scholarly journals The Junction Usage Model (JUM): A method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns

2017 ◽  
Author(s):  
Qingqing Wang ◽  
Donald C. Rio
Keyword(s):  
Intron Retention ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Mrna Splicing ◽  
Rna Seq ◽  
Detection Analysis ◽  
Positive Rate ◽  
Ir Detection ◽  
Splicing Patterns ◽  
Relevant Pattern

AbstractAlternative pre-mRNA splicing (AS) greatly diversifies metazoan transcriptomes and proteomes and is crucial for gene regulation. Current computational analysis methods of AS from Illumina RNA-seq data rely on pre-annotated libraries of known spliced transcripts, which hinders AS analysis with poorly annotated genomes and can further mask unknown AS patterns. To address this critical bioinformatics problem, we developed a method called the Junction Usage Model (JUM) that uses a bottom-up approach to identify, analyze and quantitate global AS profiles without any prior transcriptome annotations. JUM accurately reports global AS changes in terms of the five conventional AS patterns and an additional “Composite” category composed of inseparable combinations of conventional patterns. JUM stringently classifies the difficult and disease-relevant pattern of intron retention, reducing the false positive rate of IR detection commonly seen in other annotation-based methods to near negligible rates. When analyzing AS in RNA-samples derived from Drosophila heads, human tumors and human cell lines bearing cancer-associated splicing factor mutations, JUM consistently identified ~ twice the number of novel AS events missed by other methods. Computational simulations showed JUM exhibits a 1.2-4.8 times higher true positive rate at a fixed cut-off of 5% false discovery rate. In summary, JUM provides a new framework and improved method that removes the necessity for transcriptome annotations and enables the detection, analysis and quantification of AS patterns in complex metazoan transcriptomes with superior accuracy.

scholarly journals JUM is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns

2018 ◽  
Vol 115 (35) ◽  
pp. E8181-E8190 ◽  
Author(s):  
Qingqing Wang ◽  
Donald C. Rio
Keyword(s):  
Intron Retention ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Mrna Splicing ◽  
Computational Method ◽  
Sequencing Data ◽  
Detection Analysis ◽  
Positive Rate ◽  
Ir Detection ◽  
Splicing Patterns

Alternative pre-mRNA splicing (AS) greatly diversifies metazoan transcriptomes and proteomes and is crucial for gene regulation. Current computational analysis methods of AS from Illumina RNA-sequencing data rely on preannotated libraries of known spliced transcripts, which hinders AS analysis with poorly annotated genomes and can further mask unknown AS patterns. To address this critical bioinformatics problem, we developed a method called the junction usage model (JUM) that uses a bottom-up approach to identify, analyze, and quantitate global AS profiles without any prior transcriptome annotations. JUM accurately reports global AS changes in terms of the five conventional AS patterns and an additional “composite” category composed of inseparable combinations of conventional patterns. JUM stringently classifies the difficult and disease-relevant pattern of intron retention (IR), reducing the false positive rate of IR detection commonly seen in other annotation-based methods to near-negligible rates. When analyzing AS in RNA samples derived from Drosophila heads, human tumors, and human cell lines bearing cancer-associated splicing factor mutations, JUM consistently identified approximately twice the number of novel AS events missed by other methods. Computational simulations showed JUM exhibits a 1.2 to 4.8 times higher true positive rate at a fixed cutoff of 5% false discovery rate. In summary, JUM provides a framework and improved method that removes the necessity for transcriptome annotations and enables the detection, analysis, and quantification of AS patterns in complex metazoan transcriptomes with superior accuracy.

scholarly journals PSI-Sigma: a comprehensive splicing-detection method for short-read and long-read RNA-seq analysis

2019 ◽  
Vol 35 (23) ◽  
pp. 5048-5054 ◽  
Author(s):  
Kuan-Ting Lin ◽  
Adrian R Krainer
Keyword(s):  
Detection Method ◽  
Intron Retention ◽  
False Positive Rate ◽  
Mrna Splicing ◽  
Rna Seq ◽  
Synthetic Genes ◽  
Long Read ◽  
Positive Rate ◽  
Multiple Alternative ◽  
Splicing Detection

Abstract Motivation Percent Spliced-In (PSI) values are commonly used to report alternative pre-mRNA splicing (AS) changes. Previous PSI-detection tools were limited to specific AS events and were evaluated by in silico RNA-seq data. We developed PSI-Sigma, which uses a new PSI index, and we employed actual (non-simulated) RNA-seq data from spliced synthetic genes (RNA Sequins) to benchmark its performance (i.e. precision, recall, false positive rate and correlation) in comparison with three leading tools (rMATS, SUPPA2 and Whippet). Results PSI-Sigma outperformed these tools, especially in the case of AS events with multiple alternative exons and intron-retention events. We also briefly evaluated its performance in long-read RNA-seq analysis, by sequencing a mixture of human RNAs and RNA Sequins with nanopore long-read sequencers. Availability and implementation PSI-Sigma is implemented is available at https://github.com/wososa/PSI-Sigma.

