Large Genomic Databases Research Articles

FGFR2-altered gastroesophageal adenocarcinomas (GEA) are a rare clinicopathologic entity with a distinct genomic landscape.

72 Background: Therapies directed at receptor tyrosine kinases (RTKs) in GEA have met with limited success. The small molecule FGFR2 inhibitor AZD4547 failed in a phase II biomarker-selected trial, though activity has been seen with FGFR2-directed strategies, including monoclonal antibodies. Despite observed activity, very little is known about the genomic landscape of FGFR2-altered GEA. Using a large genomic database, we sought to examine the classes of FGFR2 alterations and frequency of co-occurring alterations in GEA. Methods: 6,667 tissue specimens from unique patients with advanced GEA were assayed using hybrid capture-based comprehensive genomic profiling (CGP). Tumor mutational burden (TMB) was determined on up to 1.1 Mbp of sequenced DNA and microsatellite instability (MSI) was determined on 95 or 114 loci. Descriptive statistics were used to compare among subgroups. Results: We identified a total of 269 (4.0%) FGFR2-altered cases consisting of FGFR2 amplified (amp; 209, 78% of FGFR2-altered), FGFR2 mutated (mut; 40, 15%) and FGFR2 rearranged (re, 37, 14%). There was a female predominance in FGFR2-altered cases (M:F = 1.6:1) vs FGFR wild-type (WT) (2.8:1). Cases with FGFR2amp and FGFR2re were exclusively MS-stable. The most common fusion partner was TACC2 (22%). FGFR2amp GEA had higher rates of TP53 mutation versus either FGFR2re of FGFR2mut cases (p = 4.4E-6). Co-occurring alterations in the other GEA RTK targets including ERBB2 (10%) , EGFR (8%) and MET (3%) were observed in all types of FGFR2-altered GEA. Co-occurring downstream alterations in MYC (17%), KRAS (10%) and PIK3CA (5.6%) were observed frequently in each class of FGFR2-altered GEA. The median TMB for FGFR2-altered GEA was 3.6 mut/mb, which was not significantly different from a median of 4.3 mut/mb seen in FGFR2 WT samples (p = 0.53). Conclusions: FGFR2-altered GEA is a heterogenous subgroup with approximately 20% of FGFR2-altered samples harboring concurrent RTK alterations. Putative co-occurring modifiers of FGFR2 directed therapy including oncogenic MYC, KRAS, PIK3CA alterations were also frequent, suggesting that pre-treatment CGP and combination approaches may be needed to optimize patient selection.

Combined Blockade of Activating ERBB2 Mutations and ER Results in Synthetic Lethality of ER+/HER2 Mutant Breast Cancer.

We examined the role of ERBB2-activating mutations in endocrine therapy resistance in estrogen receptor positive (ER+) breast cancer. ERBB2 mutation frequency was determined from large genomic databases. Isogenic knock-in ERBB2 mutations in ER+ MCF7 cells and xenografts were used to investigate estrogen-independent growth. Structural analysis was used to determine the molecular interaction of HER L755S with HER3. Small molecules and siRNAs were used to inhibit PI3Kα, TORC1, and HER3. Genomic data revealed a higher rate of ERBB2 mutations in metastatic versus primary ER+ tumors. MCF7 cells with isogenically incorporated ERBB2 kinase domain mutations exhibited resistance to estrogen deprivation and to fulvestrant both in vitro and in vivo, despite maintaining inhibition of ERα transcriptional activity. Addition of the irreversible HER2 tyrosine kinase inhibitor neratinib restored sensitivity to fulvestrant. HER2-mutant MCF7 cells expressed higher levels of p-HER3, p-AKT, and p-S6 than cells with wild-type HER2. Structural analysis of the HER2 L755S variant implicated a more flexible active state, potentially allowing for enhanced dimerization with HER3. Treatment with a PI3Kα inhibitor, a TORC1 inhibitor or HER3 siRNA, but not a MEK inhibitor, restored sensitivity to fulvestrant and to estrogen deprivation. Inhibition of mutant HER2 or TORC1, when combined with fulvestrant, equipotently inhibited growth of MCF7/ERBB2 V777L xenografts, suggesting a role for TORC1 in antiestrogen resistance induced by ERBB2 mutations. ERBB2 mutations hyperactivate the HER3/PI3K/AKT/mTOR axis, leading to antiestrogen resistance in ER+ breast cancer. Dual blockade of the HER2 and ER pathways is required for the treatment of ER+/HER2 mutant breast cancers.

