Predicting EGFR mutational status from pathology images using a real-world dataset.

Treatment of non-small cell lung cancer is increasingly biomarker driven with multiple genomic alterations, including those in the epidermal growth factor receptor (EGFR) gene, that benefit from targeted therapies. We developed a set of algorithms to assess EGFR status and morphology using a real-world advanced lung adenocarcinoma cohort of 2099 patients with hematoxylin and eosin (H&E) images exhibiting high morphological diversity and low tumor content relative to public datasets. The best performing EGFR algorithm was attention-based and achieved an area under the curve (AUC) of 0.870, a negative predictive value (NPV) of 0.954 and a positive predictive value (PPV) of 0.410 in a validation cohort reflecting the 15% prevalence of EGFR mutations in lung adenocarcinoma. The attention model outperformed a heuristic-based model focused exclusively on tumor regions, and we show that although the attention model also extracts signal primarily from tumor morphology, it extracts additional signal from non-tumor tissue regions. Further analysis of high-attention regions by pathologists showed associations of predicted EGFR negativity with solid growth patterns and higher peritumoral immune presence. This algorithm highlights the potential of deep learning tools to provide instantaneous rule-out screening for biomarker alterations and may help prioritize the use of scarce tissue for biomarker testing.

Humoral response to SARS-CoV-2 and seasonal coronaviruses in COVID-19 patients.

We used enzyme-linked immunoassay methods to measure the prevalence and the levels of antibody responses to the nucleocapsid (N) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and four seasonal human coronaviruses (HCoV-OC43, HCoV-HKU1, HCoV 229E, and HCoV-NL63) in a cohort of 115 convalescent plasma donors infected with SARS-CoV-2 (1-61 days after symptom onset) compared to antibody levels in 114 individuals with no evidence of a recent infection with SARS-CoV-2. In the humoral response to the four seasonal coronaviruses, only HCoV-HKU1- and HCoV-229E-assays showed slightly elevated antibody levels in the COVID group compared to the control group. While in the COVID-group the levels of SARS-CoV-2 antibodies correlated significantly with disease severity, no association was found in the levels of antibodies against the seasonal coronaviruses. The most striking result in both groups was that the levels of antibodies against all tested coronaviruses, including the new SARS-CoV-2 showed a highly significant correlation with each other. There seems to be an individual predisposition to a weaker or stronger humoral immune response against all known seasonal human coronaviruses including the new SARS-CoV-2, which could lead to a definition of low and high responders against human coronaviruses with potential impact on the assessment of postinfection antibody levels and protection.

Genetic alterations of SUGP1 mimic mutant-SF3B1 splice pattern in lung adenocarcinoma and other cancers.

Genes involved in 3'-splice site recognition during mRNA splicing constitute an emerging class of oncogenes. SF3B1 is the most frequently mutated splicing factor in cancer, and SF3B1 mutants corrupt branchpoint recognition leading to usage of cryptic 3'-splice sites and subsequent aberrant junctions. For a comprehensive determination of alterations leading to this splicing pattern, we performed a pan-TCGA screening for SF3B1-specific aberrant acceptor usage. While the most of aberrant 3'-splice patterns were explained by SF3B1 mutations, we also detected nine SF3B1 wild-type tumors (including five lung adenocarcinomas). Genomic profile analysis of these tumors identified somatic mutations combined with loss-of-heterozygosity in the splicing factor SUGP1 in five of these cases. Modeling of SUGP1 loss and mutations in cell lines showed that both alterations induced mutant-SF3B1-like aberrant splicing. Our study provides definitive evidence that genetic alterations of SUGP1 genocopy SF3B1 mutations in lung adenocarcinoma and other cancers.

Insights into coral bleaching under heat stress from analysis of gene expression in a sea anemone model system.

