Dataset Information

Long insert whole genome sequencing for copy number variant and translocation detection.

ABSTRACT: As next-generation sequencing continues to have an expanding presence in the clinic, the identification of the most cost-effective and robust strategy for identifying copy number changes and translocations in tumor genomes is needed. We hypothesized that performing shallow whole genome sequencing (WGS) of 900-1000-bp inserts (long insert WGS, LI-WGS) improves our ability to detect these events, compared with shallow WGS of 300-400-bp inserts. A priori analyses show that LI-WGS requires less sequencing compared with short insert WGS to achieve a target physical coverage, and that LI-WGS requires less sequence coverage to detect a heterozygous event with a power of 0.99. We thus developed an LI-WGS library preparation protocol based off of Illumina's WGS library preparation protocol and illustrate the feasibility of performing LI-WGS. We additionally applied LI-WGS to three separate tumor/normal DNA pairs collected from patients diagnosed with different cancers to demonstrate our application of LI-WGS on actual patient samples for identification of somatic copy number alterations and translocations. With the evolution of sequencing technologies and bioinformatics analyses, we show that modifications to current approaches may improve our ability to interrogate cancer genomes.

SUBMITTER: Liang WS

PROVIDER: S-EPMC3902897 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Long insert whole genome sequencing for copy number variant and translocation detection.

Liang Winnie S WS Aldrich Jessica J Tembe Waibhav W Kurdoglu Ahmet A Cherni Irene I Phillips Lori L Reiman Rebecca R Baker Angela A Weiss Glen J GJ Carpten John D JD Craig David W DW

Nucleic acids research 20130925 2

As next-generation sequencing continues to have an expanding presence in the clinic, the identification of the most cost-effective and robust strategy for identifying copy number changes and translocations in tumor genomes is needed. We hypothesized that performing shallow whole genome sequencing (WGS) of 900-1000-bp inserts (long insert WGS, LI-WGS) improves our ability to detect these events, compared with shallow WGS of 300-400-bp inserts. A priori analyses show that LI-WGS requires less sequ ...[more]

PMID: 24071583

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Project description:IntroductionWhole-genome sequencing using nanopore technologies can uncover structural variants, which are DNA rearrangements larger than 50 base pairs. Nanopore technologies can also characterize their boundaries with single-base accuracy, owing to the kilobase-long reads that encompass either full variants or their junctions. Other methods, such as next-generation short read sequencing or PCR assays, are limited in their capabilities to detect or characterize structural variants. However, the existing software for nanopore sequencing data analysis still reports incomplete variant sets, which also contain erroneous calls, a considerable obstacle for the molecular diagnosis or accurate genotyping of populations.MethodsWe compared multiple factors affecting variant calling, such as reference genome version, aligner (minimap2, NGMLR, and lra) choice, and variant caller combinations (Sniffles, CuteSV, SVIM, and NanoVar), to find the optimal group of tools for calling large (>50 kb) deletions and duplications, using data from seven patients exhibiting gross gene defects on SERPINC1 and from a reference variant set as the control. The goal was to obtain the most complete, yet reasonably specific group of large variants using a single cell of PromethION sequencing, which yielded lower depth coverage than short-read sequencing. We also used a custom method for the statistical analysis of the coverage value to refine the resulting datasets.ResultsWe found that for large deletions and duplications (>50 kb), the existing software performed worse than for smaller ones, in terms of both sensitivity and specificity, and newer tools had not improved this. Our novel software, disCoverage, could polish variant callers' results, improving specificity by up to 62% and sensitivity by 15%, the latter requiring other data or samples.ConclusionWe analyzed the current situation of >50-kb copy number variants with nanopore sequencing, which could be improved. The methods presented in this work could help to identify the known deletions and duplications in a set of patients, while also helping to filter out erroneous calls for these variants, which might aid the efforts to characterize a not-yet well-known fraction of genetic variability in the human genome.

Project description:BackgroundWhole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations.MethodsWe apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions.ResultsThe GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls.ConclusionsChoice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

Dataset Information

Long insert whole genome sequencing for copy number variant and translocation detection.

Publications

Long insert whole genome sequencing for copy number variant and translocation detection.

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