International Spread of Multidrug-Resistant <i>Campylobacter coli </i>in Men Who Have Sex with Men in Washington State and Quebec, 2015-2018

Background: Campylobacter spp. are among the most common causes of enteric bacterial infections worldwide. Men who have sex with men (MSM) are at increased risk for sexually-transmitted enteric infections, including globally-distributed strains of multidrug-resistant Shigella spp. Methods: Retrospective study of MSM-associated Campylobacter in Seattle and Montreal with phenotypic antimicrobial resistance profiles and whole genome sequencing. Findings: We report the isolation of two clonal lineages of multi-drug-resistant Campylobacter coli from MSM in Seattle, Washington and Montreal, Quebec. Whole genome sequencing revealed nearly identical strains obtained from the two regions over a four-year period. Comparison with large genomic databases revealed extensive Campylobacter spp. clusters carrying multiple drug resistance genes that segregated with these isolates. Examination of the genetic basis of antimicrobial resistance revealed multiple macrolide resistance determinants including a novel ribosomal RNA methyltransferase situated in a CRISPR array locus in a Campylobacter coli isolate. Interpretation: As previously reported for Shigella, specific multi-drug-resistant strains of Campylobacter are circulating by sexual transmission in MSM populations across diverse geographic locations, suggesting a need to incorporate sexual behavior in the investigation of clusters of foodborne pathogens revealed by whole-genome sequence data. Funding Statement: University of Washington Department of Laboratory Medicine. Declaration of Interests: F.C.F. reports consulting fees and non-financial support from BioFire. No other competing interests for the other authors. Ethics Approval Statement: This study was approved by the University of Washington Institutional Review Board and the Centre hospitalier de l'Universite de Montreal Research Ethics Committee.

Use of Big Data to Estimate Prevalence of Defective DNA Repair Variants in the US Population

ImportanceWide use of genomic sequencing to diagnose disease has raised concern about the extent of genotype-phenotype correlations.ObjectiveTo correlate disease-associated allele frequencies with expected and reported prevalence of clinical disease.Design, Setting, and ParticipantsXeroderma pigmentosum (XP), a recessive, cancer-prone, neurocutaneous disorder, was used as a model for this study. From January 1, 2017, to May 4, 2018, the Human Gene Mutation Database and a cohort of patients at the National Institutes of Health were searched and screened to identify reported mutations associated with XP. The clinical phenotype of these patients was confirmed from reports in the literature and National Institutes of Health medical records. The genetically predicted prevalence of disease based on frequency of known pathogenic mutations was compared with the prevalence of patients clinically diagnosed with phenotypic XP. Exome sequencing of more than 200 000 alleles from the Genome Aggregation Database, the National Cancer Institute Division of Cancer Epidemiology and Genetics database of healthy controls, and an Inova Hospital Study database was used to investigate the frequencies of these mutations in the general population.Main Outcomes and MeasuresListing of all reported mutations associated with XP, their frequencies in 3 large exome sequence databases, determination of the number of patients in the United States with XP using modeling equations, and comparison of the observed and reported numbers of patients with XP with specific mutations.ResultsA total of 156 pathogenic missense and nonsense mutations associated with XP were identified in the National Institutes of Health cohort and the Human Gene Mutation Database. The Genome Aggregation Database provided frequency data for 65 of these mutations, with a total allele frequency of 1.13%. The XPF (ERCC4) mutation, p.P379S, had an allele frequency of 0.4%, and the XPC mutation, p.P334H, had an allele frequency of 0.3%. With the Hardy-Weinberg equation, it was determined that there should be more than 8000 patients who are homozygous for these mutations in the United States. In contrast, only 3 patients with XP were reported as having the XPF mutation, and 1 patient was reported as having the XPC mutation.Conclusions and RelevanceThe findings from this study suggest that clinicians should approach large genomic databases with caution when trying to correlate the clinical implications of genetic variants with the prevalence of disease risk. Unsuspected mutations in known genes with a predisposition for skin cancer may be responsible for some of the high frequency of skin cancers in the general population.

