Review of Current Methods, Applications, and Data Management for the Bioinformatics Analysis of Whole Exome Sequencing
The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, preprocessing, alignment, post-processing, ...
Source: Cancer Informatics - September 21, 2014 Category: Cancer & Oncology Authors: Riyue BaoLei HuangJorge AndradeWei TanWarren A. KibbeHongmei JiangGang Feng Source Type: research
Comparative Study of Computational Methods for Reconstructing Genetic Networks of Cancer-Related Pathways
Network reconstruction is an important yet challenging task in systems biology. While many methods have been recently proposed for reconstructing biological networks from diverse data types, properties of estimated networks and differences between reconstruction methods are not well understood. In this paper, we conduct a comprehensive empirical evaluation of seven existing network reconstruction methods, by comparing the estimated networks with different sparsity levels for both normal and tumor samples. The results suggest substantial heterogeneity in networks reconstructed using different reconstruction methods. Our fin...
Source: Cancer Informatics - September 21, 2014 Category: Cancer & Oncology Authors: Nafiseh SedaghatTakumi SaegusaTimothy RandolphAli Shojaie Source Type: research
Integrated Analysis of Whole-Genome Paired-End and Mate-Pair Sequencing Data for Identifying Genomic Structural Variations in Multiple Myeloma
We present a pipeline to perform integrative analysis of mate-pair (MP) and paired-end (PE) genomic DNA sequencing data. Our pipeline detects structural variations (SVs) by taking aligned sequencing read pairs as input and classifying these reads into properly paired and discordantly paired categories based on their orientation and inferred insert sizes. Recurrent SV was identified from the discordant read pairs. Our pipeline takes into account genomic annotation and genome repetitive element information to increase detection specificity. Application of our pipeline to whole-genome MP and PE sequencing data from three mult...
Source: Cancer Informatics - September 21, 2014 Category: Cancer & Oncology Authors: Rendong YangLi ChenScott NewmanKhanjan GandhiGregory DohoCarlos S. MorenoPaula M. VertinoLeon Bernal-MizarchiSagar LonialLawrence H. BoiseMichael RossiJeanne Kowalskiand Zhaohui S. Qin Source Type: research
Integrative Bayesian Network Analysis of Genomic Data
In this study, we develop a novel integrative Bayesian network approach to investigate the relationships between genetic and epigenetic alterations as well as how these mutations affect a patient’s clinical outcome. We take a Bayesian network approach that admits a convenient decomposition of the joint distribution into local distributions. Exploiting the prior biological knowledge about regulatory mechanisms, we model each local distribution as linear regressions. This allows us to analyze multi-platform genome-wide data in a computationally efficient manner. We illustrate the performance of our approach through simulat...
Source: Cancer Informatics - September 21, 2014 Category: Cancer & Oncology Authors: Yang NiFrancesco C. StingoVeerabhadran Baladandayuthapani Source Type: research
A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection
We consider a Bayesian hierarchical model for the integration of gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. The approach defines a measurement error model that relates the gene expression levels to latent copy number states. In turn, the latent states are related to the observed surrogate CGH measurements via a hidden Markov model. The model further incorporates variable selection with a spatial prior based on a probit link that exploits dependencies across adjacent DNA segments. Posterior inference is carried out via Markov chain Monte Carlo stoch...
Source: Cancer Informatics - September 21, 2014 Category: Cancer & Oncology Authors: Alberto CasseseMichele GuindaniMarina Vannucci Source Type: research
A Review of Cancer Risk Prediction Models with Genetic Variants
Cancer risk prediction models are important in identifying individuals at high risk of developing cancer, which could result in targeted screening and interventions to maximize the treatment benefit and minimize the burden of cancer. The cancer-associated genetic variants identified in genome-wide or candidate gene association studies have been shown to collectively enhance cancer risk prediction, improve our understanding of carcinogenesis, and possibly result in the development of targeted treatments for patients. In this article, we review the cancer risk prediction models that have been developed for popular cancers an...
Source: Cancer Informatics - September 21, 2014 Category: Cancer & Oncology Authors: Xuexia WangMichael J. OldaniXingwang ZhaoXiaohui HuangDajun Qian Source Type: research
Functional Annotation of Putative Regulatory Elements at Cancer Susceptibility Loci
Most cancer-associated genetic variants identified from genome-wide association studies (GWAS) do not obviously change protein structure, leading to the hypothesis that the associations are attributable to regulatory polymorphisms. Translating genetic associations into mechanistic insights can be facilitated by knowledge of the causal regulatory variant (or variants) responsible for the statistical signal. Experimental validation of candidate functional variants is onerous, making bioinformatic approaches necessary to prioritize candidates for laboratory analysis. Thus, a systematic approach for recognizing functional (and...