scholarly journals Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates

2014 ◽  
Author(s):  
Andreas Tuerk ◽  
Gregor Wiktorin ◽  
Serhat Güler
Keyword(s):  
Probability Distributions ◽  
False Positive Rate ◽  
Synthetic Data ◽  
True Positive Rate ◽  
Rna Seq ◽  
Microarray Quality Control ◽  
Data Set ◽  
Rna Transcripts ◽  
Positive Rate ◽  
Fragment Distribution

Quantification of RNA transcripts with RNA-Seq is inaccurate due to positional fragment bias, which is not represented appropriately by current statistical models of RNA-Seq data. This article introduces the Mix2(rd. "mixquare") model, which uses a mixture of probability distributions to model the transcript specific positional fragment bias. The parameters of the Mix2model can be efficiently trained with the Expectation Maximization (EM) algorithm resulting in simultaneous estimates of the transcript abundances and transcript specific positional biases. Experiments are conducted on synthetic data and the Universal Human Reference (UHR) and Brain (HBR) sample from the Microarray quality control (MAQC) data set. Comparing the correlation between qPCR and FPKM values to state-of-the-art methods Cufflinks and PennSeq we obtain an increase in R2value from 0.44 to 0.6 and from 0.34 to 0.54. In the detection of differential expression between UHR and HBR the true positive rate increases from 0.44 to 0.71 at a false positive rate of 0.1. Finally, the Mix2model is used to investigate biases present in the MAQC data. This reveals 5 dominant biases which deviate from the common assumption of a uniform fragment distribution. The Mix2software is available at http://www.lexogen.com/fileadmin/uploads/bioinfo/mix2model.tgz.

contamDE-lm: linear model-based differential gene expression analysis using next-generation RNA-seq data from contaminated tumor samples

2020 ◽  
Vol 36 (8) ◽  
pp. 2492-2499
Author(s):  
Yifan Ji ◽  
Chang Yu ◽  
Hong Zhang
Keyword(s):  
Gene Expression ◽  
Linear Model ◽  
False Positive Rate ◽  
Simulated Data ◽  
True Positive Rate ◽  
Supplementary Information ◽  
Large Sample Size ◽  
Rna Seq ◽  
Next Generation ◽  
Positive Rate

Abstract Motivation Tumor and adjacent normal RNA samples are commonly used to screen differentially expressed genes between normal and tumor samples or among tumor subtypes. Such paired-sample design could avoid numerous confounders in differential expression (DE) analysis, but the cellular contamination of tumor samples can be an important noise and confounding factor, which can both inflate false-positive rate and deflate true-positive rate. The existing DE tools that use next-generation RNA-seq data either do not account for cellular contamination or are computationally extensive with increasingly large sample size. Results A novel linear model was proposed to avoid the problem that could arise from tumor–normal correlation for paired samples. A statistically robust and computationally very fast DE analysis procedure, contamDE-lm, was developed based on the novel model to account for cellular contamination, boosting DE analysis power through the reduction in individual residual variances using gene-wise information. The desired advantages of contamDE-lm over some state-of-the-art methods (limma and DESeq2) were evaluated through the applications to simulated data, TCGA database and Gene Expression Omnibus (GEO) database. Availability and implementation The proposed method contamDE-lm was implemented in an updated R package contamDE (version 2.0), which is freely available at https://github.com/zhanghfd/contamDE. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

Ascertaining an efficient eligibility cut-off for extended Medicare items for eating disorders

2021 ◽  
pp. 103985622110286
Author(s):  
Tracey Wade ◽  
Jamie-Lee Pennesi ◽  
Yuan Zhou
Keyword(s):  
Eating Disorders ◽  
Eating Disorder ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Rate Sensitivity ◽  
True Positive Rate ◽  
Eating Disorder Examination Questionnaire ◽  
Eating Disorder Examination ◽  
Positive Rate ◽  
The Relationship