PDZ Binding Kinase (PBK) - a Novel Gene Driving Genomic Evolution in Multiple Myeloma

As in all cancers, genomic instability leads to ongoing acquisition of new genetic changes in multiple myeloma (MM). This adaptability underlies the development of drug resistance and progression in MM. This genomic instability is driven by cellular processes, mainly related with DNA repair and perturbed by functional changes in limited number of genes. Since kinases play a critical role in the regulation of biological processes, including DNA damage/repair signaling and are relatively easy to screen for inhibitors, we investigated for novel genes involved in the acquisition of new genomic changes in MM. Using a large genomic database which had both the gene expression and CGH array-based copy number information (gse26863, n=246), we first identified a total of 890 expressed kinases in MM and correlated their expression with genomic instability defined as a change in ≥3 and/or 5 consecutive amplification and/or deletion events. We identified 198 kinases whose elevated expression correlated with increased genomic instability (based on FDR ≤ 0.05). Amongst these kinases, using univariate Cox survival analysis, elevated expression of 15 kinases correlated with poor overall as well as event free survival (P ≤0.05) in two MM datasets (IFM70, n=170; gse24080; n=559). We further confirmed the correlation of these 15 genes in both EFS and OS in additional two MM datasets (MMRF CoMMpass Study, IFM-DFCI 2009) as well as in additional solid tumor datasets from TCGA from patients with lung and pancreatic adenocarcinoma (P values ranging from 0.01 to <0.000002). A pathway analysis identified phosphorylation and regulation of proteasome pathway, mitotic spindle assembly/checkpoint, chromosomal segregation and cell cycle checkpoints as among major pathways regulated by these genes.To investigate the relevance of these genes with genomic instability, we performed a functional siRNA screen to evaluate impact of their suppression on homologous recombination (HR). PDZ Binding Kinase (PBK) was one of the top genes whose knockdown caused the maximal inhibition of HR activity in initial screen. To investigate it further in detail, we suppressed PBK in MM cells using shRNA and confirmed that its suppression significantly reduces HR activity. PBK-knockdown also reduced gH2AX levels (marker of DNA breaks) measured by Western blotting and decreased number of micronuclei (a marker of ongoing genomic rearrangements and instability) as assessed by flow cytometry . A small molecule inhibitor of PBK also confirmed a similar reduction in gH2AX levels as well as micronuclei, indicating inhibition of spontaneous DNA breaks and genomic instability. Using mass spectrometry and co-immunoprecipitation, we identified that PBK interacts with FEN1, a nuclease with roles in base excision repair and HR pathways. We confirmed that PBK induces phosphorylation of FEN1 and that inhibition of PBK, suppressed the phosphorylation of FEN1, RAD51 expression and gH2AX levels and it reversed FEN1-induced HR activity. These results confirm that phosphorylation of FEN1 nuclease by PBK contributes to its ability to impact DNA breaks, HR and genome stability in MM. PBK inhibition also significantly sensitized MM cells to melphalan and inhibited cell viability in a panel of MM cell lines (IC50 in MM cell lines ~20-30 nM vs ~100 nM in normal PBMCs) at the same time also reversed melphalan-induced genomic instability, as assessed by micronucleus assay. These data identify PBK as an important target affecting genomic instability, and its inhibitor as a potential drug, to inhibit genomic evolution and MM cell growth. DisclosuresMunshi:OncoPep: Other: Board of director.

Probabilistic Approach Processing Scheme Based on BLAST for Improving Search Speed of Bioinformatics

As researchers on bioinformatics using heuristic algorithms have been increasingly studied, information management used in various bioinformatics fields (new drug development, medical diagnosis, agricultural product improvement, etc.) has been studied mainly on BLAST algorithm. However, many of the algorithms that are being used in the large genome database use a complete sorting procedure, which takes a lot of time to search the database for proteins or nucleic acid sequences, which causes many problems in processing large amounts of bio information. We propose a BLAST-based probabilistic access processing method that can manage, analyze and process a large amount of bio data distributed based on information communication infrastructure and IT technology. The proposed method aims to improve the accessibility of data by linking weighted bioinformatics information with probability factors to easily access large capacity bio data. In addition, the proposed scheme classifies the priority information allocated to the bioinformatics information by hierarchical grouping according to the degree of similarity, thereby ensuring high accuracy of the search results of the bioinformatics information, and at the same time, the goal is to obtain low processing time by classifying information (type, attribute, priority, etc.) into weights by property. Previous researchers have suggested clustering algorithms for fragmentation of genetic information to solve the problem of haplotype assembly in genetics, or proposed particle swarm optimization methods similar to existing genetic algorithms using heuristic clustering method based on MEC model. In the performance evaluation, the proposed method improved the accuracy by average 13.5% and the efficiency of the data retrieval by average 19.7% more than previous scheme. The overhead of Bioinformatics information processing was 8.8% lower and the processing time was average 13.5% lower.

