Rescuing Biologically Relevant Consensus Regions Across Replicated Samples

Motivation Protein-DNA binding sites of ChIP-seq experiments are identified where the binding affinity is significant based on a given threshold. The choice of the threshold is a trade-off between conservative region identification and discarding weak, but true binding sites. Results We argue the biological relevance of weak binding sites and the information they add when rescued. The sites are rescued using MSPC, which exploits replicates to lower the threshold required to identify a binding site while keeping a low false-positive rate. We extend MSPC to call consensus regions across any number of replicated samples, accounting for differences between biological and technical replicates. We observed several master transcription regulators (e.g., SP1 and GATA3) and HDAC2-GATA1 regulatory networks on rescued regions. Availability and implementation An implementation of the proposed method and the scripts to reproduce the performed analysis are freely available at https://genometric.github.io/MSPC/, MSPC is distributed as a command-line application, an R package available from Bioconductor (https://doi.org/doi:10.18129/B9.bioc.rmspc), and a C# library.

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Programmatic access to bacterial regulatory networks with regutools

Bioinformatics ◽

10.1093/bioinformatics/btaa575 ◽

2020 ◽

Vol 36 (16) ◽

pp. 4532-4534

Author(s):

Joselyn Chávez ◽

Carmina Barberena-Jonas ◽

Jesus E Sotelo-Fonseca ◽

José Alquicira-Hernández ◽

Heladia Salgado ◽

...

Keyword(s):

Data Structures ◽

Gene Regulatory Networks ◽

Binding Sites ◽

Regulatory Networks ◽

R Package ◽

Transcriptional Regulatory Networks ◽

Dna Binding Sites ◽

Transcriptional Regulatory ◽

Gene Regulatory ◽

Programmatic Access

Abstract Summary RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. Availability and implementation regutools is an R package available through Bioconductor at bioconductor.org/packages/regutools.

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Gene set enrichment for reproducible science: comparison of CERNO and eight other algorithms

Bioinformatics ◽

10.1093/bioinformatics/btz447 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5146-5154 ◽

Cited By ~ 19

Author(s):

Joanna Zyla ◽

Michal Marczyk ◽

Teresa Domaszewska ◽

Stefan H E Kaufmann ◽

Joanna Polanska ◽

...

Keyword(s):

False Positive Rate ◽

R Package ◽

Supplementary Information ◽

Computational Time ◽

P Value ◽

Gene Set ◽

Related Data ◽

Novel Approach ◽

Positive Rate ◽

Real World Datasets

Abstract Motivation Analysis of gene set (GS) enrichment is an essential part of functional omics studies. Here, we complement the established evaluation metrics of GS enrichment algorithms with a novel approach to assess the practical reproducibility of scientific results obtained from GS enrichment tests when applied to related data from different studies. Results We evaluated eight established and one novel algorithm for reproducibility, sensitivity, prioritization, false positive rate and computational time. In addition to eight established algorithms, we also included Coincident Extreme Ranks in Numerical Observations (CERNO), a flexible and fast algorithm based on modified Fisher P-value integration. Using real-world datasets, we demonstrate that CERNO is robust to ranking metrics, as well as sample and GS size. CERNO had the highest reproducibility while remaining sensitive, specific and fast. In the overall ranking Pathway Analysis with Down-weighting of Overlapping Genes, CERNO and over-representation analysis performed best, while CERNO and GeneSetTest scored high in terms of reproducibility. Availability and implementation tmod package implementing the CERNO algorithm is available from CRAN (cran.r-project.org/web/packages/tmod/index.html) and an online implementation can be found at http://tmod.online/. The datasets analyzed in this study are widely available in the KEGGdzPathwaysGEO, KEGGandMetacoreDzPathwaysGEO R package and GEO repository. Supplementary information Supplementary data are available at Bioinformatics online.

