University Of California At Santa Cruz Research Articles

A novel immune-related prognostic signature in epithelial ovarian carcinoma.

The immune response is associated with the progression and prognosis of epithelial ovarian cancer (EOC). However, the roles of infiltrated immune cells and immune-related genes (IRGs) in EOC have not been reported comprehensively. In the current study, the differentially expressed genes (DEGs) were filtered based on the integrated gene expression data acquired from The University of California at Santa Cruz (UCSC) Genome Browser. Then, IRGs and transcriptional factors (TFs) were screened based on the ImmPort database and Cistrome database. A total of 501 differentially expressed IRGs, and 76 TFs were detected. A TF-mediated network was constructed by univariate Cox analysis to reveal the potential regulatory mechanisms of IRGs. Next, a nine immune-based prognostic risk model using nine IRGs (PI3, CXCL10, CXCL11, LCN6, CCL17, CCL25, MIF, CX3CR1, and CSPG5) was established. Based on the risk score worked out from the signature, the EOC patients could be classified into low-risk and high-risk groups. Furthermore, the immune landscapes, elevated by the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm and the Tumor Immune Estimation Resource (TIMER) database, effectuated different patterns in two groups. Thus, an immune-based prognostic risk model of EOC elucidates the immune status in the tumor microenvironment, and hence, could be used for prognosis.

Multiscale DNA partitioning: statistical evidence for segments.

DNA segmentation, i.e. the partitioning of DNA in compositionally homogeneous segments, is a basic task in bioinformatics. Different algorithms have been proposed for various partitioning criteria such as Guanine/Cytosine (GC) content, local ancestry in population genetics or copy number variation. A critical component of any such method is the choice of an appropriate number of segments. Some methods use model selection criteria and do not provide a suitable error control. Other methods that are based on simulating a statistic under a null model provide suitable error control only if the correct null model is chosen. Here, we focus on partitioning with respect to GC content and propose a new approach that provides statistical error control: as in statistical hypothesis testing, it guarantees with a user-specified probability [Formula: see text] that the number of identified segments does not exceed the number of actually present segments. The method is based on a statistical multiscale criterion, rendering this as a segmentation method that searches segments of any length (on all scales) simultaneously. It is also accurate in localizing segments: under benchmark scenarios, our approach leads to a segmentation that is more accurate than the approaches discussed in the comparative review of Elhaik et al. In our real data examples, we find segments that often correspond well to features taken from standard University of California at Santa Cruz (UCSC) genome annotation tracks. Our method is implemented in function smuceR of the R-package stepR available at http://www.stochastik.math.uni-goettingen.de/smuce.

Intercalibration of Cd and Pb concentration measurements in the northwest Pacific Ocean

Dissolved and total Cd and Pb concentration measurements in seawater were intercalibrated using 33 samples collected on the fourth cruise of the Intergovernmental Oceanographic Commission's (IOC‐4) Global Investigation of Pollution in the Marine Environment (GIPME) in the northwest Pacific Ocean, as well as in three seawater reference materials (SAFe S1, SAFe D2, and NASS‐5). Laboratories from Florida State University (FSU), University of California at Santa Cruz (UCSC), and University of Southern Mississippi (USM) participated in the Pb intercalibration, and two of them (FSU and UCSC) participated in the Cd intercalibration. While each of the laboratories employed different extraction techniques before analysis by inductively coupled plasma—mass spectrometry (ICP‐MS), the measurements of Cd and Pb concentrations for the IOC‐4 samples agreed to within 4% and 15%, respectively, and those of the reference materials agreed to within 13% and 8%, respectively. This successful intercalibration demonstrates that there now are multiple techniques available for accurately measuring Cd and Pb concentrations in seawater.

Mining susceptibility gene modules and disease risk genes from SNP data by combining network topological properties with support vector regression

Genome-wide association study is a powerful approach to identify disease risk loci. However, the molecular regulatory mechanisms for most complex diseases are still not well understood. Therefore, further investigating the interplay between genetic factors and biological networks is important for elucidating the molecular mechanisms of complex diseases. Here, we proposed a novel framework to identify susceptibility gene modules and disease risk genes by combining network topological properties with support vector regression from single nucleotide polymorphism (SNP) level. We assigned risk SNPs to genes using the University of California at Santa Cruz (UCSC) genome database, and then mapped these genes to protein–protein interaction (PPI) networks. The gene modules implicated by hub genes were extracted using the PPI networks and the topological property was analyzed for these gene modules. For each gene module, risk feature genes were determined by topological property analysis and support vector regression. As a result, five shared risk feature genes, CD80, EGFR, FN1, GSK3B and TRAF6 were found and proven to be associated with rheumatoid arthritis by previous reports. Our approach showed a good performance in comparison with other approaches and can be used for prioritizing candidate genes associated with complex diseases.