Loss of endosymbiotic algae ("bleaching") under heat stress has become a major problem for reef-building corals worldwide. To identify genes that might be involved in triggering or executing bleaching, or in protecting corals from it, we used RNAseq to analyze gene-expression changes during heat stress in a coral relative, the sea anemone Aiptasia. We identified >500 genes that showed rapid and extensive up-regulation upon temperature increase. These genes fell into two clusters. In both clusters, most genes showed similar expression patterns in symbiotic and aposymbiotic anemones, suggesting that this early stress response is largely independent of the symbiosis. Cluster I was highly enriched for genes involved in innate immunity and apoptosis, and most transcript levels returned to baseline many hours before bleaching was first detected, raising doubts about their possible roles in this process. Cluster II was highly enriched for genes involved in protein folding, and most transcript levels returned more slowly to baseline, so that roles in either promoting or preventing bleaching seem plausible. Many of the genes in clusters I and II appear to be targets of the transcription factors NFκB and HSF1, respectively. We also examined the behavior of 337 genes whose much higher levels of expression in symbiotic than aposymbiotic anemones in the absence of stress suggest that they are important for the symbiosis. Unexpectedly, in many cases, these expression levels declined precipitously long before bleaching itself was evident, suggesting that loss of expression of symbiosis-supporting genes may be involved in triggering bleaching.

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

Symbiont population control by host-symbiont metabolic interaction in Symbiodiniaceae-cnidarian associations.

In cnidarian-Symbiodiniaceae symbioses, algal endosymbiont population control within the host is needed to sustain a symbiotic relationship. However, the molecular mechanisms that underlie such population control are unclear. Here we show that a cnidarian host uses nitrogen limitation as a primary mechanism to control endosymbiont populations. Nitrogen acquisition and assimilation transcripts become elevated in symbiotic Breviolum minutum algae as they reach high-densities within the sea anemone host Exaiptasia pallida. These same transcripts increase in free-living algae deprived of nitrogen. Symbiotic algae also have an elevated carbon-to-nitrogen ratio and shift metabolism towards scavenging nitrogen from purines relative to free-living algae. Exaiptasia glutamine synthetase and glutamate synthase transcripts concomitantly increase with the algal endosymbiont population, suggesting an increased ability of the host to assimilate ammonium. These results suggest algal growth and replication in hospite is controlled by access to nitrogen, which becomes limiting for the algae as their population within the host increases.

Improved detection of gene fusions by applying statistical methods reveals oncogenic RNA cancer drivers.

The extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce Data-Enriched Efficient PrEcise STatistical fusion detection (DEEPEST), an algorithm that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling 10-fold fewer false-positive fusions in nontransformed human tissues. We leverage the increased precision of DEEPEST to discover fundamental cancer biology. Namely, 888 candidate oncogenes are identified based on overrepresentation in DEEPEST calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs, demonstrating a previously unappreciated prevalence and potential for function. DEEPEST also reveals a high enrichment for fusions involving oncogenes in cancers, including ovarian cancer, which has had minimal treatment advances in recent decades, finding that more than 50% of tumors harbor gene fusions predicted to be oncogenic. Specific protein domains are enriched in DEEPEST calls, indicating a global selection for fusion functionality: kinase domains are nearly 2-fold more enriched in DEEPEST calls than expected by chance, as are domains involved in (anaerobic) metabolism and DNA binding. The statistical algorithms, population-level analytic framework, and the biological conclusions of DEEPEST call for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.

Association of Patient Characteristics and Tumor Genomics With Clinical Outcomes Among Patients With Non-Small Cell Lung Cancer Using a Clinicogenomic Database.

