Single molecule targeted sequencing for cancer gene mutation detection

With the rapid decline cost of sequencing, it is now clinically affordable to examine multiple genes in a single disease-targeted test using next generation sequencing. Current targeted sequencing methods require a separate step of targeted capture enrichment during sample preparation before sequencing, and the library preparation process is labor intensive and time consuming. Here, we introduced an amplification-free Single Molecule Targeted Sequencing (SMTS) technology, which combined targeted capture and sequencing in one step. We demonstrated that this technology can detect low-frequency mutations of cancer genes. SMTS has several advantages, namely that it requires little sample preparation and avoids biases and errors introduced by PCR reaction. This technology can be applied in cancer gene mutation detection, inherited condition screening and high-resolution human leukocyte antigen (HLA) typing.

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Single molecule targeted sequencing for cancer gene mutation detection

Scientific Reports ◽

10.1038/srep26110 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 6

Author(s):

Yan Gao ◽

Liwei Deng ◽

Qin Yan ◽

Yongqian Gao ◽

Zengding Wu ◽

...

Keyword(s):

Gene Mutation ◽

Single Molecule ◽

Mutation Detection ◽

Targeted Sequencing ◽

Cancer Gene

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Likelihood Models of Somatic Mutation and Codon Substitution in Cancer Genes

Genetics ◽

10.1093/genetics/165.2.695 ◽

2003 ◽

Vol 165 (2) ◽

pp. 695-705 ◽

Cited By ~ 2

Author(s):

Ziheng Yang ◽

Simon Ro ◽

Bruce Rannala

Keyword(s):

Amino Acid ◽

Gene Mutation ◽

Somatic Mutation ◽

Mutation Rates ◽

Functional Domains ◽

Cancer Gene ◽

Structural Domains ◽

Specific Amino Acid ◽

Silent Substitution ◽

Codon Substitution

Abstract The role of somatic mutation in cancer is well established and several genes have been identified that are frequent targets. This has enabled large-scale screening studies of the spectrum of somatic mutations in cancers of particular organs. Cancer gene mutation databases compile the results of many studies and can provide insight into the importance of specific amino acid sequences and functional domains in cancer, as well as elucidate aspects of the mutation process. Past studies of the spectrum of cancer mutations (in particular genes) have examined overall frequencies of mutation (at specific nucleotides) and of missense, nonsense, and silent substitution (at specific codons) both in the sequence as a whole and in a specific functional domain. Existing methods ignore features of the genetic code that allow some codons to mutate to missense, or stop, codons more readily than others (i.e., by one nucleotide change, vs. two or three). A new codon-based method to estimate the relative rate of substitution (fixation of a somatic mutation in a cancer cell lineage) of nonsense vs. missense mutations in different functional domains and in different tumor tissues is presented. Models that account for several potential influences on rates of somatic mutation and substitution in cancer progenitor cells and allow biases of mutation rates for particular dinucleotide sequences (CGs and dipyrimidines), transition vs. transversion bias, and variable rates of silent substitution across functional domains (useful in detecting investigator sampling bias) are considered. Likelihood-ratio tests are used to choose among models, using cancer gene mutation data. The method is applied to analyze published data on the spectrum of p53 mutations in cancers. A novel finding is that the ratio of the probability of nonsense to missense substitution is much lower in the DNA-binding and transactivation domains (ratios near 1) than in structural domains such as the linker, tetramerization (oligomerization), and proline-rich domains (ratios exceeding 100 in some tissues), implying that the specific amino acid sequence may be less critical in structural domains (e.g., amino acid changes less often lead to cancer). The transition vs. transversion bias and effect of CpG dinucleotides on mutation rates in p53 varied greatly across cancers of different organs, likely reflecting effects of different endogenous and exogenous factors influencing mutation in specific organs.

