scholarly journals FirstSV: Fast and Accurate Approach of Structural Variations Detection for Short DNA fragments

2018 ◽  
Author(s):  
Jia Shen ◽  
Qiyang Zuo ◽  
Rongliang Wang ◽  
Xiang Li ◽  
Yuanhua Tang
Keyword(s):  
Simulated Data ◽  
Detection Methods ◽  
Read Length ◽  
Data Sets ◽  
Structural Variations ◽  
Real Patient ◽  
Public Data ◽  
Local Assembly ◽  
Current Detection ◽  
Generation Sequencing

ABSTRACTStructural variations caused by gene fusion represent a major class of somatically acquired variations in human malignancies, and include deletions, inversions, and translocations. Short fragmented reads are the main source of data from 2nd-generation sequencing, and detecting structural variations from this type of data is different from that of 1st-generation sequencing, where the read length is much longer. Current detection methods are low in specificity and are inefficient. We developed a hybrid algorithm, FirstSV, to meet the clinical demand for fast and accurate structural variation detection. Its main features include cluster analysis, realignment, and local assembly. FirstSV was validated with simulated data, with data from real patient samples, with data from standard testing samples, and with downloaded public data sets. FirstSV outperforms public-available methods in terms of sensitivity, precision, and operational efficiency. FirstSV is freely available at https://github.com/shenjia1/FirstSV.

scholarly journals Detection of structural variations and fusion genes in breast cancer samples using third-generation sequencing

2021 ◽  
Author(s):  
Taobo Hu ◽  
Jingjing Li ◽  
Mengping Long ◽  
Jinbo Wu ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Genetic Alterations ◽  
Read Length ◽  
Breast Cancer Patients ◽  
Structural Variations ◽  
Transcriptomic Sequencing ◽  
Long Read ◽  
Second Generation Sequencing ◽  
Generation Sequencing

Abstract Background: Structural variations (SVs) are common genetic alterations in the human genome that could cause different phenotypes and various diseases including cancer. However, the detection of structural variations using the second-generation sequencing was limited by its short read-length which in turn restrained our understanding of structural variations. Methods: In this study, we developed a 28-gene panel for long-read sequencing and employed it to both Oxford Nanopore Technologies and Pacific Biosciences platforms. We analyzed structural variations in the 28 breast cancer-related genes through long-read genomic and transcriptomic sequencing of tumor, para-tumor and blood samples in 19 breast cancer patients. Results: Our results showed that some somatic SVs were recurring among the selected genes, though the majority of them occurred in the non-exonic region. We found evidence supporting the existence of hotspot regions for SVs, which extended our previous understanding that they exist only for single nucleotide variations. Conclusions: In conclusion, we employed long-read genomic and transcriptomic sequencing in identifying SVs from breast cancer patients and proved that this approach holds great potential in clinical application.

scholarly journals RNA-seq gene profiling - a systematic empirical comparison

2014 ◽  
Author(s):  
Nuno A Fonseca ◽  
John A Marioni ◽  
Alvis Brazma
Keyword(s):  
Gene Expression ◽  
Simulated Data ◽  
Ground Truth ◽  
Gene Profiling ◽  
Read Length ◽  
Data Sets ◽  
Rna Seq ◽  
Expression Levels ◽  
Rnaseq Data ◽  
Gene Expression Levels

Accurately quantifying gene expression levels is a key goal of experiments using RNA-sequencing to assay the transcriptome. This typically requires aligning the short reads generated to the genome or transcriptome before quantifying expression of pre-defined sets of genes. Differences in the alignment/quantification tools can have a major effect upon the expression levels found with important consequences for biological interpretation. Here we address two main issues: do different analysis pipelines affect the gene expression levels inferred from RNA-seq data? And, how close are the expression levels inferred to the ``true'' expression levels? We evaluate fifty gene profiling pipelines in experimental and simulated data sets with different characteristics (e.g, read length and sequencing depth). In the absence of knowledge of the 'ground truth' in real RNAseq data sets, we used simulated data to assess the differences between the true expression and those reconstructed by the analysis pipelines. Even though this approach does not take into account all known biases present in RNAseq data, it still allows to assess the accuracy of the gene expression values inferred by different analysis pipelines. The results show that i) overall there is a high correlation between the expression levels inferred by the best pipelines and the true quantification values; ii) the error in the estimated gene expression values can vary considerably across genes; and iii) a small set of genes have expression estimates with consistently high error (across data sets and methods). Finally, although the mapping software is important, the quantification method makes a greater difference to the results.

scholarly journals Regional Concept Drift Detection and Density Synchronized Drift Adaptation

2017 ◽  
Author(s):  
Anjin Liu ◽  
Yiliao Song ◽  
Guangquan Zhang ◽  
Jie Lu
Keyword(s):  
Historical Data ◽  
Concept Drift ◽  
Feature Space ◽  
Small Region ◽  
Detection Methods ◽  
Data Sets ◽  
Distribution Analysis ◽  
Adaptation Algorithm ◽  
Public Data ◽  
Time Point