Importance: Data sets linking comprehensive genomic profiling (CGP) to clinical outcomes may accelerate precision medicine. Objective: To assess whether a database that combines EHR-derived clinical data with CGP can identify and extend associations in non-small cell lung cancer (NSCLC). Design, Setting, and Participants: Clinical data from EHRs were linked with CGP results for 28 998 patients from 275 US oncology practices. Among 4064 patients with NSCLC, exploratory associations between tumor genomics and patient characteristics with clinical outcomes were conducted, with data obtained between January 1, 2011, and January 1, 2018. Exposures: Tumor CGP, including presence of a driver alteration (a pathogenic or likely pathogenic alteration in a gene shown to drive tumor growth); tumor mutation burden (TMB), defined as the number of mutations per megabase; and clinical characteristics gathered from EHRs. Main Outcomes and Measures: Overall survival (OS), time receiving therapy, maximal therapy response (as documented by the treating physician in the EHR), and clinical benefit rate (fraction of patients with stable disease, partial response, or complete response) to therapy. Results: Among 4064 patients with NSCLC (median age, 66.0 years; 51.9% female), 3183 (78.3%) had a history of smoking, 3153 (77.6%) had nonsquamous cancer, and 871 (21.4%) had an alteration in EGFR, ALK, or ROS1 (701 [17.2%] with EGFR, 128 [3.1%] with ALK, and 42 [1.0%] with ROS1 alterations). There were 1946 deaths in 7 years. For patients with a driver alteration, improved OS was observed among those treated with (n = 575) vs not treated with (n = 560) targeted therapies (median, 18.6 months [95% CI, 15.2-21.7] vs 11.4 months [95% CI, 9.7-12.5] from advanced diagnosis; P < .001). TMB (in mutations/Mb) was significantly higher among smokers vs nonsmokers (8.7 [IQR, 4.4-14.8] vs 2.6 [IQR, 1.7-5.2]; P < .001) and significantly lower among patients with vs without an alteration in EGFR (3.5 [IQR, 1.76-6.1] vs 7.8 [IQR, 3.5-13.9]; P < .001), ALK (2.1 [IQR, 0.9-4.0] vs 7.0 [IQR, 3.5-13.0]; P < .001), RET (4.6 [IQR, 1.7-8.7] vs 7.0 [IQR, 2.6-13.0]; P = .004), or ROS1 (4.0 [IQR, 1.2-9.6] vs 7.0 [IQR, 2.6-13.0]; P = .03). In patients treated with anti-PD-1/PD-L1 therapies (n = 1290, 31.7%), TMB of 20 or more was significantly associated with improved OS from therapy initiation (16.8 months [95% CI, 11.6-24.9] vs 8.5 months [95% CI, 7.6-9.7]; P < .001), longer time receiving therapy (7.8 months [95% CI, 5.5-11.1] vs 3.3 months [95% CI, 2.8-3.7]; P < .001), and increased clinical benefit rate (80.7% vs 56.7%; P < .001) vs TMB less than 20. Conclusions and Relevance: Among patients with NSCLC included in a longitudinal database of clinical data linked to CGP results from routine care, exploratory analyses replicated previously described associations between clinical and genomic characteristics, between driver mutations and response to targeted therapy, and between TMB and response to immunotherapy. These findings demonstrate the feasibility of creating a clinicogenomic database derived from routine clinical experience and provide support for further research and discovery evaluating this approach in oncology.

SeqOthello: querying RNA-seq experiments at scale.

We present SeqOthello, an ultra-fast and memory-efficient indexing structure to support arbitrary sequence query against large collections of RNA-seq experiments. It takes SeqOthello only 5 min and 19.1 GB memory to conduct a global survey of 11,658 fusion events against 10,113 TCGA Pan-Cancer RNA-seq datasets. The query recovers 92.7% of tier-1 fusions curated by TCGA Fusion Gene Database and reveals 270 novel occurrences, all of which are present as tumor-specific. By providing a reference-free, alignment-free, and parameter-free sequence search system, SeqOthello will enable large-scale integrative studies using sequence-level data, an undertaking not previously practicable for many individual labs.

Using the Seven Bridges Cancer Genomics Cloud to Access and Analyze Petabytes of Cancer Data.

Next-generation sequencing has produced petabytes of data, but accessing and analyzing these data remain challenging. Traditionally, researchers investigating public datasets like The Cancer Genome Atlas (TCGA) would download the data to a high-performance cluster, which could take several weeks even with a highly optimized network connection. The National Cancer Institute (NCI) initiated the Cancer Genomics Cloud Pilots program to provide researchers with the resources to process data with cloud computational resources. We present protocols using one of these Cloud Pilots, the Seven Bridges Cancer Genomics Cloud (CGC), to find and query public datasets, bring your own data to the CGC, analyze data using standard or custom workflows, and benchmark tools for accuracy with interactive analysis features. These protocols demonstrate that the CGC is a data-analysis ecosystem that fully empowers researchers with a variety of areas of expertise and interests to collaborate in the analysis of petabytes of data. © 2017 by John Wiley & Sons, Inc.

The Cancer Genomics Cloud: Collaborative, Reproducible, and Democratized-A New Paradigm in Large-Scale Computational Research.

