp. 658-674 ◽

Cited By ~ 1

Author(s):

Mahmoud Amiri Roudbar ◽

Mohammad Reza Mohammadabadi ◽

Ahmad Ayatollahi Mehrgardi ◽

Rostam Abdollahi-Arpanahi ◽

Mehdi Momen ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Dna Methylation ◽

Whole Genome ◽

Single Nucleotide Variants ◽

Single Nucleotide

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Genome aging: somatic mutation in the brain links age-related decline with disease and nominates pathogenic mechanisms

Human Molecular Genetics ◽

10.1093/hmg/ddz191 ◽

2019 ◽

Vol 28 (R2) ◽

pp. R197-R206 ◽

Cited By ~ 10

Author(s):

Michael A Lodato ◽

Christopher A Walsh

Keyword(s):

Human Brain ◽

Somatic Mutation ◽

Somatic Mutations ◽

Late Onset ◽

Whole Genome ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Age Related ◽

The Brain ◽

Over Time

AbstractAging is a mysterious process, not only controlled genetically but also subject to random damage that can accumulate over time. While DNA damage and subsequent mutation in somatic cells were first proposed as drivers of aging more than 60 years ago, whether and to what degree these processes shape the neuronal genome in the human brain could not be tested until recent technological breakthroughs related to single-cell whole-genome sequencing. Indeed, somatic single-nucleotide variants (SNVs) increase with age in the human brain, in a somewhat stochastic process that may nonetheless be controlled by underlying genetic programs. Evidence from the literature suggests that in addition to demonstrated increases in somatic SNVs during aging in normal brains, somatic mutation may also play a role in late-onset, sporadic neurodegenerative diseases, such as Alzheimer’s disease and Parkinson’s disease. In this review, we will discuss somatic mutation in the human brain, mechanisms by which somatic mutations occur and can be controlled, and how this process can impact human health.

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Accurate identification of single-nucleotide variants in whole-genome-amplified single cells

Nature Methods ◽

10.1038/nmeth.4227 ◽

2017 ◽

Vol 14 (5) ◽

pp. 491-493 ◽

Cited By ~ 87

Author(s):

Xiao Dong ◽

Lei Zhang ◽

Brandon Milholland ◽

Moonsook Lee ◽

Alexander Y Maslov ◽

...

Keyword(s):

Single Cells ◽

Whole Genome ◽

Single Nucleotide Variants ◽

Accurate Identification ◽

Single Nucleotide

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Whole-Genome Analyses of Korean Native and Holstein Cattle Breeds by Massively Parallel Sequencing

PLoS ONE ◽

10.1371/journal.pone.0101127 ◽

2014 ◽

Vol 9 (7) ◽

pp. e101127 ◽

Cited By ~ 31

Author(s):

Jung-Woo Choi ◽

Xiaoping Liao ◽

Paul Stothard ◽

Won-Hyong Chung ◽

Heoyn-Jeong Jeon ◽

...

Keyword(s):

Massively Parallel Sequencing ◽

Massively Parallel ◽

Holstein Cattle ◽

Whole Genome ◽

Parallel Sequencing ◽

Cattle Breeds ◽

Genome Analyses

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MethylExtract: High-Quality methylation maps and SNV calling from whole genome bisulfite sequencing data

F1000Research ◽

10.12688/f1000research.2-217.v2 ◽

2014 ◽

Vol 2 ◽

pp. 217 ◽

Cited By ~ 8

Author(s):

Guillermo Barturen ◽

Antonio Rueda ◽

José L. Oliver ◽

Michael Hackenberg

Keyword(s):

High Throughput ◽

Sequence Variation ◽

High Throughput Sequencing ◽

Whole Genome ◽

Single Nucleotide Variants ◽

High Quality ◽

Single Nucleotide ◽

Error Sources ◽

Link Type ◽

Genome Methylation

Whole genome methylation profiling at a single cytosine resolution is now feasible due to the advent of high-throughput sequencing techniques together with bisulfite treatment of the DNA. To obtain the methylation value of each individual cytosine, the bisulfite-treated sequence reads are first aligned to a reference genome, and then the profiling of the methylation levels is done from the alignments. A huge effort has been made to quickly and correctly align the reads and many different algorithms and programs to do this have been created. However, the second step is just as crucial and non-trivial, but much less attention has been paid to the final inference of the methylation states. Important error sources do exist, such as sequencing errors, bisulfite failure, clonal reads, and single nucleotide variants.We developed MethylExtract, a user friendly tool to: i) generate high quality, whole genome methylation maps and ii) detect sequence variation within the same sample preparation. The program is implemented into a single script and takes into account all major error sources. MethylExtract detects variation (SNVs – Single Nucleotide Variants) in a similar way to VarScan, a very sensitive method extensively used in SNV and genotype calling based on non-bisulfite-treated reads. The usefulness of MethylExtract is shown by means of extensive benchmarking based on artificial bisulfite-treated reads and a comparison to a recently published method, called Bis-SNP.MethylExtract is able to detect SNVs within High-Throughput Sequencing experiments of bisulfite treated DNA at the same time as it generates high quality methylation maps. This simultaneous detection of DNA methylation and sequence variation is crucial for many downstream analyses, for example when deciphering the impact of SNVs on differential methylation. An exclusive feature of MethylExtract, in comparison with existing software, is the possibility to assess the bisulfite failure in a statistical way. The source code, tutorial and artificial bisulfite datasets are available at http://bioinfo2.ugr.es/MethylExtract/ and http://sourceforge.net/projects/methylextract/, and also permanently accessible from 10.5281/zenodo.7144.

