Whole exome sequencing of a single osteosarcoma case—integrative analysis with whole transcriptome RNA-seq data

Background

Osteosarcoma (OS) is a prevalent primary malignant bone tumour with unknown etiology. These highly metastasizing tumours are among the most frequent causes of cancer-related deaths. Thus, there is an urgent need for different markers, and with our study, we were aiming towards finding novel biomarkers for OS.

Methods

For that, we analysed the whole exome of the tumorous and non-tumour bone tissue from the same patient with OS applying next-generation sequencing. For data analysis, we used several softwares and combined the exome data with RNA-seq data from our previous study.

Results

In the tumour exome, we found wide genomic rearrangements, which should qualify as chromotripsis—we detected almost 3,000 somatic single nucleotide variants (SNVs) and small indels and more than 2,000 copy number variants (CNVs) in different chromosomes. Furthermore, the somatic changes seem to be associated to bone tumours, whereas germline mutations to cancer in general. We confirmed the previous findings that the most significant pathway involved in OS pathogenesis is probably the WNT/β-catenin signalling pathway. Also, the IGF1/IGF2 and IGF1R homodimer signalling and TP53 (including downstream tumour suppressor gene EI24) pathways may have a role. Additionally, the mucin family genes, especially MUC4 and cell cycle controlling gene CDC27 may be considered as potential biomarkers for OS.

Conclusions

The genes, in which the mutations were detected, may be considered as targets for finding biomarkers for OS. As the study is based on a single case and only DNA and RNA analysis, further confirmative studies are required.

     

Global analysis of a stoichiometric producer–grazer model with Holling type functional responses

Cells, the basic units of organisms, consist of multiple essential elements such as carbon, nitrogen, and phosphorus. The scarcity of any of these elements can strongly restrict cellular and organismal growth. During recent years, ecological models incorporating multiple elements have been rapidly developed in many studies, which form a new research field of mathematical and theoretical biology. Among these models, the one proposed by Loladze et al. (Bull Math Biol 62:1137–1162, 2000) is prominent and has been highly cited. However, the global analysis of this nonsmooth model has never been done. The aim of this paper is to provide the complete global analysis for the model with Holling type I functional response and perform a bifurcation analysis for the model with Holling type II functional response.     

Phylogenetic analysis of the non-structural (NS) gene of influenza a viruses isolated in Kazakhstan in 2002–2009

Although the important role of the non-structural (NS1 and NEP) gene of influenza A in virulence of the virus is well established, our knowledge about the extent of variation in the NS gene pool of influenza A viruses in their natural reservoirs in Kazakhstan is incomplete. 17 influenza A viruses of different subtypes were studied in this paper. Seven types of haemagglutinin and five different neuraminidase subtypes in eight combinations were found among the isolated viruses. A comparison of nucleotide sequences of isolated viruses revealed a substantial number of silent mutations, which results in high degree of homology in amino acid sequences. By phylogenetic analysis it was shown that two distinct gene pools, corresponding to both NS allele A with 5 Clades and B, were present at the same time in Kazakhstan. The degree of variation within the alleles was very low. In our study allele A viruses had a maximum of 5% amino acid divergence in Clade while allele B viruses had only 4% amino acid divergence.     

SC²ATmd: a tool for integration of the figure of merit with cluster analysis for gene expression data

Standard and Consensus Clustering Analysis Tool for Microarray Data (SC2ATmd) is a MATLAB-implemented application specifically designed for the exploration of microarray gene expression data via clustering. Implementation of two versions of the clustering validation method figure of merit allows for performance comparisons between different clustering algorithms, and tailors the cluster analysis process to the varying characteristics of each dataset. Along with standard clustering algorithms this application also offers a consensus clustering method that can generate reproducible clusters across replicate experiments or different clustering algorithms. This application was designed specifically for the analysis of gene expression data, but may be used with any numerical data as long as it is in the right format. AVAILABILITY: SC2ATmd may be freely downloaded from http://www.compbiosci.wfu.edu/tools.htm.     

caBIG™ VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data

Background

We sketch our species identification tool for palm sized computers that helps knowledgeable observers with census activities. An algorithm turns an identification matrix into a minimal length series of questions that guide the operator towards identification. Historic observation data from the census geographic area helps minimize question volume. We explore how much historic data is required to boost performance, and whether the use of history negatively impacts identification of rare species. We also explore how characteristics of the matrix interact with the algorithm, and how best to predict the probability of observing a previously unseen species.

Results

Point counts of birds taken at Stanford University's Jasper Ridge Biological Preserve between 2000 and 2005 were used to examine the algorithm. A computer identified species by correctly answering, and counting the algorithm's questions. We also explored how the character density of the key matrix and the theoretical minimum number of questions for each bird in the matrix influenced the algorithm. Our investigation of the required probability smoothing determined whether Laplace smoothing of observation probabilities was sufficient, or whether the more complex Good-Turing technique is required.