In data stream mining, the emergence of new patterns or a pattern ceasing to exist is called concept drift. Concept drift makes the learning process complicated because of the inconsistency between existing data and upcoming data. Since concept drift was first proposed, numerous articles have been published to address this issue in terms of distribution analysis. However, most distribution-based drift detection methods assume that a drift happens at an exact time point, and the data arrived before that time point is considered not important. Thus, if a drift only occurs in a small region of the entire feature space, the other non-drifted regions may also be suspended, thereby reducing the learning efficiency of models. To retrieve non-drifted information from suspended historical data, we propose a local drift degree (LDD) measurement that can continuously monitor regional density changes. Instead of suspending all historical data after a drift, we synchronize the regional density discrepancies according to LDD. Experimental evaluations on three public data sets show that our concept drift adaptation algorithm improves accuracy compared to other methods.

scholarly journals CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Jing Wang ◽  
Cheng Ling ◽  
Jingyang Gao
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Sequence Data ◽  
High Sensitivity ◽  
False Positives ◽  
Detection Methods ◽  
Structural Variations ◽  
Next Generation Sequencing Ngs ◽  
Low Coverage ◽  
Generation Sequencing

Many structural variations (SVs) detection methods have been proposed due to the popularization of next-generation sequencing (NGS). These SV calling methods use different SV-property-dependent features; however, they all suffer from poor accuracy when running on low coverage sequences. The union of results from these tools achieves fairly high sensitivity but still produces low accuracy on low coverage sequence data. That is, these methods contain many false positives. In this paper, we present CNNdel, an approach for calling deletions from paired-end reads. CNNdel gathers SV candidates reported by multiple tools and then extracts features from aligned BAM files at the positions of candidates. With labeled feature-expressed candidates as a training set, CNNdel trains convolutional neural networks (CNNs) to distinguish true unlabeled candidates from false ones. Results show that CNNdel works well with NGS reads from 26 low coverage genomes of the 1000 Genomes Project. The paper demonstrates that convolutional neural networks can automatically assign the priority of SV features and reduce the false positives efficaciously.

scholarly journals Detection of structural variations and fusion genes in breast cancer samples using third-generation sequencing

2021 ◽  
Author(s):  
Taobo Hu ◽  
Jingjing Li ◽  
Mengping Long ◽  
Jinbo Wu ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Genetic Alterations ◽  
Read Length ◽  
Breast Cancer Patients ◽  
Structural Variations ◽  
Transcriptomic Sequencing ◽  
Long Read ◽  
Second Generation Sequencing ◽  
Generation Sequencing

Abstract Structural variations (SVs) are common genetic alterations in the human genome that could cause different phenotypes and various diseases including cancer. However, the detection of structural variations using the second-generation sequencing was limited by its short read-length which in turn restrained our understanding of structural variations. In this study, we analyzed structural variations in 28 breast cancer-related genes through long-read genomic and transcriptomic sequencing of tumor, para-tumor and blood samples in 19 breast cancer patients. Our results showed that some somatic SVs were recurring among the selected genes, though the majority of them occurred in the non-exonic region. We found evidence supporting the existence of hotspot regions for SVs, which extended our previous understanding that they exist only for single nucleotide variations. In conclusion, we employed long-read genomic and transcriptomic sequencing in identifying SVs from breast cancer patients and proved that this approach holds great potential in clinical application.

scholarly journals OMSV enables accurate and comprehensive identification of large structural variations from nanochannel-based single-molecule optical maps

2017 ◽  
Author(s):  
Le Li ◽  
Tsz-Piu Kwok ◽  
Alden King-Yung Leung ◽  
Yvonne Y. Y. Lai ◽  
Iris K. Pang ◽  
...  
Keyword(s):  
Single Molecule ◽  
Wide Spectrum ◽  
Simulated Data ◽  
High Sensitivity ◽  
Human Cell Line ◽  
Read Length ◽  
Sequencing Data ◽  
Structural Variations ◽  
Promising Alternative ◽  
Optical Maps

AbstractHuman genomes contain structural variations (SVs) that are associated with various phenotypic variations and diseases. SV detection by sequencing is incomplete due to limited read length. Nanochannel-based optical mapping (OM) allows direct observation of SVs up to hundreds of kilo-bases in size on individual DNA molecules, making it a promising alternative technology for identifying large SVs. SV detection from optical maps is non-trivial due to complex types of error present in OM data, and no existing methods can simultaneously handle all these complex errors and the wide spectrum of SV types. Here we present a novel method, OMSV, for accurate and comprehensive identification of SVs from optical maps. OMSV detects both homozygous and heterozygous SVs, SVs of various types and sizes, and SVs with and without creating/destroying restriction sites. In an extensive series of tests based on real and simulated data, OMSV achieved both high sensitivity and specificity, with clear performance gains over the latest existing method. Applying OMSV to a human cell line, we identified hundreds of SVs >2kbp, with 65% of them missed by sequencing-based callers. Independent experimental validations confirmed the high accuracy of these SVs. We also demonstrate how OMSV can incorporate sequencing data to determine precise SV break points and novel sequences in the SVs not contained in the reference. We provide OMSV as open-source software to facilitate systematic studies of large SVs.