Objective: Currently eligibility for expanded Medicare items for eating disorders (excluding anorexia nervosa) require a score ⩾ 3 on the 22-item Eating Disorder Examination-Questionnaire (EDE-Q). We compared these EDE-Q “cases” with continuous scores on a validated 7-item version of the EDE-Q (EDE-Q7) to identify an EDE-Q7 cut-off commensurate to 3 on the EDE-Q. Methods: We utilised EDE-Q scores of female university students ( N = 337) at risk of developing an eating disorder. We used a receiver operating characteristic (ROC) curve to assess the relationship between the true-positive rate (sensitivity) and the false-positive rate (1-specificity) of cases ⩾ 3. Results: The area under the curve showed outstanding discrimination of 0.94 (95% CI: .92–.97). We examined two specific cut-off points on the EDE-Q7, which included 100% and 87% of true cases, respectively. Conclusion: Given the EDE-Q cut-off for Medicare is used in conjunction with other criteria, we suggest using the more permissive EDE-Q7 cut-off (⩾2.5) to replace use of the EDE-Q cut-off (⩾3) in eligibility assessments.

scholarly journals Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

2016 ◽  
Vol 24 (2) ◽  
pp. 263-272 ◽  
Author(s):  
Kosuke Imai ◽  
Kabir Khanna
Keyword(s):  
Mean Squared Error ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Voter Registration ◽  
Racial Groups ◽  
Ecological Inference ◽  
Inference Problem ◽  
Individual Level ◽  
Positive Rate ◽  
Election Results

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

scholarly journals Replicate sequencing libraries are important for quantification of allelic imbalance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Asia Mendelevich ◽  
Svetlana Vinogradova ◽  
Saumya Gupta ◽  
Andrey A. Mironov ◽  
Shamil R. Sunyaev ◽  
...  
Keyword(s):  
Allelic Imbalance ◽  
False Positive Rate ◽  
Error Rates ◽  
Differential Analysis ◽  
Rna Seq ◽  
Specific Expression ◽  
Technical Noise ◽  
Specific Analysis ◽  
Positive Rate ◽  
Allele Specific

AbstractA sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

Watch For Failing Objects: What Inappropriate Compliance Reveals About Shared Mental Models In Autonomous Cars

2021 ◽  
Vol 65 (1) ◽  
pp. 643-647
Author(s):  
Yosef S. Razin ◽  
Jack Gale ◽  
Jiaojiao Fan ◽  
Jaznae’ Smith ◽  
Karen M. Feigh
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
False Positive Rate ◽  
Ground Truth ◽  
True Positive Rate ◽  
Shared Mental Models ◽  
Shared Mental Model ◽  
Autonomous Cars ◽  
Positive Rate ◽  
Dispositional Factors

This paper evaluates Banks et al.’s Human-AI Shared Mental Model theory by examining how a self-driving vehicle’s hazard assessment facilitates shared mental models. Participants were asked to affirm the vehicle’s assessment of road objects as either hazards or mistakes in real-time as behavioral and subjective measures were collected. The baseline performance of the AI was purposefully low (<50%) to examine how the human’s shared mental model might lead to inappropriate compliance. Results indicated that while the participant true positive rate was high, overall performance was reduced by the large false positive rate, indicating that participants were indeed being influenced by the Al’s faulty assessments, despite full transparency as to the ground-truth. Both performance and compliance were directly affected by frustration, mental, and even physical demands. Dispositional factors such as faith in other people’s cooperativeness and in technology companies were also significant. Thus, our findings strongly supported the theory that shared mental models play a measurable role in performance and compliance, in a complex interplay with trust.

scholarly journals Exploring Author Profiling for Fake News Detection

2021 ◽  
Author(s):  
Shloak Rathod
Keyword(s):  
False Positive Rate ◽  
True Positive Rate ◽  
Supervised Machine Learning ◽  
Online Media ◽  
Fake News ◽  
Writing Style ◽  
Short Term ◽  
Positive Rate ◽  
Rapid Dissemination ◽  
Author Profiling

<div><div><div><p>The proliferation of online media allows for the rapid dissemination of unmoderated news, unfortunately including fake news. The extensive spread of fake news poses a potent threat to both individuals and society. This paper focuses on designing author profiles to detect authors who are primarily engaged in publishing fake news articles. We build on the hypothesis that authors who write fake news repeatedly write only fake news articles, at least in short-term periods. Fake news authors have a distinct writing style compared to real news authors, who naturally want to maintain trustworthiness. We explore the potential to detect fake news authors by designing authors’ profiles based on writing style, sentiment, and co-authorship patterns. We evaluate our approach using a publicly available dataset with over 5000 authors and 20000 articles. For our evaluation, we build and compare different classes of supervised machine learning models. We find that the K-NN model performed the best, and it could detect authors who are prone to writing fake news with an 83% true positive rate with only a 5% false positive rate.</p></div></div></div>

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