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Whole Genome Sequencing Identifies Single Nucleotide Variants, Structural and Copy Number Abnormalities with a High Sensitivity in Hematological Neoplasm: An All in One Diagnostic Tool Tested on 3241 Cases

Blood ◽

10.1182/blood-2018-99-113321 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 1525-1525

Author(s):

Claudia Haferlach ◽

Sven O. Twardziok ◽

Stephan Hutter ◽

Wencke Walter ◽

Wolfgang Kern ◽

...

Keyword(s):

Diagnostic Tool ◽

Copy Number ◽

Amplicon Sequencing ◽

Whole Genome ◽

Single Nucleotide Variants ◽

Employment Equity ◽

Single Nucleotide ◽

Hematological Neoplasm ◽

Equity Ownership

Abstract Background: Novel targeted treatment approaches for hematological malignancies require a comprehensive genetic characterization of patient samples. So far combinations of various techniques are used in different entities. As gross structural variants (SV) and copy number aberrations (CNA) as well as molecular mutations have to be assessed - in best case genome wide - to date no single technique is able to provide all information in a routine diagnostic setting. Whole genome sequencing (WGS) is a technology able to provide all this information in a single approach. Aim: To evaluate whether WGS qualifies as a diagnostic tool in a routine setting. Patients and Methods: 3241 bone marrow or blood samples from patients (pts) diagnosed with hematological neoplasm (including AML, ALL, MDS, CML, CLL) were evaluated by WGS. Samples had been sent for routine diagnostic work-up to our laboratory between 2005 and 2017. For WGS, 150bp paired-end sequences where generated on Illumina HiseqX and NovaSeq 6000 (Illumina, San Diego, CA). A mixture genomic DNA from multiple anonymous donors was used as normal controls. The median coverage was 104x (range: 47-196). Only cases with an estimated tumor fraction of at least 20% were included. WGS was validated against chromosome banding analysis (CBA), which was available in 2752 pts with an aberrant karyotype detected in 1513. For 334 pts genomic array data (GA) was available. CNA were called using GATK4 and SV using MANTA software accounting for missing matched-normal samples. For the validation of single nucleotide variants (SNV) and Indels we compared WGS data produced by BaseSpace WGS and Tumor/Normal app to variants classified as pathogenic during routine diagnostics using targeted amplicon sequencing (median coverage 1800x) in 70 genes known to be recurrently mutated including ASXL1, DNMT3A, RUNX1, SRSF2, TET2 , and TP53. Results: In total 475 recurrent reciprocal structural rearrangements (38 different rearrangements including BCR-ABL1, PML-RARA, CBFB-MYH11, RUNX1-RUNX1T1, IGH-BCL2, IGH-MYC, IGH-CCND1) were identified by CBA. Of these 455 (96%) rearrangements were identified by WGS. Due to the significantly lower resolution of CBA compared to WGS and the fact that in complex karyotype the precise determination of CNA in CBA is not possible the comparison with respect to CNA between CBA and WGS was restricted to 843 cases with non-complex karyotype (<4 abnormalities). 289 trisomies, 48 monosomies and 464 recurrent deletions (del) (including del(5q), del(7q), del(11q), del(17p)) were identified by CBA. Of these WGS detected 210 (73%) trisomies and 42 (88%) monosomies. For 74 of the 79 trisomies undetected by WGS the percentage of cells harboring the respective trisomy was determined by interphase FISH and was in median 8%. FISH data was available for all 6 missed monosomies, median clone size was 14%. WGS identified 420/446 (81.5%) del detected by CBA. FISH data was available for 31/44 del missed by WGS. The median proportion of cells harboring the respective del was 11%. In order to test the CNA detection of WGS on a higher resolution level GA data from 334 cases was compared to WGS data. These included 135 cases with normal and 194 with aberrant karyotype in CBA (no CBA: 5), respectively. Comparing 18,337,602 positions 18,031,728 (98%) yielded the same result with both technologies with respect to gain, loss or normal, respectively.For SNV/Indel calls we investigated 2074 mutations in 1022 pts (harboring at least 1 pathogenic mutation (range 1 - 12)). 1892/2074 (91%) were concordant between amplicon sequencing and WGS. 132 from the missed 182 mutations had a variant allele frequency of <10%, which is on the verge of the limit of detection for 100x WGS data. Only 50 cases were missed due to low coverage or very complex alterations. Conclusions: WGS can provide in an "all in one test" all relevant information required for classification and treatment decisions in hematological neoplasms with a high potential to substitute current genetic evaluation based on CBA, FISH and targeted mutation analysis. The next steps on the road towards a diagnostic tool are the validation of CNA, SV and SNV/Idel identified in addition to standard diagnostics and the determination of the coverage necessary to detect small clones relevant for patient care. Thus, a first step is taken towards a completely automated genotyping enabling a broad access to state of the art diagnostics. Disclosures Haferlach: MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Twardziok:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

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