Improved Algorithmic Complexity for the 3SEQ Recombination Detection Algorithm.

Identifying recombinant sequences in an era of large genomic databases is challenging as it requires an efficient algorithm to identify candidate recombinants and parents, as well as appropriate statistical methods to correct for the large number of comparisons performed. In 2007, a computation was introduced for an exact nonparametric mosaicism statistic that gave high-precision P values for putative recombinants. This exact computation meant that multiple-comparisons corrected P values also had high precision, which is crucial when performing millions or billions of tests in large databases. Here, we introduce an improvement to the algorithmic complexity of this computation from O(mn3) to O(mn2), where m and n are the numbers of recombination-informative sites in the candidate recombinant. This new computation allows for recombination analysis to be performed in alignments with thousands of polymorphic sites. Benchmark runs are presented on viral genome sequence alignments, new features are introduced, and applications outside recombination analysis are discussed.

Distinguishing pathogenic mutations from background genetic noise in cardiology: The use of large genome databases for genetic interpretation.

Advances in clinical genetic testing have led to increased insight into the human genome, including how challenging it is to interpret rare genetic variation. In some cases, the ability to detect genetic mutations exceeds the ability to understand their clinical impact, limiting the advantage of these technologies. Obstacles in genomic medicine are many and include: understanding the level of certainty/uncertainty behind pathogenicity determination, the numerous different variant interpretation-guidelines used by clinical laboratories, delivering the certain or uncertain result to the patient, helping patients evaluate medical decisions in light of uncertainty regarding the consequence of the findings. Through publication of large publicly available exome/genome databases, researchers and physicians are now able to highlight dubious variants previously associated with different cardiac traits. Also, continuous efforts through data sharing, international collaborative efforts to develop disease-gene-specific guidelines, and computational analyses using large data, will indubitably assist in better variant interpretation and classification. This article discusses the current, and quickly changing, state of variant interpretation resources within cardiovascular genetic research, e.g., publicly available databases and ways of how cardiovascular genetic counselors and geneticists can aid in improving variant interpretation in cardiology.

Suppressors and activators of JAK-STAT signaling at diagnosis and relapse of acute lymphoblastic leukemia in Down syndrome

Children with Down syndrome (DS) are prone to development of high-risk B-cell precursor ALL (DS-ALL), which differs genetically from most sporadic pediatric ALLs. Increased expression of cytokine receptor-like factor 2 (CRLF2), the receptor to thymic stromal lymphopoietin (TSLP), characterizes about half of DS-ALLs and also a subgroup of sporadic "Philadelphia-like" ALLs. To understand the pathogenesis of relapsed DS-ALL, we performed integrative genomic analysis of 25 matched diagnosis-remission and -relapse DS-ALLs. We found that the CRLF2 rearrangements are early events during DS-ALL evolution and generally stable between diagnoses and relapse. Secondary activating signaling events in the JAK-STAT/RAS pathway were ubiquitous but highly redundant between diagnosis and relapse, suggesting that signaling is essential but that no specific mutations are "relapse driving." We further found that activated JAK2 may be naturally suppressed in 25% of CRLF2pos DS-ALLs by loss-of-function aberrations in USP9X, a deubiquitinase previously shown to stabilize the activated phosphorylated JAK2. Interrogation of large ALL genomic databases extended our findings up to 25% of CRLF2pos, Philadelphia-like ALLs. Pharmacological or genetic inhibition of USP9X, as well as treatment with low-dose ruxolitinib, enhanced the survival of pre-B ALL cells overexpressing mutated JAK2. Thus, somehow counterintuitive, we found that suppression of JAK-STAT "hypersignaling" may be beneficial to leukemic B-cell precursors. This finding and the reduction of JAK mutated clones at relapse suggest that the therapeutic effect of JAK specific inhibitors may be limited. Rather, combined signaling inhibitors or direct targeting of the TSLP receptor may be a useful therapeutic strategy for DS-ALL.

Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations

The proliferation of large genomic databases offers the potential to perform increasingly larger-scale genome-wide association studies (GWASs). Due to privacy concerns, however, access to these data is limited, greatly reducing their usefulness for research. Here, we introduce a computational framework for performing GWASs that adapts principles of differential privacy-a cryptographic theory that facilitates secure analysis of sensitive data-to both protect private phenotype information (e.g., disease status) and correct for population stratification. This framework enables us to produce privacy-preserving GWAS results based on EIGENSTRAT and linear mixed model (LMM)-based statistics, both of which correct for population stratification. We test our differentially private statistics, PrivSTRAT and PrivLMM, on simulated and real GWAS datasets and find they are able to protect privacy while returning meaningful results. Our framework can be used to securely query private genomic datasets to discover which specific genomic alterations may be associated with a disease, thus increasing the availability of these valuable datasets.

The genetics of bone mass and susceptibility to bone diseases.

Osteoporosis is characterized by low bone mass and an increased risk of fracture. Genetic factors, environmental factors and gene-environment interactions all contribute to a person's lifetime risk of developing an osteoporotic fracture. This Review summarizes key advances in understanding of the genetics of bone traits and their role in osteoporosis. Candidate-gene approaches dominated this field 20 years ago, but clinical and preclinical genetic studies published in the past 5 years generally utilize more-sophisticated and better-powered genome-wide association studies (GWAS). High-throughput DNA sequencing, large genomic databases and improved methods of data analysis have greatly accelerated the gene-discovery process. Linkage analyses of single-gene traits that segregate in families with extreme phenotypes have led to the elucidation of critical pathways controlling bone mass. For example, components of the Wnt-β-catenin signalling pathway have been validated (in both GWAS and functional studies) as contributing to various bone phenotypes. These notable advances in gene discovery suggest that the next decade will witness cataloguing of the hundreds of genes that influence bone mass and osteoporosis, which in turn will provide a roadmap for the development of new drugs that target diseases of low bone mass, including osteoporosis.

Efficient privacy-preserving string search and an application in genomics.

Motivation: Personal genomes carry inherent privacy risks and protecting privacy poses major social and technological challenges. We consider the case where a user searches for genetic information (e.g. an allele) on a server that stores a large genomic database and aims to receive allele-associated information. The user would like to keep the query and result private and the server the database.Approach: We propose a novel approach that combines efficient string data structures such as the Burrows–Wheeler transform with cryptographic techniques based on additive homomorphic encryption. We assume that the sequence data is searchable in efficient iterative query operations over a large indexed dictionary, for instance, from large genome collections and employing the (positional) Burrows–Wheeler transform. We use a technique called oblivious transfer that is based on additive homomorphic encryption to conceal the sequence query and the genomic region of interest in positional queries.Results: We designed and implemented an efficient algorithm for searching sequences of SNPs in large genome databases. During search, the user can only identify the longest match while the server does not learn which sequence of SNPs the user queried. In an experiment based on 2184 aligned haploid genomes from the 1000 Genomes Project, our algorithm was able to perform typical queries within 4.6 s and 10.8 s for client and server side, respectively, on laptop computers. The presented algorithm is at least one order of magnitude faster than an exhaustive baseline algorithm.Availability and implementation: https://github.com/iskana/PBWT-sec and https://github.com/ratschlab/PBWT-sec.Contacts: shimizu-kana@aist.go.jp or Gunnar.Ratsch@ratschlab.orgSupplementary information: Supplementary data are available at Bioinformatics online.

GEMINI: a computationally-efficient search engine for large gene expression datasets.

