PairMotifChIP: A Fast Algorithm for Discovery of Patterns Conserved in Large ChIP-seq Data Sets

Identifying conserved patterns in DNA sequences, namely, motif discovery, is an important and challenging computational task. With hundreds or more sequences contained, the high-throughput sequencing data set is helpful to improve the identification accuracy of motif discovery but requires an even higher computing performance. To efficiently identify motifs in large DNA data sets, a new algorithm called PairMotifChIP is proposed by extracting and combining pairs of l-mers in the input with relatively small Hamming distance. In particular, a method for rapidly extracting pairs of l-mers is designed, which can be used not only for PairMotifChIP, but also for other DNA data mining tasks with the same demand. Experimental results on the simulated data show that the proposed algorithm can find motifs successfully and runs faster than the state-of-the-art motif discovery algorithms. Furthermore, the validity of the proposed algorithm has been verified on real data.

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A Clustering Approach for Motif Discovery in ChIP-Seq Dataset

Entropy ◽

10.3390/e21080802 ◽

2019 ◽

Vol 21 (8) ◽

pp. 802

Author(s):

Chun-xiao Sun ◽

Yu Yang ◽

Hua Wang ◽

Wen-hu Wang

Keyword(s):

Dna Sequences ◽

Motif Discovery ◽

Simulated Data ◽

Data Set ◽

Genome Wide ◽

A Genome ◽

Wide Scale ◽

Clustering Approach ◽

Ap Clustering ◽

Generation Sequencing

Chromatin immunoprecipitation combined with next-generation sequencing (ChIP-Seq) technology has enabled the identification of transcription factor binding sites (TFBSs) on a genome-wide scale. To effectively and efficiently discover TFBSs in the thousand or more DNA sequences generated by a ChIP-Seq data set, we propose a new algorithm named AP-ChIP. First, we set two thresholds based on probabilistic analysis to construct and further filter the cluster subsets. Then, we use Affinity Propagation (AP) clustering on the candidate cluster subsets to find the potential motifs. Experimental results on simulated data show that the AP-ChIP algorithm is able to make an almost accurate prediction of TFBSs in a reasonable time. Also, the validity of the AP-ChIP algorithm is tested on a real ChIP-Seq data set.

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A Growth Model for Multilevel Ordinal Data

Journal of Educational and Behavioral Statistics ◽

10.3102/10769986030004369 ◽

2005 ◽

Vol 30 (4) ◽

pp. 369-396 ◽

Cited By ~ 8

Author(s):

Eisuke Segawa

Keyword(s):

Latent Variable ◽

Ordinal Data ◽

Linear Models ◽

Growth Models ◽

Simulated Data ◽

Real Data ◽

Analytic Structure ◽

Data Sets ◽

Data Set ◽

Time Points

Multi-indicator growth models were formulated as special three-level hierarchical generalized linear models to analyze growth of a trait latent variable measured by ordinal items. Items are nested within a time-point, and time-points are nested within subject. These models are special because they include factor analytic structure. This model can analyze not only data with item- and time-level missing observations, but also data with time points freely specified over subjects. Furthermore, features useful for longitudinal analyses, “autoregressive error degree one” structure for the trait residuals and estimated time-scores, were included. The approach is Bayesian with Markov Chain and Monte Carlo, and the model is implemented in WinBUGS. They are illustrated with two simulated data sets and one real data set with planned missing items within a scale.

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An Affinity Propagation-Based DNA Motif Discovery Algorithm

BioMed Research International ◽

10.1155/2015/853461 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Chunxiao Sun ◽

Hongwei Huo ◽

Qiang Yu ◽

Haitao Guo ◽

Zhigang Sun

Keyword(s):

Dna Sequences ◽

Motif Discovery ◽

Simulated Data ◽

Biological Data ◽

Affinity Propagation ◽

Local Optimum ◽

Data Sets ◽

Dna Motif ◽

Challenging Tasks ◽

Dna Motif Discovery

The planted(l,d)motif search (PMS) is one of the fundamental problems in bioinformatics, which plays an important role in locating transcription factor binding sites (TFBSs) in DNA sequences. Nowadays, identifying weak motifs and reducing the effect of local optimum are still important but challenging tasks for motif discovery. To solve the tasks, we propose a new algorithm, APMotif, which first applies the Affinity Propagation (AP) clustering in DNA sequences to produce informative and good candidate motifs and then employs Expectation Maximization (EM) refinement to obtain the optimal motifs from the candidate motifs. Experimental results both on simulated data sets and real biological data sets show that APMotif usually outperforms four other widely used algorithms in terms of high prediction accuracy.