Source: Cancer Informatics - September 21, 2014 Category: Cancer & Oncology Authors: Stephanie A. RossePaul L. Auerand Christopher S. Carlson Source Type: research
Introductory Editorial: Classification, Predictive Modelling, and Statistical Analysis of Cancer Data (A)
No abstract supplied by author (Source: Cancer Informatics)
Source: Cancer Informatics - September 21, 2014 Category: Cancer & Oncology Authors: Hongmei JiangLingling AnVeerabhadran BaladandayuthapaniPaul Livermore Auer Source Type: research
Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data
This study is the first to conduct an integrative genomic pathway-based analysis with a graph-based learning algorithm. The methodology of this analysis, graph-based semi-supervised learning, detects pathways that improve prediction of a dichotomous variable, which in this study is cancer stage. This analysis integrates genome-level gene expression, methylation, and single nucleotide polymorphism (SNP) data in serous cystadenocarcinoma (OV) and colon adenocarcinoma (COAD). The top 10 ranked predictive pathways in COAD and OV were biologically relevant to their respective cancer stages and significantly enhanced prediction ...
Source: Cancer Informatics - July 28, 2014 Category: Cancer & Oncology Authors: Andrew E. DellingerAndrew B. NixonHerbert Pang Source Type: research
On the Significance of Fuzzification of the N and M in Cancer Staging
In this study, we focus on the fuzzification of N and M staging for more accurate and realistic modeling which may, in turn, lead to better treatment and medical decisions. (Source: Cancer Informatics)
Source: Cancer Informatics - July 24, 2014 Category: Cancer & Oncology Authors: Sara A. YonesAhmed S. MoussaHesham HassanNelly H. Alieldin Source Type: research
Modeling the Altered Expression Levels of Genes on Signaling Pathways in Tumors As Causal Bayesian Networks
This paper concerns a study indicating that the expression levels of genes in signaling pathways can be modeled using a causal Bayesian network (BN) that is altered in tumorous tissue. These results open up promising areas of future research that can help identify driver genes and therapeutic targets. So, it is most appropriate for the cancer informatics community.
Our central hypothesis is that the expression levels of genes that code for proteins on a signal transduction network (STP) are causally related and that this causal structure is altered when the STP is involved in cancer. To test this hypothesis, we analyzed 5 ...
Source: Cancer Informatics - May 25, 2014 Category: Cancer & Oncology Authors: Richard NeapolitanDiyang Xueand Xia Jiang Source Type: research
Nonlinear Optical Microscopy Signal Processing Strategies in Cancer
This work reviews the most relevant present-day processing methods used to improve the accuracy of multimodal nonlinear images in the detection of epithelial cancer and the supporting stroma. Special emphasis has been placed on methods of non linear optical (NLO) microscopy image processing such as: second harmonic to autofluorescence ageing index of dermis (SAAID), tumor-associated collagen signatures (TACS), fast Fourier transform (FFT) analysis, and gray level co-occurrence matrix (GLCM)-based methods. These strategies are presented as a set of potential valuable diagnostic tools for early cancer detection. It may be pr...
Source: Cancer Informatics - April 2, 2014 Category: Cancer & Oncology Authors: Javier AdurHernandes F. CarvalhoCarlos L. CesarVíctor H. Casco Source Type: research
INsPeCT: INtegrative Platform for Cancer Transcriptomics
The emergence of transcriptomics, fuelled by high-throughput sequencing technologies, has changed the nature of cancer research and resulted in a massive accumulation of data. Computational analysis, integration, and data visualization are now major bottlenecks in cancer biology and translational research. Although many tools have been brought to bear on these problems, their use remains unnecessarily restricted to computational biologists, as many tools require scripting skills, data infrastructure, and powerful computational facilities. New user-friendly, integrative, and automated analytical approaches are required to m...
Source: Cancer Informatics - March 12, 2014 Category: Cancer & Oncology Authors: Piyush B. MadhamshettiwarStefan R. MaetschkeMelissa J. DavisAntonio ReverterMark A. Ragan Source Type: research
A New Method for Predicting Patient Survivorship Using Efficient Bayesian Network Learning
In this study, we evaluate EBMC_S using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which concerns breast tumors. A 5-fold cross-validation study indicates that EMBC_S performs better than the Cox proportional hazard model and is comparable to the random survival forest method. We show that EBMC_S provides additional information such as sensitivity analyses, which covariates predict each year, and yearly areas under the ROC curve (AUROCs). We conclude that our investigation supports the central hypothesis. (Source: Cancer Informatics)
Source: Cancer Informatics - February 13, 2014 Category: Cancer & Oncology Authors: Xia JiangDiyang XueAdam BrufskySeema KhanRichard Neapolitan Source Type: research
Evaluating Gene Set Enrichment Analysis Via a Hybrid Data Model
Gene set enrichment analysis (GSA) methods have been widely adopted by biological labs to analyze data and generate hypotheses for validation. Most of the existing comparison studies focus on whether the existing GSA methods can produce accurate P-values; however, practitioners are often more concerned with the correct gene-set ranking generated by the methods. The ranking performance is closely related to two critical goals associated with GSA methods: the ability to reveal biological themes and ensuring reproducibility, especially for small-sample studies. We have conducted a comprehensive simulation study focusing on th...
Source: Cancer Informatics - February 12, 2014 Category: Cancer & Oncology Authors: Jianping HuaMichael L. BittnerEdward R. Dougherty Source Type: research