Conclusion

Historic data improved identification speed, but only impacted the top 25% most frequently observed birds. For rare birds the history based algorithms did not impose a noticeable penalty in the number of questions required for identification. For our dataset neither age of the historic data, nor the number of observation years impacted the algorithm. Density of characters for different taxa in the identification matrix did not impact the algorithms. Intrinsic differences in identifying different birds did affect the algorithm, but the differences affected the baseline method of not using historic data to exactly the same degree. We found that Laplace smoothing performed better for rare species than Simple Good-Turing, and that, contrary to expectation, the technique did not then adversely affect identification performance for frequently observed birds.     

Use of genomic DNA control features and predicted operon structure in microarray data analysis: ArrayLeaRNA – a Bayesian approach

Background  

Microarrays are widely used for the study of gene expression; however deciding on whether observed differences in expression are significant remains a challenge.     

2DDB – a bioinformatics solution for analysis of quantitative proteomics data

Background  

We present 2DDB, a bioinformatics solution for storage, integration and analysis of quantitative proteomics data. As the data complexity and the rate with which it is produced increases in the proteomics field, the need for flexible analysis software increases.     

Identification of a functional toxin–antitoxin system located in the genomic island PYG1 of piezophilic hyperthermophilic archaeon Pyrococcus yayanosii

Extremophiles - Toxin–antitoxin (TA) system is bacterial or archaeal genetic module consisting of toxin and antitoxin gene that be organized as a bicistronic operon. TA system could elicit...     

Discovery of a novel chimeric ubenimex–gemcitabine with potent oral antitumor activity

Herein, a novel mutual prodrug BC-A1 was discovered by integrating ubenimex and gemcitabine into one molecule. Biological characterization revealed that compound BC-A1 could maintain both the anti-CD13 activity of ubenimex and the cytotoxic activity of gemcitabine in vitro. Further characterization also demonstrated that compound BC-A1 exhibited significant anti-invasion and anti-angiogenesis effects in vitro. The preliminary stability test of BC-A1 revealed that it could release gemcitabine in vitro. The in vivo anti-tumor results in liver cancer showed that at the same dosage, oral administration of BC-A1 was as potent as intraperitoneal administration of gemcitabine. This warranted the further research and development of the orally active prodrug BC-A1 because gemcitabine can not be orally administrated in clinic.     

Statistical methods for temporal and space–time analysis of community composition data

This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran''s eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space–time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.     

stam – a Bioconductor compliant R package for structured analysis of microarray data

Background  

Genome wide microarray studies have the potential to unveil novel disease entities. Clinically homogeneous groups of patients can have diverse gene expression profiles. The definition of novel subclasses based on gene expression is a difficult problem not addressed systematically by currently available software tools.     

On being “actionable”: clinical sequencing and the emerging contours of a regime of genomic medicine in oncology

This article explores commercial, academic, and national initiatives aimed at using sequencing technologies to generate “actionable” genomic results that can be applied to the clinical management of oncology patients. We argue that the term “actionable” is not merely a buzzword, but signals the emergence of a distinctive sociotechnical regime of genomic medicine in oncology. Unlike other regimes of genomic medicine that are organized around assessing and managing inherited risk for developing cancer (e.g. BRCA testing), actionable regimes aim to generate predictive relationships between genetic information and drug therapies, thereby generating new kinds of clinical actions. We explore how these genomic results are made actionable by articulating them with existing clinical routines, clinical trials, regulatory regimes, and health care systems; and in turn, how clinical sequencing programs have begun to reconfigure knowledge and practices in oncology. Actionability regimes confirm the emergence of bio-clinical decision-making in oncology, whereby the articulation of molecular hypotheses and experimental therapeutics become central to patient care.     

Conformational energy analysis of the substitution of Val for Gly 233 in a functional region of platelet GPIbα in platelet-type von Willebrand disease

Platelet-type von Willebrand disease (PT-vWD) is an autosomal dominant bleeding disorder in which patient platelets exhibit an abnormally increased binding of circulating von Willebrand factor (vWF). We have recently shown that this abnormality is associated with a point mutation resulting in substitution of Val for Gly 233 in platelet membrane glycoprotein Ibα (GPIbga), a major component of the platelet (GPIb/IX receptor for vWF. To investigate the effect of this substitution on the three-dimensional structure of this region of the protein, we have generated the allowed (low energy) conformations of the region of the GPIα protein containing residues 228–238 (with 5 residues on either side of the critical residue 233) with Gly 233 (wild type) and Val 233 (PT-vWD) using the computer program ECEPP (Empirical Conformational Energies of Peptides Program). The wild-type sequence is Tyr-Val-Trp-Lys-Gln-Gly-Val-Asp-Val-Lys-Ala. We find that the Gly 233-containing peptide can exist in two low energy conformers. The lowest energy conformer is a structure containing a β-turn at Gln 232-Gly 233 while the alternative conformation is an amphipathic helical structure. Only the amphipathic helical structure is allowed for the Val 233-containing peptide which contains a hydrophobic ‘face’ consisting of Val 229, Val 233 and Val 236 and another hydrophilic surface composed of such residues as Lys 231 and Asp 235. No such surfaces exist for the lowest energy bend conformer for the Gly 233-containing peptide, but do exist in the higher energy helical structure. The amphiphatic surfaces in the 228–238 region of the Val 233-containing GPIbα protein may associate strongly with complementary surfaces during vWF binding to the GPIb/IX receptor complex and may help explain heightened association of vWF with this receptor in PT-vWD.