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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.

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A Fast Cluster Motif Finding Algorithm for ChIP-Seq Data Sets

BioMed Research International ◽

10.1155/2015/218068 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Yipu Zhang ◽

Ping Wang

Keyword(s):

High Throughput ◽

Motif Discovery ◽

Large Scale ◽

High Throughput Sequencing ◽

Es Cells ◽

Motif Finding ◽

Data Sets ◽

Data Set ◽

Binding Motifs ◽

Motif Finding Algorithm

New high-throughput technique ChIP-seq, coupling chromatin immunoprecipitation experiment with high-throughput sequencing technologies, has extended the identification of binding locations of a transcription factor to the genome-wide regions. However, the most existing motif discovery algorithms are time-consuming and limited to identify binding motifs in ChIP-seq data which normally has the significant characteristics of large scale data. In order to improve the efficiency, we propose a fast cluster motif finding algorithm, named as FCmotif, to identify the(l, d)motifs in large scale ChIP-seq data set. It is inspired by the emerging substrings mining strategy to find the enriched substrings and then searching the neighborhood instances to construct PWM and cluster motifs in different length. FCmotif is not following the OOPS model constraint and can find long motifs. The effectiveness of proposed algorithm has been proved by experiments on the ChIP-seq data sets from mouse ES cells. The whole detection of the real binding motifs and processing of the full size data of several megabytes finished in a few minutes. The experimental results show that FCmotif has advantageous to deal with the(l, d)motif finding in the ChIP-seq data; meanwhile it also demonstrates better performance than other current widely-used algorithms such as MEME, Weeder, ChIPMunk, and DREME.

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An Extensive Meta-Metagenomic Search Identifies SARS-CoV-2-Homologous Sequences in Pangolin Lung Viromes

mSphere ◽

10.1128/msphere.00160-20 ◽

2020 ◽

Vol 5 (3) ◽

Cited By ~ 9

Author(s):

Lamia Wahba ◽

Nimit Jain ◽

Andrew Z. Fire ◽

Massa J. Shoura ◽

Karen L. Artiles ◽

...

Keyword(s):

Nucleic Acid ◽

High Speed ◽

High Throughput Sequencing ◽

Biological Significance ◽

Metagenomic Data ◽

Data Sets ◽

Sequencing Data ◽

Data Set ◽

Link Type ◽

Recent Emergence

ABSTRACT In numerous instances, tracking the biological significance of a nucleic acid sequence can be augmented through the identification of environmental niches in which the sequence of interest is present. Many metagenomic data sets are now available, with deep sequencing of samples from diverse biological niches. While any individual metagenomic data set can be readily queried using web-based tools, meta-searches through all such data sets are less accessible. In this brief communication, we demonstrate such a meta-metagenomic approach, examining close matches to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in all high-throughput sequencing data sets in the NCBI Sequence Read Archive accessible with the “virome” keyword. In addition to the homology to bat coronaviruses observed in descriptions of the SARS-CoV-2 sequence (F. Wu, S. Zhao, B. Yu, Y. M. Chen, et al., Nature 579:265–269, 2020, https://doi.org/10.1038/s41586-020-2008-3; P. Zhou, X. L. Yang, X. G. Wang, B. Hu, et al., Nature 579:270–273, 2020, https://doi.org/10.1038/s41586-020-2012-7), we note a strong homology to numerous sequence reads in metavirome data sets generated from the lungs of deceased pangolins reported by Liu et al. (P. Liu, W. Chen, and J. P. Chen, Viruses 11:979, 2019, https://doi.org/10.3390/v11110979). While analysis of these reads indicates the presence of a similar viral sequence in pangolin lung, the similarity is not sufficient to either confirm or rule out a role for pangolins as an intermediate host in the recent emergence of SARS-CoV-2. In addition to the implications for SARS-CoV-2 emergence, this study illustrates the utility and limitations of meta-metagenomic search tools in effective and rapid characterization of potentially significant nucleic acid sequences. IMPORTANCE Meta-metagenomic searches allow for high-speed, low-cost identification of potentially significant biological niches for sequences of interest.

