BreakDancer: an algorithm for high-resolution mapping of genomic structural variation

Chen, Ken; Wallis, John W.; McLellan, Michael D.; Larson, David E.; Kalicki, Joelle; Pohl, Craig; McGrath, Sean; Wendl, Michael C.; Zhang, Qunyuan; Locke, Devin P.; Shi, Xiaoqi; Fulton, Robert S.; Ley, Timothy J.; Wilson, Richard K.; Li, Ding; Mardis, Elaine R.

doi:10.1038/nmeth.1363

Cited by 1,327 publications

(1,202 citation statements)

References 30 publications

Supporting

Mentioning

1,179

Contrasting

Unclassified

Order By: Relevance

“…The most basic algorithm is that applied by Breakdancer[77], Breakway[78], SVDetect[79], BreakSeq[80] and GASV[81]. These programs use mapping distance data provided through the paired-end alignment statistics to estimate the average fragment size of the library.…”

Section: Massively Parallel Sequencingmentioning

confidence: 99%

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Gundry

Vijg

2012

Mutation Research/Fundamental and Molecular Mechanisms of Mutag

View full text Add to dashboard Cite

DNA mutations are the source of genetic variation within populations. The majority of mutations with observable effects are deleterious. In humans mutations in the germ line can cause genetic disease. In somatic cells multiple rounds of mutations and selection lead to cancer. The study of genetic variation has progressed rapidly since the completion of the draft sequence of the human genome. Recent advances in sequencing technology, most importantly the introduction of massively parallel sequencing (MPS), have resulted in more than a hundred-fold reduction in the time and cost required for sequencing nucleic acids. These improvements have greatly expanded the use of sequencing as a practical tool for mutation analysis. While in the past the high cost of sequencing limited mutation analysis to selectable markers or small forward mutation targets assumed to be representative for the genome overall, current platforms allow whole genome sequencing for less than $5,000. This has already given rise to direct estimates of germline mutation rates in multiple organisms including humans by comparing whole genome sequences between parents and offspring. Here we present a brief history of the field of mutation research, with a focus on classical tools for the measurement of mutation rates. We then review MPS, how it is currently applied and the new insight into human and animal mutation frequencies and spectra that has been obtained from whole genome sequencing. While great progress has been made, we note that the single most important limitation of current MPS approaches for mutation analysis is the inability to address low-abundance mutations that turn somatic tissues into mosaics of cells. Such mutations are at the basis of intra-tumor heterogeneity, with important implications for clinical diagnosis, and could also contribute to somatic diseases other than cancer, including aging. Some possible approaches to gain access to low-abundance mutations are discussed, with a brief overview of new sequencing platforms that are currently waiting in the wings to advance this exploding field even further.

show abstract

Section: Massively Parallel Sequencingmentioning

confidence: 99%

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Gundry

Vijg

2012

Mutation Research/Fundamental and Molecular Mechanisms of Mutag

View full text Add to dashboard Cite

show abstract

“…Indels events were identified using both Pindel and the GATK Indel Genotyper V2.0, whereas translocations were found by first using Breakdancer to identify clusters of paired-end reads in which the two members mapped to different chromosomes, and then verified using Slope to confirm the presence of chimeric single-end reads in the vicinity (10 kb) of the Breakdancer calls. [20][21][22] Default parameters were used with both programs. The above actions were combined using UNIX shell scripts to create an analysis pipeline (summarized in Figure 3).…”

Section: Resultsmentioning

confidence: 99%

“…19,20 Finally, to find translocations we used the Breakdancer software package, which identifies discordant paired-end reads where one end maps to the targeted chromosome and the other to an alternate chromosome, and Slope, which identifies single end chimeric reads spanning the translocation boundary (Figure 2). 21,22 Although similar data can be obtained by commercial software packages, Figure 2 Overview of translocation identification by next generation sequencing. (a) Translocations occurring at the DNA level were identified by designing capture probes that 2 Â tiled across the both exons (dark green) and introns (light green) of gene partners commonly involved in translocations.…”

mentioning

confidence: 83%

Targeted next generation sequencing of clinically significant gene mutations and translocations in leukemia

et al. 2012

View full text Add to dashboard Cite

“…This restricts the general usage of this methodology to centers and groups with considerable amounts of computing resources and expertise. For example, widely used programs, such as BreakDancer 6 or Delly 7 , can only identify structural variants larger than 20 and 150 base pairs, respectively. Each of the methods needed for a complete structural characterization of somatic variation in tumor genomes further require complex scoring and filtering schemes to achieve acceptable levels of specificity, but such procedures drastically lower the sensitivity, leaving a substantial fraction of structural variants undetected.…”

Section: A N a Ly S I Smentioning

confidence: 99%

Comprehensive characterization of complex structural variations in cancer by directly comparing genome sequence reads

et al. 2014

View full text Add to dashboard Cite

The development of high-throughput sequencing technologies has advanced our understanding of cancer. However, characterizing somatic structural variants in tumor genomes is still challenging because current strategies depend on the initial alignment of reads to a reference genome. Here, we describe SMUFIN (somatic mutation finder), a single program that directly compares sequence reads from normal and tumor genomes to accurately identify and characterize a range of somatic sequence variation, from single-nucleotide variants (SNV) to large structural variants at base pair resolution. Performance tests on modeled tumor genomes showed average sensitivity of 92% and 74% for SNVs and structural variants, with specificities of 95% and 91%, respectively. Analyses of aggressive forms of solid and hematological tumors revealed that SMUFIN identifies breakpoints associated with chromothripsis and chromoplexy with high specificity. SMUFIN provides an integrated solution for the accurate, fast and comprehensive characterization of somatic sequence variation in cancer.The recent development of high-throughput sequencing technologies has made possible the sequencing of genomes at an unprecedented speed, allowing the identification of the genetic basis of numerous diseases. These advances have been particularly important in the study of cancer, providing information on thousands of tumor genomes and a large catalog of genomic alteration associated with oncogenesis 1 .The characterization of somatic variation in tumor samples is, therefore, rapidly becoming a standard practice in biomedicine 2 . In a large fraction of biomedical studies that rely on high-throughput sequencing, the production of genome sequence data exceeds available computer resources and the capabilities of analytic protocols. This is particularly pertinent in the field of cancer genomics, where the increasing sequencing of tumor genomes calls for faster and more accurate analyses.The identification of somatic variants associated with cancer typically requires sequencing tumor and normal genome samples from the same patient, followed by multiple sequence comparisons. Normal and pathological reads are aligned to a reference genome, and the alignment is used to identify sequence changes to isolate the somatic fraction of variants (i.e., those detected only in the tumor). In principle, this simple strategy can be used to detect single-nucleotide variants (SNVs) and structural variants. Existing methods for the detection of somatic SNVs show high sensitivity and specificity 3,4 , but identifying structural variants is still challenging and remains largely unsolved. The need for a reference sequence is particularly limiting. Reads carrying variations, such as those covering somatic changes in the tumor, are more difficult to align to the reference genome 5 , and corresponding variants might become undetectable. Moreover, reference-based methods also must discriminate germline changes from somatic variants. In addition to these limitations at detection level, this a...

show abstract

BreakDancer: an algorithm for high-resolution mapping of genomic structural variation

Cited by 1,327 publications

References 30 publications

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Targeted next generation sequencing of clinically significant gene mutations and translocations in leukemia

Comprehensive characterization of complex structural variations in cancer by directly comparing genome sequence reads

Contact Info

Product

Resources

About