State-of-the-Art Fusion-Finder Algorithms Sensitivity and Specificity

Background. Gene fusions arising from chromosomal translocations have been implicated in cancer. RNA-seq has the potential to discover such rearrangements generating functional proteins (chimera/fusion). Recently, many methods for chimeras detection have been published. However, specificity and sensitivity of those tools were not extensively investigated in a comparative way.Results. We tested eight fusion-detection tools (FusionHunter, FusionMap, FusionFinder, MapSplice, deFuse, Bellerophontes, ChimeraScan, and TopHat-fusion) to detect fusion events using synthetic and real datasets encompassing chimeras. The comparison analysis run only on synthetic data could generate misleading results since we found no counterpart on real dataset. Furthermore, most tools report a very high number of false positive chimeras. In particular, the most sensitive tool, ChimeraScan, reports a large number of false positives that we were able to significantly reduce by devising and applying two filters to remove fusions not supported by fusion junction-spanning reads or encompassing large intronic regions.Conclusions. The discordant results obtained using synthetic and real datasets suggest that synthetic datasets encompassing fusion events may not fully catch the complexity of RNA-seq experiment. Moreover, fusion detection tools are still limited in sensitivity or specificity; thus, there is space for further improvement in the fusion-finder algorithms.

Download Full-text

G-Tric: generating three-way synthetic datasets with triclustering solutions

BMC Bioinformatics ◽

10.1186/s12859-020-03925-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

João Lobo ◽

Rui Henriques ◽

Sara C. Madeira

Keyword(s):

State Of The Art ◽

Synthetic Data ◽

Ground Truth ◽

Real Data ◽

Three Dimensions ◽

Additional Advantage ◽

Urban Dynamics ◽

Data Generator ◽

Real World Datasets ◽

Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

Download Full-text

treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses

Genome Biology ◽

10.1186/s13059-021-02368-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ruizhu Huang ◽

Charlotte Soneson ◽

Pierre-Luc Germain ◽

Thomas S.B. Schmidt ◽

Christian Von Mering ◽

...

Keyword(s):

Single Cell ◽

Synthetic Data ◽

Cell Types ◽

Data Driven ◽

Rna Seq ◽

Hierarchical Trees

AbstracttreeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations.

Download Full-text

Impact of RNA degradation on fusion detection by RNA-seq

BMC Genomics ◽

10.1186/s12864-016-3161-9 ◽

2016 ◽

Vol 17 (1) ◽

Cited By ~ 21

Author(s):

Jaime I. Davila ◽

Numrah M. Fadra ◽

Xiaoke Wang ◽

Amber M. McDonald ◽

Asha A. Nair ◽

...

Keyword(s):

Rna Degradation ◽

Rna Seq ◽

Fusion Detection

Download Full-text

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

10.1101/011767 ◽

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.

Download Full-text

Inferring viral occurrence patterns through a synthetic data simulation

10.1101/2021.07.13.452220 ◽

2021 ◽

Author(s):

Ville N Pimenoff ◽

Ramon Cleries

Keyword(s):

Linear Models ◽

Population Sample ◽

Synthetic Data ◽

Interaction Patterns ◽

Viral Strain ◽

Data Simulation ◽

Synthetic Datasets ◽

Pathogen Occurrence ◽

Log Linear ◽

Occurrence Patterns

Viruses infecting humans are manifold and several of them provoke significant morbidity and mortality. Simulations creating large synthetic datasets from observed multiple viral strain infections in a limited population sample can be a powerful tool to infer significant pathogen occurrence and interaction patterns, particularly if limited number of observed data units is available. Here, to demonstrate diverse human papillomavirus (HPV) strain occurrence patterns, we used log-linear models combined with Bayesian framework for graphical independence network (GIN) analysis. That is, to simulate datasets based on modeling the probabilistic associations between observed viral data points, i.e different viral strain infections in a set of population samples. Our GIN analysis outperformed in precision all oversampling methods tested for simulating large synthetic viral strain-level prevalence dataset from observed set of HPVs data. Altogether, we demonstrate that network modeling is a potent tool for creating synthetic viral datasets for comprehensive pathogen occurrence and interaction pattern estimations.

Download Full-text

Holdout-Based Empirical Assessment of Mixed-Type Synthetic Data

Frontiers in Big Data ◽

10.3389/fdata.2021.679939 ◽

2021 ◽

Vol 4 ◽

Author(s):

Michael Platzer ◽

Thomas Reutterer

Keyword(s):

Mixed Type ◽

Synthetic Data ◽

Training Data ◽

Privacy Risk ◽

Individual Level ◽

Empirical Assessment ◽

Model Free ◽

Private Data ◽

Synthetic Datasets ◽

The Individual

AI-based data synthesis has seen rapid progress over the last several years and is increasingly recognized for its promise to enable privacy-respecting high-fidelity data sharing. This is reflected by the growing availability of both commercial and open-sourced software solutions for synthesizing private data. However, despite these recent advances, adequately evaluating the quality of generated synthetic datasets is still an open challenge. We aim to close this gap and introduce a novel holdout-based empirical assessment framework for quantifying the fidelity as well as the privacy risk of synthetic data solutions for mixed-type tabular data. Measuring fidelity is based on statistical distances of lower-dimensional marginal distributions, which provide a model-free and easy-to-communicate empirical metric for the representativeness of a synthetic dataset. Privacy risk is assessed by calculating the individual-level distances to closest record with respect to the training data. By showing that the synthetic samples are just as close to the training as to the holdout data, we yield strong evidence that the synthesizer indeed learned to generalize patterns and is independent of individual training records. We empirically demonstrate the presented framework for seven distinct synthetic data solutions across four mixed-type datasets and compare these then to traditional data perturbation techniques. Both a Python-based implementation of the proposed metrics and the demonstration study setup is made available open-source. The results highlight the need to systematically assess the fidelity just as well as the privacy of these emerging class of synthetic data generators.

