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Snakebite envenoming is a serious and neglected tropical disease that kills ~100,000 people annually. High-quality, genome-enabled comprehensive characterization of toxin genes will facilitate development of effective humanized recombinant antivenom. We report a de novo near-chromosomal genome assembly of Naja naja, the Indian cobra, a highly venomous, medically important snake. Our assembly has a scaffold N50 of 223.35 Mb, with 19 scaffolds containing 95% of the genome. Of the 23,248 predicted protein-coding genes, 12,346 venom-gland-expressed genes constitute the ‘venom-ome’ and this included 139 genes from 33 toxin families. Among the 139 toxin genes were 19 ‘venom-ome-specific toxins’ (VSTs) that showed venom-gland-specific expression, and these probably encode the minimal core venom effector proteins. Synthetic venom reconstituted through recombinant VST expression will aid in the rapid development of safe and effective synthetic antivenom. Additionally, our genome could serve as a reference for snake genomes, support evolutionary studies and enable venom-driven drug discovery., Analysis of a near-chromosomal genome assembly and transcriptome profiling of the Indian cobra identifies genes expressed in the venom glands. These data should help develop a new antivenom.

Significance Genetic heterogeneity is a significant driver of antibiotic resistance in bacteria. Understanding copy number (CN) heterogeneity is important because minority subclones with increased CN can drive resistance during antibiotic exposure, but revert and escape detection during clinical susceptibility testing. Despite its clinical relevance, CN variation has eluded quantification at single-molecule resolution. Here, we report nanopore sequencing of arylomycin-resistant mutants carrying tandem repeats ranging in size from 4.8 to 50.0 kb and encompassing the arylomycin target gene lepB. Reads spanning individual repeat arrays show vast differences in CN, underscoring the importance of amplifications in driving the emergence of genetic heterogeneity. This is a direct observation of cell-to-cell CN differences in an antibiotic-resistant bacterial population., Tandem gene amplification is a frequent and dynamic source of antibiotic resistance in bacteria. Ongoing expansions and contractions of repeat arrays during population growth are expected to manifest as cell-to-cell differences in copy number (CN). As a result, a clonal bacterial culture could comprise subpopulations of cells with different levels of antibiotic sensitivity that result from variable gene dosage. Despite the high potential for misclassification of heterogenous cell populations as either antibiotic-susceptible or fully resistant in clinical settings, and the concomitant risk of inappropriate treatment, CN distribution among cells has defied analysis. Here, we use the MinION single-molecule nanopore sequencer to uncover CN heterogeneity in clonal populations of Escherichia coli and Acinetobacter baumannii grown from single cells isolated while selecting for resistance to an optimized arylomycin, a member of a recently discovered class of Gram-negative antibiotic. We found that gene amplification of the arylomycin target, bacterial type I signal peptidase LepB, is a mechanism of unstable arylomycin resistance and demonstrate in E. coli that amplification instability is independent of RecA. This instability drives the emergence of a nonuniform distribution of lepB CN among cells with a range of 1 to at least 50 copies of lepB identified in a single clonal population. In sum, this remarkable heterogeneity, and the evolutionary plasticity it fuels, illustrates how gene amplification can enable bacterial populations to respond rapidly to novel antibiotics. This study establishes a rationale for further nanopore-sequencing studies of heterogeneous cell populations to uncover CN variability at single-molecule resolution.

Despite widespread use of CRISPR, comprehensive data on the frequency and impact of Cas9-mediated off-targets in modified rodents are limited. Here we present deep-sequencing data from 81 genome-editing projects on mouse and rat genomes at 1,423 predicted off-target sites, 32 of which were confirmed, and show that high-fidelity Cas9 versions reduced off-target mutation rates in vivo. Using whole-genome sequencing data from ten mouse embryos, treated with a single guide RNA (sgRNA), and from their genetic parents, we found 43 off-targets, 30 of which were predicted by an adapted version of GUIDE-seq.