BackgroundLow-cost DNA sequencing allows organizations to accumulate massive amounts of genomic data and use that data to answer a diverse range of research questions. Presently, users must search for relevant genomic data using a keyword, accession number of meta-data tag. However, in this search paradigm the form of the query – a text-based string – is mismatched with the form of the target – a genomic profile.ResultsTo improve access to massive genomic data resources, we have developed a fast search engine, GEMINI, that uses a genomic profile as a query to search for similar genomic profiles. GEMINI implements a nearest-neighbor search algorithm using a vantage-point tree to store a database of n profiles and in certain circumstances achieves an mathcal {O}(log n) expected query time in the limit. We tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas project and show that it achieves a query time that scales as the logarithm of the number of records in practice on genomic data. In a database with 105 samples, GEMINI identifies the nearest neighbor in 0.05 sec compared to a brute force search time of 0.6 sec.ConclusionsGEMINI is a fast search engine that uses a query genomic profile to search for similar profiles in a very large genomic database. It enables users to identify similar profiles independent of sample label, data origin or other meta-data information.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0934-8) contains supplementary material, which is available to authorized users.

A Q-gram Filter for Local Alignment in Large Genomic Database

Fast and exact searching for sequences similar to a query sequence in genomic databases remains a challenging task in molecular biology. In this paper, the problem of finding all e-matches in a large genomic database is considered, i.e. all local alignments over a given length w and an error rate of at most e. A new database searching algorithm called QFLA is designed to solve this problem. The proposed algorithm is a fullsensitivity algorithm which is a refined q-gram filter and implemented on a q-gram index. First, new features are extracted from match-regions by logically partitioning both query sequence and genomic database. Second, a large part of irrelevant subsequences are eliminated quickly by these new features during the searching process. Last, the unfiltered regions are verified by the well-known smith-waterman algorithm. The experimental results demonstrate that our algorithm saves time by improving filtration efficiency in a short filtration time.

Implications of Circadian Rhythm Regulation by microRNAs in Colorectal Cancer.

AimTo establish a connection between microRNA (miRNAs), circadian rhythm, and colorectal cancer patient survival.MethodsGenomic and clinical data were extracted from The Cancer Genome Atlas (TCGA) colorectal cancer database, and the expression levels of candidate miRNAs and a set of circadian rhythm-related genes (Per1, Per2, Per3, Bmal1), and genes associated with chemosensitivity (thymidylate synthase, dihydrofolate reductase) were assessed for any correlations among their expression. In addition, survival analyses specific to different colorectal cancer stages were performed to determine if these genes contribute to patient outcomes.ResultsSignificant inverse correlation between the expression of Per1 and that of miR-192 and miR-194 was observed. In survival analyses, high miR-192 and miR-194 correlate with better overall survival in Stage II patients, but worse survival in more advanced Stage III/IV patients. The expression of Per1, but Per2 or Bmal1, is marginally associated with patient survival for Stage II patients. Low thymidylate synthase expression correlates with better overall survival in Stage II patients but worse survival in Stage III/IV patients.ConclusionThis study establishes a foundation based on a large genomic database of colorectal cancer, for further investigation into the importance of regulatory mechanisms of circadian rhythm by miRNAs in colorectal cancer.

Next-generation sequencing of ABCA4: High frequency of complex alleles and novel mutations in patients with retinal dystrophies from Central Europe

Variation in the ABCA4 locus has emerged as the most prevalent cause of monogenic retinal diseases. The study aimed to discover causative ABCA4 mutations in a large but not previously investigated cohort with ABCA4-related diseases originating from Central Europe and to refine the genetic relevance of all identified variants based on population evidence. Comprehensive clinical studies were performed to identify patients with Stargardt disease (STGD, n = 76) and cone-rod dystrophy (CRD, n = 16). Next-generation sequencing targeting ABCA4 was applied for a widespread screening of the gene. The results were analyzed in the context of exome data from a corresponding population (n = 594) and other large genomic databases. Our data disprove the pathogenic status of p.V552I and provide more evidence against a causal role of four further ABCA4 variants as drivers of the phenotype under a recessive paradigm. The study identifies 12 novel potentially pathogenic mutations (four of them recurrent) and a novel complex allele p.[(R152*; V2050L)]. In one third (31/92) of our cohort we detected the p.[(L541P; A1038V)] complex allele, which represents an unusually high level of genetic homogeneity for ABCA4-related diseases. Causative ABCA4 mutations account for 79% of STGD and 31% of CRD cases. A combination of p.[(L541P; A1038V)] and/or a truncating ABCA4 mutation always resulted in an early disease onset. Identification of ABCA4 retinopathies provides a specific molecular diagnosis and justifies a prompt introduction of simple precautions that may slow disease progression. The comprehensive, population-specific study expands our knowledge on the genetic landscape of retinal diseases.