Download Full-text

Revisiting particle sizing using greyscale optical array probes: evaluation using laboratory experiments and synthetic data

Atmospheric Measurement Techniques ◽

10.5194/amt-12-3067-2019 ◽

2019 ◽

Vol 12 (6) ◽

pp. 3067-3079

Author(s):

Sebastian J. O'Shea ◽

Jonathan Crosier ◽

James Dorsey ◽

Waldemar Schledewitz ◽

Ian Crawford ◽

...

Keyword(s):

Climate Models ◽

Synthetic Data ◽

Mie Scattering ◽

Sample Volume ◽

Research Aircraft ◽

Order Of Magnitude ◽

Ambient Data ◽

In Situ Observations ◽

Synthetic Datasets

Abstract. In situ observations from research aircraft and instrumented ground sites are important contributions to developing our collective understanding of clouds and are used to inform and validate numerical weather and climate models. Unfortunately, biases in these datasets may be present, which can limit their value. In this paper, we discuss artefacts which may bias data from a widely used family of instrumentation in the field of cloud physics, optical array probes (OAPs). Using laboratory and synthetic datasets, we demonstrate how greyscale analysis can be used to filter data, constraining the sample volume of the OAP and improving data quality, particularly at small sizes where OAP data are considered unreliable. We apply the new methodology to ambient data from two contrasting case studies: one warm cloud and one cirrus cloud. In both cases the new methodology reduces the concentration of small particles (<60 µm) by approximately an order of magnitude. This significantly improves agreement with a Mie-scattering spectrometer for the liquid case and with a holographic imaging probe for the cirrus case. Based on these results, we make specific recommendations to instrument manufacturers, instrument operators and data processors about the optimal use of greyscale OAPs. The data from monoscale OAPs are unreliable and should not be used for particle diameters below approximately 100 µm.

Download Full-text

Validation of XRD phase quantification using semi-synthetic data

Powder Diffraction ◽

10.1017/s0885715620000573 ◽

2020 ◽

Vol 35 (4) ◽

pp. 262-275

Author(s):

Nicola Döbelin

Keyword(s):

Reference Materials ◽

Certified Reference Materials ◽

Synthetic Data ◽

Statistical Validation ◽

Validation Parameters ◽

Phase Quantification ◽

Characteristic Features ◽

Diffraction Patterns ◽

Synthetic Datasets ◽

Multi Phase

Validating phase quantification procedures of powder X-ray diffraction (XRD) data for an implementation in an ISO/IEC 17025 accredited environment has been challenging due to a general lack of suitable certified reference materials. The preparation of highly pure and crystalline reference materials and mixtures thereof may exceed the costs for a profitable and justifiable implementation. This study presents a method for the validation of XRD phase quantifications based on semi-synthetic datasets that reduces the effort for a full method validation drastically. Datasets of nearly pure reference substances are stripped of impurity signals and rescaled to 100% crystallinity, thus eliminating the need for the preparation of ultra-pure and -crystalline materials. The processed datasets are then combined numerically while preserving all sample- and instrument-characteristic features of the peak profile, thereby creating multi-phase diffraction patterns of precisely known composition. The number of compositions and repetitions is only limited by computational power and storage capacity. These datasets can be used as input files for the phase quantification procedure, in which statistical validation parameters such as precision, accuracy, linearity, and limits of detection and quantification can be determined from a statistically sound number of datasets and compositions.

Download Full-text

RNAflow: An Effective and Simple RNA-Seq Differential Gene Expression Pipeline Using Nextflow

Genes ◽

10.3390/genes11121487 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1487

Author(s):

Marie Lataretu ◽

Martin Hölzer

Keyword(s):

Gene Expression ◽

Homo Sapiens ◽

Standard Technique ◽

Common Species ◽

Rna Seq ◽

Rna Molecules ◽

Gene Filtering ◽

Differential Gene ◽

High Level ◽

Very High

RNA-Seq enables the identification and quantification of RNA molecules, often with the aim of detecting differentially expressed genes (DEGs). Although RNA-Seq evolved into a standard technique, there is no universal gold standard for these data’s computational analysis. On top of that, previous studies proved the irreproducibility of RNA-Seq studies. Here, we present a portable, scalable, and parallelizable Nextflow RNA-Seq pipeline to detect DEGs, which assures a high level of reproducibility. The pipeline automatically takes care of common pitfalls, such as ribosomal RNA removal and low abundance gene filtering. Apart from various visualizations for the DEG results, we incorporated downstream pathway analysis for common species as Homo sapiens and Mus musculus. We evaluated the DEG detection functionality while using qRT-PCR data serving as a reference and observed a very high correlation of the logarithmized gene expression fold changes.

Download Full-text

SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data

BioMed Research International ◽

10.1155/2015/780519 ◽

2015 ◽

Vol 2015 ◽

pp. 1-5 ◽

Cited By ~ 2

Author(s):

Yuxiang Tan ◽

Yann Tambouret ◽

Stefano Monti

Keyword(s):

Sample Size ◽

Rna Sequencing ◽

High Throughput Sequencing ◽

Performance Metrics ◽

Simulated Data ◽

Real Data ◽

Rna Seq ◽

Sequencing Data ◽

Detection Algorithms ◽

Fusion Detection

The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions’ background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions’ supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.

Download Full-text