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Gan-Xun Li, Ze-Yang Ding, Yu-Wei Wang, Tong-Tong Liu, Wei-Xun Chen, Jing-Jing Wu, Wei-Qi Xu, Peng Zhu, Bi-Xiang Zhang
DNA methylation is a crucial regulator of gene transcription in the etiology and pathogenesis of hepatocellular carcinoma (HCC). Thus, it is reasonable to identify DNA methylation-related prognostic markers. Currently, we aimed to make an integrative epigenetic analysis of HCC to identify the effectiveness of epigenetic drivers in predicting prognosis for HCC patients. By the software pipeline TCGA-Assembler 2, RNA-seq, and methylation data were downloaded and processed from The Cancer Genome Atlas. A bioconductor package MethylMix was utilized to incorporate gene expression and methylation data on all 363 samples and identify 589 epigenetic drivers with transcriptionally predictive...
December 7, 2018: Journal of Cellular Physiology
Sonia E Trojan, Monika Piwowar, Barbara Ostrowska, Piotr Laidler, Kinga A Kocemba-Pilarczyk
BACKGROUND/AIM: Most melanomas develop in hypoxic conditions. Since hypoxia via HIF-1 induces glycolysis, a process essential for malignant melanoma growth/survival, the goal of this study was to analyze the influence of hypoxia on the expression of HIF-1 target genes involved in glucose metabolism. MATERIALS AND METHODS: The response of melanoma cell lines to hypoxic conditions was analyzed by RT-PCR and western blotting. A Kaplan-Meier survival analysis for patients with high and low expression level of PFKFB4 was performed...
December 2018: Anticancer Research
Thomas Sherman, Jack Fu, Robert B Scharpf, Alexandre Bureau, Ingo Ruczinski
Summary: Family-based sequencing studies enable researchers to identify highly penetrant genetic variants too rare to be tested in conventional case-control studies, by studying co-segregation of variant and disease phenotype. When multiple affected subjects in a family are sequenced, the probability that a variant or a set of variants is shared identical-by-descent by some or all affected relatives provides evidence against the null hypothesis of complete absence of linkage and association...
November 30, 2018: Bioinformatics
Thomas Naake, Alisdair R Fernie
A major bottleneck of mass spectrometric metabolomic analysis is still the rapid detection and annotation of unknown m/z features across biological matrices. This kind of analysis is especially cumbersome for complex samples with hundreds to thousands of unknown features. Traditionally, the annotation was done manually imposing constraints in reproducibility and automatization. Furthermore, different analysis tools are typically used at different steps which requires parsing of data and changing of environments...
November 30, 2018: Analytical Chemistry
Sean Davis, Marcel Ramos, Lori Shepherd, Nitesh Turaga, Ludwig Geistlinger, Martin T Morgan, Benjamin Haibe-Kains, Levi Waldron
The importance of bioinformatics, computational biology, and data science in biomedical research continues to grow, driving a need for effective instruction and education. A workshop setting, with lectures and guided hands-on tutorials, is a common approach to teaching practical computational and analytical methods. Here, we detail the process we used to produce high-quality, community-authored educational materials that are available for public consumption and reuse. The coordinated efforts of 17 authors over 10 weeks resulted in 15 workshops available as a website and as a 388-page electronic book...
2018: F1000Research
Wolfgang Kaisers, Holger Schwender, Heiner Schaal
We apply hierarchical clustering (HC) of DNA k-mer counts on multiple Fastq files. The tree structures produced by HC may reflect experimental groups and thereby indicate experimental effects, but clustering of preparation groups indicates the presence of batch effects. Hence, HC of DNA k-mer counts may serve as a diagnostic device. In order to provide a simple applicable tool we implemented sequential analysis of Fastq reads with low memory usage in an R package (seqTools) available on Bioconductor. The approach is validated by analysis of Fastq file batches containing RNAseq data...