Benefits and Risks of Sharing Genomic Information.

Genetic tests can result in abundant data that must be managed, interpreted, and afforded the appropriate protections (McGuire et al., 2008). Because of the numerous potential uses of these data, ethical use requires engaged and representative governance and oversight. The movement to publish these data in large genomic databases to make it broadly accessible is generally widely supported. It is critical to weigh the benefits and the possible dangers of sharing personal data on large genomic databases.

Optimizing de novo transcriptome assembly and extending genomic resources for striped catfish (Pangasianodon hypophthalmus)

Striped catfish (Pangasianodon hypophthalmus) is a commercially important freshwater fish used in inland aquaculture in the Mekong Delta, Vietnam. The culture industry is facing a significant challenge however from saltwater intrusion into many low topographical coastal provinces across the Mekong Delta as a result of predicted climate change impacts. Developing genomic resources for this species can facilitate the production of improved culture lines that can withstand raised salinity conditions, and so we have applied high-throughput Ion Torrent sequencing of transcriptome libraries from six target osmoregulatory organs from striped catfish as a genomic resource for use in future selection strategies. We obtained 12,177,770 reads after trimming and processing with an average length of 97bp. De novo assemblies were generated using CLC Genomic Workbench, Trinity and Velvet/Oases with the best overall contig performance resulting from the CLC assembly. De novo assembly using CLC yielded 66,451 contigs with an average length of 478bp and N50 length of 506bp. A total of 37,969 contigs (57%) possessed significant similarity with proteins in the non-redundant database. Comparative analyses revealed that a significant number of contigs matched sequences reported in other teleost fishes, ranging in similarity from 45.2% with Atlantic cod to 52% with zebrafish. In addition, 28,879 simple sequence repeats (SSRs) and 55,721 single nucleotide polymorphisms (SNPs) were detected in the striped catfish transcriptome. The sequence collection generated in the current study represents the most comprehensive genomic resource for P. hypophthalmus available to date. Our results illustrate the utility of next-generation sequencing as an efficient tool for constructing a large genomic database for marker development in non-model species.

Role of genetic and molecular profiling in sarcomas.

The treatment of sarcomas has been challenging due to their heterogeneity, rarity in the general population, relative insensitivity to chemotherapeutics, and lack of effective targeted agents. One of the first major breakthroughs in the treatment of sarcomas was the use of imatinib to treat gastrointestinal stromal tumors (GISTs). Since then, advanced molecular techniques and genetic profiling have revolutionized the approach to sarcoma classification, diagnosis, prognosis and, most importantly, treatment. As the sarcoma genetic database continues to expand, the basis for how we classify, diagnose, and treat these challenging malignancies will be redefined. The overall goal of these types of techniques has been to determine a molecular blueprint for each sarcoma subtype and discover actionable alterations that lend themselves to targeted therapies. Other important information derived from these large genomic databases includes biomarkers, prognostic indicators, and information regarding tumorigenesis. Eventually, advanced molecular techniques will provide a personalized-medicine approach that tailors each treatment regimen to the patient's own tumor genome.

Whole genome sequencing of elite rice cultivars as a comprehensive information resource for marker assisted selection.

Current advances in sequencing technologies and bioinformatics revealed the genomic background of rice, a staple food for the poor people, and provided the basis to develop large genomic variation databases for thousands of cultivars. Proper analysis of this massive resource is expected to give novel insights into the structure, function, and evolution of the rice genome, and to aid the development of rice varieties through marker assisted selection or genomic selection. In this work we present sequencing and bioinformatics analyses of 104 rice varieties belonging to the major subspecies of Oryza sativa. We identified repetitive elements and recurrent copy number variation covering about 200 Mbp of the rice genome. Genotyping of over 18 million polymorphic locations within O. sativa allowed us to reconstruct the individual haplotype patterns shaping the genomic background of elite varieties used by farmers throughout the Americas. Based on a reconstruction of the alleles for the gene GBSSI, we could identify novel genetic markers for selection of varieties with high amylose content. We expect that both the analysis methods and the genomic information described here would be of great use for the rice research community and for other groups carrying on similar sequencing efforts in other crops.