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Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data

Journal of The Royal Society Interface ◽

10.1098/rsif.2009.0192 ◽

2009 ◽

Vol 7 (44) ◽

pp. 423-437 ◽

Cited By ~ 58

Author(s):

Tijana Milenković ◽

Vesna Memišević ◽

Anand K. Ganesan ◽

Nataša Pržulj

Keyword(s):

Protein Interaction ◽

Network Topology ◽

Interaction Network ◽

Biological Processes ◽

Cancer Gene ◽

Cancer Genes ◽

Clustering Methods ◽

Ppi Network ◽

Graph Theoretic ◽

Ppi Networks

Many real-world phenomena have been described in terms of large networks. Networks have been invaluable models for the understanding of biological systems. Since proteins carry out most biological processes, we focus on analysing protein–protein interaction (PPI) networks. Proteins interact to perform a function. Thus, PPI networks reflect the interconnected nature of biological processes and analysing their structural properties could provide insights into biological function and disease. We have already demonstrated, by using a sensitive graph theoretic method for comparing topologies of node neighbourhoods called ‘graphlet degree signatures’, that proteins with similar surroundings in PPI networks tend to perform the same functions. Here, we explore whether the involvement of genes in cancer suggests the similarity of their topological ‘signatures’ as well. By applying a series of clustering methods to proteins' topological signature similarities, we demonstrate that the obtained clusters are significantly enriched with cancer genes. We apply this methodology to identify novel cancer gene candidates, validating 80 per cent of our predictions in the literature. We also validate predictions biologically by identifying cancer-related negative regulators of melanogenesis identified in our siRNA screen. This is encouraging, since we have done this solely from PPI network topology. We provide clear evidence that PPI network structure around cancer genes is different from the structure around non-cancer genes. Understanding the underlying principles of this phenomenon is an open question, with a potential for increasing our understanding of complex diseases.

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Sample Preparation for Single-Molecule Enzyme Assays

Comprehensive Sampling and Sample Preparation ◽

10.1016/b978-0-12-381373-2.00087-9 ◽

2012 ◽

pp. 323-337

Author(s):

D.B. Craig ◽

E.R. Nichols

Keyword(s):

Sample Preparation ◽

Single Molecule ◽

Enzyme Assays

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P640 GENE MUTATION DETECTION ANALYSIS OF SWINE HEPATITIS E VIRUS PROPAGATED IN THE PRIMARY-CULTURED HUMAN HEPATOCYTES

Journal of Hepatology ◽

10.1016/s0168-8278(14)60802-3 ◽

2014 ◽

Vol 60 (1) ◽

pp. S282

Author(s):

Y. Oshiro ◽

H. Yasue ◽

S. Ideno ◽

S. Hattori ◽

K. Sakai ◽

...

Keyword(s):

Gene Mutation ◽

Hepatitis E Virus ◽

Mutation Detection ◽

Hepatitis E ◽

Human Hepatocytes ◽

Detection Analysis ◽

Swine Hepatitis

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SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

10.1101/739474 ◽

2019 ◽

Cited By ~ 2

Author(s):

Sushant Kumar ◽

Arif Harmanci ◽

Jagath Vytheeswaran ◽

Mark B. Gerstein

Keyword(s):

Machine Learning ◽

Large Scale ◽

Three Dimensional ◽

Point Mutations ◽

Dimensional Structure ◽

Rapid Decline ◽

Ras Signaling ◽

Cancer Genes ◽

Structural Variations ◽

Pathogenic Variants

AbstractA rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence and three-dimensional structure of the genome. Previous studies have suggested a complex interplay of genomic and epigenomic features in the emergence and distribution of SVs. However, the exact mechanism of pathogenesis for SVs in different diseases is not straightforward to decipher. Thus, we built an agnostic machine-learning-based workflow, called SVFX, to assign a “pathogenicity score” to somatic and germline SVs in various diseases. In particular, we generated somatic and germline training models, which included genomic, epigenomic, and conservation-based features for SV call sets in diseased and healthy individuals. We then applied SVFX to SVs in six different cancer cohorts and a cardiovascular disease (CVD) cohort. Overall, SVFX achieved high accuracy in identifying pathogenic SVs. Moreover, we found that predicted pathogenic SVs in cancer cohorts were enriched among known cancer genes and many cancer-related pathways (including Wnt signaling, Ras signaling, DNA repair, and ubiquitin-mediated proteolysis). Finally, we note that SVFX is flexible and can be easily extended to identify pathogenic SVs in additional disease cohorts.

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