Colin K. Watanabe

GMAP: a genomic mapping and alignment program for mRNA and EST sequences

MOTIVATION We introduce GMAP, a standalone program for mapping and aligning cDNA sequences to a genome. The program maps and aligns a single sequence with minimal startup time and memory requirements, and provides fast batch processing of large sequence sets. The program generates accurate gene structures, even in the presence of substantial polymorphisms and sequence errors, without using probabilistic splice site models. Methodology underlying the program includes a minimal sampling strategy for genomic mapping, oligomer chaining for approximate alignment, sandwich DP for splice site detection, and microexon identification with statistical significance testing. RESULTS On a set of human messenger RNAs with random mutations at a 1 and 3% rate, GMAP identified all splice sites accurately in over 99.3% of the sequences, which was one-tenth the error rate of existing programs. On a large set of human expressed sequence tags, GMAP provided higher-quality alignments more often than blat did. On a set of Arabidopsis cDNAs, GMAP performed comparably with GeneSeqer. In these experiments, GMAP demonstrated a several-fold increase in speed over existing programs. AVAILABILITY Source code for gmap and associated programs is available at http://www.gene.com/share/gmap SUPPLEMENTARY INFORMATION http://www.gene.com/share/gmap.

The mutation spectrum revealed by paired genome sequences from a lung cancer patient

Lung cancer is the leading cause of cancer-related mortality worldwide, with non-small-cell lung carcinomas in smokers being the predominant form of the disease. Although previous studies have identified important common somatic mutations in lung cancers, they have primarily focused on a limited set of genes and have thus provided a constrained view of the mutational spectrum. Recent cancer sequencing efforts have used next-generation sequencing technologies to provide a genome-wide view of mutations in leukaemia, breast cancer and cancer cell lines. Here we present the complete sequences of a primary lung tumour (60× coverage) and adjacent normal tissue (46×). Comparing the two genomes, we identify a wide variety of somatic variations, including >50,000 high-confidence single nucleotide variants. We validated 530 somatic single nucleotide variants in this tumour, including one in the KRAS proto-oncogene and 391 others in coding regions, as well as 43 large-scale structural variations. These constitute a large set of new somatic mutations and yield an estimated 17.7 per megabase genome-wide somatic mutation rate. Notably, we observe a distinct pattern of selection against mutations within expressed genes compared to non-expressed genes and in promoter regions up to 5 kilobases upstream of all protein-coding genes. Furthermore, we observe a higher rate of amino acid-changing mutations in kinase genes. We present a comprehensive view of somatic alterations in a single lung tumour, and provide the first evidence, to our knowledge, of distinct selective pressures present within the tumour environment.

Protein Identification: The Origins of Peptide Mass Fingerprinting

Peptide mass fingerprinting (PMF) grew from a need for a faster, more efficient method to identify frequently observed proteins in electrophoresis gels. We describe the genesis of the idea in 1989, and show the first demonstration with fast atom bombardment mass spectrometry. Despite its promise, the method was seldom used until 1992, with the coming of significantly more sensitive commercial instrumentation based on MALDI-TOF-MS. We recount the evolution of the method and its dependence on a number of technical breakthroughs, both in mass spectrometry and in other areas. We show how it laid the foundation for high-throughput, high-sensitivity methods of protein analysis, now known as proteomics. We conclude with recommendations for further improvements, and speculation of the role of PMF in the future.

CanPredict: a computational tool for predicting cancer-associated missense mutations

Various cancer genome projects are underway to identify novel mutations that drive tumorigenesis. While these screens will generate large data sets, the majority of identified missense changes are likely to be innocuous passenger mutations or polymorphisms. As a result, it has become increasingly important to develop computational methods for distinguishing functionally relevant mutations from other variations. We previously developed an algorithm, and now present the web application, CanPredict (http://www.canpredict.org/ or http://www.cgl.ucsf.edu/Research/genentech/canpredict/), to allow users to determine if particular changes are likely to be cancer-associated. The impact of each change is measured using two known methods: Sorting Intolerant From Tolerant (SIFT) and the Pfam-based LogR.E-value metric. A third method, the Gene Ontology Similarity Score (GOSS), provides an indication of how closely the gene in which the variant resides resembles other known cancer-causing genes. Scores from these three algorithms are analyzed by a random forest classifier which then predicts whether a change is likely to be cancer-associated. CanPredict fills an important need in cancer biology and will enable a large audience of biologists to determine which mutations are the most relevant for further study.

CCAT1 is an enhancer-templated RNA that predicts BET sensitivity in colorectal cancer

Colon tumors arise in a stepwise fashion from either discrete genetic perturbations or epigenetic dysregulation. To uncover the key epigenetic regulators that drive colon cancer growth, we used a CRISPR loss-of-function screen and identified a number of essential genes, including the bromodomain and extraterminal (BET) protein BRD4. We found that BRD4 is critical for colon cancer proliferation, and its knockdown led to differentiation effects in vivo. JQ1, a BET inhibitor, preferentially reduced growth in a subset of epigenetically dysregulated colon cancers characterized by the CpG island methylator phenotype (CIMP). Integrated transcriptomic and genomic analyses defined a distinct superenhancer in CIMP+ colon cancers that regulates cMYC transcription. We found that the long noncoding RNA colon cancer-associated transcript 1 (CCAT1) is transcribed from this superenhancer and is exquisitely sensitive to BET inhibition. Concordantly, cMYC transcription and cell growth were tightly correlated with the presence of CCAT1 RNA in a variety of tumor types. Taken together, we propose that CCAT1 is a clinically tractable biomarker for identifying patients who are likely to benefit from BET inhibitors.

