Large synteny blocks revealed between Caenorhabditis elegans and Caenorhabditis briggsae genomes using OrthoCluster

<p>Abstract</p> <p>Background</p> <p>Accurate identification of synteny blocks is an important step in comparative genomics towards the understanding of genome architecture and expression. Most computer programs developed in the last decade for identifying synteny blocks have limitations. To address these limitations, we recently developed a robust program called OrthoCluster, and an online database OrthoClusterDB. In this work, we have demonstrated the application of OrthoCluster in identifying synteny blocks between the genomes of <it>Caenorhabditis elegans </it>and <it>Caenorhabditis briggsae</it>, two closely related hermaphrodite nematodes.</p> <p>Results</p> <p>Initial identification and analysis of synteny blocks using OrthoCluster enabled us to systematically improve the genome annotation of <it>C. elegans </it>and <it>C. briggsae</it>, identifying 52 potential novel genes in <it>C. elegans</it>, 582 in <it>C. briggsae</it>, and 949 novel orthologous relationships between these two species. Using the improved annotation, we have detected 3,058 perfect synteny blocks that contain no mismatches between <it>C. elegans </it>and <it>C. briggsae</it>. Among these synteny blocks, the majority are mapped to homologous chromosomes, as previously reported. The largest perfect synteny block contains 42 genes, which spans 201.2 kb in Chromosome V of <it>C. elegans</it>. On average, perfect synteny blocks span 18.8 kb in length. When some mismatches (interruptions) are allowed, synteny blocks ("imperfect synteny blocks") that are much larger in size are identified. We have shown that the majority (80%) of the <it>C. elegans </it>and <it>C. briggsae </it>genomes are covered by imperfect synteny blocks. The largest imperfect synteny block spans 6.14 Mb in Chromosome X of <it>C. elegans </it>and there are 11 synteny blocks that are larger than 1 Mb in size. On average, imperfect synteny blocks span 63.6 kb in length, larger than previously reported.</p> <p>Conclusions</p> <p>We have demonstrated that OrthoCluster can be used to accurately identify synteny blocks and have found that synteny blocks between <it>C. elegans </it>and <it>C. briggsae </it>are almost three-folds larger than previously identified.</p

StAR: a simple tool for the statistical comparison of ROC curves

<p>Abstract</p> <p>Background</p> <p>As in many different areas of science and technology, most important problems in bioinformatics rely on the proper development and assessment of binary classifiers. A generalized assessment of the performance of binary classifiers is typically carried out through the analysis of their receiver operating characteristic (ROC) curves. The area under the ROC curve (AUC) constitutes a popular indicator of the performance of a binary classifier. However, the assessment of the statistical significance of the difference between any two classifiers based on this measure is not a straightforward task, since not many freely available tools exist. Most existing software is either not free, difficult to use or not easy to automate when a comparative assessment of the performance of many binary classifiers is intended. This constitutes the typical scenario for the optimization of parameters when developing new classifiers and also for their performance validation through the comparison to previous art.</p> <p>Results</p> <p>In this work we describe and release new software to assess the statistical significance of the observed difference between the AUCs of any two classifiers for a common task estimated from paired data or unpaired balanced data. The software is able to perform a pairwise comparison of many classifiers in a single run, without requiring any expert or advanced knowledge to use it. The software relies on a non-parametric test for the difference of the AUCs that accounts for the correlation of the ROC curves. The results are displayed graphically and can be easily customized by the user. A human-readable report is generated and the complete data resulting from the analysis are also available for download, which can be used for further analysis with other software. The software is released as a web server that can be used in any client platform and also as a standalone application for the Linux operating system.</p> <p>Conclusion</p> <p>A new software for the statistical comparison of ROC curves is released here as a web server and also as standalone software for the LINUX operating system.</p

Identification of novel transcripts with differential dorso-ventral expression in Xenopus gastrula using serial analysis of gene expression

Comparison of dorsal and ventral transcriptomes of Xenopus tropicalis gastrulae using serial analysis of gene expression provides at least 86 novel differentially expressed transcripts

Fulguración de haz izquierdo en paciente con transposición de grandes vasos

En este artículo presentamos el caso clínico de un paciente con cirugía de Senning por transposición de los grandes vasos, quien fue sometido a fulguración con radiofrecuencia de un haz paraespecífico izquierdo. Se ha documentado la asociación entre esta condición clínica y la presencia de arritmias auriculares, pera no encontramos datos publicados de tratamiento con radiofrecuencia en un paciente con cirugía de Senning y taquicardia paraxística supraventricular por un haz paraespecífico