Recent work has demonstrated that two archaic human groups (Neanderthals and Denisovans) interbred with modern humans and contributed to the contemporary human gene pool. These findings relied on the availability of high-coverage genomes from both Neanderthals and Denisovans. Here we search for evidence of archaic admixture from a worldwide panel of 1,667 individuals using an approach that does not require the presence of an archaic human reference genome. We find no evidence for archaic admixture in the Andaman Islands, as previously claimed, or on the island of Flores, where Homo floresiensis fossils have been found. However, we do find evidence for at least one archaic admixture event in sub-Saharan Africa, with the strongest signal in Khoesan and Pygmy individuals from Southern and Central Africa. The locations of these putative archaic admixture tracts are weighted against functional regions of the genome, consistent with the long-term effects of purifying selection against introgressed genetic material.

Background Data from the 1000 Genomes project is quite often used as a reference for human genomic analysis. However, its accuracy needs to be assessed to understand the quality of predictions made using this reference. We present here an assessment of the genotyping, phasing, and imputation accuracy data in the 1000 Genomes project. We compare the phased haplotype calls from the 1000 Genomes project to experimentally phased haplotypes for 28 of the same individuals sequenced using the 10X Genomics platform. Results We observe that phasing and imputation for rare variants are unreliable, which likely reflects the limited sample size of the 1000 Genomes project data. Further, it appears that using a population specific reference panel does not improve the accuracy of imputation over using the entire 1000 Genomes data set as a reference panel. We also note that the error rates and trends depend on the choice of definition of error, and hence any error reporting needs to take these definitions into account. Conclusions The quality of the 1000 Genomes data needs to be considered while using this database for further studies. This work presents an analysis that can be used for these assessments. Electronic supplementary material The online version of this article (10.1186/s12864-019-5957-x) contains supplementary material, which is available to authorized users.

Besides macaques, baboons are the most commonly used nonhuman primate in biomedical research. Despite this importance, the genomic resources for baboons are quite limited. In particular, the current baboon reference genome Panu_3.0 is a highly fragmented, reference-guided (i.e., not fully de novo) assembly, and its poor quality inhibits our ability to conduct downstream genomic analyses. Here we present a truly de novo genome assembly of the olive baboon (Papio anubis) that uses data from several recently developed single-molecule technologies. Our assembly, Panubis1.0, has an N50 contig size of ~1.46 Mb (as opposed to 139 Kb for Panu_3.0), and has single scaffolds that span each of the 20 autosomes and the X chromosome. We highlight multiple lines of evidence (including Bionano Genomics data, pedigree linkage information, and linkage disequilibrium data) suggesting that there are several large assembly errors in Panu_3.0, which have been corrected in Panubis1.0.

We analyzed transcriptomes (n = 211), whole exomes (n = 99) and targeted exomes (n = 103) from 216 malignant pleural mesothelioma (MPM) tumors. Using RNA-seq data, we identified four distinct molecular subtypes: sarcomatoid, epithelioid, biphasic-epithelioid (biphasic-E) and biphasic-sarcomatoid (biphasic-S). Through exome analysis, we found BAP1, NF2, TP53, SETD2, DDX3X, ULK2, RYR2, CFAP45, SETDB1 and DDX51 to be significantly mutated (q-score ≥ 0.8) in MPMs. We identified recurrent mutations in several genes, including SF3B1 (∼2%; 4/216) and TRAF7 (∼2%; 5/216). SF3B1-mutant samples showed a splicing profile distinct from that of wild-type tumors. TRAF7 alterations occurred primarily in the WD40 domain and were, except in one case, mutually exclusive with NF2 alterations. We found recurrent gene fusions and splice alterations to be frequent mechanisms for inactivation of NF2, BAP1 and SETD2. Through integrated analyses, we identified alterations in Hippo, mTOR, histone methylation, RNA helicase and p53 signaling pathways in MPMs.