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DECIPHER, a Search-Based Approach to Chimera Identification for 16S rRNA Sequences

Applied and Environmental Microbiology ◽

10.1128/aem.06516-11 ◽

2011 ◽

Vol 78 (3) ◽

pp. 717-725 ◽

Cited By ~ 426

Author(s):

Erik S. Wright ◽

L. Safak Yilmaz ◽

Daniel R. Noguera

Keyword(s):

16S Rrna ◽

False Positive ◽

Detection Rate ◽

False Positive Rate ◽

Query Sequence ◽

R Package ◽

Phylogenetic Group ◽

High Detection Rate ◽

Positive Rate ◽

R Programming

ABSTRACTDECIPHER is a new method for finding 16S rRNA chimeric sequences by the use of a search-based approach. The method is based upon detecting short fragments that are uncommon in the phylogenetic group where a query sequence is classified but frequently found in another phylogenetic group. The algorithm was calibrated for full sequences (fs_DECIPHER) and short sequences (ss_DECIPHER) and benchmarked against WigeoN (Pintail), ChimeraSlayer, and Uchime using artificially generated chimeras. Overall, ss_DECIPHER and Uchime provided the highest chimera detection for sequences 100 to 600 nucleotides long (79% and 81%, respectively), but Uchime's performance deteriorated for longer sequences, while ss_DECIPHER maintained a high detection rate (89%). Both methods had low false-positive rates (1.3% and 1.6%). The more conservative fs_DECIPHER, benchmarked only for sequences longer than 600 nucleotides, had an overall detection rate lower than that of ss_DECIPHER (75%) but higher than those of the other programs. In addition, fs_DECIPHER had the lowest false-positive rate among all the benchmarked programs (<0.20%). DECIPHER was outperformed only by ChimeraSlayer and Uchime when chimeras were formed from closely related parents (less than 10% divergence). Given the differences in the programs, it was possible to detect over 89% of all chimeras with just the combination of ss_DECIPHER and Uchime. Using fs_DECIPHER, we detected between 1% and 2% additional chimeras in the RDP, SILVA, and Greengenes databases from which chimeras had already been removed with Pintail or Bellerophon. DECIPHER was implemented in the R programming language and is directly accessible through a webpage or by downloading the program as an R package (http://DECIPHER.cee.wisc.edu).

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Two-way partial AUC and its properties

Statistical Methods in Medical Research ◽

10.1177/0962280217718866 ◽

2017 ◽

Vol 28 (1) ◽

pp. 184-195 ◽

Cited By ~ 2

Author(s):

Hanfang Yang ◽

Kun Lu ◽

Xiang Lyu ◽

Feifang Hu

Keyword(s):

Roc Curve ◽

False Positive ◽

False Positive Rate ◽

R Package ◽

True Positive Rate ◽

Performance Measure ◽

True Positive ◽

Cancer Data ◽

Partial Area ◽

Positive Rate

Simultaneous control on true positive rate and false positive rate is of significant importance in the performance evaluation of diagnostic tests. Most of the established literature utilizes partial area under receiver operating characteristic (ROC) curve with restrictions only on false positive rate (FPR), called FPR pAUC, as a performance measure. However, its indirect control on true positive rate (TPR) is conceptually and practically misleading. In this paper, a novel and intuitive performance measure, named as two-way pAUC, is proposed, which directly quantifies partial area under ROC curve with explicit restrictions on both TPR and FPR. To estimate two-way pAUC, we devise a nonparametric estimator. Based on the estimator, a bootstrap-assisted testing method for two-way pAUC comparison is established. Moreover, to evaluate possible covariate effects on two-way pAUC, a regression analysis framework is constructed. Asymptotic normalities of the methods are provided. Advantages of the proposed methods are illustrated by simulation and Wisconsin Breast Cancer Data. We encode the methods as a publicly available R package tpAUC.