November 21, 2018: International Journal of Molecular Sciences
Banavathy S Kruthika, Ruchi Jain, A Arivazhagan, R D Bharath, T C Yasha, Paturu Kondaiah, Vani Santosh
PURPOSE: Peritumoural brain zone (PT) of glioblastoma (GBM) is the area where tumour recurrence is often observed. We aimed to identify differentially regulated genes between tumour core (TC) and PT to understand the underlying molecular characteristics of infiltrating tumour cells in PT. METHODS: 17 each histologically characterised TC and PT tissues of GBM along with eight control tissues were subjected to cDNA Microarray. PT tissues contained 25-30% infiltrating tumour cells...
November 20, 2018: Journal of Neuro-oncology
Eralp Dogu, Sara Mohammad Taheri, Roger Olivella, Florian Marty, Ian Lienert, Lukas Reiter, Eduardo Sabidó, Olga Vitek
MSstatsQC is an R/Bioconductor package for statistical monitoring of longitudinal system suitability and quality control in mass spectrometry-based proteomics. MSstatsQC was initially designed for targeted Selected Reaction Monitoring experiments. This manuscript presents an extension, MSstatsQC 2.0, that supports experiments with global Data-Dependent and Data-Independent acquisition. The extension implements data processing and analyses that are specific to these acquisition types. It relies on the state-of-the-art methods of statistical process control to detect deviations from optimal performance of various metrics (such as intensity and retention time of chromatographic peaks), and to summarize the results across multiple metrics and analytes...
November 19, 2018: Journal of Proteome Research
Guillaume Beyrend, Koen Stam, Thomas Höllt, Ferry Ossendorp, Ramon Arens
Multi-parametric flow and mass cytometry allows exceptional high-resolution exploration of the cellular composition of the immune system. A large panel of computational tools have been developed to analyze the high-dimensional landscape of the data generated. Analysis frameworks such as FlowSOM or Cytosplore incorporate clustering and dimensionality reduction techniques and include algorithms allowing visualization of multi-parametric cytometric analysis. To additionally provide means to quantify specific cell clusters and correlations between samples, we developed an R-package, called cytofast , for further downstream analysis...
2018: Computational and Structural Biotechnology Journal
Momeneh Foroutan, Dharmesh D Bhuva, Ruqian Lyu, Kristy Horan, Joseph Cursons, Melissa J Davis
BACKGROUND: Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore ( https://bioconductor...
November 6, 2018: BMC Bioinformatics
Tiago C Silva, Simon G Coetzee, Nicole Gull, Lijing Yao, Dennis J Hazelett, Houtan Noushmehr, De-Chen Lin, Benjamin P Berman
Motivation: DNA methylation has been used to identify functional changes at transcriptional enhancers and other cisregulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set. Results: We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings...
October 26, 2018: Bioinformatics
Michael I Love, Charlotte Soneson, Rob Patro
Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU...
2018: F1000Research
Andrea Rodriguez-Martinez, Rafael Ayala, Joram M Posma, Nikita Harvey, Beatriz Jiménez, Kazuhiro Sonomura, Taka-Aki Sato, Fumihiko Matsuda, Pierre Zalloua, Dominique Gauguier, Jeremy K Nicholson, Marc-Emmanuel Dumas
Motivation: Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA ("pJRES Binning Algorithm"), which aims to extend the applicability of SRV to pJRES spectra...
October 23, 2018: Bioinformatics
Guangchuang Yu, Tommy Tsan-Yuk Lam, Huachen Zhu, Yi Guan
Ggtree is a comprehensive R package for visualizing and annotating phylogenetic trees with associated data. It can also map and visualize associated external data on phylogenies with two general methods. Method 1 allows external data to be mapped on the tree structure and used as visual characteristic in tree and data visualization. Method 2 plots the data with the tree side by side using different geometric functions after re-ordering the data based on the tree structure. These two methods integrate data with phylogeny for further exploration and comparison in the evolutionary biology context...