Identifying and understanding changes in cancer genomes is essential for the development of targeted therapeutics1. Here we analyse systematically more than 70 pairs of primary human colon tumours by applying next-generation sequencing to characterize their exomes, transcriptomes and copy-number alterations. We have identified 36,303 protein-altering somatic changes that include several new recurrent mutations in the Wnt pathway gene TCF7L2, chromatin-remodelling genes such as TET2 and TET3 and receptor tyrosine kinases including ERBB3. Our analysis for significantly mutated cancer genes identified 23 candidates, including the cell cycle checkpoint kinase ATM. Copy-number and RNA-seq data analysis identified amplifications and corresponding overexpression of IGF2 in a subset of colon tumours. Furthermore, using RNA-seq data we identified multiple fusion transcripts including recurrent gene fusions involving R-spondin family members RSPO2 and RSPO3 that together occur in 10% of colon tumours. The RSPO fusions were mutually exclusive with APC mutations, indicating that they probably have a role in the activation of Wnt signalling and tumorigenesis. Consistent with this we show that the RSPO fusion proteins were capable of potentiating Wnt signalling. The R-spondin gene fusions and several other gene mutations identified in this study provide new potential opportunities for therapeutic intervention in colon cancer.

Oncogenic mutations in PIK3CA, which encodes the phosphoinositide-3-kinase (PI3K) catalytic subunit p110α, occur in ∼25% of human breast cancers. In this study, we report the development of a knock-in mouse model for breast cancer where the endogenous Pik3ca allele was modified to allow tissue-specific conditional expression of a frequently found Pik3ca(H1047R) (Pik3ca(e20H1047R)) mutant allele. We found that activation of the latent Pik3ca(H1047R) allele resulted in breast tumors with multiple histological types. Whole-exome analysis of the Pik3ca(H1047R)-driven mammary tumors identified multiple mutations, including Trp53 mutations that appeared spontaneously during the development of adenocarinoma and spindle cell tumors. Further, we used this model to test the efficacy of GDC-0941, a PI3K inhibitor, in clinical development, and showed that the tumors respond to PI3K inhibition.

Deregulated HER2 is a target of many approved cancer drugs. We analyzed 111,176 patient tumors and identified recurrent mutations in HER2 transmembrane domain (TMD) and juxtamembrane domain (JMD) that include G660D, R678Q, E693K, and Q709L. Using a saturation mutagenesis screen and testing of patient-derived mutations we found several activating TMD and JMD mutations. Structural modeling and analysis showed that the TMD/JMD mutations function by improving the active dimer interface or stabilizing an activating conformation. Further, we found that HER2 G660D employed asymmetric kinase dimerization for activation and signaling. Importantly, anti-HER2 antibodies and small-molecule kinase inhibitors blocked the activity of TMD/JMD mutants. Consistent with this, a G660D germline mutant lung cancer patient showed remarkable clinical response to HER2 blockade.

Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas ( TCGA) pilot project aims to assess the value of large- scale multi- dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas - the most common type of primary adult brain cancer - and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol- 3- OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.

ArrayExpress is a public microarray repository founded on the Minimum Information About a Microarray Experiment (MIAME) principles that stores MIAME-compliant gene expression data. Plant-based data sets represent approximately one-quarter of the experiments in ArrayExpress. The majority are based on Arabidopsis (Arabidopsis thaliana); however, there are other data sets based on Triticum aestivum, Hordeum vulgare, and Populus subsp. AtMIAMExpress is an open-source Web-based software application for the submission of Arabidopsis-based microarray data to ArrayExpress. AtMIAMExpress exports data in MAGE-ML format for upload to any MAGE-ML-compliant application, such as J-Express and ArrayExpress. It was designed as a tool for users with minimal bioinformatics expertise, has comprehensive help and user support, and represents a simple solution to meeting the MIAME guidelines for the Arabidopsis community. Plant data are queryable both in ArrayExpress and in the Data Warehouse databases, which support queries based on gene-centric and sample-centric annotation. The AtMIAMExpress submission tool is available at http://www.ebi.ac.uk/at-miamexpress/. The software is open source and is available from http://sourceforge.net/projects/miamexpress/. For information, contact miamexpress@ebi.ac.uk.

