Correction: Development and extensive sequencing of a broadly-consented Genome in a Bottle matched tumor-normal pair.
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Published Erratum↗
Biomedical subjects
Konrad Scheffler
Publications and source records attributed to Konrad Scheffler.
Development and extensive sequencing of a broadly-consented Genome in a Bottle matched tumor-normal pair.
The Genome in a Bottle Consortium (GIAB), hosted by the National Institute of Standards and Technology (NIST), is developing new matched tumor-normal samples, the first explicitly consented for public dissemination of genomic data and cell lines. Here, we describe a comprehensive genomic dataset from the first individual, HG008, including DNA from an adherent, epithelial-like pancreatic ductal adenocarcinoma (PDAC) tumor cell line and matched normal cells from duodenal and pancreatic tissues. Data for the tumor-normal matched samples comes from seventeen distinct state-of-the-art whole genome measurement technologies, including high depth short and long-read bulk whole genome sequencing (WGS), single cell WGS, Hi-C, and karyotyping. These data will be used by the GIAB Consortium to develop matched tumor-normal benchmarks for somatic variant detection. We expect these data to facilitate innovation for whole genome measurement technologies, de novo assembly of tumor and normal genomes, and bioinformatic tools to identify small and structural somatic variants. This first-of-its-kind broadly consented open-access resource will facilitate further understanding of sequencing methods used for cancer biology.
Humans↗
Development and extensive sequencing of a broadly-consented Genome in a Bottle matched tumor-normal pair.
The Genome in a Bottle Consortium (GIAB), hosted by the National Institute of Standards and Technology (NIST), is developing new matched tumor-normal samples, the first to be explicitly consented for public dissemination of genomic data and cell lines. Here, we describe a comprehensive genomic dataset from the first individual, HG008, including DNA from an adherent, epithelial-like pancreatic ductal adenocarcinoma (PDAC) tumor cell line and matched normal cells from duodenal and pancreatic tissues. Data for the tumor-normal matched samples comes from seventeen distinct state-of-the-art whole genome measurement technologies, including high depth short and long-read bulk whole genome sequencing (WGS), single cell WGS, and Hi-C, and karyotyping. In future publications, these data will be used by the GIAB Consortium to develop matched tumor-normal benchmarks for somatic variant detection. We expect these data to facilitate innovation for whole genome measurement technologies, de novo assembly of tumor and normal genomes, and bioinformatic tools to identify small and structural somatic mutations. This first-of-its-kind broadly consented open-access resource will facilitate further understanding of sequencing methods used for cancer biology.
Journal Article↗
Maximum likelihood inference of imprinting and allele-specific expression from EST data.
MOTIVATION: In a diploid organism the proportion of transcripts that are produced from the two parental alleles can differ substantially due, for example to epigenetic modification that causes complete or partial silencing of one parental allele or to cis acting polymorphisms that affect transcriptional regulation. Counts of SNP alleles derived from EST sequences have been used to identify both novel candidates for genomic imprinting as well as examples of genes with allelic differences in expression. RESULTS: We have developed a set of statistical models in a maximum likelihood framework that can make highly efficient use of public transcript data to identify genes with unequal representation of alternative alleles in cDNA libraries. We modelled both imprinting and allele-specific expression and applied the models to a large dataset of SNPs mapped to EST sequences. Using simulations, matched closely to real data, we demonstrate significantly improved performance over existing methods that have been applied to the same data. We further validated the power of this approach to detect imprinting using a set of known imprinted genes and inferred a set of candidate imprinted genes, several of which are in close proximity to known imprinted genes. We report evidence that there are undiscovered imprinted genes in known imprinted regions. Overall, more than half of the genes for which the most data are available show some evidence of allele-specific expression. AVAILABILITY: Software is available from the authors on request.
Alleles↗
Robust inference of positive selection from recombining coding sequences.
MOTIVATION: Accurate detection of positive Darwinian selection can provide important insights to researchers investigating the evolution of pathogens. However, many pathogens (particularly viruses) undergo frequent recombination and the phylogenetic methods commonly applied to detect positive selection have been shown to give misleading results when applied to recombining sequences. We propose a method that makes maximum likelihood inference of positive selection robust to the presence of recombination. This is achieved by allowing tree topologies and branch lengths to change across detected recombination breakpoints. Further improvements are obtained by allowing synonymous substitution rates to vary across sites. RESULTS: Using simulation we show that, even for extreme cases where recombination causes standard methods to reach false positive rates >90%, the proposed method decreases the false positive rate to acceptable levels while retaining high power. We applied the method to two HIV-1 datasets for which we have previously found that inference of positive selection is invalid owing to high rates of recombination. In one of these (env gene) we still detected positive selection using the proposed method, while in the other (gag gene) we found no significant evidence of positive selection. AVAILABILITY: A HyPhy batch language implementation of the proposed methods and the HIV-1 datasets analysed are available at http://www.cbio.uct.ac.za/pub_support/bioinf06. The HyPhy package is available at http://www.hyphy.org, and it is planned that the proposed methods will be included in the next distribution. RDP2 is available at http://darwin.uvigo.es/rdp/rdp.html
Base Sequence↗