OrthoClusterDB: an online platform for synteny blocks

<p>Abstract</p> <p>Background</p> <p>The recent availability of an expanding collection of genome sequences driven by technological advances has facilitated comparative genomics and in particular the identification of synteny among multiple genomes. However, the development of effective and easy-to-use methods for identifying such conserved gene clusters among multiple genomes–synteny blocks–as well as databases, which host synteny blocks from various groups of species (especially eukaryotes) and also allow users to run synteny-identification programs, lags behind.</p> <p>Descriptions</p> <p>OrthoClusterDB is a new online platform for the identification and visualization of synteny blocks. OrthoClusterDB consists of two key web pages: <it>Run OrthoCluster </it>and <it>View Synteny</it>. The <it>Run OrthoCluster </it>page serves as web front-end to OrthoCluster, a recently developed program for synteny block detection. <it>Run OrthoCluster </it>offers full control over the functionalities of OrthoCluster, such as specifying synteny block size, considering order and strandedness of genes within synteny blocks, including or excluding nested synteny blocks, handling one-to-many orthologous relationships, and comparing multiple genomes. In contrast, the <it>View Synteny </it>page gives access to perfect and imperfect synteny blocks precomputed for a large number of genomes, without the need for users to retrieve and format input data. Additionally, genes are cross-linked with public databases for effective browsing. For both <it>Run OrthoCluster </it>and <it>View Synteny</it>, identified synteny blocks can be browsed at the whole genome, chromosome, and individual gene level. OrthoClusterDB is freely accessible.</p> <p>Conclusion</p> <p>We have developed an online system for the identification and visualization of synteny blocks among multiple genomes. The system is freely available at <url>http://genome.sfu.ca/orthoclusterdb/</url>.</p

Polymorphic segmental duplication in the nematode Caenorhabditis elegans

<p>Abstract</p> <p>Background</p> <p>The nematode <it>Caenorhabditis elegans </it>was the first multicellular organism to have its genome fully sequenced. Over the last 10 years since the original publication in 1998, the <it>C. elegans </it>genome has been scrutinized and the last gaps were filled in November 2002, which present a unique opportunity for examining genome-wide segmental duplications.</p> <p>Results</p> <p>Here, we performed analysis of the <it>C. elegans </it>genome in search for segmental duplications using a new tool–OrthoCluster–we have recently developed. We detected 3,484 duplicated segments–duplicons–ranging in size from 234 bp to 108 Kb. The largest pair of duplicons, 108 kb in length located on the left arm of <it>Chromosome V</it>, was further characterized. They are nearly identical at the DNA level (99.7% identity) and each duplicon contains 26 putative protein coding genes. Genotyping of 76 wild-type strains obtained from different labs in the <it>C. elegans </it>community revealed that not all strains contain this duplication. In fact, only 29 strains carry this large segmental duplication, suggesting a very recent duplication event in the <it>C. elegans </it>genome.</p> <p>Conclusion</p> <p>This report represents the first demonstration that the <it>C. elegans </it>laboratory wild-type N2 strains has acquired large-scale differences.</p

Transcriptome-Wide Detection of Differentially Expressed Coding and Non-Coding Transcripts and Their Clinical Significance in Prostate Cancer

Prostate cancer is a clinically and biologically heterogeneous disease. Deregulation of splice variants has been shown to contribute significantly to this complexity. High-throughput technologies such as oligonucleotide microarrays allow for the detection of transcripts that play a role in disease progression in a transcriptome-wide level. In this study, we use a publicly available dataset of normal adjacent, primary tumor, and metastatic prostate cancer samples (GSE21034) to detect differentially expressed coding and non-coding transcripts between these disease states. To achieve this, we focus on transcript-specific probe selection regions, that is, those probe sets that correspond unambiguously to a single transcript. Based on this, we are able to pinpoint at the transcript-specific level transcripts that are differentially expressed throughout prostate cancer progression. We confirm previously reported cases and find novel transcripts for which no prior implication in prostate cancer progression has been made. Furthermore, we show that transcript-specific differential expression has unique prognostic potential and provides a clinically significant source of biomarker signatures for prostate cancer risk stratification. The results presented here serve as a catalog of differentially expressed transcript-specific markers throughout prostate cancer progression that can be used as basis for further development and translation into the clinic

Genomic “Dark Matter” in Prostate Cancer: Exploring the Clinical Utility of ncRNA as Biomarkers

Prostate cancer is the most diagnosed cancer among men in the United States. While the majority of patients who undergo surgery (prostatectomy) will essentially be cured, about 30–40% men remain at risk for disease progression and recurrence. Currently, patients are deemed at risk by evaluation of clinical factors, but these do not resolve whether adjuvant therapy will significantly attenuate or delay disease progression for a patient at risk. Numerous efforts using mRNA-based biomarkers have been described for this purpose, but none have successfully reached widespread clinical practice in helping to make an adjuvant therapy decision. Here, we assess the utility of non-coding RNAs as biomarkers for prostate cancer recurrence based on high-resolution oligonucleotide microarray analysis of surgical tissue specimens from normal adjacent prostate, primary tumors, and metastases. We identify differentially expressed non-coding RNAs that distinguish between the different prostate tissue types and show that these non-coding RNAs can predict clinical outcomes in primary tumors. Together, these results suggest that non-coding RNAs are emerging from the “dark matter” of the genome as a new source of biomarkers for characterizing disease recurrence and progression. While this study shows that non-coding RNA biomarkers can be highly informative, future studies will be needed to further characterize the specific roles of these non-coding RNA biomarkers in the development of aggressive disease