iREAD: a tool for intron retention detection from RNA-seq data.

BACKGROUND: Intron retention (IR) has been traditionally overlooked as \u27noise\u27 and received negligible attention in the field of gene expression analysis. In recent years, IR has become an emerging field for interrogating transcriptomes because it has been recognized to carry out important biological functions such as gene expression regulation and it has been found to be associated with complex diseases such as cancers. However, methods for detecting IR today are limited. Thus, there is a need to develop novel methods to improve IR detection. RESULTS: Here we present iREAD (intron REtention Analysis and Detector), a tool to detect IR events genome-wide from high-throughput RNA-seq data. The command line interface for iREAD is implemented in Python. iREAD takes as input a BAM file, representing the transcriptome, and a text file containing the intron coordinates of a genome. It then 1) counts all reads that overlap intron regions, 2) detects IR events by analyzing the features of reads such as depth and distribution patterns, and 3) outputs a list of retained introns into a tab-delimited text file. iREAD provides significant added value in detecting IR compared with output from IRFinder with a higher AUC on all datasets tested. Both methods showed low false positive rates and high false negative rates in different regimes, indicating that use together is generally beneficial. The output from iREAD can be directly used for further exploratory analysis such as differential intron expression and functional enrichment. The software is freely available at https://github.com/genemine/iread. CONCLUSION: Being complementary to existing tools, iREAD provides a new and generic tool to interrogate poly-A enriched transcriptomic data of intron regions. Intron retention analysis provides a complementary approach for understanding transcriptome

Genome-Scale Consequences of Cofactor Balancing in Engineered Pentose Utilization Pathways in Saccharomyces cerevisiae

Biofuels derived from lignocellulosic biomass offer promising alternative renewable energy sources for transportation fuels. Significant effort has been made to engineer Saccharomyces cerevisiae to efficiently ferment pentose sugars such as D-xylose and L-arabinose into biofuels such as ethanol through heterologous expression of the fungal D-xylose and L-arabinose pathways. However, one of the major bottlenecks in these fungal pathways is that the cofactors are not balanced, which contributes to inefficient utilization of pentose sugars. We utilized a genome-scale model of S. cerevisiae to predict the maximal achievable growth rate for cofactor balanced and imbalanced D-xylose and L-arabinose utilization pathways. Dynamic flux balance analysis (DFBA) was used to simulate batch fermentation of glucose, D-xylose, and L-arabinose. The dynamic models and experimental results are in good agreement for the wild type and for the engineered D-xylose utilization pathway. Cofactor balancing the engineered D-xylose and L-arabinose utilization pathways simulated an increase in ethanol batch production of 24.7% while simultaneously reducing the predicted substrate utilization time by 70%. Furthermore, the effects of cofactor balancing the engineered pentose utilization pathways were evaluated throughout the genome-scale metabolic network. This work not only provides new insights to the global network effects of cofactor balancing but also provides useful guidelines for engineering a recombinant yeast strain with cofactor balanced engineered pathways that efficiently co-utilizes pentose and hexose sugars for biofuels production. Experimental switching of cofactor usage in enzymes has been demonstrated, but is a time-consuming effort. Therefore, systems biology models that can predict the likely outcome of such strain engineering efforts are highly useful for motivating which efforts are likely to be worth the significant time investment

Use of the Ober2 system for analysis of eye movements made during reading

Introduction: The Ober2 system uses infrared reflections to record and analyze eye movements made during reading. The system\u27s ability to analyze data from normal subjects, and the reliability of the data produced by subjects who read standard paragraphs were investigated in this study. Subjects: Forty-two college students and 20 junior high students participated in the project. All were self-reported normal readers. Methods: Subjects read 5 different paragraphs during each of two sessions. Ober2 analysis was attempted for each paragraph; analysis of all 10 paragraphs was successful for 38 percent of the college subjects and 20 percent of the junior high subjects. Use of manual calibration procedures did not allow any additional data to be analyzed by the Ober2 system. Results: Data from 30% of the paragraph presentations could not be analyzed by the Ober2. When analysis was successful, grade equivalent scores based on fixations, span of recognition, regressions, fixation duration, and reading rate were provided. Using mean grade equivalents from the 16 college subjects for whom all 10 paragraphs could be analyzed, significant differences were found between results for two of the test paragraphs. Split-half reliability coefficients for grade equivalent data from the two sessions ranged from 0.84 to 0.95. Conclusions: Although the Ober2 can provide valuable information on eye movements made during reading, problems exist with respect to its ability to analyze data. The analysis failures that occurred for approximately one-third of the paragraph presentations were frustrating and time consuming. With respect to the standard paragraphs, significant grade equivalent differences were found between several of them. These results suggest that caution be used when interpreting data from the Ober2 reading analysis system

