The 20th anniversary of the completion of the Human Genome Project offers an opportunity to reflect on early efforts to make biological sense out of genomic data.My interest in genomic analysis began at Washington Uni...The 20th anniversary of the completion of the Human Genome Project offers an opportunity to reflect on early efforts to make biological sense out of genomic data.My interest in genomic analysis began at Washington University in 1988 while I was a postdoc in Doug Berg’s laboratory,which was in the same building as the laboratory of Maynard Olson,one of the founders of the Human Genome Project.展开更多
Colorectal cancer is the third most common cancer and the second leading cause of cancer-related death in the United States.Three quarters of patients diagnosed with colorectal cancer will have early stage disease and...Colorectal cancer is the third most common cancer and the second leading cause of cancer-related death in the United States.Three quarters of patients diagnosed with colorectal cancer will have early stage disease and despite surgical resection for curative intent,approximately 20%-25%of patients will recur with their disease within five years.The other 25%will present with advanced or metastatic disease and clinicians are faced with the challenge of choosing the most appropriate therapy for individual patients.Despite multiple treatment options which are now available and concomitant improvements in survival,advanced colorectal cancer remains universally fatal.The challenge for clinicians is to identify prognostic and predictive biomarkers that can assist in tailoring available treatments for an individual patient to improve clinical outcomes.This review will summarize current and future biomarkers in colorectal cancer and discuss their utility in managing patient care.展开更多
Complex structural variants(CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants.However,detecting the compounded mutational...Complex structural variants(CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants.However,detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy.As a result,there has been limited progress for CSV discovery compared with simple structural variants.Here,we systematically analyzed the multi-breakpoint connection feature of CSVs,and proposed Mako,utilizing a bottom-up guided model-free strategy,to detect CSVs from paired-end short-read sequencing.Specifically,we implemented a graph-based pattern growth approach,where the graph depicts potential breakpoint connections,and pattern growth enables CSV detection without pre-defined models.Comprehensive evaluations on both simulated and real datasets revealed that Mako outperformed other algorithms.Notably,validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%,where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp,respectively.Moreover,the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types,including two novel types of adjacent segment swap and tandem dispersed duplication.Further analysis of these CSVs also revealed the impact of sequence homology on the formation of CSVs.Mako is publicly available at https://github.com/xjtu-omics/Mako.展开更多
基金This work was supported by National Institute of General Medical Sciences grant R01 GM125878.
文摘The 20th anniversary of the completion of the Human Genome Project offers an opportunity to reflect on early efforts to make biological sense out of genomic data.My interest in genomic analysis began at Washington University in 1988 while I was a postdoc in Doug Berg’s laboratory,which was in the same building as the laboratory of Maynard Olson,one of the founders of the Human Genome Project.
文摘Colorectal cancer is the third most common cancer and the second leading cause of cancer-related death in the United States.Three quarters of patients diagnosed with colorectal cancer will have early stage disease and despite surgical resection for curative intent,approximately 20%-25%of patients will recur with their disease within five years.The other 25%will present with advanced or metastatic disease and clinicians are faced with the challenge of choosing the most appropriate therapy for individual patients.Despite multiple treatment options which are now available and concomitant improvements in survival,advanced colorectal cancer remains universally fatal.The challenge for clinicians is to identify prognostic and predictive biomarkers that can assist in tailoring available treatments for an individual patient to improve clinical outcomes.This review will summarize current and future biomarkers in colorectal cancer and discuss their utility in managing patient care.
基金supported by the National Key R&D Program of China(Grant Nos.2018YFC0910400 and 2017YFC0907500)the National Science Foundation of China(Grant Nos.31671372,61702406,and 31701739)+3 种基金the Fundamental Research Funds for the Central Universitiesthe World-Class Universities(Disciplines)the Characteristic Development Guidance Funds for the Central Universitiesthe Shanghai Municipal Science and Technology Major Project(Grant No.2017SHZDZX01)。
文摘Complex structural variants(CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants.However,detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy.As a result,there has been limited progress for CSV discovery compared with simple structural variants.Here,we systematically analyzed the multi-breakpoint connection feature of CSVs,and proposed Mako,utilizing a bottom-up guided model-free strategy,to detect CSVs from paired-end short-read sequencing.Specifically,we implemented a graph-based pattern growth approach,where the graph depicts potential breakpoint connections,and pattern growth enables CSV detection without pre-defined models.Comprehensive evaluations on both simulated and real datasets revealed that Mako outperformed other algorithms.Notably,validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%,where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp,respectively.Moreover,the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types,including two novel types of adjacent segment swap and tandem dispersed duplication.Further analysis of these CSVs also revealed the impact of sequence homology on the formation of CSVs.Mako is publicly available at https://github.com/xjtu-omics/Mako.