October 23, 2018: Molecular Biology and Evolution
Ashish Jain, Geetu Tuteja
Summary: RNA-Seq data analysis results in lists of genes that may have a similar function, based on differential gene expression analysis or co-expression network analysis. While tools have been developed to identify biological processes that are enriched in the genes sets, there remains a need for tools that identify enrichment of tissue-specific genes. Therefore, we developed TissueEnrich, a tool that calculates tissue-specific gene enrichment in an input gene set. We demonstrated that TissueEnrich can assign tissue identities to single cell clusters and differentiated embryonic stem cells...
October 22, 2018: Bioinformatics
Xu Ren, Pei Fen Kuan
Motivation: An important downstream analysis following differential expression from RNA sequencing (RNA-Seq) or DNA methylation analysis is the gene set testing to relate significant genes or CpGs to known biological properties. However, the traditional gene set testing approaches result in biased p-values due to the difference in gene length. Existing methods accounting for length bias were primarily developed for RNA-Seq data. For DNA methylation data profiled using the Illumina arrays, separate methods adjusting for the number of CpGs instead of gene length are necessary...
October 22, 2018: Bioinformatics
Anand Mayakonda, De-Chen Lin, Yassen Assenov, Christoph Plass, H Phillip Koeffler
Numerous large-scale genomic studies of matched tumor-normal samples have established the somatic landscapes of most cancer types. However, the downstream analysis of data from somatic mutations entails a number of computational and statistical approaches, requiring usage of independent software and numerous tools. Here, we describe an R Bioconductor package, Maftools, which offers a multitude of analysis and visualization modules that are commonly used in cancer genomic studies, including driver gene identification, pathway, signature, enrichment, and association analyses...
November 2018: Genome Research
Bijurica Chakraborty, Payel Mondal, Pragya Gajendra, Mitashree Mitra, Chandrima Das, Sanghamitra Sengupta
BACKGROUND: Plasmodium falciparum and Plasmodium vivax are two major parasites responsible for malaria which remains a threat to almost 50% of world's population despite decade-long eradication program. One possible reason behind this conundrum is that the bases of clinical variability in malaria caused by either species are complex and poorly understood. METHODS: Whole-genome transcriptome was analyzed to identify the active and predominant pathways in the PBMC of P...
October 15, 2018: EBioMedicine
Rhonda Bacher, Ning Leng, Li-Fang Chu, Zijian Ni, James A Thomson, Christina Kendziorski, Ron Stewart
BACKGROUND: High-throughput expression profiling experiments with ordered conditions (e.g. time-course or spatial-course) are becoming more common for studying detailed differentiation processes or spatial patterns. Identifying dynamic changes at both the individual gene and whole transcriptome level can provide important insights about genes, pathways, and critical time points. RESULTS: We present an R package, Trendy, which utilizes segmented regression models to simultaneously characterize each gene's expression pattern and summarize overall dynamic activity in ordered condition experiments...
October 16, 2018: BMC Bioinformatics
Shen Sun, Yue Wang, Yue Wu, Yue Gao, Qi Li, Ayanlaja Abiola Abdulrahman, Xin-Feng Liu, Guang-Quan Ji, Jin Gao, Li Li, Fa-Ping Wan, Yun-Qing Li, Dian-Shuai Gao
Malignant astrocytoma (MA) is the most common and severe type of brain tumor. A greater understanding of the underlying mechanisms responsible for the development of MA would be beneficial for the development of targeted molecular therapies. In the present study, the upregulated differentially expressed genes (DEGs) in MA were obtained from the Gene Expression Omnibus database using R/Bioconductor software. DEGs in different World Health Organization classifications were compared using the Venny tool and 15 genes, including collagen type I α1 chain (COL1A1) and laminin subunit γ1 (LAMC1), were revealed to be involved in the malignant progression of MA...
September 21, 2018: International Journal of Oncology
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