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ESTIMATION OF EXTREME QUANTILES: EMPIRICAL TOOLS FOR METHODS ASSESSMENT AND COMPARISON

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539300000079 ◽

2000 ◽

Vol 07 (01) ◽

pp. 75-94 ◽

Cited By ~ 3

Author(s):

J. DIEBOLT ◽

M.-A. EL-AROUI ◽

V. DURBEC ◽

B. VILLAIN

Keyword(s):

Goodness Of Fit ◽

Simulated Data ◽

Real Data ◽

Data Sets ◽

Data Set ◽

Extreme Quantiles ◽

Maintenance Policies ◽

Simulated Data Sets ◽

Industrial Context

When extreme quantiles have to be estimated from a given data set, the classical parametric approach can lead to very poor estimations. This has led to the introduction of specific methods for estimating extreme quantiles (MEEQ's) in a nonparametric spirit, e.g., Pickands excess method, methods based on Hill's estimate of the Pareto index, exponential tail (ET) and quadratic tail (QT) methods. However, no practical technique for assessing and comparing these MEEQ's when they are to be used on a given data set is available. This paper is a first attempt to provide such techniques. We first compare the estimations given by the main MEEQ's on several simulated data sets. Then we suggest goodness-of-fit (Gof) tests to assess the MEEQ's by measuring the quality of their underlying approximations. It is shown that Gof techniques bring very relevant tools to assess and compare ET and excess methods. Other empirical criterions for comparing MEEQ's are also proposed and studied through Monte-Carlo analyses. Finally, these assessment and comparison techniques are experimented on real-data sets issued from an industrial context where extreme quantiles are needed to define maintenance policies.

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Discovery of tandem and interspersed segmental duplications using high throughput sequencing

10.1101/393694 ◽

2018 ◽

Cited By ~ 1

Author(s):

Arda Soylev ◽

Thong Le ◽

Hajar Amini ◽

Can Alkan ◽

Fereydoun Hormozdiari

Keyword(s):

False Discovery Rate ◽

High Throughput ◽

High Throughput Sequencing ◽

Real Data ◽

Whole Genome Sequencing Data ◽

Data Sets ◽

Segmental Duplications ◽

Sequencing Data ◽

Link Type ◽

False Discovery

AbstractMotivationSeveral algorithms have been developed that use high throughput sequencing technology to characterize structural variations. Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions, and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions; and duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants.ResultsWe developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing data sets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read. We evaluated the prediction performance of our algorithms through several experiments using both simulated and real data sets. In the simulation experiments, using a 30× coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state of the art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (less than 5% for the top 50 predictions).AvailabilityTARDIS source code is available at https://github.com/BilkentCompGen/tardis, and a corresponding Docker image is available at https://hub.docker.com/r/alkanlab/tardis/[email protected] and [email protected]

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Explaining predictive models using Shapley values and non-parametric vine copulas

Dependence Modeling ◽

10.1515/demo-2021-0103 ◽

2021 ◽

Vol 9 (1) ◽

pp. 62-81

Author(s):

Kjersti Aas ◽

Thomas Nagler ◽

Martin Jullum ◽

Anders Løland

Keyword(s):

Traditional Approach ◽

Simulated Data ◽

Real Data ◽

Data Sets ◽

Data Set ◽

Wide Range ◽

Shapley Values ◽

Non Gaussian ◽

Vine Copula ◽

Vine Copulas

Abstract In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values. The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. If the features in reality are dependent this may lead to incorrect explanations. Hence, there have recently been attempts of appropriately modelling/estimating the dependence between the features. Although the previously proposed methods clearly outperform the traditional approach assuming independence, they have their weaknesses. In this paper we propose two new approaches for modelling the dependence between the features. Both approaches are based on vine copulas, which are flexible tools for modelling multivariate non-Gaussian distributions able to characterise a wide range of complex dependencies. The performance of the proposed methods is evaluated on simulated data sets and a real data set. The experiments demonstrate that the vine copula approaches give more accurate approximations to the true Shapley values than their competitors.

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Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

10.7287/peerj.preprints.27019v2 ◽

2018 ◽

Author(s):

Sten Anslan ◽

Henrik Nilsson ◽

Christian Wurzbacher ◽

Petr Baldrian ◽

Leho Tedersoo ◽

...

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Computation Time ◽

Potential Effect ◽

Data Sets ◽

Sequencing Data ◽

Data Set ◽

Operational Taxonomic Units ◽

High Throughput Sequencing Data ◽

Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appear to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon data set. We conclude that the output of each platform require manual validation of the OTUs by examining the taxonomy assignment values.

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