2,865 research outputs found
TROM: A Testing-based Method for Finding Transcriptomic Similarity of Biological Samples
Comparative transcriptomics has gained increasing popularity in genomic
research thanks to the development of high-throughput technologies including
microarray and next-generation RNA sequencing that have generated numerous
transcriptomic data. An important question is to understand the conservation
and differentiation of biological processes in different species. We propose a
testing-based method TROM (Transcriptome Overlap Measure) for comparing
transcriptomes within or between different species, and provide a different
perspective to interpret transcriptomic similarity in contrast to traditional
correlation analyses. Specifically, the TROM method focuses on identifying
associated genes that capture molecular characteristics of biological samples,
and subsequently comparing the biological samples by testing the overlap of
their associated genes. We use simulation and real data studies to demonstrate
that TROM is more powerful in identifying similar transcriptomes and more
robust to stochastic gene expression noise than Pearson and Spearman
correlations. We apply TROM to compare the developmental stages of six
Drosophila species, C. elegans, S. purpuratus, D. rerio and mouse liver, and
find interesting correspondence patterns that imply conserved gene expression
programs in the development of these species. The TROM method is available as
an R package on CRAN (http://cran.r-project.org/) with manuals and source codes
available at http://www.stat.ucla.edu/ jingyi.li/software-and-data/trom.html
MSIQ: Joint Modeling of Multiple RNA-seq Samples for Accurate Isoform Quantification
Next-generation RNA sequencing (RNA-seq) technology has been widely used to
assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq
data offer insight into gene expression levels and transcriptome structures,
enabling us to better understand the regulation of gene expression and
fundamental biological processes. Accurate isoform quantification from RNA-seq
data is challenging due to the information loss in sequencing experiments. A
recent accumulation of multiple RNA-seq data sets from the same tissue or cell
type provides new opportunities to improve the accuracy of isoform
quantification. However, existing statistical or computational methods for
multiple RNA-seq samples either pool the samples into one sample or assign
equal weights to the samples when estimating isoform abundance. These methods
ignore the possible heterogeneity in the quality of different samples and could
result in biased and unrobust estimates. In this article, we develop a method,
which we call "joint modeling of multiple RNA-seq samples for accurate isoform
quantification" (MSIQ), for more accurate and robust isoform quantification by
integrating multiple RNA-seq samples under a Bayesian framework. Our method
aims to (1) identify a consistent group of samples with homogeneous quality and
(2) improve isoform quantification accuracy by jointly modeling multiple
RNA-seq samples by allowing for higher weights on the consistent group. We show
that MSIQ provides a consistent estimator of isoform abundance, and we
demonstrate the accuracy and effectiveness of MSIQ compared with alternative
methods through simulation studies on D. melanogaster genes. We justify MSIQ's
advantages over existing approaches via application studies on real RNA-seq
data from human embryonic stem cells, brain tissues, and the HepG2 immortalized
cell line
Slug-based epithelial-mesenchymal transition gene signature is associated with prolonged time to recurrence in glioblastoma
Background
We previously identified a precise stage-associated gene expression signature of coordinately expressed genes, including the transcription factor Slug (SNAI2) and other epithelial mesenchymal transition (EMT) markers, present in samples from publicly available gene expression datasets in multiple cancer types. The expression levels of the co-expressed genes vary in a continuous and coordinate manner across the samples, ranging from absence of expression to strong co-expression of all genes. These data suggest that tumor cells may pass through an EMT like process of mesenchymal transition to varying degrees. 

Findings
Here we show that this signature in glioblastoma multiforme (GBM) is associated with time to recurrence following initial treatment. By analyzing data from The Cancer Genome Atlas (TCGA), we found that GBM patients who responded to therapy and had long time to recurrence had low levels of the signature in their tumor samples (P = 3x10^-7^). We also found that the signature is strongly correlated in gliomas with the putative stem cell marker CD44, and is highly enriched among the differentially expressed genes in glioblastomas vs. lower grade gliomas. 

Conclusions 
Our results suggest that long delay before tumor recurrence is associated with absence of the mesenchymal transition signature, raising the possibility that inhibiting this transition might improve the durability of therapy in glioma patients
Functional Genomics Profiling of Bladder Urothelial Carcinoma MicroRNAome as a Potential Biomarker.
Though bladder urothelial carcinoma is the most common form of bladder cancer, advances in its diagnosis and treatment have been modest in the past few decades. To evaluate miRNAs as putative disease markers for bladder urothelial carcinoma, this study develops a process to identify dysregulated miRNAs in cancer patients and potentially stratify patients based on the association of their microRNAome phenotype to genomic alterations. Using RNA sequencing data for 409 patients from the Cancer Genome Atlas, we examined miRNA differential expression between cancer and normal tissues and associated differentially expressed miRNAs with patient survival and clinical variables. We then correlated miRNA expressions with genomic alterations using the Wilcoxon test and REVEALER. We found a panel of six miRNAs dysregulated in bladder cancer and exhibited correlations to patient survival. We also performed differential expression analysis and clinical variable correlations to identify miRNAs associated with tobacco smoking, the most important risk factor for bladder cancer. Two miRNAs, miR-323a and miR-431, were differentially expressed in smoking patients compared to nonsmoking patients and were associated with primary tumor size. Functional studies of these miRNAs and the genomic features we identified for potential stratification may reveal underlying mechanisms of bladder cancer carcinogenesis and further diagnosis and treatment methods for urothelial bladder carcinoma
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EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences.
The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the efficacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns
Issues arising from benchmarking single-cell RNA sequencing imputation methods
On June 25th, 2018, Huang et al. published a computational method SAVER on
Nature Methods for imputing dropout gene expression levels in single cell RNA
sequencing (scRNA-seq) data. Huang et al. performed a set of comprehensive
benchmarking analyses, including comparison with the data from RNA fluorescence
in situ hybridization, to demonstrate that SAVER outperformed two existing
scRNA-seq imputation methods, scImpute and MAGIC. However, their computational
analyses were based on semi-synthetic data that the authors had generated
following the Poisson-Gamma model used in the SAVER method. We have therefore
re-examined Huang et al.'s study. We find that the semi-synthetic data have
very different properties from those of real scRNA-seq data and that the cell
clusters used for benchmarking are inconsistent with the cell types labeled by
biologists. We show that a reanalysis based on real scRNA-seq data and grounded
on biological knowledge of cell types leads to different results and
conclusions from those of Huang et al.Comment: 5 page
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