The Seven Bridges Cancer Genomics Cloud (CGC; www.cancergenomicscloud.org) enables researchers to rapidly access and collaborate on massive public cancer genomic datasets, including The Cancer Genome Atlas. It provides secure on-demand access to data, analysis tools, and computing resources. Researchers from diverse backgrounds can easily visualize, query, and explore cancer genomic datasets visually or programmatically. Data of interest can be immediately analyzed in the cloud using more than 200 preinstalled, curated bioinformatics tools and workflows. Researchers can also extend the functionality of the platform by adding their own data and tools via an intuitive software development kit. By colocalizing these resources in the cloud, the CGC enables scalable, reproducible analyses. Researchers worldwide can use the CGC to investigate key questions in cancer genomics. Cancer Res; 77(21); e3-6. ©2017 AACR.

Storage of Erythrocytes Induces Suicidal Erythrocyte Death.

BACKGROUND/AIMS: Similar to apoptosis of nucleated cells, red blood cells (RBC) can undergo suicidal cell death - called eryptosis. It is characterized by cell shrinkage and phosphatidylserine translocation. Eryptosis is triggered by an increase of intracellular calcium concentration due to activation of nonselective cation channels. The cation channels and consequently eryptosis are inhibited by erythropoietin. Eryptotic RBC are engulfed by macrophages and thus rapidly cleared from circulating blood. In this study, we explored whether storage of RBC influences the rate of eryptosis. METHODS: Flow cytometry was employed to quantify phosphatidylserine exposing erythrocytes from annexin V binding and cytosolic Ca2+ activity from Fluo-3 fluorescence. Clearance of stored murine RBC was tested by injection of carboxyfluorescein succinimidyl ester (CFSE)-labelled erythrocytes. RESULTS: Storage for 42 days significantly increased the percentage of phosphatidylserine exposing and haemolytic erythrocytes, an effect blunted by removal of extracellular calcium. Phosphatidylserine exposure could be inhibited by addition of erythropoietin. Upon transfusion, the clearance of murine CFSE-labelled RBC from circulating blood was significantly higher following storage for 10 days when compared to 2 days of storage. CONCLUSION: Storage of RBC triggers eryptosis by Ca2+ and erythropoietin sensitive mechanisms.

Structural and functional features of a developmentally regulated lipopolysaccharide-binding protein.

UNLABELLED: Mammalian lipopolysaccharide (LPS) binding proteins (LBPs) occur mainly in extracellular fluids and promote LPS delivery to specific host cell receptors. The function of LBPs has been studied principally in the context of host defense; the possible role of LBPs in nonpathogenic host-microbe interactions has not been well characterized. Using the Euprymna scolopes-Vibrio fischeri model, we analyzed the structure and function of an LBP family protein, E. scolopes LBP1 (EsLBP1), and provide evidence for its role in triggering a symbiont-induced host developmental program. Previous studies showed that, during initial host colonization, the LPS of V. fischeri synergizes with peptidoglycan (PGN) monomer to induce morphogenesis of epithelial tissues of the host animal. Computationally modeled EsLBP1 shares some but not all structural features of mammalian LBPs that are thought important for LPS binding. Similar to human LBP, recombinant EsLBP1 expressed in insect cells bound V. fischeri LPS and Neisseria meningitidis lipooligosaccharide (LOS) with nanomolar or greater affinity but bound Francisella tularensis LPS only weakly and did not bind PGN monomer. Unlike human LBP, EsLBP1 did not bind N. meningitidis LOS:CD14 complexes. The eslbp1 transcript was upregulated ~22-fold by V. fischeri at 24 h postinoculation. Surprisingly, this upregulation was not induced by exposure to LPS but, rather, to the PGN monomer alone. Hybridization chain reaction-fluorescent in situ hybridization (HCR-FISH) and immunocytochemistry (ICC) localized eslbp1 transcript and protein in crypt epithelia, where V. fischeri induces morphogenesis. The data presented here provide a window into the evolution of LBPs and the scope of their roles in animal symbioses. IMPORTANCE: Mammalian lipopolysaccharide (LPS)-binding protein (LBP) is implicated in conveying LPS to host cells and potentiating its signaling activity. In certain disease states, such as obesity, the overproduction of this protein has been a reliable biomarker of chronic inflammation. Here, we describe a symbiosis-induced invertebrate LBP whose tertiary structure and LPS-binding characteristics are similar to those of mammalian LBPs; however, the primary structure of this distantly related squid protein (EsLBP1) differs in key residues previously believed to be essential for LPS binding, suggesting that an alternative strategy exists. Surprisingly, symbiotic expression of eslbp1 is induced by peptidoglycan derivatives, not LPS, a pattern converse to that of RegIIIγ, an important mammalian immunity protein that binds peptidoglycan but whose gene expression is induced by LPS. Finally, EsLBP1 occurs along the apical surfaces of all the host's epithelia, suggesting that it was recruited from a general defensive role to one that mediates specific interactions with its symbiont.

