Gene data to hit milestone. A. Nantel/Shutterstock DNA microarrays allow researchers to analyse the expression of a huge number of genes simultaneously. Dissecting the genomic complexity underlying medulloblastoma : Nature. As a first phase of the International Cancer Genome Consortium (ICGC) PedBrain Tumor Project ( we have collected matched tumour and germline samples from 125 medulloblastoma patients aged from 0 to 17 years (Supplementary Table 1).
Whole-genome sequencing (WGS, n = 39) and whole-exome sequencing (WES, n = 21) were applied to a ‘discovery’ set, with a custom-capture approach used to sequence 2,734 genes in an additional ‘replication’ set (n = 65). All tumour samples were obtained at primary diagnosis, before adjuvant therapy, and the distribution of molecular subgroups was similar across cohorts (Supplementary Fig. 1).
Investigation of genome-wide somatic mutation allele frequencies identified several cases with a clear peak at approximately 25%, rather than the expected approximately 50% allele frequency for early, heterozygous events (Fig. 1a). Comprehensive molecular characterization of human colon and rectal cancer : Nature. Tumour and normal pairs were analysed by different platforms.
The specific numbers of samples analysed by each platform are shown in Supplementary Table 1. Exome-sequence analysis To define the mutational spectrum, we performed exome capture DNA sequencing on 224 tumour and normal pairs (all mutations are listed in Supplementary Table 2). Sequencing achieved >20-fold coverage of at least 80% of targeted exons. The somatic mutation rates varied considerably among the samples. A, Mutation frequencies in each of the tumour samples from 224 patients. To assess the basis for the considerably different mutation rates, we evaluated MSI7 and mutations in the DNA mismatch-repair pathway8, 9, 10 genes MLH1, MLH3, MSH2, MSH3, MSH6 and PMS2. Gene mutations Overall, we identified 32 somatic recurrently mutated genes (defined by MutSig11 and manual curation) in the hypermutated and non-hypermutated cancers (Fig. 1b). Mutation rate and methylation patterns. In Living Memory: the First Steps toward Genetic Data Storage.
Drew Endy's group at Stanford has just published their latest paper1—open access of course.
It represents the first major step in a long-term ambition to create a reliable form of living memory—rewritable, retrievable digital information stored in living cells. Endy, a civil engineer by training but synthetic biologist in practice, has been at the forefront of recent innovations in genetic circuits and synthetic biology systems2. In an interview with the New Yorker in 2009, he speculated: "If the cells in our bodies had a little memory, think what we could do.
" Specifically, he entertained the idea that genetic memory could be used to encode a counter that tracks cell divisions. Besides making aging studies technically easier, anti-cancer therapies could be interfaced with the counter to specifically target cancer cells that are dividing out of control. Background The authors of this paper chose DNA flipping as the mechanism for genetic data storage.
Design Testing Conclusions Reference: 1. Correction. Sequence analysis of mutations and translocations across breast cancer subtypes : Nature. Breast cancers are classified according to gene-expression subtypes: luminal A, luminal B, Her2-enriched (Her2 is also known as ERBB2), and basal-like14.
Luminal subtypes are associated with expression of oestrogen and progesterone receptors and differentiated luminal epithelial cell markers. The subtypes differ in genomic complexity, key genetic alterations and clinical prognosis2, 3, 4, 15. To discover genomic alterations in breast cancers, we performed whole-genome and whole-exome sequencing of 108 primary, treatment-naive, breast carcinoma/normal DNA pairs from all major expression subtypes (Table 1 and Supplementary Tables 1–3), 17 cases by whole-exome and whole-genome sequencing, 5 cases by whole-genome sequencing alone, and 86 cases by whole-exome sequencing alone.
Table of contents : Nature Chemical Biology.
A User's Guide to the Encyclopedia of DNA Elements (ENCODE) II.
ENCODE Project Data The following sections describe the different types of data being produced by the ENCODE Project (Table 1). Genes and Transcripts Gene annotation. A major goal of ENCODE is to annotate all protein-coding genes, pseudogenes, and non-coding transcribed loci in the human genome and to catalog the products of transcription including splice isoforms. The result of ENCODE gene annotation (termed “GENCODE”) is a comprehensive catalog of transcripts and gene models. RNA transcripts.