Title: Adaptive Base Error Model in Ultra-Deep Sequencing Data
Authors@R: c(person("Nicola", "Casiraghi", role=c("aut", "cre"), email="casiraghinicola@gmail.com"),
Description: The use of liquid biopsies for cancer patients enables the non-invasive tracking of treatment response and tumor dynamics through single or serial blood drawn tests. Next generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single nucleotide variants (SNVs) in circulating cell free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs. We present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples.
Packaged: 2019-11-28 12:31:14 UTC; nicolacasiraghi

Packaged: 2019-12-18 14:38:59 UTC; nicolacasiraghi

Title: Adaptive Best Subset Selection in Polynomial Time
person(given = "Kangkang", family = "Jiang", email = "jiangkk3@mail2.sysu.edu.cn", role = c("aut")),
person(given = "Liyuan", family = "Hu", email = "huly5@mail2.sysu.edu.cn", role = c("aut")),
person(given = "Junxian", family = "Zhu", email = "zhujunx@mail2.sysu.edu.cn", role = c("aut")),
person(given = "Canhong", family = "Wen", email = "wencanhong@gmail.com", role = c("aut")),
person(given = "Heping", family = "Zhang", email = "heping.zhang@yale.edu", role = c("aut"), comment = c(ORCID = "0000-0002-0688-4076")),
Description: Extremely efficient toolkit for solving the best subset selection problem in linear regression, logistic regression, Poisson regression, Cox proportional hazard model, multiple-response Gaussian, and multinomial regression. It implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times.