MBASED: allele-specific expression detection in cancer tissues and cell lines

Allele-specific gene expression, ASE, is an important aspect of gene regulation. We developed a novel method MBASED, meta-analysis based allele-specific expression detection for ASE detection using RNA-seq data that aggregates information across multiple single nucleotide variation loci to obtain a gene-level measure of ASE, even when prior phasing information is unavailable. MBASED is capable of one-sample and two-sample analyses and performs well in simulations. We applied MBASED to a panel of cancer cell lines and paired tumor-normal tissue samples, and observed extensive ASE in cancer, but not normal, samples, mainly driven by genomic copy number alterations.

GEPIS---quantitative gene expression profiling in normal and cancer tissues

MOTIVATION Expression profiling in diverse tissues is fundamental to understanding gene function as well as therapeutic target identification. The vast collection of expressed sequence tags (ESTs) and the associated tissue source information provides an attractive opportunity for studying gene expression. RESULTS To facilitate EST-based expression analysis, we developed GEPIS (gene expression profiling in silico), a tool that integrates EST and tissue source information to compute gene expression patterns in a large panel of normal and tumor samples. We found EST-based expression patterns to be consistent with published papers as well as our own experimental results. We also built a GEPIS Regional Atlas that depicts expression characteristics of all genes in a selected genomic region. This program can be adapted for large-scale screening for genes with desirable expression patterns, as illustrated by our large-scale mining for tissue- and tumor-specific genes. AVAILABILITY The email server version of the GEPIS application is freely available at http://share.gene.com/share/gepis. An interactive version of GEPIS will soon be freely available at http://www.cgl.ucsf.edu/Research/genentech/gepis/. The source code, modules, data and gene lists can be downloaded at http://share.gene.com/share/gepis.

Quantitative evaluation of first, second, and third generation hairpin systems reveals the limit of mammalian vector-based RNAi

ABSTRACT Incorporating miRNA-like features into vector-based hairpin scaffolds has been shown to augment small RNA processing and RNAi efficiency. Therefore, defining an optimal, native hairpin context may obviate a need for hairpin-specific targeting design schemes, which confound the movement of functional siRNAs into shRNA/artificial miRNA backbones, or large-scale screens to identify efficacious sequences. Thus, we used quantitative cell-based assays to compare separate third generation artificial miRNA systems, miR-E (based on miR-30a) and miR-3G (based on miR-16-2 and first described in this study) to widely-adopted, first and second generation formats in both Pol-II and Pol-III expression vector contexts. Despite their unique structures and strandedness, and in contrast to first and second-generation RNAi triggers, the third generation formats operated with remarkable similarity to one another, and strong silencing was observed with a significant fraction of the evaluated target sequences within either promoter context. By pairing an established siRNA design algorithm with the third generation vectors we could readily identify targeting sequences that matched or exceeded the potency of those discovered through large-scale sensor-based assays. We find that third generation hairpin systems enable the maximal level of siRNA function, likely through enhanced processing and accumulation of precisely-defined guide RNAs. Therefore, we predict future gains in RNAi potency will come from improved hairpin expression and identification of optimal siRNA-intrinsic silencing properties rather than further modification of these scaffolds. Consequently, third generation systems should be the primary format for vector-based RNAi studies; miR-3G is advantageous due to its small expression cassette and simplified, cost-efficient cloning scheme.

Structure and Function in Recombinant HIV-1 gp120 and Speculation about the Disulfide Bonding in the gp120 Homologs of HIV-2 and SIV

The envelope glyco-proteins of the primate immunodeficiency viruses (HIV-1, HIV-2 and SIV) have been the objects of intense study since their discovery. The major envelope glycoprotein (gp120 in HIV-1) is of particular interest because it mediates the attachment of the virus to susceptible cells via the CD4 molecule1,2, it contains most of the important epitopes for neutralization of the virus by antibodies3,4,5, it plays an important role in the process by which the viral and host cell membranes fuse and the viral capsid gains access to the cytoplasm6,7, and its sequence variability is central to the ability of the virus to adapt to and escape the protective immune response of the host organism8. Complete understanding of these processes requires an understanding of the molecular structure of gp120 in detail. Such structural information has proven to be difficult to obtain because of the large size of gp120 (approximately 480 amino acids), its high degree of glycosylation (approximately 50% by weight), the high degree of heterogeneity of the oligosaccharides on the molecule, and the scarcity of material available for analysis.

Silencing of retrotransposons by SETDB1 inhibits the interferon response in acute myeloid leukemia

A propensity for rewiring genetic and epigenetic regulatory networks, thus enabling sustained cell proliferation, suppression of apoptosis, and the ability to evade the immune system, is vital to cancer cell propagation. An increased understanding of how this is achieved is critical for identifying or improving therapeutic interventions. In this study, using acute myeloid leukemia (AML) human cell lines and a custom CRISPR/Cas9 screening platform, we identify the H3K9 methyltransferase SETDB1 as a novel, negative regulator of innate immunity. SETDB1 is overexpressed in many cancers, and loss of this gene in AML cells triggers desilencing of retrotransposable elements that leads to the production of double-stranded RNAs (dsRNAs). This is coincident with induction of a type I interferon response and apoptosis through the dsRNA-sensing pathway. Collectively, our findings establish a unique gene regulatory axis that cancer cells can exploit to circumvent the immune system.