Transcript profiling is crucial to study biological systems, and various platforms have been implemented to survey mRNAs at the genome scale. We have assessed the performance of the CATMA microarray designed for Arabidopsis (Arabidopsis thaliana) transcriptome analysis and compared it with the Agilent and Affymetrix commercial platforms. The CATMA array consists of gene-specific sequence tags of 150 to 500 bp, the Agilent (Arabidopsis 2) array of 60mer oligonucleotides, and the Affymetrix gene chip (ATH1) of 25mer oligonucleotide sets. We have matched each probe repertoire with the Arabidopsis genome annotation (The Institute for Genomic Research release 5.0) and determined the correspondence between them. Array performance was analyzed by hybridization with labeled targets derived from eight RNA samples made of shoot total RNA spiked with a calibrated series of 14 control transcripts. CATMA arrays showed the largest dynamic range extending over three to four logs. Agilent and Affymetrix arrays displayed a narrower range, presumably because signal saturation occurred for transcripts at concentrations beyond 1,000 copies per cell. Sensitivity was comparable for all three platforms. For Affymetrix GeneChip data, the RMA software package outperformed Microarray Suite 5.0 for all investigated criteria, confirming that the information provided by the mismatch oligonucleotides has no added value. In addition, taking advantage of replicates in our dataset, we conducted a robust statistical analysis of the platform propensity to yield false positive and false negative differentially expressed genes, and all gave satisfactory results. The results establish the CATMA array as a mature alternative to the Affymetrix and Agilent platforms.

International audience; The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

To further understand the molecular distinctions between kidney cancer subtypes, we analyzed exome, transcriptome and copy number alteration data from 167 primary human tumors that included renal oncocytomas and non-clear cell renal cell carcinomas (nccRCCs), consisting of papillary (pRCC), chromophobe (chRCC) and translocation (tRCC) subtypes. We identified ten significantly mutated genes in pRCC, including MET, NF2, SLC5A3, PNKD and CPQ. MET mutations occurred in 15% (10/65) of pRCC samples and included previously unreported recurrent activating mutations. In chRCC, we found TP53, PTEN, FAAH2, PDHB, PDXDC1 and ZNF765 to be significantly mutated. Gene expression analysis identified a five-gene set that enabled the molecular classification of chRCC, renal oncocytoma and pRCC. Using RNA sequencing, we identified previously unreported gene fusions, including ACTG1-MITF fusion. Ectopic expression of the ACTG1-MITF fusion led to cellular transformation and induced the expression of downstream target genes. Finally, we observed upregulation of the anti-apoptotic factor BIRC7 in MiTF-high RCC tumors, suggesting a potential therapeutic role for BIRC7 inhibitors.

Tumor-derived cell lines have served as vital models to advance our understanding of oncogene function and therapeutic responses. Although substantial effort has been made to define the genomic constitution of cancer cell line panels, the transcriptome remains understudied. Here we describe RNA sequencing and single-nucleotide polymorphism (SNP) array analysis of 675 human cancer cell lines. We report comprehensive analyses of transcriptome features including gene expression, mutations, gene fusions and expression of non-human sequences. Of the 2,200 gene fusions catalogued, 1,435 consist of genes not previously found in fusions, providing many leads for further investigation. We combine multiple genome and transcriptome features in a pathway-based approach to enhance prediction of response to targeted therapeutics. Our results provide a valuable resource for studies that use cancer cell lines.