IsoDetect: Detection of splice isoforms from third generation long reads based on short feature sequences

2020 ◽  
Vol 15 ◽  
Author(s):  
Hongdong Li ◽  
Wenjing Zhang ◽  
Yuwen Luo ◽  
Jianxin Wang
Keyword(s):  
Sequence Similarity ◽  
Detection Methods ◽  
Sequence Information ◽  
Third Generation ◽  
Sequencing Data ◽  
Splice Isoforms ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Feature Sequence ◽  
Generation Sequencing

Aims: Accurately detect isoforms from third generation sequencing data. Background: Transcriptome annotation is the basis for the analysis of gene expression and regulation. The transcriptome annotation of many organisms such as humans is far from incomplete, due partly to the challenge in the identification of isoforms that are produced from the same gene through alternative splicing. Third generation sequencing (TGS) reads provide unprecedented opportunity for detecting isoforms due to their long length that exceeds the length of most isoforms. One limitation of current TGS reads-based isoform detection methods is that they are exclusively based on sequence reads, without incorporating the sequence information of known isoforms. Objective: Develop an efficient method for isoform detection. Method: Based on annotated isoforms, we propose a splice isoform detection method called IsoDetect. First, the sequence at exon-exon junction is extracted from annotated isoforms as the “short feature sequence”, which is used to distinguish different splice isoforms. Second, we aligned these feature sequences to long reads and divided long reads into groups that contain the same set of feature sequences, thereby avoiding the pair-wise comparison among the large number of long reads. Third, clustering and consensus generation are carried out based on sequence similarity. For the long reads that do not contain any short feature sequence, clustering analysis based on sequence similarity is performed to identify isoforms. Result: Tested on two datasets from Calypte Anna and Zebra Finch, IsoDetect showed higher speed and compelling accuracy compared with four existing methods. Conclusion: IsoDetect is a promising method for isoform detection. Other: This paper was accepted by the CBC2019 conference.

Mean reversion in corporate leverage: evidence from India

2019 ◽  
Vol 45 (9) ◽  
pp. 1183-1198
Author(s):  
Gaurav S. Chauhan ◽  
Pradip Banerjee
Keyword(s):  
Capital Structure ◽  
Emerging Market ◽  
Simulated Data ◽  
Mean Reversion ◽  
Developed Countries ◽  
Data Sets ◽  
Debt Ratio ◽  
Testing Strategy ◽  
Content Type ◽  
Financing Behavior

Purpose Recent papers on target capital structure show that debt ratio seems to vary widely in space and time, implying that the functional specifications of target debt ratios are of little empirical use. Further, target behavior cannot be adjudged correctly using debt ratios, as they could revert due to mechanical reasons. The purpose of this paper is to develop an alternative testing strategy to test the target capital structure. Design/methodology/approach The authors make use of a major “shock” to the debt ratios as an event and think of a subsequent reversion as a movement toward a mean or target debt ratio. By doing this, the authors no longer need to identify target debt ratios as a function of firm-specific variables or any other rigid functional form. Findings Similar to the broad empirical evidence in developed economies, there is no perceptible and systematic mean reversion by Indian firms. However, unlike developed countries, proportionate usage of debt to finance firms’ marginal financing deficits is extensive; equity is used rather sparingly. Research limitations/implications The trade-off theory could be convincingly refuted at least for the emerging market of India. The paper here stimulated further research on finding reasons for specific financing behavior of emerging market firms. Practical implications The results show that the firms’ financing choices are not only depending on their own firm’s specific variables but also on the financial markets in which they operate. Originality/value This study attempts to assess mean reversion in debt ratios in a unique but reassuring manner. The results are confirmed by extensive calibration of the testing strategy using simulated data sets.

MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble

2021 ◽  
Vol 16 (1) ◽  
pp. 1-24
Author(s):  
Yaojin Lin ◽  
Qinghua Hu ◽  
Jinghua Liu ◽  
Xingquan Zhu ◽  
Xindong Wu
Keyword(s):  
Empirical Studies ◽  
Feature Space ◽  
Training Data ◽  
Data Sets ◽  
Learning Framework ◽  
Feature Spaces ◽  
Public Data ◽  
Margin Distribution ◽  
Label Correlations ◽  
Label Correlation

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm, mu lti- l abel-specific f eature space e nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.

scholarly journals Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3536
Author(s):  
Jakub Górski ◽  
Adam Jabłoński ◽  
Mateusz Heesch ◽  
Michał Dziendzikowski ◽  
Ziemowit Dworakowski
Keyword(s):  
Novelty Detection ◽  
Feature Space ◽  
Detection Methods ◽  
Data Sets ◽  
Similar Distribution ◽  
Detection Algorithms ◽  
Rotary Machinery ◽  
Detection Approach ◽  
Input Reconstruction ◽  
Quantitative Indicators

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

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