The genome of Aiptasia, a sea anemone model for coral symbiosis.

The most diverse marine ecosystems, coral reefs, depend upon a functional symbiosis between a cnidarian animal host (the coral) and intracellular photosynthetic dinoflagellate algae. The molecular and cellular mechanisms underlying this endosymbiosis are not well understood, in part because of the difficulties of experimental work with corals. The small sea anemone Aiptasia provides a tractable laboratory model for investigating these mechanisms. Here we report on the assembly and analysis of the Aiptasia genome, which will provide a foundation for future studies and has revealed several features that may be key to understanding the evolution and function of the endosymbiosis. These features include genomic rearrangements and taxonomically restricted genes that may be functionally related to the symbiosis, aspects of host dependence on alga-derived nutrients, a novel and expanded cnidarian-specific family of putative pattern-recognition receptors that might be involved in the animal-algal interactions, and extensive lineage-specific horizontal gene transfer. Extensive integration of genes of prokaryotic origin, including genes for antimicrobial peptides, presumably reflects an intimate association of the animal-algal pair also with its prokaryotic microbiome.

AluY-mediated germline deletion, duplication and somatic stem cell reversion in UBE2T defines a new subtype of Fanconi anemia.

Fanconi anemia (FA) is a rare inherited disorder clinically characterized by congenital malformations, progressive bone marrow failure and cancer susceptibility. At the cellular level, FA is associated with hypersensitivity to DNA-crosslinking genotoxins. Eight of 17 known FA genes assemble the FA E3 ligase complex, which catalyzes monoubiquitination of FANCD2 and is essential for replicative DNA crosslink repair. Here, we identify the first FA patient with biallelic germline mutations in the ubiquitin E2 conjugase UBE2T. Both mutations were aluY-mediated: a paternal deletion and maternal duplication of exons 2-6. These loss-of-function mutations in UBE2T induced a cellular phenotype similar to biallelic defects in early FA genes with the absence of FANCD2 monoubiquitination. The maternal duplication produced a mutant mRNA that could encode a functional protein but was degraded by nonsense-mediated mRNA decay. In the patient's hematopoietic stem cells, the maternal allele with the duplication of exons 2-6 spontaneously reverted to a wild-type allele by monoallelic recombination at the duplicated aluY repeat, thereby preventing bone marrow failure. Analysis of germline DNA of 814 normal individuals and 850 breast cancer patients for deletion or duplication of UBE2T exons 2-6 identified the deletion in only two controls, suggesting aluY-mediated recombinations within the UBE2T locus are rare and not associated with an increased breast cancer risk. Finally, a loss-of-function germline mutation in UBE2T was detected in a high-risk breast cancer patient with wild-type BRCA1/2. Cumulatively, we identified UBE2T as a bona fide FA gene (FANCT) that also may be a rare cancer susceptibility gene.

Sequencing and de novo assembly of a Dahlia hybrid cultivar transcriptome.

Dahlia variabilis, with an exceptionally high diversity of floral forms and colors, is a popular flower amongst both commercial growers and hobbyists. Recently, some genetic controls of pigment patterns have been elucidated. These studies have been limited, however, by the lack of comprehensive transcriptomic resources for this species. Here we report the sequencing, assembly, and annotation of the transcriptome of the developing leaves, stems, and floral buds of D. variabilis. This resulted in 35,638 contigs, most of which seem to contain the complete coding sequence, and of which 20,881 could be successfully annotated by similarity to UniProt. Furthermore, we conducted a preliminary investigation to identify contigs with expression patterns consistent with tissue-specificity. These results will accelerate research into the genetic controls of pigmentation and floral form of D. variabilis.