Novel deletions of 14q11.2 associated with developmental delay, cognitive impairment and similar minor anomalies in three children

Methods and results: We identified de novo submicroscopic chromosome 14q11.2 deletions in two children with idiopathic developmental delay and cognitive impairment. Vancouver patient 5566 has a ∼200 kb deletion and...

DbRIP: A highly integrated database of retrotransposon insertion polymorphisms in humans

Retrotransposons constitute over 40% of the human genome and play important roles in the evolution of the genome. Since certain types of retrotransposons, particularly members of the Alu, L1, and SVA families, are still active, their recent and ongoing propagation generates a unique and important class of human genomic diversity/polymorphism (for the presence and absence of an insertion) with some elements known to cause genetic diseases. So far, over 2,300, 500, and 80 Alu, L1, and SVA insertions, respectively, have been reported to be polymorphic and many more are yet to be discovered. We present here the Database of Retrotransposon Insertion Polymorphisms (dbRIP; http://falcon.roswellpark.org:9090), a highly integrated and interactive database of human retrotransposon insertion polymorphisms (RIPs). dbRIP currently contains a nonredundant list of 1,625, 407, and 63 polymorphic Alu, L1, and SVA elements, respectively, or a total of 2,095 RIPs. In dbRIP, we deploy the utilities and annotated data of the genome browser developed at the University of California at Santa Cruz (UCSC) for user-friendly queries and integrative browsing of RIPs along with all other genome annotation information. Users can query the database by a variety of means and have access to the detailed information related to a RIP, including detailed insertion sequences and genotype data. dbRIP represents the first database providing comprehensive, integrative, and interactive compilation of RIP data, and it will be a useful resource for researchers working in the area of human genetics.

Remembering Laurence Veysey (1933-2004)

Larry Veysey was my colleague and friend at the University of California at Santa Cruz (UCSC) for almost twenty years. We shared a common fascination with Utopias past and present; we talked often; and we taught several graduate seminars together. Though Larry never aspired to be anyone's mentor, I learned much from him about intellectual history. He was a complex individual—a difficult and at times infuriating colleague but also a loyal and generous friend and a person of extraordinary intelligence and at times alarming bluntness. In faculty meetings he never failed to speak his mind, and he could rarely resist baiting and provoking colleagues with whom he disagreed. But he also remained a tireless, fair-minded, and thoroughly conscientious participant in search committees, for which he compiled detailed, carefully nuanced evaluations of the leading candidates, often with grades attached. He also graded sunsets arid dinners. He appeared to be intransigent and set in his ways, but he believed in change and relished risk-taking. He loved to walk near the edge of the cliff, pushing and testing himself.

Improvements in the HbVar database of human hemoglobin variants and thalassemia mutations for population and sequence variation studies.

HbVar (http://globin.cse.psu.edu/globin/hbvar/) is a relational database developed by a multi-center academic effort to provide up-to-date and high quality information on the genomic sequence changes leading to hemoglobin variants and all types of thalassemia and hemoglobinopathies. Extensive information is recorded for each variant and mutation, including sequence alterations, biochemical and hematological effects, associated pathology, ethnic occurrence and references. In addition to the regular updates to entries, we report two significant advances: (i) The frequencies for a large number of mutations causing beta-thalassemia in at-risk populations have been extracted from the published literature and made available for the user to query upon. (ii) HbVar has been linked with the GALA (Genome Alignment and Annotation database, available at http://globin.cse.psu.edu/gala/) so that users can combine information on hemoglobin variants and thalassemia mutations with a wide spectrum of genomic data. It also expands the capacity to view and analyze the data, using tools within GALA and the University of California at Santa Cruz (UCSC) Genome Browser.

Santa Cruz mountains (Loma Prieta) earthquake

At 5:04 P.M. on Tuesday, October 17, 1989 local time (10/18/89 00:04:15.23 UT) a large earthquake ruptured a 40‐km segment of the San Andreas fault in the Santa Cruz Mountains in northern California. The magnitude Ms was calculated at 7.1 by the National Earthquake Information Service using data from 18 stations. This report is based on information made available to geophysicists at the C. F. Richter Seismological Laboratory at the University of California at Santa Cruz (UCSC), as of 10 days following the main shock.The Santa Cruz Mountains (Loma Prieta) earthquake was the most severe in the continental U.S. since 1952, when a very large earthquake (Ms = 7.7) broke along the White Wolf fault near Bakersfield, Calif. It was the largest event on the San Andreas since the 1906 (M = 8.3) San Francisco event.