Malignant pleural mesothelioma (MPM) is an aggressive cancer arising from the mesothelial cells of the pleura. About 80% of mesothelioma cases are linked to asbestos exposure, while the remainder may be related to prior chest radiation, genetic predisposition or spontaneous occurrence. We analyzed transcriptomes (n = 211), whole exomes (n = 99), and targeted exomes (n = 103) from 216 malignant pleural mesothelioma (MPM) tumors. Four distinct molecular subtypes, sarcomatoid (S), epithelioid (E), biphasic-E, and biphasic-S were identified using RNA-seq data. Using exome analysis we found BAP1, NF2, TP53, SETD2, DDX3X, ULK2, RYR2, CFAP45, SETDB1, and DDX51 to be significantly mutated MPM genes. We identified recurrent mutations in several genes including splice complex factor SF3B1 (∼2% [4/216]) and TRAF7 (∼2% [5/216]). SF3B1 mutant samples showed a distinct splicing profile compared to wild-type tumors. Mutations in TRAF7 occurred primarily in the WD40 domain and except in one case were mutually exclusive with NF2 mutations. We found recurrent gene fusions and splice alterations to be frequent mechanisms for inactivation of NF2, BAP1 and SETD2. We analyzed the tumors for infiltrating immune cells and found that M2 macrophage to T-cell ratio was predictive of reduced overall survival. Further we found sarcomatoid subtype had a significantly higher level of PD-L1 expression suggesting that this group might be prioritized for checkpoint inhibitor therapy. Integrated analysis of alterations identified changes in Hippo, mTOR, Histone methylation, RNA helicase and p53 signaling pathways in MPMs. Overall our study significantly expands on the previous genomic studies and provides a comprehensive genomics profile of mesothelioma and identifies several previously unknown alterations and biological pathways that are potential targets for drug discovery and treatment. Incorporating genomic analysis for detection of actionable alterations as part of MPM patient care will help in developing rational individualized therapy. Citation Format: Somasekar Seshagiri, Eric Stawiski, Leonard Goldstein, Steffen Durinck, Zora Modrusan, Florian Gnad, Thong T. Nguyen, Bijay Jaiswal, Assunta De Rienzo, William Richards, Raphael Bueno. Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-331.

Small-cell lung cancer (SCLC) is an exceptionally aggressive disease with poor prognosis. Here, we obtained exome, transcriptome and copy-number alteration data from approximately 53 samples consisting of 36 primary human SCLC and normal tissue pairs and 17 matched SCLC and lymphoblastoid cell lines. We also obtained data for 4 primary tumors and 23 SCLC cell lines. We identified 22 significantly mutated genes in SCLC, including genes encoding kinases, G protein–coupled receptors and chromatin-modifying proteins. We found that several members of the SOX family of genes were mutated in SCLC. We also found SOX2 amplification in ~27% of the samples. Suppression of SOX2 using shRNAs blocked proliferation of SOX2-amplified SCLC lines. RNA sequencing identified multiple fusion transcripts and a recurrent RLF-MYCL1 fusion. Silencing of MYCL1 in SCLC cell lines that had the RLF-MYCL1 fusion decreased cell proliferation. These data provide an in-depth view of the spectrum of genomic alterations in SCLC and identify several potential targets for therapeutic intervention.

Background Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fxed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis. Results Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically signifcant negative correlation between methylation profles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Conclusions Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.

Background Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses. Results We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system. Conclusion GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.