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Predicting Flavin and Nicotinamide Adenine Dinucleotide-Binding Sites in Proteins Using the Fragment Transformation Method

BioMed Research International ◽

10.1155/2015/402536 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13

Author(s):

Chih-Hao Lu ◽

Chin-Sheng Yu ◽

Yu-Feng Lin ◽

Jin-Yi Chen

Keyword(s):

Ligand Binding ◽

Binding Sites ◽

False Positive Rate ◽

Data Bank ◽

Transformation Method ◽

Computational Method ◽

Binding Potential ◽

Binding Residue ◽

Positive Rate ◽

Fad Binding

We developed a computational method to identify NAD- and FAD-binding sites in proteins. First, we extracted from the Protein Data Bank structures of proteins that bind to at least one of these ligands. NAD-/FAD-binding residue templates were then constructed by identifying binding residues through the ligand-binding database BioLiP. The fragment transformation method was used to identify structures within query proteins that resembled the ligand-binding templates. By comparing residue types and their relative spatial positions, potential binding sites were identified and a ligand-binding potential for each residue was calculated. Setting the false positive rate at 5%, our method predicted NAD- and FAD-binding sites at true positive rates of 67.1% and 68.4%, respectively. Our method provides excellent results for identifying FAD- and NAD-binding sites in proteins, and the most important is that the requirement of conservation of residue types and local structures in the FAD- and NAD-binding sites can be verified.

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HOME: A histogram based machine learning approach for effective identification of differentially methylated regions

10.1101/228221 ◽

2017 ◽

Cited By ~ 4

Author(s):

Akanksha Srivastava ◽

Yuliya V Karpievitch ◽

Steven R Eichten ◽

Justin O Borevitz ◽

Ryan Lister

Keyword(s):

False Positive Rate ◽

Support Vector ◽

Differentially Methylated Regions ◽

Accurate Identification ◽

Entire Genome ◽

Biologically Relevant ◽

Home Based ◽

Methylome Analysis ◽

Positive Rate ◽

Genome Bisulfite Sequencing

AbstractBackgroundThe development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate.ResultsWe present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organism’s dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME.ConclusionHOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation.

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Programmatic access to bacterial regulatory networks with regutools

10.1101/2020.04.29.068551 ◽

2020 ◽

Author(s):

Joselyn Chávez ◽

Carmina Barberena-Jonas ◽

Jesus E. Sotelo-Fonseca ◽

José Alquicira-Hernández ◽

Heladia Salgado ◽

...

Keyword(s):

Data Structures ◽

Gene Regulatory Networks ◽

Binding Sites ◽

Regulatory Networks ◽

Transcriptional Regulatory Networks ◽

Link Type ◽

Dna Binding Sites ◽

Transcriptional Regulatory ◽

Gene Regulatory ◽

Programmatic Access

AbstractSummaryRegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks.Availability and Implementationregutools is an R package available through Bioconductor at bioconductor.org/packages/regutools.Contactgithub.com/ComunidadBioInfo/regutools, [email protected], [email protected].

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Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

Nuklearmedizin ◽

10.1055/s-0038-1623994 ◽

2002 ◽

Vol 41 (01) ◽

pp. 37-41 ◽

Cited By ~ 3

Author(s):

S. Shung-Shung ◽

S. Yu-Chien ◽

Y. Mei-Due ◽

W. Hwei-Chung ◽

A. Kao

Keyword(s):

Acute Appendicitis ◽

False Positive ◽

False Positive Rate ◽

Accurate Method ◽

Clinical Findings ◽

Pathological Findings ◽

Lower Quadrant ◽

Predictive Values ◽

Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

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Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

Methods of Information in Medicine ◽

10.1055/s-0038-1634910 ◽

1993 ◽

Vol 32 (02) ◽

pp. 175-179 ◽

Cited By ~ 7

Author(s):

B. Brambati ◽

T. Chard ◽

J. G. Grudzinskas ◽

M. C. M. Macintosh

Keyword(s):

Logistic Regression ◽

General Population ◽

Likelihood Ratio ◽

False Positive ◽

False Positive Rate ◽

Ratio Method ◽

Detection Rates ◽

Gaussian Distributions ◽

Positive Rate ◽

Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

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Identification of and Correction for Publication Bias: Comment

10.31222/osf.io/dh87m ◽

2019 ◽

Author(s):

Amanda Kvarven ◽

Eirik Strømland ◽

Magnus Johannesson

Keyword(s):

Publication Bias ◽

False Positive ◽

Large Scale ◽

Meta Analysis ◽

False Positive Rate ◽

Effect Sizes ◽

Replication Studies ◽

Moderate Reduction ◽

Positive Rate ◽

Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

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