Tenxsim: Simulator for Pure and Heterogeneous Genomic Sequence with 10X Genomics

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentor(s): Li Din

Reconciliation of Protein Coding Genes on the Drosophila elegans Muller F and D Elements

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 12, 05-01-2017. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentors: Sarah Elgin and Christopher D. Shaffe

Diverse biological effects of glycosyltransferase genes from Tartary buckwheat

Background: Tartary buckwheat (Fagopyrum tataricum) is an edible cereal crop whose sprouts have been marketed and commercialized for their higher levels of anti-oxidants, including rutin and anthocyanin. UDP-glucose flavonoid glycosyltransferases (UFGTs) play an important role in the biosynthesis of flavonoids in plants. So far, few studies are available on UFGT genes that may play a role in tartary buckwheat flavonoids biosynthesis. Here, we report on the identification and functional characterization of seven UFGTs from tartary buckwheat that are potentially involved in flavonoid biosynthesis (and have varying effects on plant growth and development when overexpressed in Arabidopsis thaliana.) Results: Phylogenetic analysis indicated that the potential function of the seven FtUFGT proteins, FtUFGT6, FtUFGT7, FtUFGT8, FtUFGT9, FtUFGT15, FtUFGT40, and FtUFGT41, could be divided into three Arabidopsis thaliana functional subgroups that are involved in flavonoid biosynthesis of and anthocyanin accumulation. A significant positive correlation between FtUFGT8 and FtUFGT15 expression and anthocyanin accumulation capacity was observed in the tartary buckwheat seedlings after cold stress. Overexpression in Arabidopsis thaliana showed that FtUFGT8, FtUFGT15, and FtUFGT41 significantly increased the anthocyanin content in transgenic plants. Unexpectedly, overexpression of FtUFGT6, while not leading to enhanced anthocyanin accumulation, significantly enhanced the growth yield of transgenic plants. When wild-type plants have only cotyledons, most of the transgenic plants of FtUFGT6 had grown true leaves. Moreover, the growth speed of the oxFtUFGT6 transgenic plant root was also significantly faster than that of the wild type. At later growth, FtUFGT6 transgenic plants showed larger leaves, earlier twitching times and more tillers than wild type, whereas FtUFGT15 showed opposite results. Conclusions: Seven FtUFGTs were isolated from tartary buckwheat. FtUFGT8, FtUFGT15, and FtUFGT41 can significantly increase the accumulation of total anthocyanins in transgenic plants. Furthermore, overexpression of FtUFGT6 increased the overall yield of Arabidopsis transgenic plants at all growth stages. However, FtUFGT15 shows the opposite trend at later growth stage and delays the growth speed of plants. These results suggested that the biological function of FtUFGT genes in tartary buckwheat is diverse

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20128-w

Author Correction:Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples (Nature Communications, (2020), 11, 1, (4748), 10.1038/s41467-020-18151-y)

The original version of this Article omitted from the author list the 9th author Yize Li, who is from the ‘The McDonnell Genome Institute at Washington University, St. Louis, MO 63108, USA and Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA’. This has been corrected in both the PDF and HTML versions of the Article

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.publishedVersio

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

netboxr: Automated discovery of biological process modules by network analysis in R.

SummaryLarge-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have generated high throughput sequencing and molecular profiling data sets, but it is still challenging to identify potentially causal changes in cellular processes in cancer as well as in other diseases in an automated fashion. We developed the netboxr package written in the R programming language, which makes use of the NetBox algorithm to identify candidate cancer-related functional modules. The algorithm makes use of a data-driven, network-based approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of independently curated functionally labeled gene sets. The method can combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology.Availability and implementationThe netboxr package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/netboxr.html