A Universal Theory of Pseudocodewords

Three types of pseudocodewords for LDPC codes are found in the literature: graph cover pseudocodewords, linear programming pseudocodewords, and computation tree pseudocodewords. In this paper we first review these three notions and known connections between them. We then propose a new decoding rule — universal cover decoding — for LDPC codes. This new decoding rule also has a notion of pseudocodeword attached, and this fourth notion provides a framework in which we can better understand the other three

Identifying Personalized Metabolic Signatures in Breast Cancer.

Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach

From the Cover: Assignment of an Essential Role for the Neurospora Frequency Gene in Circadian Entrainment to Temperature Cycles

Circadian systems include slave oscillators and central pacemakers, and the cores of eukaryotic circadian clocks described to date are composed of transcription and translation feedback loops (TTFLs). In the model system Neurospora, normal circadian rhythmicity requires a TTFL in which a White Collar complex (WCC) activates expression of the frequency (frq) gene, and the FRQ protein feeds back to attenuate that activation. To further test the centrality of this TTFL to the circadian mechanism in Neurospora, we used low-amplitude temperature cycles to compare WT and frq-null strains under conditions in which a banding rhythm was elicited. WT cultures were entrained to these temperature cycles. Unlike those normal strains, however, frq-null mutants did not truly entrain to the same cycles. Their peaks and troughs always occurred in the cold and warm periods, respectively, strongly suggesting that the rhythm in Neurospora lacking frq function simply is driven by the temperature cycles. Previous reports suggested that a FRQ-less oscillator (FLO) could be entrained to temperature cycles, rather than being driven, and speculated that the FLO was the underlying circadian-rhythm generator. These inferences appear to derive from the use of a phase reference point affected by both the changing waveform and the phase of the oscillation. Examination of several other phase markers as well as results of additional experimental tests indicate that the FLO is, at best, a slave oscillator to the TTFL, which underlies circadian rhythm generation in Neurospora

A Cell-Surface Membrane Protein Signature for Glioblastoma.

We present a systems strategy that facilitated the development of a molecular signature for glioblastoma (GBM), composed of 33 cell-surface transmembrane proteins. This molecular signature, GBMSig, was developed through the integration of cell-surface proteomics and transcriptomics from patient tumors in the REMBRANDT (n = 228) and TCGA datasets (n = 547) and can separate GBM patients from control individuals with a Matthew\u27s correlation coefficient value of 0.87 in a lock-down test. Functionally, 17/33 GBMSig proteins are associated with transforming growth factor β signaling pathways, including CD47, SLC16A1, HMOX1, and MRC2. Knockdown of these genes impaired GBM invasion, reflecting their role in disease-perturbed changes in GBM. ELISA assays for a subset of GBMSig (CD44, VCAM1, HMOX1, and BIGH3) on 84 plasma specimens from multiple clinical sites revealed a high degree of separation of GBM patients from healthy control individuals (area under the curve is 0.98 in receiver operating characteristic). In addition, a classifier based on these four proteins differentiated the blood of pre- and post-tumor resections, demonstrating potential clinical value as biomarkers

Health and disease markers correlate with gut microbiome composition across thousands of people.

Variation in the human gut microbiome can reflect host lifestyle and behaviors and influence disease biomarker levels in the blood. Understanding the relationships between gut microbes and host phenotypes are critical for understanding wellness and disease. Here, we examine associations between the gut microbiota and ~150 host phenotypic features across ~3,400 individuals. We identify major axes of taxonomic variance in the gut and a putative diversity maximum along the Firmicutes-to-Bacteroidetes axis. Our analyses reveal both known and unknown associations between microbiome composition and host clinical markers and lifestyle factors, including host-microbe associations that are composition-specific. These results suggest potential opportunities for targeted interventions that alter the composition of the microbiome to improve host health. By uncovering the interrelationships between host diet and lifestyle factors, clinical blood markers, and the human gut microbiome at the population-scale, our results serve as a roadmap for future studies on host-microbe interactions and interventions