Extensive differences in gene expression between symbiotic and aposymbiotic cnidarians.

Coral reefs provide habitats for a disproportionate number of marine species relative to the small area of the oceans that they occupy. The mutualism between the cnidarian animal hosts and their intracellular dinoflagellate symbionts provides the nutritional foundation for coral growth and formation of reef structures, because algal photosynthesis can provide >90% of the total energy of the host. Disruption of this symbiosis ("coral bleaching") is occurring on a large scale due primarily to anthropogenic factors and poses a major threat to the future of coral reefs. Despite the importance of this symbiosis, the cellular mechanisms involved in its establishment, maintenance, and breakdown remain largely unknown. We report our continued development of genomic tools to study these mechanisms in Aiptasia, a small sea anemone with great promise as a model system for studies of cnidarian-dinoflagellate symbiosis. Specifically, we have generated de novo assemblies of the transcriptomes of both a clonal line of symbiotic anemones and their endogenous dinoflagellate symbionts. We then compared transcript abundances in animals with and without dinoflagellates. This analysis identified >900 differentially expressed genes and allowed us to generate testable hypotheses about the cellular functions affected by symbiosis establishment. The differentially regulated transcripts include >60 encoding proteins that may play roles in transporting various nutrients between the symbiotic partners; many more encoding proteins functioning in several metabolic pathways, providing clues regarding how the transported nutrients may be used by the partners; and several encoding proteins that may be involved in host recognition and tolerance of the dinoflagellate.

Developing the anemone Aiptasia as a tractable model for cnidarian-dinoflagellate symbiosis: the transcriptome of aposymbiotic A. pallida.

BACKGROUND: Coral reefs are hotspots of oceanic biodiversity, forming the foundation of ecosystems that are important both ecologically and for their direct practical impacts on humans. Corals are declining globally due to a number of stressors, including rising sea-surface temperatures and pollution; such stresses can lead to a breakdown of the essential symbiotic relationship between the coral host and its endosymbiotic dinoflagellates, a process known as coral bleaching. Although the environmental stresses causing this breakdown are largely known, the cellular mechanisms of symbiosis establishment, maintenance, and breakdown are still largely obscure. Investigating the symbiosis using an experimentally tractable model organism, such as the small sea anemone Aiptasia, should improve our understanding of exactly how the environmental stressors affect coral survival and growth. RESULTS: We assembled the transcriptome of a clonal population of adult, aposymbiotic (dinoflagellate-free) Aiptasia pallida from ~208 million reads, yielding 58,018 contigs. We demonstrated that many of these contigs represent full-length or near-full-length transcripts that encode proteins similar to those from a diverse array of pathways in other organisms, including various metabolic enzymes, cytoskeletal proteins, and neuropeptide precursors. The contigs were annotated by sequence similarity, assigned GO terms, and scanned for conserved protein domains. We analyzed the frequency and types of single-nucleotide variants and estimated the size of the Aiptasia genome to be ~421 Mb. The contigs and annotations are available through NCBI (Transcription Shotgun Assembly database, accession numbers JV077153-JV134524) and at http://pringlelab.stanford.edu/projects.html. CONCLUSIONS: The availability of an extensive transcriptome assembly for A. pallida will facilitate analyses of gene-expression changes, identification of proteins of interest, and other studies in this important emerging model system.

Fulcrum: condensing redundant reads from high-throughput sequencing studies.

MOTIVATION: Ultra-high-throughput sequencing produces duplicate and near-duplicate reads, which can consume computational resources in downstream applications. A tool that collapses such reads should reduce storage and assembly complications and costs. RESULTS: We developed Fulcrum to collapse identical and near-identical Illumina and 454 reads (such as those from PCR clones) into single error-corrected sequences; it can process paired-end as well as single-end reads. Fulcrum is customizable and can be deployed on a single machine, a local network or a commercially available MapReduce cluster, and it has been optimized to maximize ease-of-use, cross-platform compatibility and future scalability. Sequence datasets have been collapsed by up to 71%, and the reduced number and improved quality of the resulting sequences allow assemblers to produce longer contigs while using less memory.