Loss of 1p36 heterozygosity commonly occurs with MYCN amplification in neuroblastoma tumors, and both are associated with an aggressive phenotype. Database searches identified five microRNAs that map to the commonly deleted region of 1p36 and we hypothesized that the loss of one or more of these microRNAs contributes to the malignant phenotype of MYCN-amplified tumors. By bioinformatic analysis, we identified that three out of the five microRNAs target MYCN and of these miR-34a caused the most significant suppression of cell growth through increased apoptosis and decreased DNA synthesis in neuroblastoma cell lines with MYCN amplification. Quantitative RT-PCR showed that neuroblastoma tumors with 1p36 loss expressed lower level of miR-34a than those with normal copies of 1p36. Furthermore, we demonstrated that MYCN is a direct target of miR-34a. Finally, using a series of mRNA expression profiling experiments, we identified other potential direct targets of miR-34a, and pathway analysis demonstrated that miR-34a suppresses cell-cycle genes and induces several neural-related genes. This study demonstrates one important regulatory role of miR-34a in cell growth and MYCN suppression in neuroblastoma.

The human epidermal growth factor receptor (HER) family of tyrosine kinases are deregulated in multiple cancers either through amplification, overexpression or mutation. ERBB3/HER3, the only ERBB family member with an impaired kinase domain, upon ligand binding heterodimerizes with ERBB2 to promote signaling. While amplification and overexpression of ERBB3 is observed in some cancers, occurrence and relevance of ERBB3 somatic mutations in oncogenesis is not established. Here we report the identification of ERBB3 somatic mutations in ∼11% of colon and gastric cancers. We found that the ERBB3 mutants together with ERBB2 promote oncogenic signaling and transformed colonic and breast epithelial cells in a ligand independent manner. Further, we found that multiple target therapeutics that acts on ERBB3, ERBB2 or their downstream signaling components are effective in blocking ERBB3-mutant mediated oncogenic signaling and disease progression in vivo. Identification of activating ERBB3 mutations along with the established contribution of ERBB3 to acquired resistance to EGFR/ERBB2-targeted drugs provides a rational to therapeutically target ERBB3. Citation Format: Bijay S. Jaiswal, Noelyn M. Kljavin, Eric Stawiski, Steffen Durinck, Subhra Chaudhuri, Charles Eigenbrot, Gabriele Schaefer, Frederic J. de Sauvage, Somasekar Seshagiri. Oncogenic ERBB3 mutations in human cancers. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4428. doi:10.1158/1538-7445.AM2014-4428

Microarray transcript profiling and RNA interference are two new technologies crucial for large-scale gene function studies in multicellular eukaryotes. Both rely on sequence-specific hybridization between complementary nucleic acid strands, inciting us to create a collection of gene-specific sequence tags (GSTs) representing at least 21,500 Arabidopsis genes and which are compatible with both approaches. The GSTs were carefully selected to ensure that each of them shared no significant similarity with any other region in the Arabidopsis genome. They were synthesized by PCR amplification from genomic DNA. Spotted microarrays fabricated from the GSTs show good dynamic range, specificity, and sensitivity in transcript profiling experiments. The GSTs have also been transferred to bacterial plasmid vectors via recombinational cloning protocols. These cloned GSTs constitute the ideal starting point for a variety of functional approaches, including reverse genetics. We have subcloned GSTs on a large scale into vectors designed for gene silencing in plant cells. We show that in planta expression of GST hairpin RNA results in the expected phenotypes in silenced Arabidopsis lines. These versatile GST resources provide novel and powerful tools for functional genomics.

Expression Profiler (EP, http://www.ebi.ac.uk/expressionprofiler) is a web-based platform for microarray gene expression and other functional genomics-related data analysis. The new architecture, Expression Profiler: next generation (EP:NG), modularizes the original design and allows individual analysis-task-related components to be developed by different groups and yet still seamlessly to work together and share the same user interface look and feel. Data analysis components for gene expression data preprocessing, missing value imputation, filtering, clustering methods, visualization, significant gene finding, between group analysis and other statistical components are available from the EBI (European Bioinformatics Institute) web site. The web-based design of Expression Profiler supports data sharing and collaborative analysis in a secure environment. Developed tools are integrated with the microarray gene expression database ArrayExpress and form the exploratory analytical front-end to those data. EP:NG is an open-source project, encouraging broad distribution and further extensions from the scientific community.