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High-throughput oligonucleotide microarrays are commonly employed to investigate genetic disease, including cancer. The algorithms employed to extract genotypes and copy number variation function optimally for diploid genomes usually associated with inherited disease. However, cancer genomes are aneuploid in nature leading to systematic errors when using these techniques. We introduce a preprocessing transformation and hidden Markov model algorithm bespoke to cancer. This produces genotype classification, specification of regions of loss of heterozygosity, and absolute allelic copy number segmentation. Accurate prediction is demonstrated with a combination of independent experimental techniques. These methods are exemplified with affymetrix genome-wide SNP6.0 data from 755 cancer cell lines, enabling inference upon a number of features of biological interest. These data and the coded algorithm are freely available for download.

It is a major challenge to develop effective sequence database search algorithms to translate molecular weight and fragment mass information obtained from tandem mass spectrometry into high quality peptide and protein assignments. We investigated the peptide identification performance of Mascot and X!Tandem for mass tolerance settings common for low and high accuracy mass spectrometry. We demonstrated that sensitivity and specificity of peptide identification can vary substantially for different mass tolerance settings, but this effect was more significant for Mascot. We present an adjusted Mascot threshold, which allows the user to freely select the best trade-off between sensitivity and specificity. The adjusted Mascot threshold was compared with the default Mascot and X!Tandem scoring thresholds and shown to be more sensitive at the same false discovery rates for both low and high accuracy mass spectrometry data.

Mobile group II introns encode reverse transcriptases (RTs) that function in intron mobility (“retrohoming”) by a process that requires reverse transcription of a highly structured, 2–2.5-kb intron RNA with high processivity and fidelity. Although the latter properties are potentially useful for applications in cDNA synthesis and next-generation RNA sequencing (RNA-seq), group II intron RTs have been difficult to purify free of the intron RNA, and their utility as research tools has not been investigated systematically. Here, we developed general methods for the high-level expression and purification of group II intron-encoded RTs as fusion proteins with a rigidly linked, noncleavable solubility tag, and we applied them to group II intron RTs from bacterial thermophiles. We thus obtained thermostable group II intron RT fusion proteins that have higher processivity, fidelity, and thermostability than retroviral RTs, synthesize cDNAs at temperatures up to 81°C, and have significant advantages for qRT-PCR, capillary electrophoresis for RNA-structure mapping, and next-generation RNA sequencing. Further, we find that group II intron RTs differ from the retroviral enzymes in template switching with minimal base-pairing to the 3′ ends of new RNA templates, making it possible to efficiently and seamlessly link adaptors containing PCR-primer binding sites to cDNA ends without an RNA ligase step. This novel template-switching activity enables facile and less biased cloning of nonpolyadenylated RNAs, such as miRNAs or protein-bound RNA fragments. Our findings demonstrate novel biochemical activities and inherent advantages of group II intron RTs for research, biotechnological, and diagnostic methods, with potentially wide applications.

Motivation: Whole genome and exome sequencing of matched tumor–normal sample pairs is becoming routine in cancer research. The consequent increased demand for somatic variant analysis of paired samples requires methods specialized to model this problem so as to sensitively call variants at any practical level of tumor impurity. Results: We describe Strelka, a method for somatic SNV and small indel detection from sequencing data of matched tumor–normal samples. The method uses a novel Bayesian approach which represents continuous allele frequencies for both tumor and normal samples, while leveraging the expected genotype structure of the normal. This is achieved by representing the normal sample as a mixture of germline variation with noise, and representing the tumor sample as a mixture of the normal sample with somatic variation. A natural consequence of the model structure is that sensitivity can be maintained at high tumor impurity without requiring purity estimates. We demonstrate that the method has superior accuracy and sensitivity on impure samples compared with approaches based on either diploid genotype likelihoods or general allele-frequency tests. Availability: The Strelka workflow source code is available at ftp://strelka@ftp.illumina.com/. Contact: csaunders@illumina.com Supplementary information: Supplementary data are available at Bioinformatics online

The cancer genome is moulded by the dual processes of somatic mutation and selection. Homozygous deletions in cancer genomes occur over recessive cancer genes, where they can confer selective growth advantage, and over fragile sites, where they are thought to reflect an increased local rate of DNA breakage. However, most homozygous deletions in cancer genomes are unexplained. Here we identified 2,428 somatic homozygous deletions in 746 cancer cell lines. These overlie 11% of protein-coding genes that, therefore, are not mandatory for survival of human cells. We derived structural signatures that distinguish between homozygous deletions over recessive cancer genes and fragile sites. Application to clusters of unexplained homozygous deletions suggests that many are in regions of inherent fragility, whereas a small subset overlies recessive cancer genes. The results illustrate how structural signatures can be used to distinguish between the influences of mutation and selection in cancer genomes. The extensive copy number, genotyping, sequence and expression data available for this large series of publicly available cancer cell lines renders them informative reagents for future studies of cancer biology and drug discovery.

The mammalian postsynaptic density (PSD) comprises a complex collection of approximately 1100 proteins. Despite extensive knowledge of individual proteins, the overall organization of the PSD is poorly understood. Here, we define maps of molecular circuitry within the PSD based on phosphorylation of postsynaptic proteins. Activation of a single neurotransmitter receptor, the N-methyl-D-aspartate receptor (NMDAR), changed the phosphorylation status of 127 proteins. Stimulation of ionotropic and metabotropic glutamate receptors and dopamine receptors activated overlapping networks with distinct combinatorial phosphorylation signatures. Using peptide array technology, we identified specific phosphorylation motifs and switching mechanisms responsible for the integration of neurotransmitter receptor pathways and their coordination of multiple substrates in these networks. These combinatorial networks confer high information-processing capacity and functional diversity on synapses, and their elucidation may provide new insights into disease mechanisms and new opportunities for drug discovery.